Polygenic Score Analysis Research Articles

Estimation of Direct and Indirect Polygenic Effects and Gene-Environment Interactions using Polygenic Scores in Case-Parent Trio Studies.

Family-based studies provide a unique opportunity to characterize genetic risks of diseases in the presence of population structure, assortative mating, and indirect genetic effects. We propose a novel framework, PGS-TRI, for the analysis of polygenic scores (PGS) in case-parent trio studies for estimation of the risk of an index condition associated with direct effects of inherited PGS, indirect effects of parental PGS, and gene-environment interactions. Extensive simulation studies demonstrate the robustness of PGS-TRI in the presence of complex population structure and assortative mating compared to alternative methods. We apply PGS-TRI to multi-ancestry trio studies of autism spectrum disorders (N trio = 1,517) and orofacial clefts (N trio = 1,904) to establish the first transmission-based estimates of risk associated with pre-defined PGS for these conditions and other related traits. For both conditions, we further explored offspring risk associated with polygenic gene-environment interactions, and direct and indirect effects of genetically predicted levels of gene expression and metabolite traits.

Refining the scope of genetic influences on alcohol misuse through environmental stratification and gene-environment interaction.

Gene-environment interaction (G × E) is likely an important influence shaping individual differences in alcohol misuse (AM), yet it has not been extensively studied in molecular genetic research. In this study, we use a series of genome-wide gene-environment interaction (GWEIS) and in silico annotation methods with the aim of improving gene identification and biological understanding of AM. We carried out GWEIS for four AM phenotypes in the large UK Biobank sample (N = 360,314), with trauma exposure and socioeconomic status (SES) as moderators of the genetic effects. Exploratory analyses compared stratified genome-wide association (GWAS) and GWEIS modeling approaches. We applied functional annotation, gene- and gene-set enrichment, and polygenic score analyses to interpret the GWEIS results. GWEIS models showed few genetic variants with significant interaction effects across gene-environment pairs. Enrichment analyses identified moderation by SES of the genes NOXA1, DLGAP1, and UBE2L3 on drinking quantity and the gene IFIT1B on drinking frequency. Except for DLGAP1, these genes have not previously been linked to AM. The most robust results (GWEIS interaction p = 4.59e-09) were seen for SES moderating the effects of variants linked to immune-related genes on a pattern of drinking with versus without meals. Our results highlight several genes and a potential mechanism of immune system functioning behind the moderating effect of SES on the genetic influences on AM. Although GWEIS seems to be a preferred approach over stratified GWAS, modeling G × E effects at the molecular level remains a challenge even in large samples. Understanding these effects will require substantial effort and more in-depth phenotypic measurement.

Metabolic gene function discovery platform GeneMAP identifies SLC25A48 as necessary for mitochondrial choline import.

Organisms maintain metabolic homeostasis through the combined functions of small-molecule transporters and enzymes. While many metabolic components have been well established, a substantial number remains without identified physiological substrates. To bridge this gap, we have leveraged large-scale plasma metabolome genome-wide association studies (GWAS) to develop a multiomic Gene-Metabolite Association Prediction (GeneMAP) discovery platform. GeneMAP can generate accurate predictions and even pinpoint genes that are distant from the variants implicated by GWAS. In particular, our analysis identified solute carrier family 25 member 48 (SLC25A48) as a genetic determinant of plasma choline levels. Mechanistically, SLC25A48 loss strongly impairs mitochondrial choline import and synthesis of its downstream metabolite betaine. Integrative rare variant and polygenic score analyses in UK Biobank provide strong evidence that the SLC25A48 causal effects on human disease may in part be mediated by the effects of choline. Altogether, our study provides a discovery platform for metabolic gene function and proposes SLC25A48 as a mitochondrial choline transporter.

From Atherosclerosis to Spontaneous Coronary Artery Dissection: Defining a Clinical and Genetic Risk Spectrum for Myocardial Infarction.

