Genome-Wide Association Study Identifies Protective Genetic Factors in Active Blood Donors Against Multiple Diseases

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While HDE arises due to healthier individuals being more likely to be able to donate, the extent to which it is influenced by genetic differences remains largely unclear. To elucidate the genetic basis of HDE, we conducted a genome-wide association study (GWAS) involving 53,688 active blood donors with extensive donation histories and 228,060 controls from biobank cohorts within the FinnGen project. Our results identified 2,973 genome-wide significant loci associated with several health-related endpoints and levels of proteins and laboratory values. The associated loci related not only to blood groups but also to predisposition to infections and somatic and mental diseases, suggesting that HDE genetics extends beyond blood donation eligibility criteria. In conclusion, in this study we show that HDE is partially explained by genetic factors affecting various disease categories. Biological sciences/Genetics/Genomics Health sciences/Medical research/Genetics research blood donor biobank healthy donor effect GWAS blood group HLA Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Blood donors are a highly selected population due to the health status criteria they must meet to be eligible to donate blood. Basic restrictions on blood donation suitability include serious illnesses such as severe cardiovascular conditions, cancer and epilepsy 1 . Some conditions that restrict blood donation suitability have a strong well-known genetic background, such as the association of HLA genes with type 1 diabetes (T1D) 2 and certain autoimmune diseases 3 – 5 . As a minimum hemoglobin level is required for donation eligibility, genetic variants affecting iron and hemoglobin levels may also result in donor selection. In addition to the blood donation eligibility criteria, the healthier lifestyle followed by the blood donors and self-selection caused by various factors, e.g. common cold, results in a known phenomenon, healthy donor effect , HDE, a form of membership bias, which can lead to detrimental effects in a research setting if not taken into account 6 . Comprehensive selection of different blood groups, specifically sufficiency in emergency blood, O RhD neg, suitable for most recipients, are basic requirements in blood banks. Due to high need for the emergency blood and strong immunogenicity of certain blood group antigens such as ABO, Rh and Kell 7 – 10 , donor recruitment is partially based on blood group genotype and results in enrichment of certain blood group antigens in a blood donor pool. Well-known associations between ABO blood group antigens and cardiovascular conditions exist, more precisely between red cell antigen O and reduced thromboembolic diseases 11 , 12 . Risk for bleeding disorder, von Willebrand disease, is known to be higher among individuals with red cell O antigen 13 . Pregnancy-induced hypertension has been previously described to occur more frequently in RhD positive individuals 12 . Moreover, in hemochromatosis, blood donation alleviates the symptoms, and donors with hemochromatosis-associated gene variants are known to be enriched in the blood donor population 14 . Blood donors have been used as a healthy control cohort in genetics and biomedical research due to their commitment and willingness to participate 15 – 17 . However, the enrichment of genetic variants with established disease associations or biological functions may result in unpredicted effects in biomedical research. To reveal the genetics of blood donation and to understand the possible genetic selection leading to the HDE, we conducted a genome-wide association study (GWAS) between active blood donors of the (Finnish Red Cross) Blood Service Biobank and FinnGen cohort. To the best of our knowledge, no previous genome studies have focused on the possible selection bias caused by blood groups, health questionnaire-based requirement, and self-selection of blood donors. In our analysis, we found altogether 2,973 genome-wide significant associations in 46 distinct fine-mapped loci. Genetic correlation analysis revealed strong negative correlation between blood donorship and diseases such as mental disorders, pain disorders, cardiovascular diseases, addiction disorders, autoimmune disorders, and Alzheimer’s disease. HLA association analysis confirmed the enrichment of hemochromatosis-related and protective autoimmune HLA -alleles and the lower occurrence of autoimmune-related HLA -alleles in the blood donor population. The genome-wide significant variants also associated with 181 plasma protein level alterations and several clinical blood laboratory value measurements. The results of this study demonstrate the genetic basis of the HDE, which is not limited to immediate blood donorship inclusion criteria. Results The overall study design is shown in Supplementary Fig. 2. Table 1 provides a detailed description of the study population characteristics and differences in diagnoses between the two cohorts. Blood donors, both male and female, were generally younger and taller than the controls. Additionally, male blood donors were heavier than their control counterparts. The blood donor cohort had a higher proportion of female participants. In all diagnosis categories, the prevalence of disease was significantly higher in the control group compared to the blood donors, except for the category 'XVI Certain conditions originating in the perinatal period,' where a higher prevalence of cases was observed among blood donors of both sexes. Table 1 Characteristics of the study cohorts. Age, sex, height, weight, BMI and FinnGen endpoint categories in blood donors and FinnGen control cohorts are shown. Females Males Blood donors FinnGen Blood donors FinnGen Median age at sampling, years (IQR) 44.47 (18.2–70.8) 62.7 (36.2–89.2) 50.26 (25.5–75.0) 70.46 (51.3–89.6) Sex, n (%) 32,460 (60.46) 120,279 (52.74) 21,228 (39.54) 107,781 (47.26) Median weight, kg (IQR) 72 (54–90) a 72 (51–93) a 86 (68–104) 84 (64–104) Median height, cm (IQR) 167 (159–175) 164 (156–172) 180 (171–189) 177 (168–186) Median BMI (IQR) 25.83 (19.3–32.4) 26.64 (19.1–34.2) 26.58 (21.6–31.6) 26.88 (21.2–32.6) Number of diagnoses per FinnGen endpoint category (%) b I Certain infectious and parasitic diseases (AB1) 7,466 (23) 47,901 (39.82) 5,565 (26.22) 48,487 (44.99) II Neoplasms from cancer register (ICD-O-3) 968 (2.98) 34,299 (28.52) 791 (3.73) 38,891 (36.08) II Neoplasms from hospital discharges (CD2) 6,960 (21.44) 67,026 (55.73) 2,804 (13.21) 52,708 (48.9) III Blood and immune system (D3) 964 (2.97) 17,017 (14.15) 534 (2.52) 15,606 (14.48) IV Endocrine, nutritional and metabolic (E4) 10,269 (31.64) 83,780 (69.65) 3,029 (14.27) 58,530 (54.3) IX Circulatory system (I9) 6,276 (19.33) 69,429 (57.72) 4,587 (21.61) 78,876 (73.18) V Mental and behavioural disorders (F5) 7,871 (24.25) 42,676 (35.48) 3,648 (17.18) 33,080 (30.69) VI Nervous system (G6) 6,431 (19.81) 54,710 (45.49) 4,501 (21.2) 51,142 (47.45) VII Eye and adnexa (H7) 8,088 (24.92) 58,567 (48.69) 4,660 (21.95) 47,174 (43.77) VIII Ear and mastoid process (H8) 5,316 (16.38) 29,249 (24.32) 3,893 (18.34) 26,561 (24.64) X Respiratory system (J10) 14,408 (44.39) 69,022 (57.38) 11,532 (54.32) 69,209 (64.21) XI Digestive system (K11) 27,542 (84.85) 106,938 (88.91) 17,037 (80.26) 90,482 (83.95) XII Skin and subcutaneous tissue (L12) 7,710 (23.75) 43,402 (36.08) 4,096 (19.3) 32,486 (30.14) XIII Musculoskeletal and connective tissue (M13) 13,715 (42.25) 83,931 (69.78) 9,118 (42.95) 68,570 (63.62) XIV Genitourinary system (N14) 17,334 (53.4) 91,665 (76.21) 4,463 (21.02) 49,729 (46.14) XIX Injury, poisoning, external (ST19) 15,936 (49.09) 73,725 (61.29) 13,051 (61.48) 70,375 (65.29) XV Pregnancy, childbirth and the puerperium (O15) 20,560 (63.34) 86,799 (72.16) 0 (0) 0 (0) XVI Perinatal period (P16) 535 (1.65) 1,000 (0.83) 263 (1.24) 514 (0.48) XVII Congenital malformations (Q17) 2,018 (6.22) 10,032 (8.34) 1,231 (5.8) 7,258 (6.73) XVIII Abnormal clinical and laboratory findings (R18) 16,800 (51.76) 93,458 (77.7) 9,125 (42.99) 80,419 (74.61) XX External causes of mortality (VWXY20) 13,025 (40.13) 64,874 (53.94) 10,741 (50.6) 62,034 (57.56) XXI Contact with health services (Z21) 24,581 (75.73) 10,4068 (86.52) 10,544 (49.67) 72,915 (67.65) XXII Codes for special purposes (U22) 1,417 (4.37) 5,906 (4.91) 595 (2.8) 4,310 (4) a Not significant b Shortened for clarity Genome-wide association analysis GWAS revealed 2,973 significant (p < 5x10 − 8 ) SNPs associated with blood donorship. After fine-mapping, 5 coding (4 missense and 1 inframe deletion) and 36 non-coding variants were detected (Fig. 1 A). In addition to these 41 variants, 5 non-coding lead variants in the extended MHC region (Fig. 1 B and C) were included in the downstream analyses (Table 2 ). The strongest genome-wide associations were seen in variants related to blood group antigens: rs55794721-A (p = 8.88x10 − 89 ) in chr 1, rs687621-A (p = 5.70x10 − 81 ) on chr 9 in ABO gene, and rs8176058-A (p = 1.16x10 − 31 ) on chr 7 in KEL gene (Fig. 1 A, Table 2 ). Despite of the adjustment of the GWAS for BMI, we observed significant variants influencing height, weight, and BMI. rs66781921-T, an inframe deletion variant in chr 2, previously shown to have a negative association with height, weight and body mass index 18 , was rarer in blood donors (beta=-1.97, p = 1.89x10 − 11 ). The missense variant rs34811474-A, previously shown to be negatively associated with body mass index 18 , was found to be more frequent in blood donors (beta = 0.064, p = 1.21x10 − 12 ). Variants rs10947114-T (beta=-0.055, p = 2.88x10 − 09 ), rs3130906-A (beta=-0.055, p = 5.17x10 − 11 ) and rs4713637-C (beta=-0.047, p = 1.39x10 − 08 ), previously described to be associated with spondylopathies and celiac disease 18 , were negatively associated with blood donorship. A rare intron variant in chromosome 3, rs528492111-T, previously shown to have a weak association to benign neoplasm of conjunctiva 18 , was more frequent in blood donors (beta = 1.87, p = 4.09x10 − 10 ). All the fine-mapped and MHC lead variants in the are listed in Table 2 . Table 2 Fine-mapped (41) and MHC lead variants (5) from blood donorship GWAS. P-value, effect size, the predicted most serious effect type and target gene for each variant are shown. Variants are ordered according to association effect size. In case the target gene is not known, the nearest gene is indicated by asterisk (*). Chromosome Position Effect allele rsID number p-value Beta Variant type Target gene 2 231460727 T rs66781921 1,89E-11 -1,973 inframe deletion NCL 11 5226799 A rs1135071 2,93E-13 -0,573 missense variant HBB 17 58358769 T rs199598395 1,55E-25 -0,383 missense variant RNF43 7 142957921 A rs8176058 1,16E-31 -0,327 missense variant KEL 5 174443703 C rs191302298 3,89E-08 -0,292 intron variant LINC01411 8 105038155 C rs10096658 1,35E-08 -0,277 intron variant ZFPM2 19 5149612 A rs62113181 8,06E-09 -0,135 intron variant KDM4B 4 37135112 C rs75738358 4,89E-08 -0,080 intergenic variant MIR4801* 7 101559560 A rs12539059 5,68E-09 -0,071 downstream gene variant COL26A1 6 32636375 G rs9272324 1,36E-16 -0,064 intron variant HLA-DQA1* 19 18707615 AT rs36035346 5,48E-09 -0,062 intron variant CRTC1 7 23484043 A rs60678519 1,06E-08 -0,055 downstream gene variant AC079780 ,1 6 30934404 T rs10947114 2,88E-09 -0,055 intron variant SFTA2* 6 31457404 A rs3130906 5,17E-11 -0,055 intron variant HCP5* 10 77157477 A rs80319585 1,92E-08 -0,055 intron variant KCNMA1 7 82855734 A rs7795945 1,28E-09 -0,049 intron variant PCLO 2 186695046 T rs34483988 1,65E-08 -0,049 intron variant FAM171B 3 18717009 T rs6775319 2,17E-09 -0,048 intron variant SATB1-AS1 10 105646256 A rs7907026 4,63E-10 -0,048 intergenic variant SORCS3* 1 153658595 A rs3806234 5,16E-09 -0,047 upstream gene variant SNAPIN 6 33602120 C rs4713637 1,39E-08 -0,047 intergenic variant LINC00336* 18 52600634 C rs62083414 5,35E-09 -0,046 intron variant DCC 4 19015485 A rs207350 3,03E-08 -0,045 intergenic variant LCORL* 17 48880334 C rs594398 1,18E-08 -0,044 intron variant SUMO2P17 12 38272211 T rs10880819 2,64E-08 0,043 downstream gene variant RF00019 2 71295511 CA rs35440643 4,54E-08 0,044 intron variant ZNF638 2 26992548 A rs10166897 1,57E-08 0,044 intron variant MAPRE3 15 75059054 TG rs113236240 6,53E-09 0,045 intron variant PPCDC 9 16695862 CAAGCA rs55763604 2,84E-08 0,049 intron variant BNC2 1 27087829 T rs55732343 2,31E-10 0,049 regulatory region variant SLC9A1* 6 26071867 C rs9968910 1,12E-09 0,050 intergenic variant HIST1H1C* 17 67991393 T rs5821444 7,98E-09 0,051 3 prime UTR variant C17orf58 16 13464582 C rs6498415 5,47E-10 0,053 intron variant U91319 ,1 16 90023620 G rs12444334 1,51E-09 0,053 intron variant GAS8 2 59856883 A rs11675100 3,88E-09 0,054 intron variant AC007100 ,1 6 17127475 A rs9367942 3,81E-08 0,055 intron variant STMND1 12 57289173 T rs61937595 1,25E-08 0,062 intron variant R3HDM2 4 25407216 A rs34811474 1,21E-12 0,064 missense variant ANAPC4 1 66016472 C rs1500956 5,14E-10 0,075 intron variant PDE4B 6 102401219 C rs77448558 1,28E-08 0,078 intergenic variant GRIK2* 7 97662657 T rs10268629 1,71E-09 0,078 intergenic variant TAC1* 13 50130043 T rs75350584 1,58E-08 0,094 intron variant DLEU1 9 133261662 A rs687621 5,70E-81 0,147 intron variant ABO 1 25235176 A rs55794721 8,88E-89 0,157 upstream gene variant SYF2 19 50410043 C rs118101548 1,90E-08 0,299 intron variant POLD1 3 49008876 T rs528492111 4,09E-10 1,875 intron variant WDR6 Genetic correlation To assess genetic similarity between blood donors and various disease phenotypes, we performed a genetic correlation analysis comparing the blood donor GWAS result with over 2,400 FinnGen core endpoint GWAS results. Altogether 593 phenotypes were negatively correlated with blood donorship (FDR < 0.05, Supplementary File 2). Height and hemoglobin were the only phenotypes showing weak positive correlation with blood donorship, r g =0.07 and r g =0.18, respectively (Fig. 3 ), despite that the blood donorship GWAS had been adjusted for BMI. Blood donorship was negatively correlated (r g <-0.5) with 30 phenotypes (Supplementary File 2). The strongest negative correlations were seen with ‘Anemias’ (r g =-0.66, FDR = 2.81x10 − 112 ), ´Other reaction to severe stress and adjustment disorders´ (r g =-0.64, FDR = 3.36x10 − 40 ) and ´Other disorders of teeth and supporting structures´ (r g =-0.62, FDR = 2.74x10 − 03 ). Malignancy-related phenotypes showed moderate negative correlations (r g <-0.2). In addition, negative correlations were observed for addiction phenotypes such as smoking dependency (r g =-0.29, FDR = 1.57x10 − 05 ), alcohol dependence (r g =-0.31, FDR = 8.26x10 − 20 ) and substance abuse (r g =-0.39, FDR = 2.88x10 − 45 ). Expectedly, blood donors displayed negative correlation towards autoimmune-related phenotypes, such as autoimmune diseases in general (r g =-0.33, FDR = 1.87x10 − 52 ) and ‘Irritable bowel syndrome’ (r g =-0.50, FDR = 4.05x10 − 26 ). A wide variety of mental illnesses (in total 49 phenotypes) were negatively correlated with blood donorship. The strongest mental illness-related phenotype with negative genetic correlation was ‘Mental disorders, not otherwise specified’ (rg = 0.59, FDR = 0.001). Another example of phenotypes not directly restricted by blood donation eligibility criteria but with relatively strong negative correlation were infectious phenotypes, such as acute bronchitis (r g =-0.58, FDR = 1.76x10 − 16 ), viral hepatitis (r g =-0.51, FDR = 1.88x10 − 7 ) and ‘Other or unspecified bacterial infection’ (r g =-0.50, FDR = 1.83x10 − 09 ) (Fig. 3 , Supplementary File 2). Lastly, phenotypes not directly linked to blood donation eligibility criteria, but which undeniably affect donation suitability or donor’s knowledge of their current state of health, such as Alzheimer’s disease (r g =-0.24, FDR = 0.03), were also negatively correlated with blood donorship. Blood group antigen-related associations To quantify the enrichment of blood group antigens among blood donors, we evaluated the association between imputed red cell antigens and blood donorship using a logistic regression model. The model revealed a strong negative association for the Kell, RhD, and AB blood group antigens, and a positive association for the O blood group antigen (Supplementary Fig. 3). The effect sizes for the red cell antigens and other GWAS covariates and more detailed description of the blood