A multi-phenotype approach implicatesSH2B3in the genetics of chronic kidney disease

A multi-phenotype approach implicates SH2B3 in the genetics of chronic kidney disease | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search A multi-phenotype approach implicates SH2B3 in the genetics of chronic kidney disease View ORCID Profile Kim N. Tran , View ORCID Profile Heidi G. Sutherland , View ORCID Profile Andrew J. Mallett , View ORCID Profile Lyn R. Griffiths , View ORCID Profile Rodney A. Lea doi: https://doi.org/10.1101/2024.06.29.24309709 Kim N. Tran 1 Queensland University of Technology (QUT), Centre for Genomics and Personalised Health, Genomics Research Centre, School of Biomedical Sciences , QLD, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Kim N. Tran Heidi G. Sutherland 1 Queensland University of Technology (QUT), Centre for Genomics and Personalised Health, Genomics Research Centre, School of Biomedical Sciences , QLD, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Heidi G. Sutherland Andrew J. Mallett 2 Institute for Molecular Bioscience & Faculty of Medicine, The University of Queensland , Brisbane, QLD, Australia 3 Department of Renal Medicine, Townsville University Hospital , Townsville, QLD, Australia 4 College of Medicine & Dentistry, James Cook University , Townsville, QLD, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Andrew J. Mallett Lyn R. Griffiths 1 Queensland University of Technology (QUT), Centre for Genomics and Personalised Health, Genomics Research Centre, School of Biomedical Sciences , QLD, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Lyn R. Griffiths Rodney A. Lea 1 Queensland University of Technology (QUT), Centre for Genomics and Personalised Health, Genomics Research Centre, School of Biomedical Sciences , QLD, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Rodney A. Lea For correspondence: rodney.lea{at}qut.edu.au Abstract Full Text Info/History Metrics Data/Code Preview PDF ABSTRACT Chronic kidney disease (CKD) is a complex condition with diverse underlying causes that lead to a progressive decline in kidney function. Genome-wide association studies (GWASs) have identified numerous genetic loci associated with CKD, yet much of the genetic basis remains unexplained. Part of the reason is that most GWASs have only assessed kidney function via single biomarkers such as estimated glomerular filtration rate (eGFR). This study employs a novel multi-phenotype approach, combinatorial Principal Component Analysis (cPCA), to better understand the genetic architecture of CKD. Utilizing a discovery cohort of white British individuals from the UK Biobank (n=337,112), we analyzed 21 CKD-related phenotypes using cPCA to generate over 2 million composite phenotypes (CPs). More than 46,000 CPs demonstrated superior performance in classifying clinical CKD compared to any single biomarker, and those CPs were most frequently comprised of eGFR, cystatin C, HbA1c, microalbuminuria, albumin, and LDL. GWASs of the top 1,000 CPs revealed seven novel genetic loci, with CST3 and SH2B3 successfully replicated in an independent Irish cohort (n=11,106). Notably, the index SNP of the SH2B3 gene, which encodes a regulator in immune responses and cytokine signaling, is a loss-of-function variant with a combined beta of −0.046 and a p-value of 3.1E-56. These results highlight the effectiveness of a multi-phenotype approach in GWASs and implicate a novel functional variant in SH2B3 in CKD phenotypes. TRANSLATIONAL STATEMENT The application of combinatorial Principal Component Analysis (cPCA) in our study has identified SH2B3 as a novel genetic locus associated with chronic kidney disease (CKD). This discovery advances our understanding of CKD’s genetic architecture beyond single biomarker analyses, potentially leading to more precise diagnostic tools and personalized treatment strategies. Future research should focus on validating these findings in diverse populations and integrating cPCA-derived biomarkers into clinical practice to enhance CKD prediction and management, ultimately improving patient outcomes. INTRODUCTION Chronic kidney disease (CKD) is a collective term encompassing a range of heterogeneous diseases characterized by persistent structural or functional kidney abnormalities. CKD is stratified into five stages, culminating in kidney failure, which necessitates consideration of interventions such as kidney transplantation or dialysis. This condition has a high prevalence, affecting approximately 10-15% of the global population, resulting in significant burden on both public health and the economy. 