Spontaneous coronary artery dissection (SCAD) has been increasingly recognized as a significant cause of acute myocardial infarction (AMI) in young and middle-aged women and arises through mechanisms independent of atherosclerosis. SCAD has a multifactorial etiology that includes environmental, individual, and genetic factors distinct from those typically associated with coronary artery disease. Here, we summarize the current understanding of the genetic factors contributing to the development of SCAD and highlight those factors which differentiate SCAD from atherosclerotic coronary artery disease. Recent studies have revealed several associated variants with varying effect sizes for SCAD, giving rise to a complex genetic architecture. Associated genes highlight an important role for arterial cells and their extracellular matrix in the pathogenesis of SCAD, as well as notable genetic overlap between SCAD and other systemic arteriopathies such as fibromuscular dysplasia and vascular connective tissue diseases. Further investigation of individual variants (including in the associated gene PHACTR1) along with polygenic score analysis have demonstrated an inverse genetic relationship between SCAD and atherosclerosis as distinct causes of AMI. SCAD represents an increasingly recognized cause of AMI with opposing clinical and genetic risk factors from that of AMI due to atherosclerosis, and it is often associated with complex underlying genetic conditions. Genetic study of SCAD on a larger scale and with more diverse cohorts will not only further our evolving understanding of a newly defined genetic spectrum for AMI, but it will also inform the clinical utility of integrating genetic testing in AMI prevention and management moving forward.

Genetic evidence for the causal effects of C–reactive protein on self-reported habitual sleep duration

Inflammatory responses to acute stimuli are proposed to regulate sleep, but the relationship between chronic inflammation and habitual sleep duration is elusive. Here, we study this relation using genetically predicted level of chronic inflammation, indexed by CRP and IL6 signaling, and self-reported sleep duration. By Mendelian randomization analysis, we show that elevated CRP level within <10 mg/L has a homeostatic effect that facilitates maintaining 7–8 h sleep duration per day — making short-sleepers sleep longer (p = 2.42 × 10−2) and long-sleepers sleep shorter (1.87 × 10−7); but it is not associated with the overall sleep duration (p = 0.17). This homeostatic effect replicated in an independent CRP dataset. We observed causal effects of the soluble interleukin 6 receptor and gp130 on overall sleep duration (p = 1.62 × 10−8, p = 2.61 × 10−58, respectively), but these effects disappeared when CRP effects were accounted for in the model. Using polygenic score analysis, we found that the homeostatic effect of CRP on sleep duration stems primarily from the genetic variants within the CRP gene region: when genetic variants outside of this region were used to predict CRP levels, the opposite direction of effect was observed. In conclusion, we show that elevated CRP level may causally facilitate maintaining an optimal sleep duration that is beneficial to health, thus updating our current knowledge of immune regulation on sleep.

Pharmacogenomic overlap between antidepressant treatment response in major depression & antidepressant associated treatment emergent mania in bipolar disorder

There is increasing interest in individualizing treatment selection for more than 25 regulatory approved treatments for major depressive disorder (MDD). Despite an inconclusive efficacy evidence base, antidepressants (ADs) are prescribed for the depressive phase of bipolar disorder (BD) with oftentimes, an inadequate treatment response and or clinical concern for mood destabilization. This study explored the relationship between antidepressant response in MDD and antidepressant-associated treatment emergent mania (TEM) in BD. We conducted a genome-wide association study (GWAS) and polygenic score analysis of TEM and tested its association in a subset of BD-type I patients treated with SSRIs or SNRIs. Our results did not identify any genome-wide significant variants although, we found that a higher polygenic score (PGS) for antidepressant response in MDD was associated with higher odds of TEM in BD. Future studies with larger transdiagnostic depressed cohorts treated with antidepressants are encouraged to identify a neurobiological mechanism associated with a spectrum of depression improvement from response to emergent mania.