group antigen association analysis is provided in the Supplementary information. HLA -allele associations Because of the known strong role of the HLA region in immunology and the predisposing risk of certain HLA -alleles in autoimmunity 2 – 5 , 19 , we performed an association study with imputed HLA -alleles for blood donor endpoint. HLA association analysis for blood donorship (Fig. 5 , upper panel) revealed a negative association with HLA -alleles in linkage with the well-known T1D risk genotype HLA-DQ2/DQ8 . A known risk allele for ankylosing spondylitis 19 HLA-B*27:05 was negatively associated with blood donorship. Positive association was seen with HLA -alleles previously shown to be associated with HFE C282Y allele in Finland 14 or HLA class II alleles with known protection from T1D 20 . To fine-map the MHC GWAS signal (Fig. 1 b), we adjusted the analysis with the MHC lead variant on chromosome 6 in HLA-DQA1 , rs9272324-G (Fig. 5 , middle panel). Controlling for rs9272324-G decreased the effect sizes of the above-mentioned HLA -alleles in linkage with HLA-DQ8 and HLA-B*27:05 and increased the p-values of the above mentioned HFE C282Y related HLA -alleles below the significance level. When adjusted for the lead HLA -allele, DRB1*04:01 (Fig. 5 , lower panel), HLA-B*27:05 and HLA-C*02:02 in LD (r 2 = 0.3) with HLA-B*27:05 and HLA-alleles in linkage with HLA-DQ2 were negatively associated with blood donorship. Adjustment with DRB1*04:01 revealed also the negative association of HLA-B*08:01 , a known HLA-B allele in linkage with HLA-DQ2 in Finland, with blood donorship. In HLA-DRB1*04:01 adjusted analysis, HFE C282Y-related HLA -alleles B*07:02 , DRB1*15:01 , DQA1*01:02 and DQB1*06:02 were positively associated with blood donorship, but with less significant p-values than in HLA -association analysis without any additional adjustment (Supplementary File 2). Association of the GWAS lead variants with molecular traits To better understand the functional effects of the 46 selected GWAS lead variants, we used quantitative trait locus (QTL) analysis for protein expression levels, metabolites, and common electronic health care record-derived laboratory measurements as well as functional enrichment analysis. Altogether 470,490 associations were tested, and 327 pQTL and labQTL associations reached the significance level of FDR < 0.05. Among the 41 fine-mapped and five MHC region variants, 19 were significantly associated with 184 unique plasma protein expression levels and 18 with 128 clinical laboratory value measurements (Table 3 ). However, no significant associations were observed between the 46 genetic variants and metabolite levels. Table 3 Significant associations (FDR < 0.05) of quantitative trait locus analysis for protein expression level and clinical laboratory values with the GWAS fine-mapped and MHC lead variants. In case the target gene is not known, the nearest gene is indicated by asterisk (*). pQTL labQTL rsID number Effect allele Target gene Associated with protein level alteration (FDR < 0.05) Associated with laboratory value in a given category (FDR < 0.05) rs1135071 A HBB 52 different proteins Iron rs199598395 T RNF43 PARP1 Iron, liver function rs8176058 A KEL APBB3, ETV5, NCR3, NT5M, TPH1, YTHDC1, KEL - rs191302298 C LINC01411 21 different proteins - rs10096658 C ZFPM2 14 different proteins - rs62113181 A KDM4B RXRA, SLMAP - rs9272324 G HLA-DQA1* HERC5, MICB, SH3GL3-1, HLA-DRA Electrolytes, glucose, hemostasis, immune system, iron, lipids, thyroid gland function rs60678519 A AC079780 ,1 GPNMB, GPNMB-1, GPNMB-2 - rs10947114 C SFTA2* DDR1 Hemostasis, iron, liver function rs3130906 A HCP5* CDSN, TEK Iron, thyroid gland function, immune system, lipids, electrolytes, hemostasis, glucose rs34483988 T FAM171B - Iron rs6775319 A SATB1-AS1 - Glucose rs3806234 A SNAPIN - Immune system rs4713637 C LINC00336* - Iron, immune system, hemostasis rs594398 G SUMO2P17 - Glucose rs10166897 A MAPRE3 CGREF1, KHK - rs113236240 T PPCDC PPCDC - rs9968910 C HIST1H1C* - Iron, glucose, immune system, hemostasis rs5821444 TTAA C17orf58 - Liver function, lipids, immune system rs12444334 G GAS8 CDH15 Iron, liver function rs11675100 A AC007100 ,1 - Glucose rs61937595 T R3HDM2 INHBC, TDP1, IL21R - rs34811474 A ANAPC4 - Immune system, glucose rs77448558 C GRIK2* ARCV1 - rs10268629 T TAC1* NADK Iron rs687621 G ABO 54 different proteins Thyroid gland function, liver function, lipids, iron, immune system, hemostasis, glucose, electrolytes rs55794721 A SYF2 ICAM4, ICAM4-1 Immune system, hemostasis, iron, lipids rs118101548 C POLD1 15 different proteins - Variants regulating protein expression levels Two variants were shown to be associated with expression levels of more than 50 unique proteins (Supplementary Fig. 4). rs678621-G located on the ABO gene regulated expression levels of 54 proteins, and rs1135071-A in the HBB gene regulated 52 proteins. rs678621-G pQTLs included proteins essential for the histo-blood group ABO formation, such as ABO itself and various N-acetylgalactosaminyltransferases such as GALNT9. Additionally, due to the enrichment of the rs687621-A allele in blood donors, decreased expression was observed for proteins such as F8 and MUC2, while the strongest increasing effect were seen for ALPI and SELE. Only two of the 52 associations identified with rs1135071-A showed decreasing effects on protein levels, the strongest effect was seen with TLR2, while the rest of the associations showed increasing effects, such as AHSP and MTIF3. Other variants with more than 10 associations with protein expression levels included rs191302298-C (21 unique proteins), rs118101548-C (15 unique proteins) and rs10096658-C (14 unique proteins). In blood donors, the rs55794721-A variant exerted a decreasing effect on ICAM4, a LW glycoprotein present in the RhD protein complex. Additionally, an association was observed between rs60678519-A and GPNMB. Due to the enrichment of rs60678519-AAAT in blood donors, GPNMB expression was decreased in blood donors. Overexpression of GPNMB has been linked to immunosuppression within the tumor immune microenvironment, leading to poorer prognosis in various cancers 21 . The STRING 22 functional enrichment analysis tool identified several Gene Ontology (GO) functional processes for the 184 proteins. Furthermore, the 54 proteins regulated by rs687621-G alone showed significant enrichments (Fig. 6 ). We detected 60 pQTLs that were not involved with the enriched biological processes or pathways. Of these, the strongest increasing and decreasing effect was seen with rs1135071-A on AHSP (beta = 2.59) and ACP6 (beta=-0.97), respectively (Supplementary Fig. 4). Since the proteomics data originated solely from the blood donor cohort, we were unable to analyze whether the significant pQTLs acted as mediators. Variants regulating clinical laboratory test values We observed 128 significant labQTLs that comprised nine variants and 50 different measurements and represented eight different clinical laboratory value categories (Supplementary File 4). We performed mediation analysis for the observed significant labQTLs to estimate the indirect effect of a genetic variant on blood donorship through a measured laboratory value. Test of mediation for the top QTL of each 50 laboratory measurements showed significant mediation effect for 9 variants in 43 clinical laboratory measurements (Fig. 7 a). The highest number of mediated associations were seen in Iron (13 associations), Lipids (10 associations), Immune System (5 associations) and Liver Function (5 associations) related laboratory categories. The highest number of mediated associations, 16, on different laboratory values was seen for the MHC variant rs9272324. The results of the labQTL analysis are shown in detail in Supplementary File 4. Strongest mediation effects, as measured by the proportion of mediated effect relative to the total SNP effect, was seen for iron-related laboratory values. Glucose, hemostasis, lipids, immune and liver function related labQTLs also showed proportion of mediation > 0.2. Of the 43 significant mediation effects, 25 showed lower and 18 higher clinical laboratory values in blood donors when compared to control population (Fig. 7 b). However, direction of the QTL effect on a laboratory value for the allele enriched in blood donors was sometimes opposite to the laboratory value difference between blood donors and controls. For example, blood donors had lower plasma ferritin levels (likely caused by donation), but they also harbored an enriched genetic variant that upregulates plasma ferritin. Discussion The healthy donor effect is a well-documented phenomenon, but its genetic basis has remained unclear. We conducted a genome-wide comparison between blood donor biobank donors with relatively long and active blood donation history 14 and the FinnGen control population. The FinnGen controls included individuals from hospital biobanks, population cohorts, and patients at occupational health clinics. The latter two cohorts primarily comprise healthy individuals, and as disease-specific legacy cohorts were excluded from the analysis, the study controls can be assumed to be relatively unbiased, representing the general population of Finland. The observed protection of blood donors from certain diseases can be directly attributed to donor eligibility criteria that select healthy individuals. However, some of the observed protective genetic factors may be secondary. For instance, the strongest negative genetic correlation was seen with anemias. This correlation could be due to individuals with a higher tendency for diseases causing iron loss being less likely to start donating blood or to withstand repeated donations. Additionally, it may be a secondary result of autoimmune diseases, such as Crohn’s disease, which can restrict blood donation eligibility and cause secondary anemia. Surprisingly, our results revealed that blood donors not only exhibit a protective genetic profile against diseases explicitly targeted by donor eligibility criteria, but also show a broader negative genetic correlation with conditions beyond these criteria, including various mental illnesses, infectious diseases and Alzheimer’s disease. This suggests that blood donors harbor a lower genetic disease burden than the general biobank population. While pleiotropic effects and/or self-selection may explain this phenomenon, our analyses indicate that genetic correlations with the phenotypes targeted by the eligibility criteria are not consistently strong. Further investigations are required to fully understand pleiotropy in blood donor selection and to explore the potential benefits of an active blood donor population for genetic and biomedical research. Expectedly, genetic variants in the MHC region played strong role in several genetic associations and in QTLs. HLA -alleles with known autoimmune associations occurred with lower frequency in the blood donor population than in the control population, whereas HFE C282Y-linked HLA -alleles 14 and HLA -alleles with known protective role in autoimmunity were more frequent among the blood donors. Furthermore, the negative selection for T1D in blood donors leading to lower frequency of HLA-DQ2/8 genotype, could explain some of the protective genetic correlation. HLA -alleles of this genotype, have known predisposing associations, including certain infectious diseases and use of antidepressants 5 . These findings show the simultaneous genetic selection in the MHC for hemochromatosis and against autoimmune conditions among blood donors, while the pleiotropic role of the associated HLA -alleles could explain some of the protective genetic correlation shown in this study. In addition to tagging certain autoimmune-related HLA -alleles or haplotypes (e.g. T1D and ankylosing spondylitis), rs9272324-G was shown to enhance the protein levels of HLA-DRA , which encodes the nonpolymorphic DR alpha-chain 23 , essential for the stable expression of DR molecules on the cell surface. Considering this, the known associations of HLA-DRB1*15:01 with multiple sclerosis 3 , 24 and the lower frequency of the rs9272324-G in blood donors, further investigation of the pleiotropic role of rs9272324 in multiple sclerosis and in autoimmunity in general would be of interest. In addition to hemochromatosis, the results of the present study highlight genetic factors that may affect recipient matching or donation activity. We show an increasing expression level of AHSP protein due to Hb Tacoma variant rs1135071-A. AHSP plays a crucial role in hemoglobin assembly by stabilizing the αHb subunits and preventing the self-aggregation of excess αHb subunits prior the formation of HbA1 subunit 28 . As AHSP may have a role as a genetic modifier in β-thalassemia 28 , 29 and the normal clinical phenotype of Hb Tacoma heterozygotes 30 , rs1135071 could be of further interest in β-thalassemia research. Since the rs1135071-A allele is less common in blood donors compared to controls and strongly downregulates Hb and methemoglobin, it may be useful to analyze whether blood donation is harmless for Hb Tacoma heterozygotes or whether the blood products of carries are suitable for recipients with conditions such as sickle cell disease 31 . We demonstrated that blood donors differ from controls in several clinical blood laboratory measurements, such as ferritin and lipoproteins, that are regulated by the 41 fine-mapped and 5 MHC lead variants. Consistently with low prevalence of diabetes, blood donors exhibit lower plasma glucose levels, partly due to genetics, but also have higher LDL levels due to enriched QTLs. Interestingly, HDL levels are higher in blood donors, despite the presence of a variant that typically lowers HDL. This could be attributed to the U-shaped relationship between HDL and overall health 32 , where extremely high HDL levels are harmful and thus less common among blood donors. The mediation analysis revealed that blood donors had lower ferritin and higher hemoglobin levels than controls. This finding is in line with our previous studies; donation activity is a major determinant of ferritin in donors 33 , yet iron deficiency anemia is less common in donors (0.5%) than in the Finnish general population (2.6%) 34 . Consistent with this result, blood donors exhibited an enrichment of a compensatory variant that upregulates ferritin levels. In this study we present the genetic underpinnings of HDE and blood group-based selection in active blood donors. Our findings highlight the unique genetic makeup of this donor population and its impact on health and blood donation suitability. The results demonstrate the role of genetics in maintaining good health status and active blood donation career which is also connected to genetic variation associated with a good mental health. Furthermore, research using blood donors as a control group should account for this distinct genetic structure, even after donation ceases. Additionally, the relatively predominant enrichment of specific blood group antigens among blood donors warrants consideration in research as these antigens have several known disease associations. No unambiguous solution exists to control collider biases when conducting research within blood donors, but rather a case-by-case evaluation e.g. through directed acyclic graphs can be helpful when evaluating the possibility of collider biases 35 – 37 . Further investigations are necessary to fully elucidate the implications of these discoveries, but they underscore the genetic structure behind HDE and the potential of active blood donor cohorts for advancing biomedical genetic research. Material and Methods Ethics statement Study subjects in FinnGen provided informed consent for biobank research, based on the Finnish Biobank Act. Alternatively, separate research cohorts, collected prior the Finnish Biobank Act came into effect (in September 2013) and start of FinnGen (August 2017), were collected based on study-specific consents and later transferred to the Finnish biobanks after approval by Fimea (Finnish Medicines Agency), the National Supervisory Authority for Welfare and Health. Data acquisition Genome data used in the present study originated from samples that were genotyped in the FinnGen project using the FinnGen ThermoFisher Axiom custom array v1 or v2 and genome imputation version R12. To minimize potential bias in downstream analyses, legacy samples that were highly enriched in certain common diseases ( https://finngen.gitbook.io/documentation/ ), were excluded from the study controls. Individuals under 18 years of age or lacking BMI data were removed. After filtering, total number of FinnGen genome data used in this study was 53,688 blood donors and 228,060 controls. Statistical analyses If not stated otherwise, all statistical analyses were performed in R 38 version 4.3 or 4.4, with RStudio 39 . The genome-wide significance p-value threshold p < 5 x 10 − 8 was used in GWAS. In all other analyses, p-values were adjusted with the Benjamini–Yekutieli procedure 40 , and significance threshold of 0.05 was used. All the phenotypes analyzed here have been described in FinnGen DF12 dataset. The FinnGen summary statistics data are publicly available ( https://r12.finngen.fi/ ) 18 , and analysis methods are available at https://finngen.gitbook.io/documentation/methods/ . Association analysis and fine-mapping GWAS was performed with an additive genetic model using Regenie 41 v2.2.4 in FinnGen pipeline ( https://github.com/FINNGEN/regenie-pipelines ). Blood donor status was defined as a binary endpoint. Sex, age, BMI, PC1-10, birth region, and FinnGen genotyping array version were used as model covariates. To discover associations independent of type 1 diabetes (T1D) in chromosome 6, the FinnGen T1D endpoint 42 was used as an additional covariate in the chromosome 6-specific association analysis. Fine-mapping for the genome-wide significant GWAS signals (p < 5 x 10 − 8 ) was performed in FinnGen pipeline ( https://github.com/FINNGEN/ ) with SuSiE 43 excluding MHC region (GRCh38, chr6:25–34 Mb) 42 , 44 . The lead variant in the MHC region in the non-T1D adjusted GWAS, rs9272324, was included in further analyses due to its strong association with autoimmunity 18 . Association testing of each classical HLA -allele HLA-A , - B , - C , - DRB1 , - DQA1 , - DQB1 , DPB1 and HLA-DRB3-5 with the blood donor endpoint was performed using Regenie v3.0.1 with the same covariates as in GWAS. ABO association analysis was performed using Regenie v2.2.4 with the same covariated as in GWAS, except region of birth. Genetic correlation All pair-wise genetic correlations between blood donorship and all the 2,470 phenotypes previously studied as part of FinnGen study were computed using LD Score Regression v1.0.1 using Finnish LD panel in FinnGen pipeline ( https://github.com/FINNGEN/ ) 45 . HLA and blood group imputation The alleles of the classical HLA genes, HLA-A , -C , -B , -DRB1 , DRB3-5 , -DQA1 , -DQB1 , and -DPB1 , were previously imputed at four-field resolution (defining protein sequence variation) in FinnGen using HIBAG 46 algorithm with population-specific reference panel, as reported earlier 47 . The dosage value of each HLA -allele was used in downstream analyses. Altogether 37 red blood cell antigens in 14 different blood group systems were imputed using population-specific random forest models as described by Hyvärinen et al 48 . QTL analysis Proteomics data were previously generated in FinnGen on blood donors using multiplex antibody-based immunoassay (Olink) and multiplex aptamer-based immunoassay (SomaScan) 49 . We analyzed protein QTLs by performing an association between the 46 GWAS lead variants and protein expression data using the glm function of Plink2 v2.00a4LM. Age, sex, sampling year and PC1-10 were used as model covariates, and first-degree relatives were removed. To evaluate the enrichment of the associated proteins in functional processes, we used STRING interaction network and functional enrichment analysis tool 22 . The KANTA clinical laboratory test data in FinnGen spans from 2014 to 2023, as detailed in the FinnGen documentation ( https://finngen.gitbook.io/finngen-handbook ). We tested the associations of the 46 GWAS lead variants with the laboratory values using continuous regression analysis, adjusting for age, sex, sampling year, and PC1-10 as covariates using the glm function in Plink2. For mediation analysis 50 , we selected significant (FDR < 0.05) labQTLs limiting to the smallest p-value per each KANTA lab value, and included both the SNP and the corresponding lab value as independent variables in a logistic regression model of blood donorship. We accepted lab value associations reaching FDR < 0.05, indicating that these values associate with blood donorship when adjusted for their QTL SNPs. To further validate the causal effect of each GWAS lead variant on laboratory values, we employed the Sobel mediation test 51 using the R package bda v18.3.2, accepting FDR < 0.05. Further details on the analyses are described in Supplementary Methods. Declarations Data Availability FinnGen summary statistics are available at https://r12.finngen.fi/. Data supporting the current study are available from the authors upon reasonable request and with permission of FinnGen. The GWAS summary statistics for the blood donor phenotype will be available at a public repository to be determined later. Code Availability Code will be available in https://github.com/FRCBS/HDE-GWAS. Competing Interests The authors declare no competing interests. Acknowledgements We want to acknowledge the participants and investigators of FinnGen study. The FinnGen project is funded by two grants from Business Finland (HUS 4685/31/2016 and UH 4386/31/2016) and the following industry partners: AbbVie Inc., AstraZeneca UK Ltd, Biogen MA Inc., Bristol Myers Squibb (and Celgene Corporation & Celgene International II Sàrl), Genentech Inc., Merck Sharp & Dohme LCC, Pfizer Inc., GlaxoSmithKline Intellectual Property Development Ltd., Sanofi US Services Inc., Maze Therapeutics Inc., Janssen Biotech Inc, Novartis Pharma AG, and Boehringer Ingelheim International GmbH. Following biobanks are acknowledged for delivering biobank samples to FinnGen: Auria Biobank (www.auria.fi/biopankki), THL Biobank (www.thl.fi/biobank), Helsinki Biobank (www.helsinginbiopankki.fi), Biobank Borealis of Northern Finland (https://www.ppshp.fi/Tutkimus-ja-opetus/Biopankki/Pages/Biobank-Borealis-briefly-in-English.aspx), Finnish Clinical Biobank Tampere (www.tays.fi/en-US/Research_and_development/Finnish_Clinical_Biobank_Tampere), Biobank of Eastern Finland (www.ita-suomenbiopankki.fi/en), Central Finland Biobank (www.ksshp.fi/fi-FI/Potilaalle/Biopankki), Finnish Red Cross Blood Service Biobank (www.veripalvelu.fi/verenluovutus/biopankkitoiminta), Terveystalo Biobank (www.terveystalo.com/fi/Yritystietoa/Terveystalo-Biopankki/Biopankki/) and Arctic Biobank (https://www.oulu.fi/en/university/faculties-and-units/faculty-medicine/northern-finland-birth-cohorts-and-arctic-biobank). All Finnish Biobanks are members of BBMRI.fi infrastructure (www.bbmri.fi). Finnish Biobank Cooperative -FINBB (https://finbb.fi/) is the coordinator of BBMRI-ERIC operations in Finland. The Finnish biobank data can be accessed through the Fingenious ® services (https://site.fingenious.fi/en/) managed by FINBB. In addition, we want to acknowledge Dr Katri Haimila for her valuable advice with red cell antigen immunogenetics, Dr Kati Hyvärinen for her expertise and kind help with red cell antigen imputation and the personnel of Blood Service Biobank for their kind help and support for the study. Author Contributions Original study concept by JC and JR. Genetic and statistical analysis of the study: JC, JT, MA and JR. Visualization: JC, JT and JR. Medical expertise: JL. FinnGen: data curation and resources. Contributed to the study design: JC, JR, JT, MA, SK, JP. Original manuscript: JC. All the authors read, commented, and approved the final manuscript. The authors declare that they have no conflict of interest. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6663925","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":467551896,"identity":"7039a3dd-f4b7-473c-9074-fd6400311fde","order_by":0,"name":"Jonna Clancy","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0002-0568-6676","institution":"Finnish Red Cross Blood Service","correspondingAuthor":true,"prefix":"","firstName":"Jonna","middleName":"","lastName":"Clancy","suffix":""},{"id":467551897,"identity":"55989822-b4b4-4456-a597-39f47ff00337","order_by":1,"name":"Jarkko Toivonen","email":"","orcid":"","institution":"Finnish Red Cross Blood Service","correspondingAuthor":false,"prefix":"","firstName":"Jarkko","middleName":"","lastName":"Toivonen","suffix":""},{"id":467551898,"identity":"3eb96d7f-e2e3-4fdc-b4ed-67119f263ed4","order_by":2,"name":"Jouni Lauronen","email":"","orcid":"","institution":"Finnish Red Cross Blood Service","correspondingAuthor":false,"prefix":"","firstName":"Jouni","middleName":"","lastName":"Lauronen","suffix":""},{"id":467551899,"identity":"e5976165-db27-4669-a419-e32526c4e18b","order_by":3,"name":"Satu Koskela","email":"","orcid":"https://orcid.org/0000-0001-9258-9163","institution":"Finnish Red Cross Blood Service","correspondingAuthor":false,"prefix":"","firstName":"Satu","middleName":"","lastName":"Koskela","suffix":""},{"id":467551900,"identity":"4ed204b5-6d08-4a10-9319-b439e2e5e6fc","order_by":4,"name":"Jukka Partanen","email":"","orcid":"https://orcid.org/0000-0001-6681-4734","institution":"Finnish Red Cross Blood Service","correspondingAuthor":false,"prefix":"","firstName":"Jukka","middleName":"","lastName":"Partanen","suffix":""},{"id":467551901,"identity":"05074083-9948-4f06-9f46-91d7aa5abf17","order_by":5,"name":"FinnGen -","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"FinnGen","middleName":"","lastName":"-","suffix":""},{"id":467551902,"identity":"fbce6586-5d70-4fda-a952-d67d838b3c55","order_by":6,"name":"Mikko Arvas","email":"","orcid":"https://orcid.org/0000-0002-6902-8488","institution":"Finnish Red Cross Blood Service","correspondingAuthor":false,"prefix":"","firstName":"Mikko","middleName":"","lastName":"Arvas","suffix":""},{"id":467551903,"identity":"36c2a69f-8698-42fc-a44e-798279ae928b","order_by":7,"name":"Jarmo Ritari","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Jarmo","middleName":"","lastName":"Ritari","suffix":""}],"badges":[],"createdAt":"2025-05-14 11:56:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6663925/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6663925/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41431-026-02100-2","type":"published","date":"2026-04-27T04:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":84297497,"identity":"5736cdbd-22cf-4d3d-9e9b-4d75e0543863","added_by":"auto","created_at":"2025-06-10 09:47:15","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":281294,"visible":true,"origin":"","legend":"\u003cp\u003eResults of the genome-wide association study (GWAS) for blood donorship. \u003cstrong\u003ea)\u003c/strong\u003e Manhattan plot of the whole genome. \u003cstrong\u003eb)\u003c/strong\u003e Extended major histocompatibility (MHC) region on chromosome 6. \u003cstrong\u003ec)\u003c/strong\u003e MHC association adjusted for type 1 diabetes (T1D). In panel \u003cstrong\u003ea)\u003c/strong\u003e, red dots indicate fine-mapped variants. In panels \u003cstrong\u003eb)\u003c/strong\u003e and \u003cstrong\u003ec)\u003c/strong\u003e, the highest variant in each peak reaching genome-wide significance (p \u0026lt; 5 x 10\u003csup\u003e-8\u003c/sup\u003e) is highlighted in red. For variants with p-values ≤ 5 x 10\u003csup\u003e-9\u003c/sup\u003e, the target gene or nearest gene is named. The horizontal line represents significance threshold of 5 x 10\u003csup\u003e-8\u003c/sup\u003e.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6663925/v1/d50330fed052bb769e4f25ba.png"},{"id":84297155,"identity":"987ed633-e4e8-40cd-a407-88ea6b9daa03","added_by":"auto","created_at":"2025-06-10 09:39:15","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":240848,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 3\u003c/strong\u003e. Genetic correlations of selected phenotypes against blood donorship phenotype. Each correlation is statistically significant at an FDR threshold of 0.05. The proportion of genetic variance shared between blood donorship, and a given phenotype (r\u003csub\u003eg\u003c/sub\u003e) is shown on the x-axis. Error bars represent the 95% confidence intervals. Apart from height and hemoglobin, all the phenotypes were negatively correlated with blood donorship.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6663925/v1/ee984c3b4d0431e424eb416f.png"},{"id":84297160,"identity":"21a28ee6-5d63-419d-ac7e-ecf0305680c5","added_by":"auto","created_at":"2025-06-10 09:39:15","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":151998,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 5\u003c/strong\u003e. Conditional \u003cem\u003eHLA\u003c/em\u003e association analysis. Upper panel: \u003cem\u003eHLA\u003c/em\u003e association analysis for blood donorship. Middle panel: \u003cem\u003eHLA\u003c/em\u003eassociation analysis for blood donorship adjusted for rs9272324-G. Lower panel: \u003cem\u003eHLA\u003c/em\u003e association analysis for blood donorship adjusted for \u003cem\u003eHLA-DRB1\u003c/em\u003e*04:01. The other covariates used in all analyses were age, sex, BMI, PC1-10, birth region, and FinnGen genotyping array version. The dotted horizontal line represents the highest p-value that meets the FDR limit of 0.05.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6663925/v1/813a644cad116c11ebde0877.png"},{"id":84297162,"identity":"9f4e8b37-3208-4f01-a0c9-5eb1acd45b21","added_by":"auto","created_at":"2025-06-10 09:39:15","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":204196,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 6\u003c/strong\u003e. Enriched functional processes among the protein levels regulated by the 41 fine-mapped and 5 MHC lead variants. \u003cstrong\u003ea)\u003c/strong\u003eKEGG pathway enrichments among the 181 significant QTL target proteins. \u003cstrong\u003eb)\u003c/strong\u003eFive Gene Ontology (GO) functional processes enriched among the 54 proteins regulated by rs687621-A located in \u003cem\u003eABO\u003c/em\u003e. Three variants were found to regulate these functional processes. The associated variants and the effect alleles are shown on the x-axis and the proteins on the y-axis. The protein levels were measured in blood donors.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6663925/v1/6b7a64787ae14d6eea102d18.png"},{"id":84297170,"identity":"030d07bc-2043-4fc4-96e5-95b7eef107c5","added_by":"auto","created_at":"2025-06-10 09:39:16","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":241783,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 7\u003c/strong\u003e. Mediation analysis of labQTLs. The analysis estimates the indirect effect of a genetic variant on blood donorship through a measured laboratory value. \u003cstrong\u003ea)\u003c/strong\u003e 43 significant mediation effects plotted in the top right quarter as defined by FDR \u0026lt; 0.05 difference in a lab value between blood donors and FinnGen controls adjusted for the QTL SNP (x-axis) and Sobel’s mediation test (y-axis). FDR limits are indicated by dashed lines. Proportion of mediation relative to the total effect of the variant is depicted by symbol size. \u003cstrong\u003eb)\u003c/strong\u003e Barplot of lab values with a significant QTL. The y-axis shows lab value effect sizes toward blood donorship, i.e. the effect size between blood donors and FinnGen controls when adjusted for the QTL SNP. The labQTL direction for the allele enriched in blood donors is depicted by the color bar on the x-axis. A lab measurement can have a lower value in blood donors than in controls (negative beta), but the QTL direction can still be up (or \u003cem\u003evice versa\u003c/em\u003e).