1 Genome-wide association studies (GWAS) investigating CKD have traditionally focused on evaluating kidney function using single biomarkers, such as estimated glomerular filtration rate (eGFR), microalbuminuria, or blood urea nitrogen. 2 – 5 For example, a robust GWAS analysis of eGFR in a cohort of over 1.2 million individuals identified 634 independent genetic signals, collectively accounting for 9.8% of the eGFR variance. 4 However, a portion of the heritability of CKD remains unexplained. This gap in understanding can be attributed, in part, to the fact that eGFR and other individual biomarkers do not fully capture the underlying causes of CKD nor accurately predict an individual’s risk of CKD or progression to kidney failure. 6 For a comprehensive diagnosis and prognosis of systemic CKD, it is recommended to employ a combination of various markers that collectively reflect the diverse alterations occurring during the course of CKD. 7 Previously, we employed principal component analysis (PCA) on multiple quantitative phenotypes associated with CKD, uncovering a novel susceptibility gene for kidney function that remained undetected in single-phenotype GWASs. 8 In this study, we introduce and implement a new approach termed combinatorial PCA to further investigate the genetic basis of CKD within the UK Biobank dataset. As a result, we identified a new locus, SH2B3 (SH2B adaptor protein 3), to be associated with CKD. METHODS Research cohort The UK Biobank (UKB) is a longitudinal cohort study examining the interplay between genes, the environment, and health. It encompasses over 500,000 participants aged 40-69 years, recruited between 2006 and 2010 from 22 assessment centers across England, Scotland, and Wales. Approval for the UKB study was obtained from the North West Multi-Centre Research Ethics Committee, and all participants provided written informed consent. This research has been conducted utilizing the UK Biobank Resource under Application Number 60111. We selected White-British samples, constituting the largest ethnic group within the UKB dataset, for the discovery cohort based on both the ‘Ethnic background’ and ‘Genetic ethnic grouping’ data. This approach allowed us to accurately identify individuals who self-identified as ‘White British’ and exhibited very similar genetic ancestry profiles, as determined by a principal components analysis of their genotypic data. Additionally, we excluded individuals whose genetic sex differed from their self-identified sex, those with sex chromosome aneuploidy, or those who were not included in the genetic principal components analysis conducted by the UKB research team. The final sample size was 337,112. Finally, we included individuals from the Irish ethnicity within the UKB dataset for the replication analyses (n=11,106). The data processing steps were performed similarly to those used for the discovery cohort. Phenotype data In total, 21 biomarkers relevant to chronic kidney disease (CKD) were included in this study ( Table 1 ). These phenotypes were assessed based on the correlations of the measurements with prevalence of CKD, CKD stages, kidney function, and an increased risk of adverse outcomes in individuals with CKD. All measurements were collected at baseline for all participants. Details of the assay manufacturers, analytical platforms, and analysis methodologies can be found at https://www.ukbiobank.ac.uk/enable-your-research/about-our-data/biomarker-data . Quantitative measures outside their respective analytical ranges were treated as missing data. Estimated GFR (eGFR) was calculated using the creatinine-based CKD-EPI-2021 equation without race coefficient. 9 Samples with more than 30% missing data points were excluded. Remaining missing phenotypic values were imputed to obtain a complete dataset using the R package missMDA v1.11, 10 ensuring that the imputed values had no effect on the principal component analysis (PCA) results. PCA was performed using the R package FactoMineR v1.34. 11 View this table: View inline View popup Table 1. 21 kidney function related phenotypes selected from the UK Biobank dataset. Genotype data Genome-wide genotyping was conducted on all UKB participants using the UK Biobank Axiom Array. Approximately 850,000 variants were directly measured, while over 90 million variants were imputed using the Haplotype Reference Consortium and UK10K + 1000 Genomes reference panels. Imputation data were stored in the compressed and indexed BGENv1.2 format. We converted the data from BGEN format into binary PGEN files and performed quality control procedures within PLINK2.0. 