Heritable Variation in Lymphocyte-Related Traits and Risk of Down Syndrome Acute Lymphoblastic Leukemia: A Mendelian Randomization Study

Introduction: The risk of developing acute lymphoblastic leukemia (ALL) is up to 30-fold higher in children with Down syndrome (DS). While trisomy 21 increases the risk of leukemia, only ~1% of children with DS develop ALL (DS-ALL), suggesting that additional modifying risk factors exist. A recent study in individuals without DS revealed that a genetic propensity for increased lymphocyte production was associated with an increased risk of childhood ALL. Additionally, a previous genome-wide association study (GWAS) of DS-ALL identified associations in genes involved in lymphoid development. Here, we investigated whether genetic variation in lymphocyte-related traits is associated with DS-ALL risk, with the hypothesis that a genetic predisposition for lymphocyte overproduction is associated with an increased risk of DS-ALL. Methods: We performed Mendelian randomization (MR) analyses to investigate the potential causal relationship between variation in four lymphocyte-related traits - lymphocyte counts, the lymphocytes to monocytes ratio (LMR), the neutrophils to lymphocytes ratio (NLR), and the platelets to lymphocytes ratio (PLR) - and DS-ALL risk. Genetic variants associated with each lymphocyte-related trait (“exposure”) at P &amp;lt; 5x10 -8 and their corresponding effect sizes and standard errors were obtained from GWAS of blood cell traits performed in 413,810 cancer-free individuals of European ancestry in the UK Biobank. A GWAS of DS-ALL (“outcome”) was carried out using germline whole-genome sequencing data from 222 DS-ALL cases and 1,010 individuals without ALL from DS research cohorts, limited to individuals of non-Hispanic White race/ethnicity. We examined consistency across multiple MR methods, including the inverse-variance weighted (IVW), weighted median (WM), MR robust adjusted profile score (RAPS), and MR pleiotropy residual sum and outlier (PRESSO) estimators. Results: Among the lymphocyte-related traits, we found a significant association between LMR and DS-ALL risk that was consistent across MR estimators. Specifically, each 1-unit increase in LMR was associated with a 41%-53% decrease in ALL risk in children with DS (odds ratio [OR IVW] = 0.56; 95% CI: 0.35-0.90, p = 0.016; OR WM = 0.47; 95% CI: 0.21-1.04, p = 0.051; OR RAPS = 0.59; 95% CI: 0.36-0.97, p = 0.038; OR PRESSO = 0.55; 95% CI: 0.34-0.88, p = 0.014). There were no statistically significant associations between the remaining blood cell traits (lymphocyte count, PLR, and NLR) and DS-ALL risk, although increased lymphocyte counts conferred a modest increase in risk of DS-ALL (OR IVW=1.24, p=0.40). Conclusions: We discovered that genetic predisposition to shifts in the ratio of lymphocytes relative to monocytes may be a novel risk factor for the development of DS-ALL, with a deficit of lymphocytes to monocytes conferring an increased risk of DS-ALL. This observed direction of effect contrasts with previous findings for ALL in the non-DS population, suggesting a distinct etiology of ALL in the context of DS that may be influenced by the profound effects of trisomy 21 on immune function. Replication of our findings in independent DS-ALL studies is underway, and analysis of polygenic risk scores for lymphocyte traits may further improve identification of children with a high risk of DS-ALL.

A genome-wide association scan reveals novel loci for facial traits of Koreans

DNA-based prediction of externally visible characteristics (EVC) with SNPs is one of the research areas of interest in the forensic field. Based on a previous study performing GWAS on facial traits in a Korean population, herein, we present results stemming from GWA analysis with KoreanChip and novel genetic loci satisfying genome-wide significant level. We discovered a total of 20 signals and 12 loci were found to have novel associations with facial traits, including six loci located in intergenic regions and six loci located at UBE2O, HECTD2, CCDC108, TPK1, FCN2, and FRMPD1. Additionally, we performed a polygenic score analysis for 33 distance-related traits in facial phenotyping and determined genetic relationships between facial traits and SNPs using the GCTA program. The results of the current study offer an understanding of how facial morphology is influenced by complex genetic structures and provide insights into forensic investigation and population genetics.

