scholarly journals Trans-biobank analysis with 676,000 individuals elucidates the association of polygenic risk scores of complex traits with human lifespan

2020 ◽  
Vol 26 (4) ◽  
pp. 542-548 ◽  
Author(s):  
Saori Sakaue ◽  
◽  
Masahiro Kanai ◽  
Juha Karjalainen ◽  
Masato Akiyama ◽  
...  
Keyword(s):  
Complex Traits ◽  
Risk Scores ◽  
Polygenic Risk ◽  
Human Lifespan

scholarly journals Trans-biobank analysis with 676,000 individuals elucidates the association of polygenic risk scores of complex traits with human lifespan

2019 ◽  
Author(s):  
Saori Sakaue ◽  
Masahiro Kanai ◽  
Juha Karjalainen ◽  
Masato Akiyama ◽  
Mitja Kurki ◽  
...  
Keyword(s):  
Risk Factors ◽  
Blood Pressure ◽  
Genetic Susceptibility ◽  
Population Health ◽  
Complex Traits ◽  
European Ancestry ◽  
Risk Scores ◽  
Genome Wide Association Studies ◽  
Polygenic Risk ◽  
Human Lifespan

AbstractHuman genetics seeks a way to improve human health on a global scale. Expectations are running high for polygenic risk scores (PRSs) to be translated into clinical practice to predict an inborn susceptibility to health risks. While risk stratification based on PRS is one way to promote population health, a strategy to utilize genetics to prioritize modifiable risk factors and biomarkers driving heath outcome is also warranted. To this end, here we utilized PRSs to comprehensively investigate the association of the genetic susceptibility to complex traits with human lifespan in collaboration with three worldwide biobanks (ntotal = 675,898). First, we conducted genome-wide association studies for 45 quantitative clinical phenotypes, constructed the individual PRSs, and associated them with the age at death of 179,066 participants in BioBank Japan. The PRSs revealed that the genetic susceptibility of high systolic blood pressure (sBP) was strongly associated with a shorter lifespan (hazard ratio [HR] = 1.03, P = 1.4×10-7). Next, we sought to replicate these associations in individuals of European ancestry in UK Biobank (n = 361,194) and FinnGen (n = 135,638). Among the investigated traits, the individuals with higher blood pressure-related PRSs were trans-ethnically associated with a shorter lifespan (HR = 1.03, Pmeta = 3.9×10-13 for sBP) and parental lifespan (HR = 1.06, PUKBB = 2.0×10-86 for sBP). Further, our trans-biobank study identified additional complex traits associated with lifespan (e.g., obesity, height, serum lipids, and platelet counts). Of them, obesity-related traits showed strikingly heterogeneous effects on lifespan between Japanese and European populations (Pheterogeneity = 9.5×10-8 for body mass index). Through trans-ethnic biobank collaboration, we elucidated the novel value of the PRS study in genetics-driven prioritization of risk factors and biomarkers which can be medically intervened to improve population health.

scholarly journals Polygenic interactions with environmental adversity in the aetiology of major depressive disorder

2015 ◽  
Vol 46 (4) ◽  
pp. 759-770 ◽  
Author(s):  
N. Mullins ◽  
R. A. Power ◽  
H. L. Fisher ◽  
K. B. Hanscombe ◽  
J. Euesden ◽  
...  
Keyword(s):  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Environmental Risk ◽  
Complex Traits ◽  
Risk Scores ◽  
Environment Interaction ◽  
Inverse Association ◽  
Major Depressive ◽  
Polygenic Risk ◽  
Gene Environment

BackgroundMajor depressive disorder (MDD) is a common and disabling condition with well-established heritability and environmental risk factors. Gene–environment interaction studies in MDD have typically investigated candidate genes, though the disorder is known to be highly polygenic. This study aims to test for interaction between polygenic risk and stressful life events (SLEs) or childhood trauma (CT) in the aetiology of MDD.MethodThe RADIANT UK sample consists of 1605 MDD cases and 1064 controls with SLE data, and a subset of 240 cases and 272 controls with CT data. Polygenic risk scores (PRS) were constructed using results from a mega-analysis on MDD by the Psychiatric Genomics Consortium. PRS and environmental factors were tested for association with case/control status and for interaction between them.ResultsPRS significantly predicted depression, explaining 1.1% of variance in phenotype (p= 1.9 × 10−6). SLEs and CT were also associated with MDD status (p= 2.19 × 10−4andp= 5.12 × 10−20, respectively). No interactions were found between PRS and SLEs. Significant PRSxCT interactions were found (p= 0.002), but showed an inverse association with MDD status, as cases who experienced more severe CT tended to have a lower PRS than other cases or controls. This relationship between PRS and CT was not observed in independent replication samples.ConclusionsCT is a strong risk factor for MDD but may have greater effect in individuals with lower genetic liability for the disorder. Including environmental risk along with genetics is important in studying the aetiology of MDD and PRS provide a useful approach to investigating gene–environment interactions in complex traits.