38 The criteria for selecting variants were: (1) autosomal variants; (2) missing rate of less than 5%; (3) not significantly deviated from Hardy-Weinberg equilibrium (p-value=10E-10); (4) minor allele frequency (MAF) of at least 0.001; and (5) imputation score of more than 0.8. After quality control, we retained 12.7 million SNPs for subsequent analysis. CKD clinical outcome data Health-related outcome data are available in death, hospital, and primary care records. Using the ICD-10 and ICD-9 codes (International Classification of Diseases, tenth and ninth editions), we categorized individuals diagnosed with chronic kidney disease, renal failure, renal sclerosis, chronic glomerulonephritis, nephritis, nephropathy, hypertensive chronic kidney disease, hypertensive heart and kidney disease, diabetes with renal complications, kidney replaced by transplant, disorders resulting from impaired renal function, or unspecified disorders of the kidney and ureter as CKD cases. Combinatorial principal component analysis (cPCA) Principles We developed an approach called combinatorial PCA (cPCA) to identify combinations of biomarkers that collectively offer improved discriminatory power in disease classification compared to individual biomarkers alone. In cPCA, various combinations with varying numbers of biomarkers are generated from a fixed set of input biomarkers. The number of possible combinations generated can be calculated as .The first principal component, denoted as CP, is then extracted to represent each combination. CP serves as a comprehensive biomarker signature, representing the maximum variance direction within the biomarker combination. Finally, the performance of each CP in disease classification is evaluated and compared to that of single biomarkers. Implementation Details To systematically explore and identify potential superior components for CKD classification beyond conventional biomarkers, we applied cPCA to a set of 21 CKD-related phenotypes. Initially, we generated 2,097,130 unique combinations out of the 21 phenotypes. These combinations encompassed all possible subsets of the 21 phenotypes with varying numbers, ranging from 2 to the complete set of 21. For each combination, we extracted CP, resulting in 2 million CPs. Subsequently, we evaluated the performance of each CP in CKD classification and compared it to that of CYSC, which served as the best single marker for CKD classification. To validate the efficacy of the identified combinations, we partitioned the dataset into a training set (70%) and a test set (30%). Notably, cPCA was exclusively performed on the training set, encompassing the 2 million combinations. The performance evaluation involved comparing the ROC curves (Receiver Operating Characteristic curves) of each CP against those of individual phenotypes. Confidence intervals for the calculated AUCs (Area Under the Curve) were computed using bootstrap methods with 2000 stratified bootstrap replicates, implemented within the R package pROC. Combinations exhibiting significantly higher AUCs compared to CYSC were further validated using the independent test set. The final AUCs were calculated based on the entire dataset. Genome-wide analyses Genome-wide association studies (GWAS) were performed by fitting linear models (for quantitative traits) or logistic models (for binary traits) implemented in PLINK2.0. 38 All the input phenotypes were inverse-normal transformed prior to GWAS. Age, sex, and the first 20 genetic principal components were integrated into the models as covariates. SNP-based heritability and genetic correlation were estimated based on the GWAS summary statistics using linkage disequilibrium score regression (LDSC) v1.0.1 39 RESULTS Best single-markers for CKD classification In this study, our objective was to identify novel genetic loci associated with CKD through a comprehensive multi-phenotype analysis. Prior to conducting the multi-phenotype analysis, we examined the 21 phenotypes previously linked to CKD ( Table 1 ) in terms of their performance in classifying clinical CKD. This was evaluated by the area under the curve (AUC) of receiver operating characteristic (ROC) curves using the ICD codes for CKD as clinical outcomes ( Figure 1 ). The biomarkers encompassed a range of physiological indicators of CKD risk, including markers of renal function, metabolic parameters, inflammation, lipid profile, and blood pressure. Notably, cystatin C (CYSC) exhibited the highest discriminatory power among the biomarkers, with an AUC range