Guidelines for Evaluating the Comparability of Down-Sampled GWAS Summary Statistics

Proprietary genetic datasets are valuable for boosting the statistical power of genome-wide association studies (GWASs), but their use can restrict investigators from publicly sharing the resulting summary statistics. Although researchers can resort to sharing down-sampled versions that exclude restricted data, down-sampling reduces power and might change the genetic etiology of the phenotype being studied. These problems are further complicated when using multivariate GWAS methods, such as genomic structural equation modeling (Genomic SEM), that model genetic correlations across multiple traits. Here, we propose a systematic approach to assess the comparability of GWAS summary statistics that include versus exclude restricted data. Illustrating this approach with a multivariate GWAS of an externalizing factor, we assessed the impact of down-sampling on (1) the strength of the genetic signal in univariate GWASs, (2) the factor loadings and model fit in multivariate Genomic SEM, (3) the strength of the genetic signal at the factor level, (4) insights from gene-property analyses, (5) the pattern of genetic correlations with other traits, and (6) polygenic score analyses in independent samples. For the externalizing GWAS, although down-sampling resulted in a loss of genetic signal and fewer genome-wide significant loci; the factor loadings and model fit, gene-property analyses, genetic correlations, and polygenic score analyses were found robust. Given the importance of data sharing for the advancement of open science, we recommend that investigators who generate and share down-sampled summary statistics report these analyses as accompanying documentation to support other researchers’ use of the summary statistics.

Mental illness and cardiovascular health: observational and polygenic score analyses in a population-based cohort study.

Individuals with serious mental illness have a markedly shorter life expectancy. A major contributor to premature death is cardiovascular disease (CVD). We investigated associations of (genetic liability for) depressive disorder, bipolar disorder and schizophrenia with a range of CVD traits and examined to what degree these were driven by important confounders. We included participants of the Dutch Lifelines cohort (N = 147 337) with information on self-reported lifetime diagnosis of depressive disorder, bipolar disorder, or schizophrenia and CVD traits. Employing linear mixed-effects models, we examined associations between mental illness diagnoses and CVD, correcting for psychotropic medication, demographic and lifestyle factors. In a subsample (N = 73 965), we repeated these analyses using polygenic scores (PGSs) for the three mental illnesses. There was strong evidence that depressive disorder diagnosis is associated with increased arrhythmia and atherosclerosis risk and lower heart rate variability, even after confounder adjustment. Positive associations were also found for the depression PGSs with arrhythmia and atherosclerosis. Bipolar disorder was associated with a higher risk of nearly all CVD traits, though most diminished after adjustment. The bipolar disorder PGSs did not show any associations. While the schizophrenia PGSs was associated with increased arrhythmia risk and lower heart rate variability, schizophrenia diagnosis was not. All mental illness diagnoses were associated with lower blood pressure and a lower risk of hypertension. Our study shows widespread associations of (genetic liability to) mental illness (primarily depressive disorder) with CVD, even after confounder adjustment. Future research should focus on clarifying potential causal pathways between mental illness and CVD.

Educational Attainment and Intergenerational Mobility: A Polygenic Score Analysis

Previous articleNext article No AccessEducational Attainment and Intergenerational Mobility: A Polygenic Score AnalysisAldo Rustichini, William Iacono, James Lee, and Matt McGueAldo Rustichini Search for more articles by this author , William Iacono Search for more articles by this author , James Lee Search for more articles by this author , and Matt McGue Search for more articles by this author PDFPDF PLUS Add to favoritesDownload CitationTrack CitationsPermissionsReprints Share onFacebookTwitterLinkedInRedditEmail SectionsMoreDetailsFiguresReferencesCited by Journal of Political Economy Just Accepted Article DOIhttps://doi.org/10.1086/724860 HistoryAccepted February 22, 2023 © 2023 The University of Chicago. All Rights reserved.PDF download Crossref reports no articles citing this article.

The genetic architecture and evolution of the human skeletal form.

The human skeletal form underlies bipedalism, but the genetic basis of skeletal proportions (SPs) is not well characterized. We applied deep-learning models to 31,221 x-rays from the UK Biobank to extract a comprehensive set of SPs, which were associated with 145 independent loci genome-wide. Structural equation modeling suggested that limb proportions exhibited strong genetic sharing but were independent of width and torso proportions. Polygenic score analysis identified specific associations between osteoarthritis and hip and knee SPs. In contrast to other traits, SP loci were enriched in human accelerated regions and in regulatory elements of genes that are differentially expressed between humans and great apes. Combined, our work identifies specific genetic variants that affect the skeletal form and ties a major evolutionary facet of human anatomical change to pathogenesis.

