Longitudinal Genome-Wide Study Reveals Genetic Architecture of Resilience Using a Novel Phenotype

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While earlier genetic studies of resilience investigated effects of specific candidate genes, small sample sizes and the narrow focus on one target gene provided a limited perspective on genetic architecture. Genome-wide association studies (GWAS) can overcome these issues but have been rarely applied to resilience. To date, only two GWAS are reported, because few sufficiently large-scale datasets have a measure of resilience, and those that do may not have genetic data. Here we used a novel longitudinal resilience phenotype with genomic data from the Avon Longitudinal Study on Parent and Children (ALSPAC) to establish resilience trajectories in response to adverse childhood experiences (ACEs). Our results identify the SMARCA2 and OPRM1 genes as significant genetic markers, highlighting their roles in epigenetic mechanisms and dendritic functions associated with resilience. Post-GWAS analyses revealed enrichment of genes linked to dendritic and axonal functions, supporting the hypothesis that dendritic spine plasticity is crucial for cognitive resilience. Our approach offers novel functional insights into how resilience across early life is underpinned by genetic factors, emphasising the importance of dynamic, longitudinal phenotyping. Biological sciences/Genetics/Genetic association study/Genome-wide association studies Biological sciences/Genetics/Development Health sciences/Biomarkers/Diagnostic markers Figures Figure 1 Figure 2 Figure 3 Introduction While there is an increasing amount of preclinical and translational research shedding light on the biological mechanisms of stress resilience ( 1 ), defined as the ability to maintain or regain stability during and after stressful events, our understanding of the genetic architecture of psychological resilience in humans remains limited (( 2 , 3 ) for a review). The limited research of genetic studies of resilience to date have mostly investigated candidate genes, with protective variants identified in genes related to serotonergic systems ( 4 ), the HPA axis ( 5 ), the norepinephrine stress response ( 6 ), influencing temporal lobe grey matter volume ( 7 ) or amygdala and hippocampal activation to threat ( 8 ) (see ( 9 ) for a review). While candidate gene association studies offer a step towards explaining variation in responses to adversity, there are notable limitations to the candidate gene approach such as lack of statistical power associated with small sample sizes, and potential bias in selecting for candidate genes e.g. ( 10 – 12 ). Genome-wide association studies (GWAS) have been used to estimate associations between millions of genetic variants and diverse phenotypes, ranging from health conditions to psychiatric disorders such as schizophrenia and major depression ( 13 – 16 ). However, when it comes to research into the genetics of resilience, few studies have used such approaches ( 17 ). Indeed, there have only been two GWAS of resilience to date ( 18 , 19 ). The reasons for this are two-fold. First, while most studies share some commonality in the definition of resilience as the ability to maintain or regain functioning in the face of adversity e.g. ( 20 – 22 ), there is no gold standard definition or operationalisation of resilience. Second, GWAS require large sample sizes to be sufficiently powered to detect genetic variants with small effect sizes ( 23 , 24 ). Therefore, few large-scale datasets have a measure of resilience, and those that do, may not have genetic data. While the two GWAS of resilience have been able to identify suggestive genetic variants associated with resilience, none of the key variants were replicated ( 18 , 19 ). Resilience research now agrees that resilience is an active, dynamic process that fluctuates across time ( 25 ). Therefore, resilience reflects an individual’s trajectory of functioning following exposure to heightened risk or adversity ( 26 ). Given that the two GWAS of resilience to date use cross sectional measures of resilience, there is a possibility that utilising a single time point for phenotypic assessment contributes to the inconsistent results. One less frequently employed method in GWAS involves utilising detailed phenotyping that can be obtained by monitoring phenotypes over a period of time, known as longitudinal data analysis. By examining longitudinal data, it becomes easier to detect trends in complex traits, particularly those with a late or varying onset. Longitudinal data has been proven to enhance the efficacy of GWAS, as well as boost heritability estimates in certain phenotypes like blood pressure, body mass index (BMI) and cardiovascular phenotypes ( 27 – 30 ). In principle, deep phenotyping increases the granularity of a phenotype and a more precise phenotype will increase the power of a GWAS and lead to larger effect size estimates ( 31 ). Extending a phenotype over time by harnessing the information contained in longitudinal data instead of simple aggregation is one strategy to deepen phenotype ( 32 , 33 ). Therefore, a promising avenue of research is the identification of genetic factors associated with resilience trajectories. This approach diverges from traditional, cross-sectional trait measurement and encapsulates the recognized complexity of resilience, providing granular data in one phenotypic measurement. For the first time, we apply this measurement approach to a large dataset, enhancing the robustness and reliability of our understanding of the genetic architecture of resilience. Adverse childhood experiences (ACEs) can have a significant impact on an individuals' mental, physical, and social well-being. Studies have shown that individuals who have experienced ACEs, such as abuse, neglect, or household dysfunction, are more likely to develop mental health disorders, chronic diseases, and engage in risky behaviours ( 34 – 36 ). While ACEs are prevalent and pose a transdiagnostic risk for the emergence of psychiatric issues, it is widely recognised that the psychological responses to adversity typically exhibit diverse patterns of symptoms (trajectories) over time, with the most common trajectory one of stable good health ( 37 – 40 ). This resilience to adversity has been associated with various intrinsic and extrinsic resilience factors at the individual, family and community level ( 41 , 42 ), but their individual effect sizes have been limited, suggesting that additional key factors may be needed to increase explanatory power ( 43 ). The consistent observation of trajectories of resilience being the modal response to adversity indicates the presence of underlying genetic factors ( 37 , 40 , 44 , 45 ). Although resilience is multifactorial, the genetic component is significant and warrants focused investigation. Twin studies have shown that genetic differences account for 31–52% of the variation in resilience phenotypes ( 46 – 48 ) and that heritability differs across the sexes ( 46 , 49 ). Statistical-genetic innovations such as genome wide complex trait analysis (GCTA) allow for molecular level investigations of psychiatric phenotypes ( 50 ). It is important to note that heritability estimates from twin studies provide insights into the genetic and environmental contributions; whereas GCTA offers a molecular-level perspective by examining the variance explained by all SNPs. To date, only two studies have applied GCTA in relation to resilience. Stein et al. ( 18 ) estimated the single-nucleotide polymorphism (SNP)-based heritability of self-assessed resilience to be 16% (h2 = 0.16, SE = 0.050, p = 5.37 × 10 − 4, N = 9,932), while Cusack et al. ( 19 ) found no significant heritability estimates in two sub-populations (N = 908, & N = 2,371), with notable standard error estimates suggesting inadequate power. In sum, while the detrimental effects of ACEs on mental, physical, and social wellbeing are well-documented, the mechanisms underlying resilience remain elusive. This gap in understanding combined with the lack of a gold standard definition of resilience, has hindered the comprehensive application of GWAS in the context of resilience. Understanding the biological mechanisms and genetics of resilience to ACEs will also contribute to the development of strategies that aim to mitigate the negative outcomes associated with childhood adversity. By leveraging the power of GWAS and the granularity of longitudinal data, this study aims to shed light on the genetic factors that contribute to resilient trajectories in the face of adversity. Our approach, which emphasises the dynamic nature of resilience and the importance of deep phenotyping, provides novel insights into the genetic architecture of resilience and can inform future interventions aimed at fostering resilience in individuals exposed to ACEs. Within this paper, we operationalize resilient functioning as having better than expected psychosocial functioning, given specific levels of ACEs across the early lifecourse ( 51 ). Our GWAS phenotype is derived from patterns of this resilient functioning across time, informed by previously identified group-based trajectories of longitudinal functioning ( 38 ). By harnessing the power of genome-wide data, our methodology offers a novel functional insight into how membership to the resilient trajectory is determined at the biological level. Our objectives are threefold: to better understand the molecular underpinnings of resilience, to estimate its molecular heritability, and to uncover the broader biological processes, molecular functions, and cellular components associated with trajectories of resilience. Methods Study population: ALSPAC is a transgenerational cohort study designed to investigate the influence of genetic and environmental factors on the health of both parents and children ( 52 , 53 ). In brief, the study recruited 13,761 pregnant women who lived in Southwest England and were due to deliver between 1st April 1991 and 31st December 1992. These women and their children have been followed up at regular intervals over the past 30 years. Detailed phenotypic information, biological samples and genetic data have been collected from the participants which are available through a searchable data dictionary ( http://www.bris.ac.uk/alspac/researchers/our-data/ ). Written informed consent was obtained for all study participants. Ethical approval for this study was obtained from the ALSPAC Ethics and Law Committee and the Local Research Ethics Committees. All analyses were performed on the eligible children with genotype and phenotype data (N = 7975). See supplementary table 1 for the sociodemographic characteristics of the sample, grouped by their binary trajectory resilient phenotype. Resilient Trajectories: The ALSPAC offspring cohort has been previously characterised into seven distinct trajectories of resilient functioning using growth mixture modelling. For a comprehensive description of this method, refer to Cahill et al. ( 38 ), with a visual representation in Fig. 1 . The process of modelling resilience involved constructing resilience residuals at each time point individually. In these models, the binary exposures of ACEs from early life periods prior to the outcomes were regressed on the total difficulties score of the Strength and Difficulties Questionnaire (SDQ) and the Rutter Behaviour Scale (RBS) at that specific time point. The SDQ is one of the most commonly used ratings of child psychopathology in epidemiological studies ( 54 ), with the RBS used to assess child mental health and behavioural/emotional problems in younger children ( 55 ). We extracted the residuals from each regression model as these reflect a spectrum ranging from risk to resilient functioning i.e., the extent to which an individual has better, or worse, SDQ outcomes than the average score expected given their exposure to ACEs over the early life periods. This provides six separate quantitative measures of resilience across each individual life course. The residuals obtained from these regressions were then used as outcomes for a Growth Mixture Model (GMM). This model aimed to identify group-based longitudinal trajectories of resilient functioning by using a longitudinal dataset composed of the six repeated measures of resilience derived from the earlier step. GMM combines latent growth modelling with latent class analysis ( 56 ), allowing the classification of study participants into distinct groups that represent different levels of resilient functioning over time. The optimal number of trajectories was determined using a multi-step approach and selecting the model with the lowest Bayesian Information Criterion (BIC), with 7 trajectories being the optimal solution (Table 1 ) All GMM analyses were conducted using the ‘lcmm’ package version 1.9.5 ( 57 ). All analyses were performed in R version 4.0.3 (2020-10-10), Rstudio version 1.3.1093 for Windows. Table 1 Seven trajectories of resilience as identified in GMM Class Description % of Study Population 1 Participants who showed increasing vulnerability from infancy through adolescence 14.09 2 Participants who were initially resilient, became extremely vulnerable in mid-childhood, and then experienced a decrease in vulnerability. 7.67 3 Participants who started vulnerable, became resilient around age 5–6, but faced increasing vulnerability again in early adolescence. 4.47 4 Participants who were vulnerable in early childhood, became resilient around age 6–7, and maintained high resilience into early adolescence. 10.23 5 Participants who maintained moderate levels of resilience consistently throughout their early life. 28.44 6 Participants who consistently maintained high levels of resilience throughout their early life. 24.88 7 Participants who were vulnerable in early childhood but became resilient by adolescence. 