of 0.832-0.842, closely followed by estimated glomerular filtration rate (eGFR) with an AUC range of 0.825-0.835. Other biomarkers, such as blood urea nitrogen (BUN), uric acid (UA), and glycated hemoglobin (HbA1c), demonstrated moderate discriminatory performance, with AUCs ranging from 0.658 to 0.742. Conversely, other biomarkers such as vitamin D (VITD), calcium (CALC), and diastolic blood pressure (DBP) exhibited low AUC values, ranging from 0.489 to 0.525. Download figure Open in new tab Figure 1. ROC curves for CKD classification of the 21 CKD-related phenotypes. Composite phenotypes better than single markers in CKD classification In this multi-phenotype analysis, we developed and applied a method called combinatorial PCA (cPCA) to identify combinations of biomarkers that outperformed single markers. General steps of the cPCA method are illustrated in Figure 2A . Through cPCA application, a total of 2,097,130 composite phenotypes (CPs) were extracted from all unique combinations of 21 CKD-related biomarkers, and then evaluated for performance in CKD classification. As a result, we identified 46,562 CPs with significantly better disease classification compared to CYSC (p<2.5e-8), as assessed using ROC curves and AUC. The top ten CPs with the highest performance are listed in Table 2 . Download figure Open in new tab Figure 2. Combinatorial Principal Component Analysis (cPCA). A. Flowchart of the cPCA method. B. The ROC curve of the top CP extracted from eGFR, CYSC, MA, HbA1c, LDL, ALB, and GGT in comparison to the ROC curves of the 21 biomarkers in terms of CKD classification. C. Top 10 of the phentoypes that appeared most frequently in the 46,000 CPs. D. Top 10 of the phenoyptes pairs or triples that appeared most frequently in the 46,000 CPs. View this table: View inline View popup Download powerpoint Table 2. Top 10 CPs with the highest AUCs. P-values were of the tests comparing the CPs’ ROC curves to the CYSC’s ROC curve. We analyzed the phenotypic components of the 46,562 CPs that exhibited statistically significantly better performance in CKD classification compared to CYSC ( Figure 2B, 2C and 2D ). The top ranked CP was represented by albumin (ALB), CYSC, eGFR, gamma glutamintransferase (GGT), HbA1c, low density lipoprotein (LDL), and microalbuminuria (MA) (AUC=0.878, 95%CI=0.873-0.882). Among the other combinations, CYSC and eGFR were consistently present, with HbA1c appearing in nearly all instances. Other notable phenotypes included MA, ALB, and LDL, with appearances ranging from 75% to 55% across the combinations. Regarding pairs or triples of phenotypes, as expected, the most frequent combinations included CYSC, eGFR, and HbA1c: CYSC-eGFR pairs were present in all combinations, while CYSC-HbA1c, eGFR-HbA1c, and CYSC-eGFR-HbA1c were found in 99% of combinations. Genetic associations of the top 1000 CPs The cPCA analysis identified a total of 46,562 CPs with significantly higher AUCs than that of CYSC. To uncover genetic loci associated with kidney function, we conducted a genome-wide analysis of the top 1000 CPs with the highest AUCs, as well as all 21 individual phenotypes. This analysis yielded 82 loci consistently identified in the GWASs of the top 1000 CPs (p=5e-8, Figure 3 ). Most of these loci were also observed in the eGFR GWAS. However, seven loci – CST3, SH2B3 , FTO , SEMA3F-AS1 , AL359852.2 , AC128707.1 , and AL049757.1 - were not identified in the eGFR GWAS and were instead found in GWASs of other individual phenotypes. SH2B3 was found in 12 out of the 21 individual-phenotype GWASs, FTO was found in 7 and SEMA3F-AS 1 in 5. These 7 loci represented potentially novel genetic associations with kidney function, discovered through the multi-phenotype approach. Download figure Open in new tab Figure 3. The 82 genetic loci identified in all the top 1000 CPs. Among these loci, 75 were found to overlap with those identified in the GWAS of eGFR, while 7 loci were not. Instead, these 8 loci were discovered in GWASs of other individual phenotypes, each represented by a distinct color. Replication analysis We utilised the independent cohort of Irish ethnicity in the UKB dataset for our replication analysis. In the discovery group, i.e. the British cohort, the CP extracted from the combination of {eGFR, CYSC, ALB, HbA1c, GGT, LDL, and MA} was among those that had the highest AUCs for CKD classification and at the same time had the least number of phenotypes ( Table 2 ). Therefore, we selected this combination of phenotypes to generate a new CP for the replication cohort. As a result, the new CP in the