\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-6663925/v1/31be523cd9d91223564dca0b.png"},{"id":107973474,"identity":"335ae14c-b0d6-401a-b5d2-43e02be02a54","added_by":"auto","created_at":"2026-04-28 07:11:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1714143,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6663925/v1/3e826fb3-776f-449f-9456-c85590532db9.pdf"},{"id":84298223,"identity":"fdea7bf2-ee07-45b9-ba23-526b04db300c","added_by":"auto","created_at":"2025-06-10 09:55:15","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1158596,"visible":true,"origin":"","legend":"Supplementary information","description":"","filename":"JulkaisuSupplementaryinformationID136762.docx","url":"https://assets-eu.researchsquare.com/files/rs-6663925/v1/9dec29eb7997a84d744408f2.docx"}],"financialInterests":"There is no duality of interest","formattedTitle":"Genome-Wide Association Study Identifies Protective Genetic Factors in Active Blood Donors Against Multiple Diseases","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBlood donors are a highly selected population due to the health status criteria they must meet to be eligible to donate blood. Basic restrictions on blood donation suitability include serious illnesses such as severe cardiovascular conditions, cancer and epilepsy\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Some conditions that restrict blood donation suitability have a strong well-known genetic background, such as the association of \u003cem\u003eHLA\u003c/em\u003e genes with type 1 diabetes (T1D)\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e and certain autoimmune diseases\u003csup\u003e\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. As a minimum hemoglobin level is required for donation eligibility, genetic variants affecting iron and hemoglobin levels may also result in donor selection. In addition to the blood donation eligibility criteria, the healthier lifestyle followed by the blood donors and self-selection caused by various factors, e.g. common cold, results in a known phenomenon, \u003cem\u003ehealthy donor effect\u003c/em\u003e, HDE, a form of membership bias, which can lead to detrimental effects in a research setting if not taken into account\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eComprehensive selection of different blood groups, specifically sufficiency in emergency blood, O RhD neg, suitable for most recipients, are basic requirements in blood banks. Due to high need for the emergency blood and strong immunogenicity of certain blood group antigens such as ABO, Rh and Kell\u003csup\u003e\u003cspan additionalcitationids=\"CR8 CR9\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e, donor recruitment is partially based on blood group genotype and results in enrichment of certain blood group antigens in a blood donor pool. Well-known associations between ABO blood group antigens and cardiovascular conditions exist, more precisely between red cell antigen O and reduced thromboembolic diseases\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Risk for bleeding disorder, von Willebrand disease, is known to be higher among individuals with red cell O antigen\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Pregnancy-induced hypertension has been previously described to occur more frequently in RhD positive individuals\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Moreover, in hemochromatosis, blood donation alleviates the symptoms, and donors with hemochromatosis-associated gene variants are known to be enriched in the blood donor population\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eBlood donors have been used as a healthy control cohort in genetics and biomedical research due to their commitment and willingness to participate\u003csup\u003e\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. However, the enrichment of genetic variants with established disease associations or biological functions may result in unpredicted effects in biomedical research. To reveal the genetics of blood donation and to understand the possible genetic selection leading to the HDE, we conducted a genome-wide association study (GWAS) between active blood donors of the (Finnish Red Cross) Blood Service Biobank and FinnGen cohort.\u003c/p\u003e \u003cp\u003eTo the best of our knowledge, no previous genome studies have focused on the possible selection bias caused by blood groups, health questionnaire-based requirement, and self-selection of blood donors. In our analysis, we found altogether 2,973 genome-wide significant associations in 46 distinct fine-mapped loci. Genetic correlation analysis revealed strong negative correlation between blood donorship and diseases such as mental disorders, pain disorders, cardiovascular diseases, addiction disorders, autoimmune disorders, and Alzheimer\u0026rsquo;s disease. \u003cem\u003eHLA\u003c/em\u003e association analysis confirmed the enrichment of hemochromatosis-related and protective autoimmune \u003cem\u003eHLA\u003c/em\u003e-alleles and the lower occurrence of autoimmune-related \u003cem\u003eHLA\u003c/em\u003e-alleles in the blood donor population. The genome-wide significant variants also associated with 181 plasma protein level alterations and several clinical blood laboratory value measurements. The results of this study demonstrate the genetic basis of the HDE, which is not limited to immediate blood donorship inclusion criteria.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe overall study design is shown in Supplementary Fig.\u0026nbsp;2. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e provides a detailed description of the study population characteristics and differences in diagnoses between the two cohorts. Blood donors, both male and female, were generally younger and taller than the controls. Additionally, male blood donors were heavier than their control counterparts. The blood donor cohort had a higher proportion of female participants. In all diagnosis categories, the prevalence of disease was significantly higher in the control group compared to the blood donors, except for the category 'XVI Certain conditions originating in the perinatal period,' where a higher prevalence of cases was observed among blood donors of both sexes.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of the study cohorts. Age, sex, height, weight, BMI and FinnGen endpoint categories in blood donors and FinnGen control cohorts are shown.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eFemales\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eMales\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBlood donors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFinnGen\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBlood donors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFinnGen\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian age at sampling, years (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44.47 (18.2\u0026ndash;70.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62.7 (36.2\u0026ndash;89.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50.26 (25.5\u0026ndash;75.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e70.46 (51.3\u0026ndash;89.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32,460 (60.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e120,279 (52.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21,228 (39.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e107,781 (47.26)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian weight, kg (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72 (54\u0026ndash;90) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72 (51\u0026ndash;93) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e86 (68\u0026ndash;104)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e84 (64\u0026ndash;104)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian height, cm (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e167 (159\u0026ndash;175)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e164 (156\u0026ndash;172)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e180 (171\u0026ndash;189)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e177 (168\u0026ndash;186)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian BMI (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.83 (19.3\u0026ndash;32.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.64 (19.1\u0026ndash;34.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.58 (21.6\u0026ndash;31.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26.88 (21.2\u0026ndash;32.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of diagnoses per FinnGen endpoint category (%)\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI Certain infectious and parasitic diseases (AB1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7,466 (23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47,901 (39.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5,565 (26.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e48,487 (44.99)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eII Neoplasms from cancer register (ICD-O-3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e968 (2.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34,299 (28.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e791 (3.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38,891 (36.08)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eII Neoplasms from hospital discharges (CD2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6,960 (21.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67,026 (55.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,804 (13.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e52,708 (48.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIII Blood and immune system (D3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e964 (2.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17,017 (14.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e534 (2.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15,606 (14.48)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIV Endocrine, nutritional and metabolic (E4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10,269 (31.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83,780 (69.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,029 (14.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e58,530 (54.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIX Circulatory system (I9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6,276 (19.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69,429 (57.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4,587 (21.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e78,876 (73.18)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eV Mental and behavioural disorders (F5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7,871 (24.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42,676 (35.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,648 (17.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33,080 (30.69)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVI Nervous system (G6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6,431 (19.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54,710 (45.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4,501 (21.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e51,142 (47.45)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVII Eye and adnexa (H7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8,088 (24.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58,567 (48.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4,660 (21.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e47,174 (43.77)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVIII Ear and mastoid process (H8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5,316 (16.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29,249 (24.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,893 (18.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26,561 (24.64)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eX Respiratory system (J10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14,408 (44.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69,022 (57.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11,532 (54.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e69,209 (64.21)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eXI Digestive system (K11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27,542 (84.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e106,938 (88.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17,037 (80.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e90,482 (83.95)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eXII Skin and subcutaneous tissue (L12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7,710 (23.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43,402 (36.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4,096 (19.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32,486 (30.14)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eXIII Musculoskeletal and connective tissue (M13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13,715 (42.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83,931 (69.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9,118 (42.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e68,570 (63.62)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eXIV Genitourinary system (N14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17,334 (53.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e91,665 (76.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4,463 (21.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e49,729 (46.14)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eXIX Injury, poisoning, external (ST19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15,936 (49.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73,725 (61.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13,051 (61.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e70,375 (65.29)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eXV Pregnancy, childbirth and the puerperium (O15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20,560 (63.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86,799 (72.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eXVI Perinatal period (P16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e535 (1.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,000 (0.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e263 (1.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e514 (0.48)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eXVII Congenital malformations (Q17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,018 (6.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10,032 (8.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,231 (5.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7,258 (6.73)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eXVIII Abnormal clinical and laboratory findings (R18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16,800 (51.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e93,458 (77.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9,125 (42.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e80,419 (74.61)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eXX External causes of mortality (VWXY20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13,025 (40.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64,874 (53.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10,741 (50.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e62,034 (57.56)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eXXI Contact with health services (Z21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24,581 (75.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10,4068 (86.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10,544 (49.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e72,915 (67.65)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eXXII Codes for special purposes (U22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,417 (4.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5,906 (4.