scholarly journals Gamete simulation improves polygenic transmission disequilibrium analysis

2020 ◽  
Author(s):  
Jiawen Chen ◽  
Jing You ◽  
Zijie Zhao ◽  
Zheng Ni ◽  
Kunling Huang ◽  
...  
Keyword(s):  
Complex Traits ◽  
Statistical Power ◽  
Association Studies ◽  
Autism Spectrum ◽  
Genetic Maps ◽  
Risk Scores ◽  
Parental Genotype ◽  
Genome Wide Association Studies ◽  
Transmission Disequilibrium ◽  
Polygenic Risk

AbstractPolygenic risk scores (PRS) derived from summary statistics of genome-wide association studies (GWAS) have enjoyed great popularity in human genetics research. Applied to population cohorts, PRS can effectively stratify individuals by risk group and has promising applications in early diagnosis and clinical intervention. However, our understanding of within-family polygenic risk is incomplete, in part because the small samples per family significantly limits power. Here, to address this challenge, we introduce ORIGAMI, a computational framework that uses parental genotype data to simulate offspring genomes. ORIGAMI uses state-of-the-art genetic maps to simulate realistic recombination events on phased parental genomes and allows quantifying the prospective PRS variability within each family. We quantify and showcase the substantially reduced yet highly heterogeneous PRS variation within families for numerous complex traits. Further, we incorporate within-family PRS variability to improve polygenic transmission disequilibrium test (pTDT). Through simulations, we demonstrate that modeling within-family risk substantially improves the statistical power of pTDT. Applied to 7,805 trios of autism spectrum disorder (ASD) probands and healthy parents, we successfully replicated previously reported over-transmission of ASD, educational attainment, and schizophrenia risk, and identified multiple novel traits with significant transmission disequilibrium. These results provided novel etiologic insights into the shared genetic basis of various complex traits and ASD.

scholarly journals Polygenic risk modeling with latent trait-related genetic components

2019 ◽  
Author(s):  
Matthew Aguirre ◽  
Yosuke Tanigawa ◽  
Guhan Ram Venkataraman ◽  
Rob Tibshirani ◽  
Trevor Hastie ◽  
...  
Keyword(s):  
Fat Mass ◽  
Genetic Risk ◽  
Complex Traits ◽  
Fat Free Mass ◽  
Risk Scores ◽  
Polygenic Risk Score ◽  
Genetic Associations ◽  
Polygenic Risk ◽  
Genetic Components ◽  
Mass Component

AbstractPolygenic risk models have led to significant advances in understanding complex diseases and their clinical presentation. While models like polygenic risk scores (PRS) can effectively predict outcomes, they do not generally account for disease subtypes or pathways which underlie within-trait diversity. Here, we introduce a latent factor model of genetic risk based on components from Decomposition of Genetic Associations (DeGAs), which we call the DeGAs polygenic risk score (dPRS). We compute DeGAs using genetic associations for 977 traits in the UK Biobank and find that dPRS performs comparably to standard PRS while offering greater interpretability. We show how to decompose an individual’s genetic risk for a trait across DeGAs components, highlighting specific results for body mass index (BMI), myocardial infarction (heart attack), and gout in 337,151 white British individuals, with replication in a further set of 25,486 non-British white individuals from the Biobank. We find that BMI polygenic risk factorizes into components relating to fat-free mass, fat mass, and overall health indicators like physical activity measures. Most individuals with high dPRS for BMI have strong contributions from both a fat mass component and a fat-free mass component, whereas a few ‘outlier’ individuals have strong contributions from only one of the two components. Overall, our method enables fine-scale interpretation of the drivers of genetic risk for complex traits.