replication cohort also had significantly better performance in CKD classification compared to those of individual phenotypes ( Figure 4 ). Out of the 7 potentially novel loci associated with kidney function, CST3 and SH2B3 were replicated in the Irish cohort as outlined in Table 3 . Download figure Open in new tab Figure 4. ROC curves for CKD classification of the CP and the 21 CKD-related phenotypes in the replication Irish cohort. The CP was extracted from the combinations of phenotypes {eGFR, CYSC, ALB, HbA1c, GGT, LDL, and MA}. View this table: View inline View popup Table 3. Association results of the potentially novel kidney-function loci in the discovery British group and the replication Irish group. The phenotypic outcomes were CP extracted from {eGFR, CYSC, ALB, HbA1c, GGT, LDL, and MA} for both the British group and the Irish group, respectively. DISCUSSION CKD is a common term to describe a range of diseases characterized by impaired kidney structure, or reduced kidney function over time. Because there is an incomplete understanding of the genetics for different CKD subtypes, the identification of effective drug targets has been hindered. Research has tended to focus on eGFR or other single CKD-related biomarkers, yet this approach could be inadequate for capturing the underlying CKD etiology or pathophysiology. Since CKD is associated with many individual phenotypes we reasoned that a multi-phenotype analytical approach may identify novel genetic loci relevant to CKD. Specifically we designed a combinatorial PCA algorithm (cPCA), the aim of which was to extract relevant composite phenotypes for accurate CKD classification. This involved iteratively exploring all possible combinations of the 21 input phenotypes to identify composite phenotypes that outperformed individual biomarkers in CKD classification. Over 2 million phenotypic combinations were analyzed, resulting in the identification of over 46,000 composite phenotypes with significantly higher AUCs than CYSC or any individual phenotype. CYSC, eGFR, HbA1c, MA, ALB, LDL, and GGT were the most frequently observed phenotypes, appearing in 75% to 48% of those combinations. The frequent presence of HbA1c, ALB, LDL, and GGT alongside well-established CKD phenotypes such as CYSC, eGFR, and MA highlighted the overlap between kidney function and other aspects of human health, including blood glucose levels, cardiovascular health, and liver function. Furthermore, we observed that although BUN and UA, which are highly correlated with eGFR, exhibited higher performance in CKD classification than HbA1c, MA, and others, they appeared less frequently in the 46,000 combinations (BUN: 22.4% and UA: 0%). This suggested that cPCA could mitigate multicollinearity by ensuring the inclusion of independent phenotypes that are not highly correlated. Consequently, we performed GWAS of the top 1000 composite phenotypes with the highest performance in CKD classification and identified 82 loci that were consistently found in all the 1000 GWASs. As expected, most of these were also found in eGFR GWAS, including well-known CKD loci such as UMOD/PDILT , SHROOM3 , and PRKAG2 . Noteworthy was that there were 7 loci that were not identified in eGFR. They were CST3, SH2B3 , FTO , SEMA3F-AS1 , AL359852.2 , AC128707.1 , and AL049757.1 . Finally, CST3 and SH2B3 were successfully replicated in an independent cohort. As noted, CST3 was, in fact, already an established kidney function gene. On the other hand, SH2B3 , which encoded for a cytokin-signalling regulator, was less recognized for its involvement in kidney function. The index SNP rs3184504 mapped to the SH2B3 locus is a loss-of-function variant and has been found to be associated with multiple phenotypes and diseases relating to blood pressure, blood cells, cholesterol levels, as well as cardiovascular diseases and type-1 diabetes ( https://www.ebi.ac.uk/gwas/variants/rs3184504 ). Notably, murine animal models that were modified to be homozygous for the minor allele at rs3184504 using CRISPR-Cas9 exhibited higher blood pressure and exacerbated kidney dysfunction compared to control mice following angiotensin II infusion. 