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e595 (2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4,310 (4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003csup\u003ea\u003c/sup\u003e Not significant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003csup\u003eb\u003c/sup\u003e Shortened for clarity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eGenome-wide association analysis\u003c/p\u003e \u003cp\u003eGWAS revealed 2,973 significant (p\u0026thinsp;\u0026lt;\u0026thinsp;5x10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e) SNPs associated with blood donorship. After fine-mapping, 5 coding (4 missense and 1 inframe deletion) and 36 non-coding variants were detected (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). In addition to these 41 variants, 5 non-coding lead variants in the extended MHC region (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB and C) were included in the downstream analyses (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe strongest genome-wide associations were seen in variants related to blood group antigens: rs55794721-A (p\u0026thinsp;=\u0026thinsp;8.88x10\u003csup\u003e\u0026minus;\u0026thinsp;89\u003c/sup\u003e) in chr 1, rs687621-A (p\u0026thinsp;=\u0026thinsp;5.70x10\u003csup\u003e\u0026minus;\u0026thinsp;81\u003c/sup\u003e) on chr 9 in \u003cem\u003eABO\u003c/em\u003e gene, and rs8176058-A (p\u0026thinsp;=\u0026thinsp;1.16x10\u003csup\u003e\u0026minus;\u0026thinsp;31\u003c/sup\u003e) on chr 7 in \u003cem\u003eKEL\u003c/em\u003e gene (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite of the adjustment of the GWAS for BMI, we observed significant variants influencing height, weight, and BMI. rs66781921-T, an inframe deletion variant in chr 2, previously shown to have a negative association with height, weight and body mass index\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, was rarer in blood donors (beta=-1.97, p\u0026thinsp;=\u0026thinsp;1.89x10\u003csup\u003e\u0026minus;\u0026thinsp;11\u003c/sup\u003e). The missense variant rs34811474-A, previously shown to be negatively associated with body mass index\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, was found to be more frequent in blood donors (beta\u0026thinsp;=\u0026thinsp;0.064, p\u0026thinsp;=\u0026thinsp;1.21x10\u003csup\u003e\u0026minus;\u0026thinsp;12\u003c/sup\u003e). Variants rs10947114-T (beta=-0.055, p\u0026thinsp;=\u0026thinsp;2.88x10\u003csup\u003e\u0026minus;\u0026thinsp;09\u003c/sup\u003e), rs3130906-A (beta=-0.055, p\u0026thinsp;=\u0026thinsp;5.17x10\u003csup\u003e\u0026minus;\u0026thinsp;11\u003c/sup\u003e) and rs4713637-C (beta=-0.047, p\u0026thinsp;=\u0026thinsp;1.39x10\u003csup\u003e\u0026minus;\u0026thinsp;08\u003c/sup\u003e), previously described to be associated with spondylopathies and celiac disease\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, were negatively associated with blood donorship. A rare intron variant in chromosome 3, rs528492111-T, previously shown to have a weak association to benign neoplasm of conjunctiva\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, was more frequent in blood donors (beta\u0026thinsp;=\u0026thinsp;1.87, p\u0026thinsp;=\u0026thinsp;4.09x10\u003csup\u003e\u0026minus;\u0026thinsp;10\u003c/sup\u003e). All the fine-mapped and MHC lead variants in the are listed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFine-mapped (41) and MHC lead variants (5) from blood donorship GWAS. P-value, effect size, the predicted most serious effect type and target gene for each variant are shown. Variants are ordered according to association effect size. In case the target gene is not known, the nearest gene is indicated by asterisk (*).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChromosome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePosition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEffect allele\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ersID number\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBeta\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eVariant type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTarget gene\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e231460727\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers66781921\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,89E-11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1,973\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003einframe deletion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNCL\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5226799\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers1135071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,93E-13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0,573\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003emissense variant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHBB\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e58358769\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers199598395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,55E-25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0,383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003emissense variant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRNF43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e142957921\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers8176058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,16E-31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0,327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003emissense variant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eKEL\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e174443703\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers191302298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,89E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0,292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eintron variant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eLINC01411\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e105038155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers10096658\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,35E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0,277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eintron variant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eZFPM2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5149612\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers62113181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8,06E-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0,135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eintron variant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eKDM4B\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e37135112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers75738358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4,89E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0,080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eintergenic variant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMIR4801*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e101559560\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers12539059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5,68E-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0,071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003edownstream gene variant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCOL26A1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32636375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers9272324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,36E-16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0,064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eintron variant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHLA-DQA1*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18707615\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers36035346\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5,48E-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0,062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eintron variant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCRTC1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23484043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers60678519\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,06E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0,055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003edownstream gene variant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAC079780 ,1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30934404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers10947114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,88E-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0,055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eintron variant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSFTA2*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31457404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers3130906\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5,17E-11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0,055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eintron variant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHCP5*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e77157477\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers80319585\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,92E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0,055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eintron variant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eKCNMA1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e82855734\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers7795945\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,28E-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0,049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eintron variant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePCLO\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e186695046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers34483988\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,65E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0,049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eintron variant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eFAM171B\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18717009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers6775319\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,17E-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0,048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eintron variant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSATB1-AS1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e105646256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers7907026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4,63E-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0,048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eintergenic variant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSORCS3*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e153658595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers3806234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5,16E-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0,047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eupstream gene variant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSNAPIN\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33602120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers4713637\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,39E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0,047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eintergenic variant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eLINC00336*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e52600634\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers62083414\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5,35E-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0,046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eintron variant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDCC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19015485\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers207350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,03E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0,045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eintergenic variant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eLCORL*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e48880334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers594398\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,18E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0,044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eintron variant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSUMO2P17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e38272211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers10880819\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,64E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003edownstream gene variant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRF00019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e71295511\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers35440643\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4,54E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eintron variant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eZNF638\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26992548\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers10166897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,57E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eintron variant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMAPRE3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e75059054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers113236240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6,53E-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eintron variant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePPCDC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16695862\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCAAGCA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers55763604\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,84E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eintron variant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eBNC2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27087829\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers55732343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,31E-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eregulatory region variant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSLC9A1*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26071867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers9968910\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,12E-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eintergenic variant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHIST1H1C*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e67991393\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers5821444\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7,98E-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3 prime UTR variant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eC17orf58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13464582\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers6498415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5,47E-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eintron variant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eU91319 ,1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e90023620\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers12444334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,51E-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eintron variant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eGAS8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e59856883\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers11675100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,88E-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eintron variant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAC007100 ,1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17127475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers9367942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,81E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eintron variant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSTMND1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e57289173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers61937595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,25E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eintron variant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eR3HDM2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25407216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers34811474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,21E-12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003emissense variant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eANAPC4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e66016472\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers1500956\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5,14E-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eintron