10.13 To create a binary phenotype, we have merged class 5 and 6 and defined this as the resilient phenotype, because they consistently maintain better than expected psychosocial functioning, given the levels of ACEs they were exposed to across the early lifecourse. The remaining classes ( 1 – 4 and 7 ) are the vulnerable phenotype. Within the participants 4,190 (53.3%) were identified as resilient and 3,669 (46.7%) as non-resilient. This decision to dichotomise the trajectories into resilient and vulnerable phenotypes is based on two considerations. First, a binary classification simplifies the analytical approach, enhancing the clarity and interpretability of the results. While the seven trajectories provide a nuanced understanding of resilience patterns, for the purpose of GWAS, a binary phenotype can increase statistical power and reduce the risk of type I errors. Second, classes 5 and 6 consistently demonstrate stable resilience throughout the early life course, making them ideal representatives of the resilient phenotype. In contrast, the other classes, despite their varied patterns, all exhibit periods of vulnerability, justifying their categorisation under the vulnerable phenotype. Genotyping: Genome-wide genotyping was undertaken on ALSPAC offspring at a cohort level with quality control, data cleaning and imputation, as described previously ( 52 ), leaving 8,237 eligible children. Genotype data on participants was derived using the Illumina HumanHap550 quad genome-wide single nucleotide polymorphism (SNP) genotyping platform (Illumina Inc, San Diego, USA) by the Wellcome Trust Sanger Institute (WTSI, Cambridge, UK) and the Laboratory Corporation of America (LCA, Burlington, NC, USA). Statistical Analyses QC and GWAS: In our study, we initially considered a dataset of 7,975 participants: 4,092 males and 3,883 females, comprising 27,449,291 SNPs. Preliminary quality control (QC) procedures, performed using QCtool, a command-line utility program for manipulation and quality control of GWAS datasets and other genome-wide data ( 58 ), filtered variants based on an imputation quality (INFO) score < 0.8, narrowing down our dataset to 10,885,703 variants for further analytical steps in PLINK v1.90b6.16 64-bit ( 59 ). We implemented QC filters via PLINK adopting the following best practice criteria: SNPs with < 0.05% genotype missingness were discarded (excluding 978,364). We filtered out SNPs with a minor allele frequency (MAF) < 0.05 (excluding 4,809,144 SNPs) and excluded variants not conforming to the Hardy-Weinberg equilibrium (HWE), specifically those with a p-value below 0.01 (excluding 63,397 SNPs). Following QC, 5,034,798 variants remained eligible for further analysis. All 7,975 participants passed the QC checks. We conducted a genome-wide association test for resilience using logistic regression (for dichotomised trajectories of resilience vs non-resilient) using PLINK. A post-QC total genotyping rate of 0.98296 was achieved. A p-value < 5 x 10 − 8 was used as the threshold for genome-wide significance, whereas results at p-value < 5 x 10 − 5 are reported as genome-wide suggestive ( 60 ). Clumping: After passing the stringent QC criteria, the dataset underwent a clumping procedure to handle the issue of linkage disequilibrium (LD) between markers. The clumping procedure in PLINK serves as a method to identify and retain the most statistically significant SNP (known as the "index" SNP) within regions of LD, while "clumping" or grouping together other SNPs (referred to as "clumped" SNPs) that are in close LD with this index SNP. PLINK starts by identifying the most significant SNP based on its p-value. Around this index SNP, a physical window is defined by default to 250kb on either side. Within this window, other SNPs that are in strong LD (r 2 > 0.25) with the index SNP are grouped or 'clumped' together. Only the index SNP is retained for subsequent analyses, and other SNPs within the LD region that meet the clumping criterion are noted but not considered independently in subsequent steps to avoid redundant information. The procedure is then reiterated for the next most significant SNP outside the previously defined clumped regions until all SNPs have either been designated as an index SNP or been clumped with one. Genome-wide Complex Trait Analysis (GCTA): GCTA is a software tool developed to quantify the variance explained by genome-wide SNPs for complex traits in quantitative genetic studies, especially in GWAS ( 50 ). GCTA is employed to estimate the heritability explained by all SNPs, giving insight into the genetic component of the variance observed in resilience. We applied GCTA's GREML method to estimate the variance explained by all SNPs on our phenotype ( 56 ). Post-GWAS Analyses Variant Effect Predictor (VEP): VEP is a powerful bioinformatics tool developed by the Ensembl project to analyse and predict the functional effects of genetic variants (e.g., SNPs, insertions, deletions) on genes, transcripts, and protein sequences ( 61 ). It offers insight into the potential consequences of discovered variants, including their impact on protein function, involvement in regulatory regions, and associations with known phenotypes or diseases. Significant and suggestive variants were uploaded to the VEP platform. Each variant was annotated with its predicted functional consequences, including missense mutations, synonymous changes, or regulatory region alterations. Special attention was given to variants predicted to have high or moderate impact on protein function, as these could be pivotal in affecting resilience phenotypes. The Database for Annotation, Visualisation and Integrated Discovery (DAVID): DAVID is an integrative online platform designed to provide a comprehensive set of functional annotation tools for analysing the biological meanings behind a vast list of genes/proteins ( 62 ). We used DAVID to undertake a gene ontology (GO) and pathway enrichment analysis. This aids in understanding the broader biological processes, molecular functions, and cellular components associated with our gene list. Through DAVID, we condense large gene lists into functionally related gene groups, providing a holistic overview of the gene functional classifications. Uploaded gene lists were subjected to functional annotation clustering, with a focus on GO terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Significance values were adjusted using the Benjamini-Hochberg method to control the false discovery rate. Overview of GWAS Results We conducted a GWAS to identify genetic variants associated with resilience, using resilient and non-resilient trajectories as outcomes. The analysis included 5,035,004 markers. The lambda value (λ = 0.993 at 50th percentile) indicated minimal inflation across the p-value distribution, suggesting that population stratification was well-controlled. This control was achieved by including principal components derived from PCA as covariates in the analysis, thereby accounting for potential confounding due to population structure (Fig. 1 ). The QQ plot of our GWAS data showed the observed distribution closely following the expected distribution under the null hypothesis until a -log10(p) value of 4. Beyond this point, there was a slight deviation from the expected line, which suggests the possibility of true genetic associations with resilience, although these associations are not statistically significant at the genome-wide level (Fig. 1 ). The clumping procedure identified 79 distinct clumps from 472 top variants, capturing the most significant association signals from their respective LD regions. (Supplementary Table 2). The Manhattan plot of the clumped data (Fig. 2 ) shows the distribution of the association signals across the genome. Several SNPs were close to achieving genome-wide significance ( p < 5 × 10 − 8 ), although none reached it. 42 SNPs surpassed the suggestive association threshold ( p < 5 × 10 − 5 ; Fig. 2 and Table 2 ). The top SNP, rs77374979, located on chromosome 9, showed the strongest association with resilience (p = 4.02 x 10 − 7 ) Individuals carrying the A allele of rs77374979 have 1.466 times higher odds of being resilient compared to non-carriers. This variation is located in an intronic region of the SWI/SNF related, matrix associated, actin dependent regulator of chromatin, subfamily a, member 2 ( SMARCA2 ) gene, which has been implicated in chromatin remodeling and stress response pathways. Table 2 provides detailed information on the top SNPs that met the suggestive threshold. These findings point to the involvement of genetic factors in resilience and highlight specific genomic regions and genes for further investigation. Table 2 Summary information of SNPs meeting suggestive threshold. CHR = Chromosome, Allele = The allele for which the association test statistics are calculated. SNP Chr Base Pair Position Test Allele Odds Ratio P-value Wald Test Statistic Gene rs77374979 9 2081278 A 1.466 4.02E-07 5.068 SMARCA2 rs1906656 11 6468326 T 1.172 1.07E-06 4.879 None rs10079405 5 124782491 A 1.417 1.66E-06 4.791 None rs2326459 16 84995658 G 1.159 4.41E-06 4.591 None rs4961 4 2906707 T 1.208 5.03E-06 4.564 ADD1 rs11705732 3 131529862 A 0.8155 6.26E-06 -4.517 CPNE4 rs9808926 3 187607504 T 1.194 1.29E-05 4.362 LOC105374264 rs2881053 16 25201470 G 0.8478 1.30E-05 -4.36 None rs1147931 10 44922347 T 1.158 1.64E-05 4.309 None rs10474761 5 3204386 C 1.38 1.70E-05 4.301 None rs35939580 17 62973078 A 0.7575 1.85E-05 -4.282 AMZ2P1 rs6950250 7 95853837 A 0.8323 1.96E-05 -4.269 SLC25A13 rs4712864 6 24891756 T 0.8402 1.98E-05 -4.268 RIPOR2 rs55964566 8 81497393 G 1.187 2.13E-05 4.25 None rs2757630 6 7561359 T 1.147 2.56E-05 4.21 DSP rs199631776 9 22774473 A 0.7777 2.57E-05 -4.209 LINC01239 rs1991394 10 54086988 G 1.292 2.65E-05 4.202 None rs11772117 7 138010190 A 1.146 2.85E-05 4.185 None rs11677946 2 200670394 G 0.8727 2.94E-05 -4.178 FTCDNL1 rs2911319 16 62921503 T 0.8209 2.96E-05 -4.176 None rs55728401 3 125240861 G 1.249 3.10E-05 4.166 SNX4 rs1570925 10 14555086 T 0.8312 3.11E-05 -4.165 None rs8058900 16 85026218 G 1.209 3.12E-05 4.165 ZDHHC7 rs9397696 6 154532278 T 0.8441 3.14E-05 -4.163 OPRM1 rs147898448 8 47709375 C 1.168 3.14E-05 4.163 SPIDR rs10202078 2 37727116 C 0.7632 3.19E-05 -4.159 LOC105374464 rs4288315 7 122708549 T 0.7618 3.22E-05 -4.157 None rs79044013 16 84984096 G 0.8726 3.26E-05 -4.154 None rs17650551 11 106618077 G 0.7953 3.27E-05 -4.154 GUCY1A2 rs13053888 22 34366372 T 0.7846 3.34E-05 -4.149 None rs7975048 12 128000853 C 0.8717 3.39E-05 -4.146 None rs73136137 20 51349009 A 1.266 3.52E-05 4.137 None rs35116986 20 45092334 CT 1.212 3.54E-05 4.135 MKRN7P rs7721856 5 26108316 A 1.148 3.74E-05 4.123 None rs754415 14 104558319 C 1.213 3.96E-05 4.11 ASPG rs28470926 16 19323261 A 1.287 4.15E-05 4.099 LOC105371114 rs73043693 7 3315440 T 1.185 4.45E-05 4.083 SDK1-AS1 rs9901183 17 49740054 C 0.8387 4.48E-05 -4.081 CA10 rs76728099 1 244364363 T 1.215 4.56E-05 4.077 None rs357373 7 137983003 C 0.8707 4.64E-05 -4.073 None rs1543994 8 46939842 T 1.166 4.77E-05 4.067 None rs7255568 19 605985 A 0.8767 4.99E-05 -4.056 HCN2 Among the SNPs that passed the suggestive association threshold, the opioid receptor mu 1 ( OPRM1) gene, specifically SNP rs9397696, emerged as a notable candidate (p = 3.14 x 10 − 5 ). OPRM1 , located on chromosome 6, has been previously associated with resilience in both human and animal studies ( 9 ). In our dataset, it further strengthens the evidence for OPRM1 's role in resilience, suggesting that it might modulate an individual's response to stressors or adverse environments. Heritability of Resilience Using GCTA ( 50 ) we estimated the SNP-based, narrow-sense heritability (h 2 ) of resilience to be 1.97% (SE = 0.043). Due to the binary nature of our phenotype, we further transformed these estimates to the underlying liability scale, which better approximates a normal distribution. The liability scale is a theoretical construct in genetic studies of binary traits, where the observed binary outcome is considered a threshold on an unobserved continuous liability distribution. By assuming a disease prevalence of 0.5, meaning that resilience is equally likely to be present or absent in the population, the transformed heritability was 3.11% (SE = 0.067). Variant Effect Predictor We entered the list of 79 variants obtained post-clumping into VEP (Supplementary table 2). This list, representing the most statistically significant variants from distinct LD regions, serves as a curated starting point to ensure that our functional annotations and predictions are directed at variants of biological relevance. A significant majority of our variants (73 out of 79) overlap with genes, which potentially means they could have direct gene-related functional effects. These variants also intersect with a total of 348 transcripts, signifying that they might have impacts on different gene isoforms or across multiple genes. Additionally, 21 variants overlap with regulatory features, highlighting their potential role in gene regulation. Three (or 3.8%) of these variants are novel, meaning they might not have been previously documented in major variant databases. This could indicate unique or understudied genetic variations in the dataset. DAVID Our DAVID analysis identified specific cellular components, particularly dendrites and axons, that were significantly associated with our gene set based on their p-values and Benjamini scores. Benjamini scores are used to control the false discovery rate (FDR) in multiple hypothesis testing. A lower Benjamini score indicates that the term is less likely to be a false positive. In many studies, a Benjamini score below 0.05 is considered significant, indicating that the term is likely to be truly associated with the genes and not a result of random chance. Several significant