40 This study marks the first time this missense SNP has been linked to human kidney function. Another noteworthy finding was the identification of the FTO locus among the top 1000 CPs. Although the locus only reached a nominal significance level in the replication analysis, this was potentially attributed to the much lower power of replication analysis. The FTO gene has been associated with CKD in case-control studies, 41 . Interstingly, the identified FTO SNP rs17817449 in Hubacek et al 41 was within 5.5kbp of the index SNP in our discovery GWAS and, more importantly, also reached the genome-wide significane threshold (p= 8.13E-18). In conclusion, The cPCA method developed and applied in this study successfully identified a novel CKD locus, SH2B3 . Moreover, this study highlighted the effectiveness of multi-phenotype approaches in uncovering novel genetic loci associated with complex diseases such as CKD, which exhibit substantial overlap with multiple other physiological components. DISCLOSURE All the authors declared no competing interests. Data Availability The summary statistics are publicly available in Figshare repository at https://doi.org/10.6084/m9.figshare.26122540.v1 . https://doi.org/10.6084/m9.figshare.26122540.v1 DATA SHARING STATEMENT The summary statistics are publicly available in Figshare repository at https://doi.org/10.6084/m9.figshare.26122540.v1 . ACKNOWLEDGEMENTS This research has been conducted using the UK Biobank Resource under Application Number 60111. NKT has been supported by a QUT postgraduate scholarship. AJM has been supported by a Queensland Health Advancing Clinical Research Fellowship. REFERENCES 1. ↵ Levin A , Tonelli M , Bonventre J , et al. Global kidney health 2017 and beyond: a roadmap for closing gaps in care, research, and policy . The Lancet . 2017 ; 390 ( 10105 ): 1888 – 1917 . doi: 10.1016/S0140-6736(17)30788-2 OpenUrl CrossRef PubMed 2. ↵ Wuttke M , Li Y , Li M , et al. A catalog of genetic loci associated with kidney function from analyses of a million individuals . Nat Genet . 2019 ; 51 ( 6 ): 957 – 972 . doi: 10.1038/s41588-019-0407-x OpenUrl CrossRef PubMed 3. Köttgen A , Pattaro C . The CKDGen Consortium: ten years of insights into the genetic basis of kidney function . Kidney Int . 2020 ; 97 ( 2 ): 236 – 242 . doi: 10.1016/j.kint.2019.10.027 OpenUrl CrossRef 4. ↵ Stanzick KJ , Li Y , Schlosser P , et al. Discovery and prioritization of variants and genes for kidney function in >1.2 million individuals . Nat Commun . 2021/07/16 2021 ; 12 ( 1 ): 4350 . doi: 10.1038/s41467-021-24491-0 OpenUrl CrossRef 5. ↵ Teumer A , Li Y , Ghasemi S , et al. Genome-wide association meta-analyses and fine-mapping elucidate pathways influencing albuminuria . Nat Commun . 2019/09/11 2019 ; 10 ( 1 ): 4130 . doi: 10.1038/s41467-019-11576-0 OpenUrl CrossRef PubMed 6. ↵ Cañadas-Garre M , Anderson K , Cappa R , et al. Genetic Susceptibility to Chronic Kidney Disease - Some More Pieces for the Heritability Puzzle . Front Genet . 2019 ; 10 : 453 . doi: 10.3389/fgene.2019.00453 OpenUrl CrossRef 7. ↵ Rysz J , Gluba-Brzózka A , Franczyk B , Jabłonowski Z , Ciałkowska-Rysz A . Novel Biomarkers in the Diagnosis of Chronic Kidney Disease and the Prediction of Its Outcome . Int J Mol Sci. Aug 4 2017 ; 18 ( 8 ) doi: 10.3390/ijms18081702 OpenUrl CrossRef 8. ↵ Tran NK , Lea RA , Holland S , et al. Multi-phenotype genome-wide association studies of the Norfolk Island isolate implicate pleiotropic loci involved in chronic kidney disease . Sci Rep . 2021/09/30 2021 ; 11 ( 1 ): 19425 . doi: 10.1038/s41598-021-98935-4 OpenUrl CrossRef 9. ↵ Inker LA , Eneanya ND , Coresh J , et al. New Creatinine- and Cystatin C–Based Equations to Estimate GFR without Race . New England Journal of Medicine . 2021/11/04 2021 ; 385 ( 19 ): 1737 - 1749 . doi: 10.1056/NEJMoa2102953 OpenUrl CrossRef PubMed 10. ↵ Josse J , Husson F . missMDA: a package for handling missing values in multivariate data analysis . Journal of Statistical Software . 2016 ; 70 ( 1 ): 1 – 31 . doi: 10.18637/jss.v070.i01 OpenUrl CrossRef PubMed 11. ↵ Lê S , Josse J , Husson F . FactoMineR: an R package for multivariate analysis . Journal of statistical software . 2008 ; 25 ( 1 ): 1 – 18 . doi: 10.18637/jss.v025.i01 OpenUrl CrossRef 12. Lang J , Katz R , Ix JH , et al. Association of serum albumin levels with kidney function decline and incident chronic kidney disease in elders . Nephrology Dialysis Transplantation . 2018 ; 33 ( 6 ): 986 – 992 . OpenUrl PubMed 13. Lang J , Scherzer R , Tien PC , et al. Serum albumin and kidney function decline in HIV-infected women . Am J Kidney Dis . 2014 ; 64 ( 4 ): 584 – 591 . OpenUrl 14. Goek ON , Köttgen A , Hoogeveen RC , Ballantyne CM , Coresh J , Astor BC . Association of apolipoprotein A1 and B with kidney function and chronic kidney disease in two multiethnic population samples . Nephrol Dial Transplant . Jul 2012 ; 27 ( 7 ): 2839 – 47 . doi: 10.1093/ndt/gfr795 OpenUrl CrossRef PubMed Web of Science 15. Zhao W , Li J , Zhang X , et al. Apolipoprotein B and renal function: across-sectional study from the China health and nutrition survey . Lipids Health Dis . 