Polygenic risk prediction: why and when out-of-sample prediction R2 can exceed SNP-based heritability

In polygenic score (PGS) analysis, the coefficient of determination (R2) is a key statistic to evaluate efficacy. R2 is the proportion of phenotypic variance explained by the PGS, calculated in a cohort that is independent of the genome-wide association study (GWAS) that provided estimates of allelic effect sizes. The SNP-based heritability (hSNP2, the proportion of total phenotypic variances attributable to all common SNPs) is the theoretical upper limit of the out-of-sample prediction R2. However, in real data analyses R2 has been reported to exceed hSNP2, which occurs in parallel with the observation that hSNP2 estimates tend to decline as the number of cohorts being meta-analyzed increases. Here, we quantify why and when these observations are expected. Using theory and simulation, we show that if heterogeneities in cohort-specific hSNP2 exist, or if genetic correlations between cohorts are less than one, hSNP2 estimates can decrease as the number of cohorts being meta-analyzed increases. We derive conditions when the out-of-sample prediction R2 will be greater than hSNP2 and show the validity of our derivations with real data from a binary trait (major depression) and a continuous trait (educational attainment). Our research calls for a better approach to integrating information from multiple cohorts to address issues of between-cohort heterogeneity.

Evaluating the Genetic Effects of Gut Microbiota on the Development of Neuroticism and General Happiness: A Polygenic Score Analysis and Interaction Study Using UK Biobank Data.

Limited efforts have been invested in exploring the interaction effects between genetic factors and gut microbiota on neuroticism and general happiness. The polygenic risk scores (PRS) of gut microbiota were calculated from individual-level genotype data of the UK Biobank cohort. Linear regression models were then used to assess the associations between individual PRS of gut microbiota and mental traits and interaction analysis was performed by PLINK2.0. KOBAS-i was used to conduct gene ontology (GO) enrichment analysis of the identified genes. We observed suggestive significant associations between neuroticism and PRS for the genus Bifidobacterium (rank-normal transformation, RNT) (beta = -1.10, P = 4.16 × 10-3) and the genus Desulfovibrio (RNT) (beta = 0.54, P = 7.46 × 10-3). PRS for the genus Bifidobacterium (hurdle binary, HB) (beta = 1.99, P = 5.24 × 10-3) and the genus Clostridium (RNT) (beta = 1.26, P = 9.27 × 10-3) were found to be suggestive positively associated with general happiness. Interaction analysis identified several significant genes that interacted with gut microbiota, such as RORA (rs575949009, beta = -45.00, P = 1.82 × 10-9) for neuroticism and ASTN2 (rs36005728, beta = 19.15, P = 3.37 × 10-8) for general happiness. Our study results support the genetic effects of gut microbiota on the development of neuroticism and general happiness.

EraSOR: a software tool to eliminate inflation caused by sample overlap in polygenic score analyses.

Polygenic risk score (PRS) analyses are now routinely applied across biomedical research. However, as PRS studies grow in size, there is an increased risk of sample overlap between the genome-wide association study (GWAS) from which the PRS is derived and the "target sample," in which PRSs are computed and hypotheses are tested. Despite the wide recognition of the sample overlap problem, its potential impact on the results from PRS studies has not yet been quantified, and no analytical solution has been provided. Here, we first conduct a comprehensive investigation into the scale of the sample overlap problem, finding that PRS results can be substantially inflated even in the presence of minimal overlap. Next, we introduce a method and software, EraSOR (Erase Sample Overlap and Relatedness), which eliminates the inflation caused by sample overlap (and close relatedness) in almost all settings tested here. EraSOR could be useful in PRS studies (with target sample >1,000) similar to those investigated here, either (i) to mitigate the potential effects of known or unknown intercohort overlap and close relatedness or (ii) as a sensitivity tool to highlight the possible presence of sample overlap before its direct removal, when possible, or else to provide a lower bound on PRS analysis results after accounting for potential sample overlap.