annotation clusters were associated with the genes of interest (Supplementary Table 3). The most significant cluster was enriched for dendritic functions GO:0030425 ~ dendrite: This term was significantly enriched with a p-value of 1.56 x 10 − 4 and a Benjamini score of 0.017. The genes associated with this term were DSCAM, TRIM3, HTT, OPRM1, SLC8A1, HCN2 , and RAP1GAP . The fold enrichment for this term was 8.26. Another notable cluster showed a positive association with axonal components. GO:0030424 ~ axon: This term was significantly enriched with a p-value of 4.93 x 10 − 4 and a Benjamini score of 0.0278. The genes associated with this term were DSCAM, HTT, OPRM1, SLC8A1, HCN2 , and RAP1GAP. The fold enrichment for this term was 8.81. In our gene set analysis, we observed a notable enrichment in specific protein domains (Supplementary table 4). According to the UniProt Knowledgebase (UniProtKB) domain annotations under the UP_KW_DOMAIN category, there was a significant association with the "Repeat" protein domain (KW-0677). This domain was found in several genes, including DSP, DGKG, UNC13C, WWOX, KRTAP12-3, DSCAM, KRTAP12-4, CPNE4, MMP2, TTC1, HTT, TSPEAR, AHR, SLC8A1, PREX2, PTPRD, HPX, DCHS2, ASPG, TRIM3 , and SLC25A13 . The statistical significance of this association is underscored by a p-value of 0.0019 and a Benjamini score of 0.025, indicating a low likelihood of this result being due to random chance. The fold enrichment value of 1.79 further emphasizes the overrepresentation of the "Repeat" domain in our gene set. Proteins with repeat domains often suggest evolutionary patterns through repetitive sequences, potentially bestowing them with unique structural or functional attributes. Discussion Our study conducted a genome-wide association analysis on resilience trajectories, revealing several suggestively significant genetic markers, with the top SNP, rs77374979, located in the SMARCA2 gene. Furthermore, the suggestive significance of OPRM1 in our dataset underscores its potential as a key genetic factor in resilience pathways. By harnessing the information contained in longitudinal data, we provide a more granular and accurate representation of resilience over time ( 32 , 33 ). This approach, rooted in the principles of deep phenotyping, is likely to enhance the efficacy of GWAS and lead to more accurate heritability estimates ( 31 ). Our study design advances on previous work in using a more biologically relevant phenotype of resilience, accounting for the impact of childhood adversity, and with a larger sample size than previous GWAS of resilience. These factors may contribute to the differences in heritability estimates between our study and previous work. Our comprehensive approach not only enhances the efficacy of GWAS, but also ensures that the genetic foundations of resilience are more accurately captured. The identification of the top SNP in the SMARCA2 gene suggests a potential epigenetic mechanism underlying resilience. Recent research has highlighted the SWI/SNF chromatin remodeler complex, particularly the ATPase subunits BRM (Brahma, encoded by the SMARCA2 gene) and BRG1 (Brahma Related Gene 1), for its pivotal role in behavioural adaptations to stress ( 78 ), specifically within the mesocorticolimbic pathway, a critical neural circuitry for stress response and resilience ( 79 ). Zayed et al. ( 64 ) demonstrated that mice with inactive Brg1/Smarca4 gene in dopamine-innervated regions, or those with constitutive inactivation of the Brm/Smarca2 gene, exhibited resilience to repeated social defeat. This resilience was further evidenced by decreased behavioural responses to cocaine, independent of any alterations in midbrain dopamine neuron activity. A study on the effects of heat shock on human brain development found that the expression of the SMARCA2 gene, among others, was significantly altered, suggesting that environmental stressors could modulate the expression of genes associated with neuropsychiatric disorders such as schizophrenia and autism ( 80 ). Furthermore, insights into the role of nucleosome remodelling, particularly by the neuron-specific CRC nBAF, underscore its significance in long-term memory formation and synaptic plasticity ( 81 ). Given that SMARCA2 is an integral component of the BAF complex, it is plausible that disruptions in its function could influence cognitive processes and thus resilience. nBAF has been shown to regulate gene expression essential for dendritic arborization during development ( 82 ) and contributes to long-term potentiation in adults. Impaired nBAF function has been linked to human cognitive disorders through exome-sequencing and GWAS ( 81 ), further emphasising the potential significance of the SMARCA2 gene in resilience. Given our results suggest a role of the SMARCA2 gene in resilience, investigating the SWI/SNF chromatin remodeler complex, especially the SMARCA2 gene, should be a focus for future research. The collective evidence supports the hypothesis that SMARCA2 plays a crucial role in modulating an individual's response to environmental stressors and may thus offer a novel therapeutic target for enhancing resilience. Our findings also provide additional support for a role of OPRM1 in resilience, adding to studies in both human and animal studies ( 9 ) that suggest OPRM1 might modulate an individual's response to stressors or adverse environments. Previous research has highlighted the G-allele of OPRM1 A118G polymorphism as a resilience factor against relapse to heroin use ( 83 ) and its role in modulating attachment behaviours based on early life experiences ( 84 ). In line with this, Daniel et al. ( 85 ) found that carriers of the G-allele of the OPRM1 A118G polymorphism exhibited reduced resilience, such as slower recovery following a reward downshift and a heightened sensitivity to physical pain. This underscores the potential influence of genetic variations in the opioid system on resilience to emotional disturbances. Our Variant Effect Predictor (VEP) analysis highlighted the potential functional relevance of the identified genetic variants. Many of these variants were found to overlap with genes and regulatory elements, suggesting they could play a role in influencing gene expression. Our DAVID analysis revealed a neurobiological basis for resilience, with a significant enrichment of genes associated with dendritic and axonal functions. Dendrites and axons are critical components of neuronal communication, and their proper functioning is essential for cognitive processes, learning, and memory ( 86 ). Disruptions in these structures have been linked to various neuropsychiatric disorders ( 87 ). Dendritic spines, integral to neuronal communication, have been observed to undergo plasticity in response to stress. Rodent models have shown regional differences in dendritic spine density in response to stress, with certain stress models leading to decreased spine density in specific brain regions, such as the prelimbic medial prefrontal cortex and the hippocampus ( 88 , 89 ). Notably, resilience to stress appears to be associated with maintenance of dendritic spine densities, suggesting a potential neurobiological mechanism for resilience. The evidence of dendritic spine involvement in resilience to stress, and the association of OPRM1 and the nBAF complex with dendritic functions, supports the hypothesis that dendritic spine plasticity is a mechanism for cognitive resilience. The SNP-based heritability of resilience, as estimated in our study, is notably lower than previous GCTA analyses ( 18 , 19 ) and twin studies ( 46 , 49 ). SNP-based heritability captures the proportion of phenotypic variance explained by common genetic variants that are typically assayed on SNP arrays. In contrast, twin studies capture both the additive genetic variance due to all genetic variants (common and rare) and potential non-additive genetic effects, such as dominance and epistasis. The discrepancy between SNP-based heritability and twin study heritability estimates has been a topic of discussion in the field of genetics for several complex traits, not just resilience ( 63 ). This phenomenon, often termed "missing heritability", suggests that the heritability captured by common SNPs on arrays is often substantially lower than the heritability estimated from twin or family studies ( 64 – 66 ). Several factors could contribute to this "missing heritability". Firstly, common SNPs typically assayed in GWAS might not capture the effects of rare genetic variants. These rare variants, although individually of low frequency, might collectively explain a significant portion of the genetic variance. Over the past two decades, our understanding of the genetic and epigenetic contributions to common complex traits and disease has significantly evolved, enabled by large-scale meta-analyses based on genome-wide SNP arrays ( 67 ). However, genetic variants that elude detection by even the most statistically powerful association studies ( 68 ) are believed to contribute to the missing heritability of many human traits. This includes common variants (defined as having a minor allele frequency [MAF] > 5%) with very weak effects, low-frequency variants (MAF 1–5%) with small to modest effects, and rare variants (MAF < 1%) also with small to modest effects ( 69 ). Advancements in custom genotyping arrays and whole-exome sequencing have identified numerous novel associations with diseases such as type 2 diabetes, coronary artery disease and various immune disorders ( 70 , 71 ). The Haplotype Reference Consortium has aggregated whole-genome sequencing data to create an extensive reference panel, enhancing the accuracy of genotype imputation for rare variants down to a minor allele frequency (MAF) of 0.1% ( 72 ). Additionally, research has demonstrated the significant impact of rare copy number variations (CNVs) on disease susceptibility, as evidenced by large-scale genomic studies using data from the UK Biobank and other population-based cohorts ( 73 ). Furthermore, the combined effect of multiple genes might not be purely additive. Interactions between genes, where the effect of one gene is modified by the presence of another (epistasis), might contribute to the heritability not captured by individual SNPs ( 74 ). Secondly, the effect of genes can also vary across different environmental contexts. For example, certain genetic variants might act as differential susceptibility variants, making individuals more sensitive to both positive and negative environmental factors ( 75 , 76 ). This means that the manifestation of resilience might be contingent upon specific environmental conditions. Such gene-environment interactions, pivotal in understanding how genetic variant effect depend on environmental context ("biological sensitivity to context"; ( 77 )), are often overlooked in standard GWAS. This oversight can lead to underestimated heritability, as the full spectrum of genetic influence, especially in tandem with environmental factors, is not captured. Furthermore, while our focus has been on SNPs, it is crucial to acknowledge that other genetic variations, like insertions, deletions, and copy number variants, can also contribute to the genetic architecture of resilience and other complex traits. Lastly, the way resilience is measured and defined can introduce variability. If the phenotype is not measured accurately or consistently, heritability may be underestimated because measurement error can obscure the true genetic contributions to resilience. In our study, we have operationalised resilience as an active, dynamic process that reflects an individual’s trajectory of functioning following exposure to adversity that captures temporal variation. In sum, one should interpret SNP-based heritability estimates with caution. While such estimates provide valuable insights into the genetic architecture of resilience, they represent only a piece of the broader heritability puzzle. Future studies employing whole-genome sequencing, multi-omics approaches, and more comprehensive models might shed further light on the "missing heritability" of resilience. Our results pave the way for more targeted genetic research on resilience. A deeper understanding of the genetic architecture can lead to the development of personalised interventions. The novel variants identified in this study, combined with the suggestive significance of OPRM1 and SMARCA2 , warrant further validation in diverse cohorts. Future studies could benefit from a more integrative approach, combining genetic data with other omics data, such as transcriptomics or epigenomics, to provide a holistic understanding of resilience. However, we note that the cohort in our study, specific to a particular region, and lacking diversity in ethnic background and socioeconomic status, might not be representative of broader populations, emphasising the need for replication in diverse ethnic and geographical cohorts. Given the consistent findings related to OPRM1 and SMARCA2 across different studies and models, these two genes emerge as promising therapeutic targets. We recommend longitudinal studies focusing on genetic variants of OPRM1 and SMARCA2 , considering factors like developmental timing, type and severity of stressor, sex, and ethnicity. Declarations Competing Interests None Acknowledgments We are extremely grateful to all the families who took part in the ALSPAC study, the midwives for their help in recruiting them, and the whole ALSPAC team, which includes interviewers, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, receptionists and nurses References McEwen BS, Bowles NP, Gray JD, Hill MN, Hunter RG, Karatsoreos IN, et al. Mechanisms of stress in the brain. Nature neuroscience. 