variant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePDE4B\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e102401219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers77448558\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,28E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eintergenic variant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eGRIK2*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e97662657\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers10268629\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,71E-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eintergenic variant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTAC1*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50130043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers75350584\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,58E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eintron variant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDLEU1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e133261662\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers687621\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5,70E-81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eintron variant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eABO\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25235176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers55794721\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8,88E-89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eupstream gene variant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSYF2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50410043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers118101548\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,90E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eintron variant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePOLD1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e49008876\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers528492111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4,09E-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eintron variant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eWDR6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eGenetic correlation\u003c/p\u003e \u003cp\u003eTo assess genetic similarity between blood donors and various disease phenotypes, we performed a genetic correlation analysis comparing the blood donor GWAS result with over 2,400 FinnGen core endpoint GWAS results.\u003c/p\u003e \u003cp\u003eAltogether 593 phenotypes were negatively correlated with blood donorship (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Supplementary File 2). Height and hemoglobin were the only phenotypes showing weak positive correlation with blood donorship, r\u003csub\u003eg\u003c/sub\u003e=0.07 and r\u003csub\u003eg\u003c/sub\u003e=0.18, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e), despite that the blood donorship GWAS had been adjusted for BMI.\u003c/p\u003e \u003cp\u003eBlood donorship was negatively correlated (r\u003csub\u003eg\u003c/sub\u003e\u0026lt;-0.5) with 30 phenotypes (Supplementary File 2). The strongest negative correlations were seen with \u0026lsquo;Anemias\u0026rsquo; (r\u003csub\u003eg\u003c/sub\u003e=-0.66, FDR\u0026thinsp;=\u0026thinsp;2.81x10\u003csup\u003e\u0026minus;\u0026thinsp;112\u003c/sup\u003e), \u0026acute;Other reaction to severe stress and adjustment disorders\u0026acute; (r\u003csub\u003eg\u003c/sub\u003e=-0.64, FDR\u0026thinsp;=\u0026thinsp;3.36x10\u003csup\u003e\u0026minus;\u0026thinsp;40\u003c/sup\u003e) and \u0026acute;Other disorders of teeth and supporting structures\u0026acute; (r\u003csub\u003eg\u003c/sub\u003e=-0.62, FDR\u0026thinsp;=\u0026thinsp;2.74x10\u003csup\u003e\u0026minus;\u0026thinsp;03\u003c/sup\u003e). Malignancy-related phenotypes showed moderate negative correlations (r\u003csub\u003eg\u003c/sub\u003e\u0026lt;-0.2). In addition, negative correlations were observed for addiction phenotypes such as smoking dependency (r\u003csub\u003eg\u003c/sub\u003e=-0.29, FDR\u0026thinsp;=\u0026thinsp;1.57x10\u003csup\u003e\u0026minus;\u0026thinsp;05\u003c/sup\u003e), alcohol dependence (r\u003csub\u003eg\u003c/sub\u003e=-0.31, FDR\u0026thinsp;=\u0026thinsp;8.26x10\u003csup\u003e\u0026minus;\u0026thinsp;20\u003c/sup\u003e) and substance abuse (r\u003csub\u003eg\u003c/sub\u003e=-0.39, FDR\u0026thinsp;=\u0026thinsp;2.88x10\u003csup\u003e\u0026minus;\u0026thinsp;45\u003c/sup\u003e). Expectedly, blood donors displayed negative correlation towards autoimmune-related phenotypes, such as autoimmune diseases in general (r\u003csub\u003eg\u003c/sub\u003e=-0.33, FDR\u0026thinsp;=\u0026thinsp;1.87x10\u003csup\u003e\u0026minus;\u0026thinsp;52\u003c/sup\u003e) and \u0026lsquo;Irritable bowel syndrome\u0026rsquo; (r\u003csub\u003eg\u003c/sub\u003e=-0.50, FDR\u0026thinsp;=\u0026thinsp;4.05x10\u003csup\u003e\u0026minus;\u0026thinsp;26\u003c/sup\u003e). A wide variety of mental illnesses (in total 49 phenotypes) were negatively correlated with blood donorship. The strongest mental illness-related phenotype with negative genetic correlation was \u0026lsquo;Mental disorders, not otherwise specified\u0026rsquo; (rg\u0026thinsp;=\u0026thinsp;0.59, FDR\u0026thinsp;=\u0026thinsp;0.001). Another example of phenotypes not directly restricted by blood donation eligibility criteria but with relatively strong negative correlation were infectious phenotypes, such as acute bronchitis (r\u003csub\u003eg\u003c/sub\u003e=-0.58, FDR\u0026thinsp;=\u0026thinsp;1.76x10\u003csup\u003e\u0026minus;\u0026thinsp;16\u003c/sup\u003e), viral hepatitis (r\u003csub\u003eg\u003c/sub\u003e=-0.51, FDR\u0026thinsp;=\u0026thinsp;1.88x10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e) and \u0026lsquo;Other or unspecified bacterial infection\u0026rsquo; (r\u003csub\u003eg\u003c/sub\u003e=-0.50, FDR\u0026thinsp;=\u0026thinsp;1.83x10\u003csup\u003e\u0026minus;\u0026thinsp;09\u003c/sup\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Supplementary File 2). Lastly, phenotypes not directly linked to blood donation eligibility criteria, but which undeniably affect donation suitability or donor\u0026rsquo;s knowledge of their current state of health, such as Alzheimer\u0026rsquo;s disease (r\u003csub\u003eg\u003c/sub\u003e=-0.24, FDR\u0026thinsp;=\u0026thinsp;0.03), were also negatively correlated with blood donorship.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBlood group antigen-related associations\u003c/p\u003e \u003cp\u003eTo quantify the enrichment of blood group antigens among blood donors, we evaluated the association between imputed red cell antigens and blood donorship using a logistic regression model. The model revealed a strong negative association for the Kell, RhD, and AB blood group antigens, and a positive association for the O blood group antigen (Supplementary Fig.\u0026nbsp;3). The effect sizes for the red cell antigens and other GWAS covariates and more detailed description of the blood group antigen association analysis is provided in the Supplementary information.\u003c/p\u003e \u003cp\u003e \u003cem\u003eHLA\u003c/em\u003e-allele associations\u003c/p\u003e \u003cp\u003eBecause of the known strong role of the \u003cem\u003eHLA\u003c/em\u003e region in immunology and the predisposing risk of certain \u003cem\u003eHLA\u003c/em\u003e-alleles in autoimmunity\u003csup\u003e\u003cspan additionalcitationids=\"CR3 CR4\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e, we performed an association study with imputed \u003cem\u003eHLA\u003c/em\u003e-alleles for blood donor endpoint. \u003cem\u003eHLA\u003c/em\u003e association analysis for blood donorship (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e5\u003c/span\u003e, upper panel) revealed a negative association with \u003cem\u003eHLA\u003c/em\u003e-alleles in linkage with the well-known T1D risk genotype \u003cem\u003eHLA-DQ2/DQ8\u003c/em\u003e. A known risk allele for ankylosing spondylitis\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e \u003cem\u003eHLA-B*27:05\u003c/em\u003e was negatively associated with blood donorship. Positive association was seen with \u003cem\u003eHLA\u003c/em\u003e-alleles previously shown to be associated with HFE C282Y allele in Finland\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e or HLA class II alleles with known protection from T1D\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. To fine-map the MHC GWAS signal (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb), we adjusted the analysis with the MHC lead variant on chromosome 6 in \u003cem\u003eHLA-DQA1\u003c/em\u003e, rs9272324-G (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e5\u003c/span\u003e, middle panel). Controlling for rs9272324-G decreased the effect sizes of the above-mentioned \u003cem\u003eHLA\u003c/em\u003e-alleles in linkage with \u003cem\u003eHLA-DQ8\u003c/em\u003e and \u003cem\u003eHLA-B*27:05\u003c/em\u003e and increased the p-values of the above mentioned HFE C282Y related \u003cem\u003eHLA\u003c/em\u003e-alleles below the significance level. When adjusted for the lead \u003cem\u003eHLA\u003c/em\u003e-allele, \u003cem\u003eDRB1*04:01\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e5\u003c/span\u003e, lower panel), \u003cem\u003eHLA-B*27:05\u003c/em\u003e and \u003cem\u003eHLA-C*02:02\u003c/em\u003e in LD (r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.3) with \u003cem\u003eHLA-B*27:05\u003c/em\u003e and HLA-alleles in linkage with HLA-DQ2 were negatively associated with blood donorship. Adjustment with \u003cem\u003eDRB1*04:01\u003c/em\u003e revealed also the negative association of \u003cem\u003eHLA-B*08:01\u003c/em\u003e, a known \u003cem\u003eHLA-B\u003c/em\u003e allele in linkage with \u003cem\u003eHLA-DQ2\u003c/em\u003e in Finland, with blood donorship. In \u003cem\u003eHLA-DRB1*04:01\u003c/em\u003e adjusted analysis, HFE C282Y-related \u003cem\u003eHLA\u003c/em\u003e-alleles \u003cem\u003eB*07:02\u003c/em\u003e, \u003cem\u003eDRB1*15:01\u003c/em\u003e, \u003cem\u003eDQA1*01:02\u003c/em\u003e and \u003cem\u003eDQB1*06:02\u003c/em\u003e were positively associated with blood donorship, but with less significant p-values than in \u003cem\u003eHLA\u003c/em\u003e-association analysis without any additional adjustment (Supplementary File 2).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAssociation of the GWAS lead variants with molecular traits\u003c/p\u003e \u003cp\u003eTo better understand the functional effects of the 46 selected GWAS lead variants, we used quantitative trait locus (QTL) analysis for protein expression levels, metabolites, and common electronic health care record-derived laboratory measurements as well as functional enrichment analysis. Altogether 470,490 associations were tested, and 327 pQTL and labQTL associations reached the significance level of FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Among the 41 fine-mapped and five MHC region variants, 19 were significantly associated with 184 unique plasma protein expression levels and 18 with 128 clinical laboratory value measurements (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). However, no significant associations were observed between the 46 genetic variants and metabolite levels.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSignificant associations (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05) of quantitative trait locus analysis for protein expression level and clinical laboratory values with the GWAS fine-mapped and MHC lead variants. In case the target gene is not known, the nearest gene is indicated by asterisk (*).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003epQTL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003elabQTL\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ersID number\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eEffect allele\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eTarget gene\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eAssociated with protein level alteration\u003c/b\u003e (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eAssociated with laboratory value in a given category\u003c/b\u003e (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers1135071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHBB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52 different proteins\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIron\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers199598395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRNF43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePARP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIron, liver function\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers8176058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKEL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAPBB3, ETV5, NCR3, NT5M, TPH1, YTHDC1, KEL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers191302298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLINC01411\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21 different proteins\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers10096658\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eZFPM2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14 different proteins\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers62113181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKDM4B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRXRA, SLMAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers9272324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHLA-DQA1*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHERC5, MICB, SH3GL3-1, HLA-DRA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eElectrolytes, glucose, hemostasis, immune system, iron, lipids, thyroid gland function\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers60678519\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAC079780 ,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGPNMB, GPNMB-1, GPNMB-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers10947114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSFTA2*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDDR1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHemostasis, iron, liver function\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers3130906\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHCP5*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCDSN, TEK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIron, thyroid gland function, immune system, lipids, electrolytes, hemostasis, glucose\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers34483988\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFAM171B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIron\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers6775319\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSATB1-AS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGlucose\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers3806234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSNAPIN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eImmune system\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers4713637\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLINC00336*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIron, immune system, hemostasis\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers594398\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSUMO2P17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGlucose\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers10166897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMAPRE3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCGREF1, KHK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers113236240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePPCDC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePPCDC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers9968910\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHIST1H1C*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIron, glucose, immune system, hemostasis\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers5821444\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTTAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC17orf58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLiver function, lipids, immune system\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers12444334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGAS8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCDH15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIron, liver function\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers11675100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAC007100 ,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGlucose\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers61937595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR3HDM2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eINHBC, TDP1, IL21R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers34811474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eANAPC4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eImmune system, glucose\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers77448558\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGRIK2*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eARCV1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers10268629\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTAC1*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNADK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIron\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers687621\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eABO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54 different proteins\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eThyroid gland function, liver function, lipids, iron, immune system, hemostasis, glucose, electrolytes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers55794721\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSYF2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eICAM4, ICAM4-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eImmune system, hemostasis, iron, lipids\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers118101548\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePOLD1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15 different proteins\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eVariants regulating protein expression levels\u003c/p\u003e \u003cp\u003eTwo variants were shown to be associated with expression levels of more than 50 unique proteins (Supplementary Fig.