Gene‐based polygenic score analysis identifies novel immune genes with deleterious variants that associate with Alzheimer’s disease

AbstractBackgroundIn the Alzheimer’s disease sequencing project (ADSP) whole exome dataset, only the well‐known TREM2.pR47H variant shows genome‐wide significance by conventional single variant analysis. To identify novel AD genes related to TREM2, we used gene‐based polygenic score analysis (PGSA) to evaluate an immune, temporal cortical co‐expression network with 229 genes including TREM2.MethodThe most recent whole exome variant call files from the ADSP (NG00067.v7) were subjected to stringent quality control. Post‐QC samples were divided into discovery (4174 samples sequenced at Broad), test (3299 samples sequenced at WashU), and follow‐up (2273 samples sequenced at Baylor) sets. The pruned variants (r2 &lt; 0.2) with a minor allele count over 20 in each gene were analyzed by Broad/WashU PGSA thereby obtaining P‐values (gPbr.wa) for each gene in WashU samples. To assess significance in independent Baylor samples, cumulative Broad/Baylor PGSA was performed using a novel approach in which variants were stratified by both gene (gPbr.wa) and variant (Broad.P).ResultAfter excluding TREM2, the five variants with gPbr.wa ≤ 0.08 and Broad.P ≤ 0.03 showed significant association in Broad/Baylor PGSA. One of these variants showed no evidence of association with AD in the combined dataset. Polygenic scores for the other four variants, which were in three genes, showed significant association in Baylor samples, and significant, essentially identical association in WashU samples. Polygenic scores for nine additional variants in these genes (0.03&gt; Broad.P ≤ 0.13) also showed significant, essentially identical association in Baylor and WashU samples. The association of these 13 variants with AD was driven primarily by six deleterious, protein‐altering variants with minor allele frequencies less than 1%, but the other 7 variants also contributed.ConclusionUsing gene‐based polygenic score analysis to analyze an immune co‐expression network, we identify three novel immune genes with actionable nonsynonymous and stopgain variants that show significant, replicable association with AD in the most recent release of the ADSP whole exome dataset.

A comprehensive evaluation of polygenic score and genotype imputation performances of human SNP arrays in diverse populations

Regardless of the overwhelming use of next-generation sequencing technologies, microarray-based genotyping combined with the imputation of untyped variants remains a cost-effective means to interrogate genetic variations across the human genome. This technology is widely used in genome-wide association studies (GWAS) at bio-bank scales, and more recently, in polygenic score (PGS) analysis to predict and stratify disease risk. Over the last decade, human genotyping arrays have undergone a tremendous growth in both number and content making a comprehensive evaluation of their performances became more important. Here, we performed a comprehensive performance assessment for 23 available human genotyping arrays in 6 ancestry groups using diverse public and in-house datasets. The analyses focus on performance estimation of derived imputation (in terms of accuracy and coverage) and PGS (in terms of concordance to PGS estimated from whole-genome sequencing data) in three different traits and diseases. We found that the arrays with a higher number of SNPs are not necessarily the ones with higher imputation performance, but the arrays that are well-optimized for the targeted population could provide very good imputation performance. In addition, PGS estimated by imputed SNP array data is highly correlated to PGS estimated by whole-genome sequencing data in most cases. When optimal arrays are used, the correlations of PGS between two types of data are higher than 0.97, but interestingly, arrays with high density can result in lower PGS performance. Our results suggest the importance of properly selecting a suitable genotyping array for PGS applications. Finally, we developed a web tool that provides interactive analyses of tag SNP contents and imputation performance based on population and genomic regions of interest. This study would act as a practical guide for researchers to design their genotyping arrays-based studies. The tool is available at: https://genome.vinbigdata.org/tools/saa/.