2015;18(10):1353-63. Maul S, Giegling I, Fabbri C, Corponi F, Serretti A, Rujescu D. Genetics of resilience: Implications from genome-wide association studies and candidate genes of the stress response system in posttraumatic stress disorder and depression. American Journal of Medical Genetics, Part B: Neuropsychiatric Genetics. 2020;183(2):77-94. Ryan M, Ryznar R. The molecular basis of resilience: a narrative review. Frontiers in psychiatry. 2022;13. 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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-4861048","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":343868004,"identity":"a199ae37-6283-4c61-b8be-23ec16f44430","order_by":0,"name":"Stephanie Cahill","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFElEQVRIiWNgGAWjYJACZiDmAWI2hoQCCzkDsJiBBW71bChaDCSMDcB8AwmCWqBMA4nEDRA+bi3y85uPPS6ouSPDz3742YMHBhLp29n7j274USDBwN/enYBNi8ExtnTjGcee8Uj2pJkbAB2Wu7PnMNvNHqDDJM6c3YBVCxuPmTQP22EegxsMZhIgLRtuJLPd4AFqAbGxOqyN/5s0z7/DPPY32L+BtKQbALXc/INHC8MxHjZp3jagLRI8YFsSQFpu47PF4FiauTFv32EeiTM5ZSAthhvOHDa7LQM0AZdf5JsPP3vM8+2wPX/78W2SPyps5A2ONz67+eaPjRx/ey92h4GjAxvgwaEcj5ZRMApGwSgYBTAAAL00WNaJWwqdAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-2075-5510","institution":"Manchester Metropolitan University","correspondingAuthor":true,"prefix":"","firstName":"Stephanie","middleName":"","lastName":"Cahill","suffix":""},{"id":343868005,"identity":"3b04a8ca-39b9-4748-b0ef-e864a2ceb86e","order_by":1,"name":"Krisztina Mekli","email":"","orcid":"","institution":"University of Manchester","correspondingAuthor":false,"prefix":"","firstName":"Krisztina","middleName":"","lastName":"Mekli","suffix":""},{"id":343868006,"identity":"9e2de17b-a876-4c10-976c-ff844dd8dc77","order_by":2,"name":"Reinmar Hager","email":"","orcid":"","institution":"University of Manchester","correspondingAuthor":false,"prefix":"","firstName":"Reinmar","middleName":"","lastName":"Hager","suffix":""}],"badges":[],"createdAt":"2024-08-05 09:55:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4861048/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4861048/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":78825587,"identity":"a668e524-7bfd-41bb-b84a-3a8b16c73608","added_by":"auto","created_at":"2025-03-19 12:25:22","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":226678,"visible":true,"origin":"","legend":"\u003cp\u003eVisual representation of the trajectories of resilience to ACEs across the early lifecourse\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4861048/v1/f6e935b549758cd6e00ba189.png"},{"id":78826699,"identity":"68575560-b10c-417a-b54c-5d8795a3c1aa","added_by":"auto","created_at":"2025-03-19 12:41:22","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":58675,"visible":true,"origin":"","legend":"\u003cp\u003eQuantile-Quantile (QQ) plot for the GWAS of Resilience. The expected distribution of p-values is shown on the x-axis, while the observed distribution of p-values from GWAS of resilience is shown on the y-axis. All p-values are represented as – log10(P\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4861048/v1/2f65c794a66435360bcf3646.png"},{"id":78825589,"identity":"076944d6-463f-4ff1-ac49-82588cc15a26","added_by":"auto","created_at":"2025-03-19 12:25:22","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":247709,"visible":true,"origin":"","legend":"\u003cp\u003eManhattan Plot for Resilience. This figure plots the -log10(p) values of associations for resilience by chromosome. The blue line represents genome-wide significance (P = 5 x 10\u003csup\u003e-8\u003c/sup\u003e), while the green line indicates a suggestive association threshold (p = 5 x 10\u003csup\u003e-5\u003c/sup\u003e)\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4861048/v1/9dfd810a8274aef09b020751.png"},{"id":78827103,"identity":"ecc26ed5-7d2b-4395-bdcf-3b8e39afb32d","added_by":"auto","created_at":"2025-03-19 12:49:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1234948,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4861048/v1/b07d0fb1-7576-42a7-93ed-4b701db7b4b1.pdf"},{"id":78826001,"identity":"bf29d332-5eda-4548-9ab4-78c69bbca799","added_by":"auto","created_at":"2025-03-19 12:33:22","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":69440,"visible":true,"origin":"","legend":"Supplementary Files","description":"","filename":"CahilletalSupplementaryfiles.docx","url":"https://assets-eu.researchsquare.com/files/rs-4861048/v1/97b43af0ef826d340a16bedf.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Longitudinal Genome-Wide Study Reveals Genetic Architecture of Resilience Using a Novel Phenotype","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWhile there is an increasing amount of preclinical and translational research shedding light on the biological mechanisms of stress resilience (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e), defined as the ability to maintain or regain stability during and after stressful events, our understanding of the genetic architecture of psychological resilience in humans remains limited ((\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) for a review). The limited research of genetic studies of resilience to date have mostly investigated candidate genes, with protective variants identified in genes related to serotonergic systems (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e), the HPA axis (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e), the norepinephrine stress response (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e), influencing temporal lobe grey matter volume (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e) or amygdala and hippocampal activation to threat (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e) (see (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e) for a review). While candidate gene association studies offer a step towards explaining variation in responses to adversity, there are notable limitations to the candidate gene approach such as lack of statistical power associated with small sample sizes, and potential bias in selecting for candidate genes e.g. (\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e–\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGenome-wide association studies (GWAS) have been used to estimate associations between millions of genetic variants and diverse phenotypes, ranging from health conditions to psychiatric disorders such as schizophrenia and major depression (\u003cspan additionalcitationids=\"CR14 CR15\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e–\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). However, when it comes to research into the genetics of resilience, few studies have used such approaches (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Indeed, there have only been two GWAS of resilience to date (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). The reasons for this are two-fold. First, while most studies share some commonality in the definition of resilience as the ability to maintain or regain functioning in the face of adversity e.g. (\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e–\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e), there is no gold standard definition or operationalisation of resilience. Second, GWAS require large sample sizes to be sufficiently powered to detect genetic variants with small effect sizes (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Therefore, few large-scale datasets have a measure of resilience, and those that do, may not have genetic data.\u003c/p\u003e \u003cp\u003eWhile the two GWAS of resilience have been able to identify suggestive genetic variants associated with resilience, none of the key variants were replicated (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Resilience research now agrees that resilience is an active, dynamic process that fluctuates across time (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Therefore, resilience reflects an individual’s trajectory of functioning following exposure to heightened risk or adversity (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Given that the two GWAS of resilience to date use cross sectional measures of resilience, there is a possibility that utilising a single time point for phenotypic assessment contributes to the inconsistent results.\u003c/p\u003e \u003cp\u003eOne less frequently employed method in GWAS involves utilising detailed phenotyping that can be obtained by monitoring phenotypes over a period of time, known as longitudinal data analysis. By examining longitudinal data, it becomes easier to detect trends in complex traits, particularly those with a late or varying onset. Longitudinal data has been proven to enhance the efficacy of GWAS, as well as boost heritability estimates in certain phenotypes like blood pressure, body mass index (BMI) and cardiovascular phenotypes (\u003cspan additionalcitationids=\"CR28 CR29\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e–\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). In principle, deep phenotyping increases the granularity of a phenotype and a more precise phenotype will increase the power of a GWAS and lead to larger effect size estimates (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Extending a phenotype over time by harnessing the information contained in longitudinal data instead of simple aggregation is one strategy to deepen phenotype (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Therefore, a promising avenue of research is the identification of genetic factors associated with resilience trajectories. This approach diverges from traditional, cross-sectional trait measurement and encapsulates the recognized complexity of resilience, providing granular data in one phenotypic measurement. For the first time, we apply this measurement approach to a large dataset, enhancing the robustness and reliability of our understanding of the genetic architecture of resilience.\u003c/p\u003e \u003cp\u003eAdverse childhood experiences (ACEs) can have a significant impact on an individuals' mental, physical, and social well-being. Studies have shown that individuals who have experienced ACEs, such as abuse, neglect, or household dysfunction, are more likely to develop mental health disorders, chronic diseases, and engage in risky behaviours (\u003cspan additionalcitationids=\"CR35\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e–\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). While ACEs are prevalent and pose a transdiagnostic risk for the emergence of psychiatric issues, it is widely recognised that the psychological responses to adversity typically exhibit diverse patterns of symptoms (trajectories) over time, with the most common trajectory one of stable good health (\u003cspan additionalcitationids=\"CR38 CR39\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e–\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). This resilience to adversity has been associated with various intrinsic and extrinsic resilience factors at the individual, family and community level (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e), but their individual effect sizes have been limited, suggesting that additional key factors may be needed to increase explanatory power (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). The consistent observation of trajectories of resilience being the modal response to adversity indicates the presence of underlying genetic factors (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). Although resilience is multifactorial, the genetic component is significant and warrants focused investigation. Twin studies have shown that genetic differences account for 31–52% of the variation in resilience phenotypes (\u003cspan additionalcitationids=\"CR47\" citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e–\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e) and that heritability differs across the sexes (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). Statistical-genetic innovations such as genome wide complex trait analysis (GCTA) allow for molecular level investigations of psychiatric phenotypes (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e). It is important to note that heritability estimates from twin studies provide insights into the genetic and environmental contributions; whereas GCTA offers a molecular-level perspective by examining the variance explained by all SNPs. To date, only two studies have applied GCTA in relation to resilience. Stein et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e) estimated the single-nucleotide polymorphism (SNP)-based heritability of self-assessed resilience to be 16% (h2 = 0.16, SE = 0.050, p = 5.37 × 10 − 4, N = 9,932), while Cusack et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e) found no significant heritability estimates in two sub-populations (N = 908, \u0026amp; N = 2,371), with notable standard error estimates suggesting inadequate power.\u003c/p\u003e \u003cp\u003eIn sum, while the detrimental effects of ACEs on mental, physical, and social wellbeing are well-documented, the mechanisms underlying resilience remain elusive. This gap in understanding combined with the lack of a gold standard definition of resilience, has hindered the comprehensive application of GWAS in the context of resilience. Understanding the biological mechanisms and genetics of resilience to ACEs will also contribute to the development of strategies that aim to mitigate the negative outcomes associated with childhood adversity. By leveraging the power of GWAS and the granularity of longitudinal data, this study aims to shed light on the genetic factors that contribute to resilient trajectories in the face of adversity. Our approach, which emphasises the dynamic nature of resilience and the importance of deep phenotyping, provides novel insights into the genetic architecture of resilience and can inform future interventions aimed at fostering resilience in individuals exposed to ACEs.