2020/05/27 2020 ; 19 ( 1 ): 110 . doi: 10.1186/s12944-020-01241-7 OpenUrl CrossRef 16. Zhao WB , Zhu L , Rahman T . Increased serum concentration of apolipoprotein B is associated with an increased risk of reaching renal replacement therapy in patients with diabetic kidney disease . Ren Fail . Nov 2020 ; 42 ( 1 ): 323 – 328 . doi: 10.1080/0886022x.2020.1745235 OpenUrl CrossRef PubMed 17. Kwon S , Kim DK , Oh K-H , et al. Apolipoprotein B is a risk factor for end-stage renal disease . Clinical Kidney Journal . 2021 ; 14 ( 2 ): 617 – 623 . doi: 10.1093/ckj/sfz186 OpenUrl CrossRef 18. Ejerblad E , Fored CM , Lindblad P , Fryzek J , McLaughlin JK , Nyrén O . Obesity and risk for chronic renal failure . J Am Soc Nephrol . Jun 2006 ; 17 ( 6 ): 1695 – 702 . doi: 10.1681/asn.2005060638 OpenUrl Abstract / FREE Full Text 19. Lu JL , Molnar MZ , Naseer A , Mikkelsen MK , Kalantar-Zadeh K , Kovesdy CP . Association of age and BMI with kidney function and mortality: a cohort study . Lancet Diabetes Endocrinol . Sep 2015 ; 3 ( 9 ): 704 – 14 . doi: 10.1016/s2213-8587(15)00128-x OpenUrl CrossRef 20. Herrington WG , Smith M , Bankhead C , et al. Body-mass index and risk of advanced chronic kidney disease: Prospective analyses from a primary care cohort of 1.4 million adults in England . PLoS One . 2017 ; 12 ( 3 ): e0173515 . doi: 10.1371/journal.pone.0173515 OpenUrl CrossRef PubMed 21. Janmaat CJ , van Diepen M , Gasparini A , et al. Lower serum calcium is independently associated with CKD progression . Sci Rep . 2018/03/26 2018 ; 8 ( 1 ): 5148 . doi: 10.1038/s41598-018-23500-5 OpenUrl CrossRef 22. Fox ER , Benjamin EJ , Sarpong DF , et al. The relation of C - reactive protein to chronic kidney disease in African Americans: the Jackson Heart Study . BMC Nephrol . 2010/01/15 2010 ; 11 ( 1 ): 1 . doi: 10.1186/1471-2369-11-1 OpenUrl CrossRef PubMed 23. Benoit SW , Ciccia EA , Devarajan P . Cystatin C as a biomarker of chronic kidney disease: latest developments . Expert Rev Mol Diagn . Oct 2020 ; 20 ( 10 ): 1019 – 1026 . doi: 10.1080/14737159.2020.1768849 OpenUrl CrossRef 24. Agarwal R . Blood pressure components and the risk for end-stage renal disease and death in chronic kidney disease . Clin J Am Soc Nephrol . Apr 2009 ; 4 ( 4 ): 830 – 7 . doi: 10.2215/cjn.06201208 OpenUrl Abstract / FREE Full Text 25. Mitka M . Low Diastolic Blood Pressure and Chronic Kidney Disease Are Associated With Increased Mortality . JAMA . 2013 ; 310 ( 12 ): 1215 – 1216 . doi: 10.1001/jama.2013.277706 OpenUrl CrossRef 26. Lee DY , Han K , Yu JH , et al. Gamma-glutamyl transferase variability can predict the development of end-stage of renal disease: a nationwide population-based study . Sci Rep . 2020/07/15 2020 ; 10 ( 1 ): 11668 . doi: 10.1038/s41598-020-68603-0 OpenUrl CrossRef PubMed 27. Hernandez D , Espejo-Gil A , Bernal-Lopez MR , et al. Association of HbA1c and cardiovascular and renal disease in an adult Mediterranean population . BMC Nephrol . 2013/07/17 2013 ; 14 ( 1 ): 151 . doi: 10.1186/1471-2369-14-151 OpenUrl CrossRef PubMed 28. Iseki K , Ikemiya Y , Iseki C , Takishita S . Haematocrit and the risk of developing end stage renal disease . Nephrology Dialysis Transplantation . 2003 ; 18 ( 5 ): 899 – 905 . doi: 10.1093/ndt/gfg021 OpenUrl CrossRef PubMed Web of Science 29. Chen TK , Estrella MM , Astor BC , et al. Longitudinal changes in hematocrit in hypertensive chronic kidney disease: results from the African-American Study of Kidney Disease and Hypertension (AASK) . Nephrol Dial Transplant . Aug 2015 ; 30 ( 8 ): 1329 – 35 . doi: 10.1093/ndt/gfv037 OpenUrl CrossRef PubMed 30. Nam KH , Chang TI , Joo YS , et al. Association Between Serum High-Density Lipoprotein Cholesterol Levels and Progression of Chronic Kidney Disease: Results From the KNOW-CKD . J Am Heart Assoc. Mar 19 2019 ; 8 ( 6 ): e011162 . doi: 10.1161/jaha.118.011162 OpenUrl CrossRef 31. De Nicola L , Provenzano M , Chiodini P , et al. Prognostic role of LDL cholesterol in non-dialysis chronic kidney disease: Multicenter prospective study in Italy . Nutr Metab Cardiovasc Dis . Aug 2015 ; 25 ( 8 ): 756 – 62 . doi: 10.1016/j.numecd.2015.04.001 OpenUrl CrossRef 32. Glassock RJ . Is the presence of microalbuminuria a relevant marker of kidney disease? Curr Hypertens Rep . Oct 2010 ; 12 ( 5 ): 364 – 8 . doi: 10.1007/s11906-010-0133-3 OpenUrl CrossRef PubMed 33. Vervloet MG , Sezer S , Massy ZA , et al. The role of phosphate in kidney disease . Nature Reviews Nephrology . 