Exploring the clinical and genetic associations of adult weight trajectories using electronic health records in a racially diverse biobank: a phenome-wide and polygenic risk study

SummaryBackgroundWeight trajectories might reflect individual health status. In this study, we aimed to examine the clinical and genetic associations of adult weight trajectories using electronic health records (EHRs) in the BioMe Biobank.MethodsWe constructed four weight trajectories based on a-priori definitions of weight changes (5% or 10%) using annual weight in EHRs (stable weight, weight gain, weight loss, and weight cycle); the final weight dataset included 21 487 participants with 162 783 annual weight measures. To confirm accurate assignment of weight trajectories, we manually reviewed weight trajectory plots for 100 random individuals. We then did a hypothesis-free phenome-wide association study (PheWAS) to identify diseases associated with each weight trajectory. Next, we estimated the single-nucleotide polymorphism-based heritability (hSNP2) of weight trajectories using GCTA-GREML, and we did a hypothesis-driven analysis of anorexia nervosa and depression polygenic risk scores (PRS) on these weight trajectories, given both diseases are associated with weight changes. We extended our analyses to the UK Biobank to replicate findings from a patient population to a generally healthy population.FindingsWe found high concordance between manually assigned weight trajectories and those assigned by the algorithm (accuracy ≥98%). Stable weight was consistently associated with lower disease risks among those passing Bonferroni-corrected p value in our PheWAS (p≤4·4 × 10−5). Additionally, we identified an association between depression and weight cycle (odds ratio [OR] 1·42, 95% CI 1·31–1·55, p≤7·7 × 10−16). The adult weight trajectories were heritable (using 5% weight change as the cutoff: hSNP2 of 2·1%, 95% CI 0·9–3·3, for stable weight; 4·1%, 1·4–6·8, for weight gain; 5·5%, 2·8–8·2, for weight loss; and 4·7%, 2·3–7·1%, for weight cycle). Anorexia nervosa PRS was positively associated with weight loss trajectory among individuals without eating disorder diagnoses (OR1SD 1·16, 95% CI 1·07–1·26, per 1 SD higher PRS, p=0·011), and the association was not attenuated by obesity PRS. No association was found between depression PRS and weight trajectories after permutation tests. All main findings were replicated in the UK Biobank (p<0·05).InterpretationOur findings suggest the importance of considering weight from a longitudinal aspect for its association with health and highlight a crucial role of weight management during disease development and progression.FundingKlarman Family Foundation, US National Institute of Mental Health (NIMH).

Gene-Environment Correlation over Time: A Longitudinal Analysis of Polygenic Risk Scores for Schizophrenia and Major Depression in Three British Cohorts Studies.

Research suggests that both genetic and environmental risk factors are involved in the aetiology of schizophrenia (SCZ) and major depressive disorder (MDD). Importantly, environmental and genetic risk factors are often related as evidenced in gene–environment correlation (rGE), which describes the observation that genetic and environmental factors are associated with each other. It is understood that rGE gets stronger over time as individuals select their environments more actively based on their genetic propensities. However, little is known whether rGEs remain stable over time or change across different development periods. Using data from three British longitudinal cohorts, we investigated whether rGE patterns of polygenic risk scores (PRS) for SCZ and MDD changed over time across childhood and adulthood, as well as across both from birth to age 55 and whether results differed between SCZ and MDD. Overall, the majority of rGEs remained stable across the investigated development periods. Furthermore, the few detected rGE changes which did differ between SCZ and MDD, could not be explained by the confounding of clinical cases and are therefore likely the result of actual changes in environmental and cultural risk factors with genetic susceptibility to SCZ and MDD likely playing a less significant role.

Feasibility and application of polygenic score analysis to the morphology of human-induced pluripotent stem cells

Genome-wide association studies have identified thousands of significant associations between genetic variants and complex traits. Inferring biological insights from these associations has been challenging. One approach attempted has been to examine the effects of individual variants in cellular models. Here, I demonstrate the feasibility of examining the aggregate effect of many variants on cellular phenotypes. I examine the effects of polygenic scores for cross-psychiatric disorder risk, schizophrenia, body mass index and height on cellular morphology, using 1.5 million induced pluripotent stem cells (iPSC) from 60 European-ancestry donors from the Human iPSC Initiative dataset. I show that measuring multiple cells per donor provides sufficient power for polygenic score analyses, and that cross-psychiatric disorder risk is associated with cell area (p = 0.004). Combined with emerging methods of high-throughput iPSC phenotyping, cellular polygenic scoring is a promising method for understanding potential biological effects of the polygenic component of complex traits.