\u003c/p\u003e \u003cp\u003eWithin this paper, we operationalize resilient functioning as having better than expected psychosocial functioning, given specific levels of ACEs across the early lifecourse (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e). Our GWAS phenotype is derived from patterns of this resilient functioning across time, informed by previously identified group-based trajectories of longitudinal functioning (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). By harnessing the power of genome-wide data, our methodology offers a novel functional insight into how membership to the resilient trajectory is determined at the biological level. Our objectives are threefold: to better understand the molecular underpinnings of resilience, to estimate its molecular heritability, and to uncover the broader biological processes, molecular functions, and cellular components associated with trajectories of resilience.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \n\n "},{"header":"Methods","content":"\u003cp\u003eStudy population: ALSPAC is a transgenerational cohort study designed to investigate the influence of genetic and environmental factors on the health of both parents and children (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e). In brief, the study recruited 13,761 pregnant women who lived in Southwest England and were due to deliver between 1st April 1991 and 31st December 1992. These women and their children have been followed up at regular intervals over the past 30 years. Detailed phenotypic information, biological samples and genetic data have been collected from the participants which are available through a searchable data dictionary (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.bris.ac.uk/alspac/researchers/our-data/\u003c/span\u003e\u003cspan address=\"http://www.bris.ac.uk/alspac/researchers/our-data/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Written informed consent was obtained for all study participants. Ethical approval for this study was obtained from the ALSPAC Ethics and Law Committee and the Local Research Ethics Committees. All analyses were performed on the eligible children with genotype and phenotype data (N = 7975). See supplementary table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003e1\u003c/span\u003e for the sociodemographic characteristics of the sample, grouped by their binary trajectory resilient phenotype.\u003c/p\u003e\u003cp\u003eResilient Trajectories: The ALSPAC offspring cohort has been previously characterised into seven distinct trajectories of resilient functioning using growth mixture modelling. For a comprehensive description of this method, refer to Cahill et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e), with a visual representation in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eThe process of modelling resilience involved constructing resilience residuals at each time point individually. In these models, the binary exposures of ACEs from early life periods prior to the outcomes were regressed on the total difficulties score of the Strength and Difficulties Questionnaire (SDQ) and the Rutter Behaviour Scale (RBS) at that specific time point. The SDQ is one of the most commonly used ratings of child psychopathology in epidemiological studies (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e), with the RBS used to assess child mental health and behavioural/emotional problems in younger children (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e). We extracted the residuals from each regression model as these reflect a spectrum ranging from risk to resilient functioning i.e., the extent to which an individual has better, or worse, SDQ outcomes than the average score expected given their exposure to ACEs over the early life periods. This provides six separate quantitative measures of resilience across each individual life course.\u003c/p\u003e\u003cp\u003eThe residuals obtained from these regressions were then used as outcomes for a Growth Mixture Model (GMM). This model aimed to identify group-based longitudinal trajectories of resilient functioning by using a longitudinal dataset composed of the six repeated measures of resilience derived from the earlier step. GMM combines latent growth modelling with latent class analysis (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e), allowing the classification of study participants into distinct groups that represent different levels of resilient functioning over time. The optimal number of trajectories was determined using a multi-step approach and selecting the model with the lowest Bayesian Information Criterion (BIC), with 7 trajectories being the optimal solution (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) All GMM analyses were conducted using the ‘lcmm’ package version 1.9.5 (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e). All analyses were performed in R version 4.0.3 (2020-10-10), \u003cem\u003eRstudio\u003c/em\u003e version 1.3.1093 for Windows.\u003c/p\u003e\u003cdiv class=\"gridtable\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\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\u003eSeven trajectories of resilience as identified in GMM\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e% of Study Population\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eParticipants who showed increasing vulnerability from infancy through adolescence\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.09\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=\"left\" colname=\"c2\"\u003e \u003cp\u003eParticipants who were initially resilient, became extremely vulnerable in mid-childhood, and then experienced a decrease in vulnerability.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.67\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=\"left\" colname=\"c2\"\u003e \u003cp\u003eParticipants who started vulnerable, became resilient around age 5–6, but faced increasing vulnerability again in early adolescence.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.47\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=\"left\" colname=\"c2\"\u003e \u003cp\u003eParticipants who were vulnerable in early childhood, became resilient around age 6–7, and maintained high resilience into early adolescence.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.23\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=\"left\" colname=\"c2\"\u003e \u003cp\u003eParticipants who maintained moderate levels of resilience consistently throughout their early life.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28.44\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=\"left\" colname=\"c2\"\u003e \u003cp\u003eParticipants who consistently maintained high levels of resilience throughout their early life.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24.88\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=\"left\" colname=\"c2\"\u003e \u003cp\u003eParticipants who were vulnerable in early childhood but became resilient by adolescence.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.13\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003eTo create a binary phenotype, we have merged class 5 and 6 and defined this as the resilient phenotype, because they consistently maintain better than expected psychosocial functioning, given the levels of ACEs they were exposed to across the early lifecourse. The remaining classes (\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e–\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e and \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e) are the vulnerable phenotype. Within the participants 4,190 (53.3%) were identified as resilient and 3,669 (46.7%) as non-resilient.\u003c/p\u003e\u003cp\u003eThis decision to dichotomise the trajectories into resilient and vulnerable phenotypes is based on two considerations. First, a binary classification simplifies the analytical approach, enhancing the clarity and interpretability of the results. While the seven trajectories provide a nuanced understanding of resilience patterns, for the purpose of GWAS, a binary phenotype can increase statistical power and reduce the risk of type I errors. Second, classes 5 and 6 consistently demonstrate stable resilience throughout the early life course, making them ideal representatives of the resilient phenotype. In contrast, the other classes, despite their varied patterns, all exhibit periods of vulnerability, justifying their categorisation under the vulnerable phenotype.\u003c/p\u003e\u003cp\u003eGenotyping: Genome-wide genotyping was undertaken on ALSPAC offspring at a cohort level with quality control, data cleaning and imputation, as described previously (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e), leaving 8,237 eligible children. Genotype data on participants was derived using the Illumina HumanHap550 quad genome-wide single nucleotide polymorphism (SNP) genotyping platform (Illumina Inc, San Diego, USA) by the Wellcome Trust Sanger Institute (WTSI, Cambridge, UK) and the Laboratory Corporation of America (LCA, Burlington, NC, USA).\u003c/p\u003e\u003cp\u003eStatistical Analyses\u003c/p\u003e\u003cp\u003eQC and GWAS: In our study, we initially considered a dataset of 7,975 participants: 4,092 males and 3,883 females, comprising 27,449,291 SNPs. Preliminary quality control (QC) procedures, performed using QCtool, a command-line utility program for manipulation and quality control of GWAS datasets and other genome-wide data (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e), filtered variants based on an imputation quality (INFO) score \u0026lt; 0.8, narrowing down our dataset to 10,885,703 variants for further analytical steps in PLINK v1.90b6.16 64-bit (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e). We implemented QC filters via PLINK adopting the following best practice criteria: SNPs with \u0026lt; 0.05% genotype missingness were discarded (excluding 978,364). We filtered out SNPs with a minor allele frequency (MAF) \u0026lt; 0.05 (excluding 4,809,144 SNPs) and excluded variants not conforming to the Hardy-Weinberg equilibrium (HWE), specifically those with a p-value below 0.01 (excluding 63,397 SNPs). Following QC, 5,034,798 variants remained eligible for further analysis. All 7,975 participants passed the QC checks.\u003c/p\u003e\u003cp\u003eWe conducted a genome-wide association test for resilience using logistic regression (for dichotomised trajectories of resilience vs non-resilient) using PLINK. A post-QC total genotyping rate of 0.98296 was achieved. A p-value \u0026lt; 5 x 10\u003csup\u003e− 8\u003c/sup\u003e was used as the threshold for genome-wide significance, whereas results at p-value \u0026lt; 5 x 10\u003csup\u003e− 5\u003c/sup\u003e are reported as genome-wide suggestive (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eClumping: After passing the stringent QC criteria, the dataset underwent a clumping procedure to handle the issue of linkage disequilibrium (LD) between markers. The clumping procedure in PLINK serves as a method to identify and retain the most statistically significant SNP (known as the \"index\" SNP) within regions of LD, while \"clumping\" or grouping together other SNPs (referred to as \"clumped\" SNPs) that are in close LD with this index SNP. PLINK starts by identifying the most significant SNP based on its p-value. Around this index SNP, a physical window is defined by default to 250kb on either side. Within this window, other SNPs that are in strong LD (r\u003csup\u003e2\u003c/sup\u003e \u0026gt; 0.25) with the index SNP are grouped or 'clumped' together. Only the index SNP is retained for subsequent analyses, and other SNPs within the LD region that meet the clumping criterion are noted but not considered independently in subsequent steps to avoid redundant information. The procedure is then reiterated for the next most significant SNP outside the previously defined clumped regions until all SNPs have either been designated as an index SNP or been clumped with one.\u003c/p\u003e\u003cp\u003eGenome-wide Complex Trait Analysis (GCTA): GCTA is a software tool developed to quantify the variance explained by genome-wide SNPs for complex traits in quantitative genetic studies, especially in GWAS (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e). GCTA is employed to estimate the heritability explained by all SNPs, giving insight into the genetic component of the variance observed in resilience. We applied GCTA's GREML method to estimate the variance explained by all SNPs on our phenotype (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e).\u003c/p\u003e\u003cp\u003ePost-GWAS Analyses\u003c/p\u003e\u003cp\u003eVariant Effect Predictor (VEP): VEP is a powerful bioinformatics tool developed by the Ensembl project to analyse and predict the functional effects of genetic variants (e.g., SNPs, insertions, deletions) on genes, transcripts, and protein sequences (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e). It offers insight into the potential consequences of discovered variants, including their impact on protein function, involvement in regulatory regions, and associations with known phenotypes or diseases. Significant and suggestive variants were uploaded to the VEP platform. Each variant was annotated with its predicted functional consequences, including missense mutations, synonymous changes, or regulatory region alterations. Special attention was given to variants predicted to have high or moderate impact on protein function, as these could be pivotal in affecting resilience phenotypes.