2017/01/01 2017 ; 13 ( 1 ): 27 - 38 . doi: 10.1038/nrneph.2016.164 OpenUrl CrossRef PubMed 34. Zubovic SV , Kristic S , Prevljak S , Pasic IS. Chronic Kidney Disease and Lipid Disorders . Med Arch . Jun 2016 ; 70 ( 3 ): 191 – 2 . doi: 10.5455/medarh.2016.70.191-192 OpenUrl CrossRef 35. Oluwo O , Scialla JJ . Uric Acid and CKD Progression Matures with Lessons for CKD Risk Factor Discovery . Clin J Am Soc Nephrol . 2021 ; 16 ( 3 ): 476 . doi: 10.2215/CJN.10650620 OpenUrl FREE Full Text 36. Seki M , Nakayama M , Sakoh T , et al. Blood urea nitrogen is independently associated with renal outcomes in Japanese patients with stage 3-5 chronic kidney disease: a prospective observational study . BMC Nephrol . 2019 ; 20 ( 1 ): 115 – 115 . doi: 10.1186/s12882-019-1306-1 OpenUrl CrossRef 37. Kim CS , Kim SW . Vitamin D and chronic kidney disease . Korean J Intern Med . Jul 2014 ; 29 ( 4 ): 416 – 27 . doi: 10.3904/kjim.2014.29.4.416 OpenUrl CrossRef PubMed 38. ↵ Chang CC , Chow CC , Tellier LC , Vattikuti S , Purcell SM , Lee JJ . Second-generation PLINK: rising to the challenge of larger and richer datasets . Gigascience . 2015 ; 4 ( 1 ): 7 . OpenUrl CrossRef PubMed 39. ↵ Bulik-Sullivan BK , Loh P-R , Finucane HK , et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies . Nat Genet . 2015/03/01 2015 ; 47 ( 3 ): 291 - 295 . doi: 10.1038/ng.3211 OpenUrl CrossRef PubMed 40. ↵ Alexander MR , Hank S , Dale BL , et al. A Single Nucleotide Polymorphism in SH2B3/LNK Promotes Hypertension Development and Renal Damage . Circ Res. Oct 14 2022 ; 131 ( 9 ): 731 – 747 . doi: 10.1161/circresaha.121.320625 OpenUrl CrossRef 41. ↵ Hubacek JA , Viklicky O , Dlouha D , et al. The FTO gene polymorphism is associated with end-stage renal disease: two large independent case-control studies in a general population . Nephrol Dial Transplant . Mar 2012 ; 27 ( 3 ): 1030 – 5 . doi: 10.1093/ndt/gfr418 OpenUrl CrossRef PubMed Web of Science View the discussion thread. Back to top Previous Next Posted June 30, 2024. Download PDF Data/Code Email Thank you for your interest in spreading the word about medRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following A multi-phenotype approach implicates SH2B3 in the genetics of chronic kidney disease Message Subject (Your Name) has forwarded a page to you from medRxiv Message Body (Your Name) thought you would like to see this page from the medRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share A multi-phenotype approach implicates SH2B3 in the genetics of chronic kidney disease Kim N. Tran , Heidi G. Sutherland , Andrew J. Mallett , Lyn R. Griffiths , Rodney A. Lea medRxiv 2024.06.29.24309709; doi: https://doi.org/10.1101/2024.06.29.24309709 Share This Article: Copy Citation Tools A multi-phenotype approach implicates SH2B3 in the genetics of chronic kidney disease Kim N. Tran , Heidi G. Sutherland , Andrew J. Mallett , Lyn R. Griffiths , Rodney A. Lea medRxiv 2024.06.29.24309709; doi: https://doi.org/10.1101/2024.06.29.24309709 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Nephrology Subject Areas All Articles Addiction Medicine (573) Allergy and Immunology (865) Anesthesia (302) Cardiovascular Medicine (4453) Dentistry and Oral Medicine (444) Dermatology (383) Emergency Medicine (609) Endocrinology (including Diabetes Mellitus and Metabolic Disease) (1515) Epidemiology (15242) Forensic Medicine (30) Gastroenterology (1131) Genetic and Genomic Medicine (6615) Geriatric Medicine (669) Health Economics (1001) Health Informatics (4552) Health Policy (1372) Health Systems and Quality Improvement (1614) Hematology (543) HIV/AIDS (1270) Infectious Diseases (except HIV/AIDS) (15929) Intensive Care and Critical Care Medicine (1106) Medical Education (624) Medical Ethics (147) Nephrology (670) Neurology (6625) Nursing (346) Nutrition (999) Obstetrics and Gynecology (1148) Occupational and Environmental Health (957) Oncology (3344) Ophthalmology (979) Orthopedics (369) Otolaryngology (421) Pain Medicine (436) Palliative Medicine (130) Pathology (665) Pediatrics (1696) Pharmacology and Therapeutics (693) Primary Care Research (714) Psychiatry and Clinical Psychology (5461) Public and Global Health (9252) Radiology and Imaging (2207) Rehabilitation Medicine and Physical Therapy (1371) Respiratory Medicine (1197) Rheumatology (597) Sexual and Reproductive Health (715) Sports Medicine (530) Surgery (714) Toxicology (99) Transplantation (289) Urology (265) (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'a02f17c5594ae2c5',t:'MTc3OTk5MDUzMw=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();