\u0026nbsp;4). rs678621-G located on the \u003cem\u003eABO\u003c/em\u003e gene regulated expression levels of 54 proteins, and rs1135071-A in the \u003cem\u003eHBB\u003c/em\u003e gene regulated 52 proteins. rs678621-G pQTLs included proteins essential for the histo-blood group ABO formation, such as ABO itself and various N-acetylgalactosaminyltransferases such as GALNT9. Additionally, due to the enrichment of the rs687621-A allele in blood donors, decreased expression was observed for proteins such as F8 and MUC2, while the strongest increasing effect were seen for ALPI and SELE. Only two of the 52 associations identified with rs1135071-A showed decreasing effects on protein levels, the strongest effect was seen with TLR2, while the rest of the associations showed increasing effects, such as AHSP and MTIF3.\u003c/p\u003e \u003cp\u003eOther variants with more than 10 associations with protein expression levels included rs191302298-C (21 unique proteins), rs118101548-C (15 unique proteins) and rs10096658-C (14 unique proteins). In blood donors, the rs55794721-A variant exerted a decreasing effect on ICAM4, a LW glycoprotein present in the RhD protein complex. Additionally, an association was observed between rs60678519-A and GPNMB. Due to the enrichment of rs60678519-AAAT in blood donors, GPNMB expression was decreased in blood donors. Overexpression of GPNMB has been linked to immunosuppression within the tumor immune microenvironment, leading to poorer prognosis in various cancers\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe STRING\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e functional enrichment analysis tool identified several Gene Ontology (GO) functional processes for the 184 proteins. Furthermore, the 54 proteins regulated by rs687621-G alone showed significant enrichments (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe detected 60 pQTLs that were not involved with the enriched biological processes or pathways. Of these, the strongest increasing and decreasing effect was seen with rs1135071-A on AHSP (beta\u0026thinsp;=\u0026thinsp;2.59) and ACP6 (beta=-0.97), respectively (Supplementary Fig.\u0026nbsp;4).\u003c/p\u003e \u003cp\u003eSince the proteomics data originated solely from the blood donor cohort, we were unable to analyze whether the significant pQTLs acted as mediators.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eVariants regulating clinical laboratory test values\u003c/p\u003e \u003cp\u003eWe observed 128 significant labQTLs that comprised nine variants and 50 different measurements and represented eight different clinical laboratory value categories (Supplementary File 4). We performed mediation analysis for the observed significant labQTLs to estimate the indirect effect of a genetic variant on blood donorship through a measured laboratory value. Test of mediation for the top QTL of each 50 laboratory measurements showed significant mediation effect for 9 variants in 43 clinical laboratory measurements (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e7\u003c/span\u003ea). The highest number of mediated associations were seen in Iron (13 associations), Lipids (10 associations), Immune System (5 associations) and Liver Function (5 associations) related laboratory categories. The highest number of mediated associations, 16, on different laboratory values was seen for the MHC variant rs9272324. The results of the labQTL analysis are shown in detail in Supplementary File 4.\u003c/p\u003e \u003cp\u003eStrongest mediation effects, as measured by the proportion of mediated effect relative to the total SNP effect, was seen for iron-related laboratory values. Glucose, hemostasis, lipids, immune and liver function related labQTLs also showed proportion of mediation\u0026thinsp;\u0026gt;\u0026thinsp;0.2.\u003c/p\u003e \u003cp\u003eOf the 43 significant mediation effects, 25 showed lower and 18 higher clinical laboratory values in blood donors when compared to control population (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e7\u003c/span\u003eb). However, direction of the QTL effect on a laboratory value for the allele enriched in blood donors was sometimes opposite to the laboratory value difference between blood donors and controls. For example, blood donors had lower plasma ferritin levels (likely caused by donation), but they also harbored an enriched genetic variant that upregulates plasma ferritin.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe healthy donor effect is a well-documented phenomenon, but its genetic basis has remained unclear. We conducted a genome-wide comparison between blood donor biobank donors with relatively long and active blood donation history\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e and the FinnGen control population. The FinnGen controls included individuals from hospital biobanks, population cohorts, and patients at occupational health clinics. The latter two cohorts primarily comprise healthy individuals, and as disease-specific legacy cohorts were excluded from the analysis, the study controls can be assumed to be relatively unbiased, representing the general population of Finland.\u003c/p\u003e \u003cp\u003eThe observed protection of blood donors from certain diseases can be directly attributed to donor eligibility criteria that select healthy individuals. However, some of the observed protective genetic factors may be secondary. For instance, the strongest negative genetic correlation was seen with anemias. This correlation could be due to individuals with a higher tendency for diseases causing iron loss being less likely to start donating blood or to withstand repeated donations. Additionally, it may be a secondary result of autoimmune diseases, such as Crohn\u0026rsquo;s disease, which can restrict blood donation eligibility and cause secondary anemia. Surprisingly, our results revealed that blood donors not only exhibit a protective genetic profile against diseases explicitly targeted by donor eligibility criteria, but also show a broader negative genetic correlation with conditions beyond these criteria, including various mental illnesses, infectious diseases and Alzheimer\u0026rsquo;s disease. This suggests that blood donors harbor a lower genetic disease burden than the general biobank population. While pleiotropic effects and/or self-selection may explain this phenomenon, our analyses indicate that genetic correlations with the phenotypes targeted by the eligibility criteria are not consistently strong. Further investigations are required to fully understand pleiotropy in blood donor selection and to explore the potential benefits of an active blood donor population for genetic and biomedical research.\u003c/p\u003e \u003cp\u003eExpectedly, genetic variants in the MHC region played strong role in several genetic associations and in QTLs. \u003cem\u003eHLA\u003c/em\u003e-alleles with known autoimmune associations occurred with lower frequency in the blood donor population than in the control population, whereas \u003cem\u003eHFE\u003c/em\u003e C282Y-linked \u003cem\u003eHLA\u003c/em\u003e-alleles\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e and \u003cem\u003eHLA\u003c/em\u003e-alleles with known protective role in autoimmunity were more frequent among the blood donors. Furthermore, the negative selection for T1D in blood donors leading to lower frequency of \u003cem\u003eHLA-DQ2/8\u003c/em\u003e genotype, could explain some of the protective genetic correlation. \u003cem\u003eHLA\u003c/em\u003e-alleles of this genotype, have known predisposing associations, including certain infectious diseases and use of antidepressants\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. These findings show the simultaneous genetic selection in the MHC for hemochromatosis and against autoimmune conditions among blood donors, while the pleiotropic role of the associated \u003cem\u003eHLA\u003c/em\u003e-alleles could explain some of the protective genetic correlation shown in this study.\u003c/p\u003e \u003cp\u003eIn addition to tagging certain autoimmune-related \u003cem\u003eHLA\u003c/em\u003e-alleles or haplotypes (e.g. T1D and ankylosing spondylitis), rs9272324-G was shown to enhance the protein levels of \u003cem\u003eHLA-DRA\u003c/em\u003e, which encodes the nonpolymorphic \u003cem\u003eDR\u003c/em\u003e alpha-chain\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e, essential for the stable expression of DR molecules on the cell surface. Considering this, the known associations of \u003cem\u003eHLA-DRB1*15:01\u003c/em\u003e with multiple sclerosis\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003eand the lower frequency of the rs9272324-G in blood donors, further investigation of the pleiotropic role of rs9272324 in multiple sclerosis and in autoimmunity in general would be of interest.\u003c/p\u003e \u003cp\u003eIn addition to hemochromatosis, the results of the present study highlight genetic factors that may affect recipient matching or donation activity. We show an increasing expression level of AHSP protein due to \u003cem\u003eHb Tacoma\u003c/em\u003e variant rs1135071-A. AHSP plays a crucial role in hemoglobin assembly by stabilizing the αHb subunits and preventing the self-aggregation of excess αHb subunits prior the formation of HbA1 subunit\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. As AHSP may have a role as a genetic modifier in β-thalassemia\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e and the normal clinical phenotype of \u003cem\u003eHb Tacoma\u003c/em\u003e heterozygotes\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e, rs1135071 could be of further interest in β-thalassemia research. Since the rs1135071-A allele is less common in blood donors compared to controls and strongly downregulates Hb and methemoglobin, it may be useful to analyze whether blood donation is harmless for \u003cem\u003eHb Tacoma\u003c/em\u003e heterozygotes or whether the blood products of carries are suitable for recipients with conditions such as sickle cell disease\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWe demonstrated that blood donors differ from controls in several clinical blood laboratory measurements, such as ferritin and lipoproteins, that are regulated by the 41 fine-mapped and 5 MHC lead variants. Consistently with low prevalence of diabetes, blood donors exhibit lower plasma glucose levels, partly due to genetics, but also have higher LDL levels due to enriched QTLs. Interestingly, HDL levels are higher in blood donors, despite the presence of a variant that typically lowers HDL. This could be attributed to the U-shaped relationship between HDL and overall health\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e, where extremely high HDL levels are harmful and thus less common among blood donors. The mediation analysis revealed that blood donors had lower ferritin and higher hemoglobin levels than controls. This finding is in line with our previous studies; donation activity is a major determinant of ferritin in donors\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e, yet iron deficiency anemia is less common in donors (0.5%) than in the Finnish general population (2.6%)\u003csup\u003e34\u003c/sup\u003e. Consistent with this result, blood donors exhibited an enrichment of a compensatory variant that upregulates ferritin levels.\u003c/p\u003e \u003cp\u003eIn this study we present the genetic underpinnings of HDE and blood group-based selection in active blood donors. Our findings highlight the unique genetic makeup of this donor population and its impact on health and blood donation suitability. The results demonstrate the role of genetics in maintaining good health status and active blood donation career which is also connected to genetic variation associated with a good mental health. Furthermore, research using blood donors as a control group should account for this distinct genetic structure, even after donation ceases. Additionally, the relatively predominant enrichment of specific blood group antigens among blood donors warrants consideration in research as these antigens have several known disease associations. No unambiguous solution exists to control collider biases when conducting research within blood donors, but rather a case-by-case evaluation e.g. through directed acyclic graphs can be helpful when evaluating the possibility of collider biases\u003csup\u003e\u003cspan additionalcitationids=\"CR36\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Further investigations are necessary to fully elucidate the implications of these discoveries, but they underscore the genetic structure behind HDE and the potential of active blood donor cohorts for advancing biomedical genetic research.\u003c/p\u003e"},{"header":"Material and Methods","content":"\u003cp\u003eEthics statement\u003c/p\u003e \u003cp\u003eStudy subjects in FinnGen provided informed consent for biobank research, based on the Finnish Biobank Act. Alternatively, separate research cohorts, collected prior the Finnish Biobank Act came into effect (in September 2013) and start of FinnGen (August 2017), were collected based on study-specific consents and later transferred to the Finnish biobanks after approval by Fimea (Finnish Medicines Agency), the National Supervisory Authority for Welfare and Health.\u003c/p\u003e \u003cp\u003eData acquisition\u003c/p\u003e \u003cp\u003eGenome data used in the present study originated from samples that were genotyped in the FinnGen project using the FinnGen ThermoFisher Axiom custom array v1 or v2 and genome imputation version R12. To minimize potential bias in downstream analyses, legacy samples that were highly enriched in certain common diseases (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://finngen.gitbook.io/documentation/\u003c/span\u003e\u003cspan address=\"https://finngen.gitbook.io/documentation/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), were excluded from the study controls. Individuals under 18 years of age or lacking BMI data were removed. After filtering, total number of FinnGen genome data used in this study was 53,688 blood donors and 228,060 controls.