\u003c/p\u003e\u003cp\u003eThe Database for Annotation, Visualisation and Integrated Discovery (DAVID): DAVID is an integrative online platform designed to provide a comprehensive set of functional annotation tools for analysing the biological meanings behind a vast list of genes/proteins (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e). We used DAVID to undertake a gene ontology (GO) and pathway enrichment analysis. This aids in understanding the broader biological processes, molecular functions, and cellular components associated with our gene list. Through DAVID, we condense large gene lists into functionally related gene groups, providing a holistic overview of the gene functional classifications. Uploaded gene lists were subjected to functional annotation clustering, with a focus on GO terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Significance values were adjusted using the Benjamini-Hochberg method to control the false discovery rate.\u003c/p\u003e\u003cp\u003eOverview of GWAS Results\u003c/p\u003e\u003cp\u003eWe conducted a GWAS to identify genetic variants associated with resilience, using resilient and non-resilient trajectories as outcomes. The analysis included 5,035,004 markers. The lambda value (λ = 0.993 at 50th percentile) indicated minimal inflation across the p-value distribution, suggesting that population stratification was well-controlled. This control was achieved by including principal components derived from PCA as covariates in the analysis, thereby accounting for potential confounding due to population structure (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The QQ plot of our GWAS data showed the observed distribution closely following the expected distribution under the null hypothesis until a -log10(p) value of 4. Beyond this point, there was a slight deviation from the expected line, which suggests the possibility of true genetic associations with resilience, although these associations are not statistically significant at the genome-wide level (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe clumping procedure identified 79 distinct clumps from 472 top variants, capturing the most significant association signals from their respective LD regions. (Supplementary Table\u0026nbsp;2). The Manhattan plot of the clumped data (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) shows the distribution of the association signals across the genome.\u003c/p\u003e\u003cp\u003eSeveral SNPs were close to achieving genome-wide significance (\u003cem\u003ep\u003c/em\u003e \u0026lt; 5 × 10\u003csup\u003e− 8\u003c/sup\u003e), although none reached it. 42 SNPs surpassed the suggestive association threshold (\u003cem\u003ep\u003c/em\u003e \u0026lt; 5 × 10\u003csup\u003e− 5\u003c/sup\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The top SNP, rs77374979, located on chromosome 9, showed the strongest association with resilience (p = 4.02 x 10\u003csup\u003e− 7\u003c/sup\u003e) Individuals carrying the A allele of rs77374979 have 1.466 times higher odds of being resilient compared to non-carriers. This variation is located in an intronic region of the SWI/SNF related, matrix associated, actin dependent regulator of chromatin, subfamily a, member 2 (\u003cem\u003eSMARCA2\u003c/em\u003e) gene, which has been implicated in chromatin remodeling and stress response pathways.\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e provides detailed information on the top SNPs that met the suggestive threshold. These findings point to the involvement of genetic factors in resilience and highlight specific genomic regions and genes for further investigation.\u003c/p\u003e\u003cdiv class=\"gridtable\"\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=\"char\" char=\".\" 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=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\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\u003eSummary information of SNPs meeting suggestive threshold. CHR = Chromosome, Allele = The allele for which the association test statistics are calculated.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSNP\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChr\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBase Pair Position\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTest Allele\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOdds Ratio\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eWald Test Statistic\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers77374979\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2081278\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.466\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.02E-07\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5.068\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eSMARCA2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers1906656\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6468326\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.172\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.07E-06\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.879\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers10079405\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e124782491\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.417\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.66E-06\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.791\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers2326459\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e84995658\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.159\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.41E-06\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.591\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers4961\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2906707\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.208\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.03E-06\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.564\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eADD1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers11705732\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e131529862\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.8155\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.26E-06\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-4.517\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eCPNE4\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers9808926\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e187607504\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.194\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.29E-05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.362\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eLOC105374264\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers2881053\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25201470\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.8478\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.30E-05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-4.36\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers1147931\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44922347\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.158\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.64E-05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.309\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers10474761\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3204386\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.38\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.70E-05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.301\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers35939580\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e62973078\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.7575\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.85E-05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-4.282\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eAMZ2P1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers6950250\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e95853837\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.8323\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.96E-05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-4.269\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eSLC25A13\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers4712864\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24891756\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.8402\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.98E-05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-4.268\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eRIPOR2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers55964566\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e81497393\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.187\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.13E-05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.25\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers2757630\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7561359\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.147\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.56E-05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.21\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eDSP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers199631776\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22774473\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.7777\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.57E-05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-4.209\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eLINC01239\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers1991394\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54086988\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.292\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.65E-05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.202\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers11772117\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e138010190\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.146\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.85E-05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.185\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers11677946\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e200670394\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.8727\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.94E-05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-4.178\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eFTCDNL1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers2911319\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e62921503\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.8209\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.96E-05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-4.176\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers55728401\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e125240861\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.249\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.10E-05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.166\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eSNX4\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers1570925\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14555086\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.8312\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.11E-05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-4.165\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers8058900\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e85026218\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.209\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.12E-05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.165\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eZDHHC7\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers9397696\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e154532278\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.8441\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.14E-05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-4.163\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eOPRM1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers147898448\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47709375\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.168\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.14E-05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.163\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eSPIDR\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers10202078\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37727116\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.7632\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.19E-05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-4.159\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eLOC105374464\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers4288315\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e122708549\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.7618\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.22E-05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-4.157\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers79044013\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e84984096\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.8726\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.26E-05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-4.154\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers17650551\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e106618077\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.7953\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.27E-05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-4.154\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eGUCY1A2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers13053888\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34366372\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.7846\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.34E-05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-4.149\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers7975048\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e128000853\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.8717\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.39E-05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-4.146\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers73136137\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51349009\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.266\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.52E-05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.137\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers35116986\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45092334\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCT\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.212\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.54E-05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.135\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eMKRN7P\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers7721856\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26108316\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.148\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.74E-05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.123\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers754415\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e104558319\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.213\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.96E-05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.11\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eASPG\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers28470926\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19323261\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.287\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.15E-05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.099\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eLOC105371114\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers73043693\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3315440\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.185\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.45E-05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.083\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eSDK1-AS1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers9901183\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49740054\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.8387\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.48E-05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-4.081\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eCA10\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers76728099\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e244364363\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.215\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.56E-05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.077\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers357373\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e137983003\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.8707\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.64E-05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-4.073\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers1543994\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e46939842\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.166\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.77E-05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.067\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers7255568\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e605985\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.8767\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.99E-05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-4.056\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eHCN2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003eAmong the SNPs that passed the suggestive association threshold, the opioid receptor mu 1 (\u003cem\u003eOPRM1)\u003c/em\u003e gene, specifically SNP rs9397696, emerged as a notable candidate (p = 3.14 x 10\u003csup\u003e− 5\u003c/sup\u003e). \u003cem\u003eOPRM1\u003c/em\u003e, located on chromosome 6, has been previously associated with resilience in both human and animal studies (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). In our dataset, it further strengthens the evidence for \u003cem\u003eOPRM1\u003c/em\u003e's role in resilience, suggesting that it might modulate an individual's response to stressors or adverse environments.\u003c/p\u003e\u003cp\u003eHeritability of Resilience\u003c/p\u003e\u003cp\u003eUsing GCTA (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e) we estimated the SNP-based, narrow-sense heritability (h\u003csup\u003e2\u003c/sup\u003e) of resilience to be 1.97% (SE = 0.043). Due to the binary nature of our phenotype, we further transformed these estimates to the underlying liability scale, which better approximates a normal distribution. The liability scale is a theoretical construct in genetic studies of binary traits, where the observed binary outcome is considered a threshold on an unobserved continuous liability distribution. By assuming a disease prevalence of 0.5, meaning that resilience is equally likely to be present or absent in the population, the transformed heritability was 3.11% (SE = 0.067).\u003c/p\u003e\u003cp\u003eVariant Effect Predictor\u003c/p\u003e\u003cp\u003eWe entered the list of 79 variants obtained post-clumping into VEP (Supplementary table 2). This list, representing the most statistically significant variants from distinct LD regions, serves as a curated starting point to ensure that our functional annotations and predictions are directed at variants of biological relevance. A significant majority of our variants (73 out of 79) overlap with genes, which potentially means they could have direct gene-related functional effects. These variants also intersect with a total of 348 transcripts, signifying that they might have impacts on different gene isoforms or across multiple genes. Additionally, 21 variants overlap with regulatory features, highlighting their potential role in gene regulation. Three (or 3.8%) of these variants are novel, meaning they might not have been previously documented in major variant databases. This could indicate unique or understudied genetic variations in the dataset.\u003c/p\u003e\u003ch3\u003eDAVID\u003c/h3\u003e\u003cp\u003eOur DAVID analysis identified specific cellular components, particularly dendrites and axons, that were significantly associated with our gene set based on their p-values and Benjamini scores. Benjamini scores are used to control the false discovery rate (FDR) in multiple hypothesis testing. A lower Benjamini score indicates that the term is less likely to be a false positive. In many studies, a Benjamini score below 0.05 is considered significant, indicating that the term is likely to be truly associated with the genes and not a result of random chance.\u003c/p\u003e\u003cp\u003eSeveral significant annotation clusters were associated with the genes of interest (Supplementary Table\u0026nbsp;3). The most significant cluster was enriched for dendritic functions GO:0030425 ~ dendrite: This term was significantly enriched with a p-value of 1.56 x 10\u003csup\u003e− 4\u003c/sup\u003e and a Benjamini score of 0.017. The genes associated with this term were \u003cem\u003eDSCAM, TRIM3, HTT, OPRM1, SLC8A1, HCN2\u003c/em\u003e, and \u003cem\u003eRAP1GAP\u003c/em\u003e. The fold enrichment for this term was 8.26.\u003c/p\u003e\u003cp\u003eAnother notable cluster showed a positive association with axonal components. GO:0030424 ~ axon: This term was significantly enriched with a p-value of 4.93 x 10\u003csup\u003e− 4\u003c/sup\u003e and a Benjamini score of 0.0278. The genes associated with this term were \u003cem\u003eDSCAM, HTT, OPRM1, SLC8A1, HCN2\u003c/em\u003e, and \u003cem\u003eRAP1GAP.\u003c/em\u003e The fold enrichment for this term was 8.81.\u003c/p\u003e\u003cp\u003eIn our gene set analysis, we observed a notable enrichment in specific protein domains (Supplementary table 4). According to the UniProt Knowledgebase (UniProtKB) domain annotations under the UP_KW_DOMAIN category, there was a significant association with the \"Repeat\" protein domain (KW-0677). This domain was found in several genes, including \u003cem\u003eDSP, DGKG, UNC13C, WWOX, KRTAP12-3, DSCAM, KRTAP12-4, CPNE4, MMP2, TTC1, HTT, TSPEAR, AHR, SLC8A1, PREX2, PTPRD, HPX, DCHS2, ASPG, TRIM3\u003c/em\u003e, and \u003cem\u003eSLC25A13\u003c/em\u003e. The statistical significance of this association is underscored by a p-value of 0.0019 and a Benjamini score of 0.025, indicating a low likelihood of this result being due to random chance. The fold enrichment value of 1.79 further emphasizes the overrepresentation of the \"Repeat\" domain in our gene set. Proteins with repeat domains often suggest evolutionary patterns through repetitive sequences, potentially bestowing them with unique structural or functional attributes.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur study conducted a genome-wide association analysis on resilience trajectories, revealing several suggestively significant genetic markers, with the top SNP, rs77374979, located in the \u003cem\u003eSMARCA2\u003c/em\u003e gene. Furthermore, the suggestive significance of \u003cem\u003eOPRM1\u003c/em\u003e in our dataset underscores its potential as a key genetic factor in resilience pathways. By harnessing the information contained in longitudinal data, we provide a more granular and accurate representation of resilience over time (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). This approach, rooted in the principles of deep phenotyping, is likely to enhance the efficacy of GWAS and lead to more accurate heritability estimates (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Our study design advances on previous work in using a more biologically relevant phenotype of resilience, accounting for the impact of childhood adversity, and with a larger sample size than previous GWAS of resilience. These factors may contribute to the differences in heritability estimates between our study and previous work. Our comprehensive approach not only enhances the efficacy of GWAS, but also ensures that the genetic foundations of resilience are more accurately captured.\u003c/p\u003e \u003cp\u003eThe identification of the top SNP in the \u003cem\u003eSMARCA2\u003c/em\u003e gene suggests a potential epigenetic mechanism underlying resilience. Recent research has highlighted the SWI/SNF chromatin remodeler complex, particularly the ATPase subunits BRM (Brahma, encoded by the \u003cem\u003eSMARCA2\u003c/em\u003e gene) and \u003cem\u003eBRG1\u003c/em\u003e (Brahma Related Gene 1), for its pivotal role in behavioural adaptations to stress (\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e), specifically within the mesocorticolimbic pathway, a critical neural circuitry for stress response and resilience (\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e). Zayed et al. (\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e) demonstrated that mice with inactive \u003cem\u003eBrg1/Smarca4\u003c/em\u003e gene in dopamine-innervated regions, or those with constitutive inactivation of the \u003cem\u003eBrm/Smarca2\u003c/em\u003e gene, exhibited resilience to repeated social defeat. This resilience was further evidenced by decreased behavioural responses to cocaine, independent of any alterations in midbrain dopamine neuron activity. A study on the effects of heat shock on human brain development found that the expression of the \u003cem\u003eSMARCA2\u003c/em\u003e gene, among others, was significantly altered, suggesting that environmental stressors could modulate the expression of genes associated with neuropsychiatric disorders such as schizophrenia and autism (\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e). Furthermore, insights into the role of nucleosome remodelling, particularly by the neuron-specific CRC nBAF, underscore its significance in long-term memory formation and synaptic plasticity (\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e). Given that \u003cem\u003eSMARCA2\u003c/em\u003e is an integral component of the BAF complex, it is plausible that disruptions in its function could influence cognitive processes and thus resilience. nBAF has been shown to regulate gene expression essential for dendritic arborization during development (\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e) and contributes to long-term potentiation in adults. Impaired nBAF function has been linked to human cognitive disorders through exome-sequencing and GWAS (\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e), further emphasising the potential significance of the \u003cem\u003eSMARCA2\u003c/em\u003e gene in resilience.\u003c/p\u003e \u003cp\u003eGiven our results suggest a role of the SMARCA2 gene in resilience, investigating the SWI/SNF chromatin remodeler complex, especially the \u003cem\u003eSMARCA2\u003c/em\u003e gene, should be a focus for future research. The collective evidence supports the hypothesis that \u003cem\u003eSMARCA2\u003c/em\u003e plays a crucial role in modulating an individual's response to environmental stressors and may thus offer a novel therapeutic target for enhancing resilience.