\u003c/p\u003e \u003cp\u003eStatistical analyses\u003c/p\u003e \u003cp\u003eIf not stated otherwise, all statistical analyses were performed in R\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e version 4.3 or 4.4, with RStudio\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. The genome-wide significance p-value threshold p\u0026thinsp;\u0026lt;\u0026thinsp;5 x 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e was used in GWAS. In all other analyses, p-values were adjusted with the Benjamini\u0026ndash;Yekutieli procedure\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e, and significance threshold of 0.05 was used. All the phenotypes analyzed here have been described in FinnGen DF12 dataset. The FinnGen summary statistics data are publicly available (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://r12.finngen.fi/\u003c/span\u003e\u003cspan address=\"https://r12.finngen.fi/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003csup\u003e18\u003c/sup\u003e, and analysis methods are available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://finngen.gitbook.io/documentation/methods/\u003c/span\u003e\u003cspan address=\"https://finngen.gitbook.io/documentation/methods/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eAssociation analysis and fine-mapping\u003c/p\u003e \u003cp\u003eGWAS was performed with an additive genetic model using Regenie\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e v2.2.4 in FinnGen pipeline (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/FINNGEN/regenie-pipelines\u003c/span\u003e\u003cspan address=\"https://github.com/FINNGEN/regenie-pipelines\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Blood donor status was defined as a binary endpoint. Sex, age, BMI, PC1-10, birth region, and FinnGen genotyping array version were used as model covariates. To discover associations independent of type 1 diabetes (T1D) in chromosome 6, the FinnGen T1D endpoint\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e was used as an additional covariate in the chromosome 6-specific association analysis.\u003c/p\u003e \u003cp\u003eFine-mapping for the genome-wide significant GWAS signals (p\u0026thinsp;\u0026lt;\u0026thinsp;5 x 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e) was performed in FinnGen pipeline (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/FINNGEN/\u003c/span\u003e\u003cspan address=\"https://github.com/FINNGEN/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) with SuSiE\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e excluding MHC region (GRCh38, chr6:25\u0026ndash;34 Mb)\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e,\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. The lead variant in the MHC region in the non-T1D adjusted GWAS, rs9272324, was included in further analyses due to its strong association with autoimmunity\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAssociation testing of each classical \u003cem\u003eHLA\u003c/em\u003e-allele \u003cem\u003eHLA-A\u003c/em\u003e, -\u003cem\u003eB\u003c/em\u003e, -\u003cem\u003eC\u003c/em\u003e, -\u003cem\u003eDRB1\u003c/em\u003e, -\u003cem\u003eDQA1\u003c/em\u003e, -\u003cem\u003eDQB1\u003c/em\u003e, \u003cem\u003eDPB1\u003c/em\u003e and \u003cem\u003eHLA-DRB3-5\u003c/em\u003e with the blood donor endpoint was performed using Regenie v3.0.1 with the same covariates as in GWAS. ABO association analysis was performed using Regenie v2.2.4 with the same covariated as in GWAS, except region of birth.\u003c/p\u003e \u003cp\u003eGenetic correlation\u003c/p\u003e \u003cp\u003eAll pair-wise genetic correlations between blood donorship and all the 2,470 phenotypes previously studied as part of FinnGen study were computed using LD Score Regression v1.0.1 using Finnish LD panel in FinnGen pipeline (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/FINNGEN/\u003c/span\u003e\u003cspan address=\"https://github.com/FINNGEN/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003csup\u003e45\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHLA and blood group imputation\u003c/p\u003e \u003cp\u003eThe alleles of the classical HLA genes, \u003cem\u003eHLA-A\u003c/em\u003e, \u003cem\u003e-C\u003c/em\u003e, \u003cem\u003e-B\u003c/em\u003e, \u003cem\u003e-DRB1\u003c/em\u003e, \u003cem\u003eDRB3-5\u003c/em\u003e, \u003cem\u003e-DQA1\u003c/em\u003e, \u003cem\u003e-DQB1\u003c/em\u003e, and \u003cem\u003e-DPB1\u003c/em\u003e, were previously imputed at four-field resolution (defining protein sequence variation) in FinnGen using HIBAG\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e algorithm with population-specific reference panel, as reported earlier\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. The dosage value of each \u003cem\u003eHLA\u003c/em\u003e-allele was used in downstream analyses.\u003c/p\u003e \u003cp\u003eAltogether 37 red blood cell antigens in 14 different blood group systems were imputed using population-specific random forest models as described by Hyv\u0026auml;rinen et al\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eQTL analysis\u003c/p\u003e \u003cp\u003eProteomics data were previously generated in FinnGen on blood donors using multiplex antibody-based immunoassay (Olink) and multiplex aptamer-based immunoassay (SomaScan)\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. We analyzed protein QTLs by performing an association between the 46 GWAS lead variants and protein expression data using the glm function of Plink2 v2.00a4LM. Age, sex, sampling year and PC1-10 were used as model covariates, and first-degree relatives were removed. To evaluate the enrichment of the associated proteins in functional processes, we used STRING interaction network and functional enrichment analysis tool\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe KANTA clinical laboratory test data in FinnGen spans from 2014 to 2023, as detailed in the FinnGen documentation (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://finngen.gitbook.io/finngen-handbook\u003c/span\u003e\u003cspan address=\"https://finngen.gitbook.io/finngen-handbook\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). We tested the associations of the 46 GWAS lead variants with the laboratory values using continuous regression analysis, adjusting for age, sex, sampling year, and PC1-10 as covariates using the glm function in Plink2. For mediation analysis\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e, we selected significant (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05) labQTLs limiting to the smallest p-value per each KANTA lab value, and included both the SNP and the corresponding lab value as independent variables in a logistic regression model of blood donorship. We accepted lab value associations reaching FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05, indicating that these values associate with blood donorship when adjusted for their QTL SNPs. To further validate the causal effect of each GWAS lead variant on laboratory values, we employed the Sobel mediation test\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e using the R package bda v18.3.2, accepting FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003cp\u003eFurther details on the analyses are described in Supplementary Methods.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eFinnGen summary statistics are available at https://r12.finngen.fi/. Data supporting the current study are available from the authors upon reasonable request and with permission of FinnGen.\u003c/p\u003e\n\u003cp\u003eThe GWAS summary statistics for the blood donor phenotype will be available at a public repository to be determined later.\u003c/p\u003e\n\u003ch2\u003eCode Availability \u003c/h2\u003e\n\u003cp\u003eCode will be available in https://github.com/FRCBS/HDE-GWAS.\u003c/p\u003e\n\u003ch1\u003eCompeting Interests\u003c/h1\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003ch1\u003eAcknowledgements\u003c/h1\u003e\n\u003cp\u003eWe want to acknowledge the participants and investigators of FinnGen study. The FinnGen project is funded by two grants from Business Finland (HUS 4685/31/2016 and UH 4386/31/2016) and the following industry partners: AbbVie Inc., AstraZeneca UK Ltd, Biogen MA Inc., Bristol Myers Squibb (and Celgene Corporation \u0026amp; Celgene International II S\u0026agrave;rl), Genentech Inc., Merck Sharp \u0026amp; Dohme LCC, Pfizer Inc., GlaxoSmithKline Intellectual Property Development Ltd., Sanofi US Services Inc., Maze Therapeutics Inc., Janssen Biotech Inc, Novartis Pharma AG, and Boehringer Ingelheim International GmbH. Following biobanks are acknowledged for delivering biobank samples to FinnGen: Auria Biobank (www.auria.fi/biopankki), THL Biobank (www.thl.fi/biobank), Helsinki Biobank (www.helsinginbiopankki.fi), Biobank Borealis of Northern Finland (https://www.ppshp.fi/Tutkimus-ja-opetus/Biopankki/Pages/Biobank-Borealis-briefly-in-English.aspx), Finnish Clinical Biobank Tampere (www.tays.fi/en-US/Research_and_development/Finnish_Clinical_Biobank_Tampere), Biobank of Eastern Finland (www.ita-suomenbiopankki.fi/en), Central Finland Biobank (www.ksshp.fi/fi-FI/Potilaalle/Biopankki), Finnish Red Cross Blood Service Biobank (www.veripalvelu.fi/verenluovutus/biopankkitoiminta), Terveystalo Biobank (www.terveystalo.com/fi/Yritystietoa/Terveystalo-Biopankki/Biopankki/) and Arctic Biobank (https://www.oulu.fi/en/university/faculties-and-units/faculty-medicine/northern-finland-birth-cohorts-and-arctic-biobank). All Finnish Biobanks are members of BBMRI.fi infrastructure (www.bbmri.fi). Finnish Biobank Cooperative -FINBB (https://finbb.fi/) is the coordinator of BBMRI-ERIC operations in Finland. The Finnish biobank data can be accessed through the Fingenious\u003csup\u003e\u0026reg; \u003c/sup\u003eservices (https://site.fingenious.fi/en/) managed by FINBB.\u003c/p\u003e\n\u003cp\u003eIn addition, we want to acknowledge Dr Katri Haimila for her valuable advice with red cell antigen immunogenetics, Dr Kati Hyv\u0026auml;rinen for her expertise and kind help with red cell antigen imputation and the personnel of Blood Service Biobank for their kind help and support for the study.\u003c/p\u003e\n\u003ch1\u003eAuthor Contributions\u003c/h1\u003e\n\u003cp\u003eOriginal study concept by JC and JR. Genetic and statistical analysis of the study: JC, JT, MA and JR. Visualization: JC, JT and JR. Medical expertise: JL. FinnGen: data curation and resources. Contributed to the study design: JC, JR, JT, MA, SK, JP. Original manuscript: JC. All the authors read, commented, and approved the final manuscript. The authors declare that they have no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eFinnish Red Cross Blood Service - donation eligibility. https://www.veripalvelu.fi/en/faq/?search=\u0026amp;category=eligibility.\u003c/li\u003e\n\u003cli\u003eHu, X. \u003cem\u003eet al.\u003c/em\u003e Additive and interaction effects at three amino acid positions in HLA-DQ and HLA-DR molecules drive type 1 diabetes risk. \u003cem\u003eNat. Genet.\u003c/em\u003e \u003cstrong\u003e47\u003c/strong\u003e, 898\u0026ndash;905 (2015).\u003c/li\u003e\n\u003cli\u003eDe Silvestri, A. \u003cem\u003eet al.\u003c/em\u003e The Involvement of HLA Class II Alleles in Multiple Sclerosis: A Systematic Review with Meta-analysis. \u003cem\u003eDis. Markers\u003c/em\u003e \u003cstrong\u003e2019\u003c/strong\u003e, 1409069 (2019).\u003c/li\u003e\n\u003cli\u003eDendrou, C. A., Petersen, J., Rossjohn, J. \u0026amp; Fugger, L. HLA variation and disease. \u003cem\u003eNat. Rev. Immunol.\u003c/em\u003e \u003cstrong\u003e18\u003c/strong\u003e, 325\u0026ndash;339 (2018).\u003c/li\u003e\n\u003cli\u003eRitari, J., Koskela, S., Hyv\u0026auml;rinen, K., FinnGen \u0026amp; Partanen, J. HLA-disease association and pleiotropy landscape in over 235,000 Finns. \u003cem\u003eHum. Immunol.\u003c/em\u003e \u003cstrong\u003e83\u003c/strong\u003e, 391\u0026ndash;398 (2022).\u003c/li\u003e\n\u003cli\u003eAtsma, F., Veldhuizen, I., Kort, W. De \u0026amp; Vegt, F. De. Healthy donor effect: its magnitude in health research among blood donors. \u003cem\u003eTransfusion\u003c/em\u003e \u003cstrong\u003e51\u003c/strong\u003e, 1820\u0026ndash;1828 (2011).\u003c/li\u003e\n\u003cli\u003eMarsh, W. L. \u0026amp; Redman, C. M. 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Methods\u003c/em\u003e \u003cstrong\u003e40\u003c/strong\u003e, 879\u0026ndash;891 (2008).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-human-genetics","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"ejhg","sideBox":"Learn more about [European Journal of Human Genetics](http://www.nature.com/ejhg/)","snPcode":"41431","submissionUrl":"https://mts-ejhg.nature.com/cgi-bin/main.plex","title":"European Journal of Human Genetics","twitterHandle":"@ejhg_journal","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"blood donor, biobank, healthy donor effect, GWAS, blood group, HLA","lastPublishedDoi":"10.21203/rs.3.rs-6663925/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6663925/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe healthy donor effect (HDE) refers to the observed lower mortality rate among blood donors compared to the general population. While HDE arises due to healthier individuals being more likely to be able to donate, the extent to which it is influenced by genetic differences remains largely unclear. To elucidate the genetic basis of HDE, we conducted a genome-wide association study (GWAS) involving 53,688 active blood donors with extensive donation histories and 228,060 controls from biobank cohorts within the FinnGen project. Our results identified 2,973 genome-wide significant loci associated with several health-related endpoints and levels of proteins and laboratory values. The associated loci related not only to blood groups but also to predisposition to infections and somatic and mental diseases, suggesting that HDE genetics extends beyond blood donation eligibility criteria. In conclusion, in this study we show that HDE is partially explained by genetic factors affecting various disease categories.\u003c/p\u003e","manuscriptTitle":"Genome-Wide Association Study Identifies Protective Genetic Factors in Active Blood Donors Against Multiple Diseases","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-10 09:39:10","doi":"10.21203/rs.3.rs-6663925/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"revise","date":"2025-11-07T10:41:44+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"This content is not available.","date":"2025-07-01T13:56:53+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2025-06-15T08:36:22+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewersInvited","content":"","date":"2025-06-06T12:26:14+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-02T10:07:31+00:00","index":"","fulltext":""},{"type":"submitted","content":"European Journal of Human Genetics","date":"2025-05-27T12:25:32+00:00","index":"","fulltext":""},{"type":"checksFailed","content":"","date":"2025-05-14T13:56:47+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-14T11:54:53+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-human-genetics","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"ejhg","sideBox":"Learn more about [European Journal of Human Genetics](http://www.nature.com/ejhg/)","snPcode":"41431","submissionUrl":"https://mts-ejhg.nature.com/cgi-bin/main.plex","title":"European Journal of Human Genetics","twitterHandle":"@ejhg_journal","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"83a4576a-942f-4114-a475-d043785eda97","owner":[],"postedDate":"June 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":49644860,"name":"Biological sciences/Genetics/Genomics"},{"id":49644861,"name":"Health sciences/Medical research/Genetics research"}],"tags":[],"updatedAt":"2026-04-28T07:11:05+00:00","versionOfRecord":{"articleIdentity":"rs-6663925","link":"https://doi.org/10.1038/s41431-026-02100-2","journal":{"identity":"european-journal-of-human-genetics","isVorOnly":false,"title":"European Journal of Human Genetics"},"publishedOn":"2026-04-27 04:00:00","publishedOnDateReadable":"April 27th, 2026"},"versionCreatedAt":"2025-06-10 09:39:10","video":"","vorDoi":"10.1038/s41431-026-02100-2","vorDoiUrl":"https://doi.org/10.1038/s41431-026-02100-2","workflowStages":[]},"version":"v1","identity":"rs-6663925","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6663925","identity":"rs-6663925","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}