\u003c/p\u003e \u003cp\u003eOur findings also provide additional support for a role of \u003cem\u003eOPRM1\u003c/em\u003e in resilience, adding to studies in both human and animal studies (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e) that suggest \u003cem\u003eOPRM1\u003c/em\u003e might modulate an individual's response to stressors or adverse environments. Previous research has highlighted the G-allele of \u003cem\u003eOPRM1\u003c/em\u003e A118G polymorphism as a resilience factor against relapse to heroin use (\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e) and its role in modulating attachment behaviours based on early life experiences (\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e). In line with this, Daniel et al. (\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e) found that carriers of the G-allele of the \u003cem\u003eOPRM1\u003c/em\u003e A118G polymorphism exhibited reduced resilience, such as slower recovery following a reward downshift and a heightened sensitivity to physical pain. This underscores the potential influence of genetic variations in the opioid system on resilience to emotional disturbances. Our Variant Effect Predictor (VEP) analysis highlighted the potential functional relevance of the identified genetic variants. Many of these variants were found to overlap with genes and regulatory elements, suggesting they could play a role in influencing gene expression.\u003c/p\u003e \u003cp\u003eOur DAVID analysis revealed a neurobiological basis for resilience, with a significant enrichment of genes associated with dendritic and axonal functions. Dendrites and axons are critical components of neuronal communication, and their proper functioning is essential for cognitive processes, learning, and memory (\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e). Disruptions in these structures have been linked to various neuropsychiatric disorders (\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e). Dendritic spines, integral to neuronal communication, have been observed to undergo plasticity in response to stress. Rodent models have shown regional differences in dendritic spine density in response to stress, with certain stress models leading to decreased spine density in specific brain regions, such as the prelimbic medial prefrontal cortex and the hippocampus (\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e, \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e). Notably, resilience to stress appears to be associated with maintenance of dendritic spine densities, suggesting a potential neurobiological mechanism for resilience. The evidence of dendritic spine involvement in resilience to stress, and the association of \u003cem\u003eOPRM1\u003c/em\u003e and the nBAF complex with dendritic functions, supports the hypothesis that dendritic spine plasticity is a mechanism for cognitive resilience.\u003c/p\u003e \u003cp\u003eThe SNP-based heritability of resilience, as estimated in our study, is notably lower than previous GCTA analyses (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e) and twin studies (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). SNP-based heritability captures the proportion of phenotypic variance explained by common genetic variants that are typically assayed on SNP arrays. In contrast, twin studies capture both the additive genetic variance due to all genetic variants (common and rare) and potential non-additive genetic effects, such as dominance and epistasis.\u003c/p\u003e \u003cp\u003eThe discrepancy between SNP-based heritability and twin study heritability estimates has been a topic of discussion in the field of genetics for several complex traits, not just resilience (\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e). This phenomenon, often termed \"missing heritability\", suggests that the heritability captured by common SNPs on arrays is often substantially lower than the heritability estimated from twin or family studies (\u003cspan additionalcitationids=\"CR65\" citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e). Several factors could contribute to this \"missing heritability\".\u003c/p\u003e \u003cp\u003eFirstly, common SNPs typically assayed in GWAS might not capture the effects of rare genetic variants. These rare variants, although individually of low frequency, might collectively explain a significant portion of the genetic variance. Over the past two decades, our understanding of the genetic and epigenetic contributions to common complex traits and disease has significantly evolved, enabled by large-scale meta-analyses based on genome-wide SNP arrays (\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e). However, genetic variants that elude detection by even the most statistically powerful association studies (\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e) are believed to contribute to the missing heritability of many human traits. This includes common variants (defined as having a minor allele frequency [MAF]\u0026thinsp;\u0026gt;\u0026thinsp;5%) with very weak effects, low-frequency variants (MAF 1\u0026ndash;5%) with small to modest effects, and rare variants (MAF\u0026thinsp;\u0026lt;\u0026thinsp;1%) also with small to modest effects (\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e). Advancements in custom genotyping arrays and whole-exome sequencing have identified numerous novel associations with diseases such as type 2 diabetes, coronary artery disease and various immune disorders (\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e). The Haplotype Reference Consortium has aggregated whole-genome sequencing data to create an extensive reference panel, enhancing the accuracy of genotype imputation for rare variants down to a minor allele frequency (MAF) of 0.1% (\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e). Additionally, research has demonstrated the significant impact of rare copy number variations (CNVs) on disease susceptibility, as evidenced by large-scale genomic studies using data from the UK Biobank and other population-based cohorts (\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e). Furthermore, the combined effect of multiple genes might not be purely additive. Interactions between genes, where the effect of one gene is modified by the presence of another (epistasis), might contribute to the heritability not captured by individual SNPs (\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSecondly, the effect of genes can also vary across different environmental contexts. For example, certain genetic variants might act as differential susceptibility variants, making individuals more sensitive to both positive and negative environmental factors (\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e). This means that the manifestation of resilience might be contingent upon specific environmental conditions. Such gene-environment interactions, pivotal in understanding how genetic variant effect depend on environmental context (\"biological sensitivity to context\"; (\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e)), are often overlooked in standard GWAS. This oversight can lead to underestimated heritability, as the full spectrum of genetic influence, especially in tandem with environmental factors, is not captured. Furthermore, while our focus has been on SNPs, it is crucial to acknowledge that other genetic variations, like insertions, deletions, and copy number variants, can also contribute to the genetic architecture of resilience and other complex traits.\u003c/p\u003e \u003cp\u003eLastly, the way resilience is measured and defined can introduce variability. If the phenotype is not measured accurately or consistently, heritability may be underestimated because measurement error can obscure the true genetic contributions to resilience. In our study, we have operationalised resilience as an active, dynamic process that reflects an individual\u0026rsquo;s trajectory of functioning following exposure to adversity that captures temporal variation. In sum, one should interpret SNP-based heritability estimates with caution. While such estimates provide valuable insights into the genetic architecture of resilience, they represent only a piece of the broader heritability puzzle. Future studies employing whole-genome sequencing, multi-omics approaches, and more comprehensive models might shed further light on the \"missing heritability\" of resilience.\u003c/p\u003e \u003cp\u003eOur results pave the way for more targeted genetic research on resilience. A deeper understanding of the genetic architecture can lead to the development of personalised interventions. The novel variants identified in this study, combined with the suggestive significance of \u003cem\u003eOPRM1\u003c/em\u003e and \u003cem\u003eSMARCA2\u003c/em\u003e, warrant further validation in diverse cohorts. Future studies could benefit from a more integrative approach, combining genetic data with other omics data, such as transcriptomics or epigenomics, to provide a holistic understanding of resilience. However, we note that the cohort in our study, specific to a particular region, and lacking diversity in ethnic background and socioeconomic status, might not be representative of broader populations, emphasising the need for replication in diverse ethnic and geographical cohorts.\u003c/p\u003e \u003cp\u003eGiven the consistent findings related to \u003cem\u003eOPRM1\u003c/em\u003e and \u003cem\u003eSMARCA2\u003c/em\u003e across different studies and models, these two genes emerge as promising therapeutic targets. We recommend longitudinal studies focusing on genetic variants of \u003cem\u003eOPRM1\u003c/em\u003e and \u003cem\u003eSMARCA2\u003c/em\u003e, considering factors like developmental timing, type and severity of stressor, sex, and ethnicity.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting Interests\u003c/h2\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eWe are extremely grateful to all the families who took part in the ALSPAC study, the midwives for their help in recruiting them, and the whole ALSPAC team, which includes interviewers, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, receptionists and nurses\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMcEwen BS, Bowles NP, Gray JD, Hill MN, Hunter RG, Karatsoreos IN, et al. Mechanisms of stress in the brain. 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Int J Neuropsychopharmacol. 2015;18(7):pyu121.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"molecular-psychiatry","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"mp","sideBox":"Learn more about [Molecular Psychiatry](http://www.nature.com/mp/)","snPcode":"41380","submissionUrl":"https://mts-mp.nature.com/cgi-bin/main.plex","title":"Molecular Psychiatry","twitterHandle":"@molpsychiatry","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4861048/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4861048/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe biological mechanisms underlying resilience have been extensively studied, yet our understanding of the genetic architecture of resilience in humans remains limited. While earlier genetic studies of resilience investigated effects of specific candidate genes, small sample sizes and the narrow focus on one target gene provided a limited perspective on genetic architecture. Genome-wide association studies (GWAS) can overcome these issues but have been rarely applied to resilience. To date, only two GWAS are reported, because few sufficiently large-scale datasets have a measure of resilience, and those that do may not have genetic data. Here we used a novel longitudinal resilience phenotype with genomic data from the Avon Longitudinal Study on Parent and Children (ALSPAC) to establish resilience trajectories in response to adverse childhood experiences (ACEs). Our results identify the \u003cem\u003eSMARCA2\u003c/em\u003e and \u003cem\u003eOPRM1\u003c/em\u003e genes as significant genetic markers, highlighting their roles in epigenetic mechanisms and dendritic functions associated with resilience. Post-GWAS analyses revealed enrichment of genes linked to dendritic and axonal functions, supporting the hypothesis that dendritic spine plasticity is crucial for cognitive resilience. Our approach offers novel functional insights into how resilience across early life is underpinned by genetic factors, emphasising the importance of dynamic, longitudinal phenotyping.\u003c/p\u003e","manuscriptTitle":"Longitudinal Genome-Wide Study Reveals Genetic Architecture of Resilience Using a Novel Phenotype","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-19 12:25:17","doi":"10.21203/rs.3.rs-4861048/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"molecular-psychiatry","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"mp","sideBox":"Learn more about [Molecular Psychiatry](http://www.nature.com/mp/)","snPcode":"41380","submissionUrl":"https://mts-mp.nature.com/cgi-bin/main.plex","title":"Molecular Psychiatry","twitterHandle":"@molpsychiatry","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"fc87e6e9-4fa5-4a3d-bc27-f1d429442539","owner":[],"postedDate":"March 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":36442581,"name":"Biological sciences/Genetics/Genetic association study/Genome-wide association studies"},{"id":36442582,"name":"Biological sciences/Genetics/Development"},{"id":36442583,"name":"Health sciences/Biomarkers/Diagnostic markers"}],"tags":[],"updatedAt":"2025-03-19T12:25:18+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-19 12:25:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4861048","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4861048","identity":"rs-4861048","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}