Stressful Life Events and Self-Control: Testing Gene x Environment Interaction with a Polygenic Approach

2019 ◽  
Author(s):  
Yayouk Willems ◽  
Jouke-Jan Hottenga ◽  
Lannie Ligthart ◽  
Gonneke WIllemsen ◽  
Dorret Boomsma ◽  
...  
Keyword(s):  
Complex Traits ◽  
Large Population ◽  
Self Control ◽  
Risk Scores ◽  
Life Stressors ◽  
Control Problems ◽  
Environment Interaction ◽  
Polygenic Risk ◽  
Polygenic Scores ◽  
Low Self Control

Background: Ill decisions and reckless behaviors due to low self-control are concurrently and longitudinally costly, and revealing possible factors contributing to individual differences in self-control is necessary. It is hypothesized that genetically sensitivity interacts with life stressors in the prediction of the development of low self-control (gene environment interaction), yet attempts to test this hypothesis mostly concern candidate gene studies yielding inconclusive results. The goal of this research was to bring findings from large scale gene identification studies into the developmental psychology framework, taking the polygenic nature of complex traits into account. Methods: Using data of a large population-based twin sample, we tested whether polygenic risk scores for self-control problems – based on the most recent ADHD GWAS – predict self-control problems in adults, and whether this polygenic risk scores interact with the presence of environmental stressors. Results: While polygenic scores and life stressors significantly predicted low self-control, we did not find a significant interaction effect. Conclusions: Empirically, finding statistical evidence for this hypothesis remains a challenge, and more research is needed to investigate how to better detect G x E.

scholarly journals Genetic pleiotropy between mood disorders, metabolic, and endocrine traits in a multigenerational pedigree

2017 ◽  
Author(s):  
Rachel L. Kember ◽  
Liping Hou ◽  
Xiao Ji ◽  
Lars H. Andersen ◽  
Arpita Ghorai ◽  
...  
Keyword(s):  
General Population ◽  
Complex Traits ◽  
Family Members ◽  
Complex Diseases ◽  
Risk Scores ◽  
Disease Genes ◽  
Mendelian Disease ◽  
Polygenic Risk ◽  
Genetic Pleiotropy ◽  
Increased Risk

AbstractBipolar disorder (BD) is a mental disorder characterized by alternating periods of depression and mania. Individuals with BD have higher levels of early mortality than the general population, and a substantial proportion of this may be due to increased risk for comorbid diseases. Recent evidence suggests that pleiotropy, either in the form of a single risk-allele or the combination of multiple loci genome-wide, may underlie medical comorbidity between traits and diseases. To identify the molecular events that underlie BD and related medical comorbidities, we generated imputed whole genome sequence (WGS) data using a population specific reference panel, for an extended multigenerational Old Order Amish pedigree (400 family members) segregating BD and related disorders. First, we investigated all putative disease-causing variants at known Mendelian disease loci present in this pedigree. Second, we performed genomic profiling using polygenic risk scores to establish each individual's risk for several complex diseases. To explore the contribution of disease genes to BD we performed gene-based and variant-based association tests for BD, and found that Mendelian disease genes are enriched in the top results from both tests (OR=20.3, p=1×10−3; OR=2.2, p=1×10−2). We next identified a set of Mendelian variants that co-occur in individuals with BD more frequently than their unaffected family members, including the R3527Q mutation inAPOBassociated with hypercholesterolemia. Using polygenic risk scores, we demonstrated that BD individuals from this pedigree were enriched for the same common risk-alleles for BD as in the general population (β=0.416, p=6×10−4). Furthermore, in the extended Amish family we find evidence for a common genetic etiology between BD and clinical autoimmune thyroid disease (p=1×10−4), diabetes (p=1×10−3), and lipid traits such as triglyceride levels (p=3×10−4). We identify genomic regions that contribute to the differences between BD individuals and unaffected family members by calculating local genetic risk for independent LD blocks. Our findings provide evidence for the extensive genetic pleiotropy that can drive epidemiological findings of comorbidities between diseases and other complex traits. Identifying such patterns may enable the subtyping of complex diseases and facilitate our understanding of the genetic mechanisms underlying phenotypic heterogeneity.

scholarly journals Polygenic inheritance, GWAS, polygenic risk scores, and the search for functional variants

2020 ◽  
Vol 117 (32) ◽  
pp. 18924-18933
Author(s):  
Daniel J. M. Crouch ◽  
Walter F. Bodmer
Keyword(s):  
Genetic Association ◽  
Complex Traits ◽  
Rare Variants ◽  
Association Studies ◽  
Genetic Association Studies ◽  
Mendelian Inheritance ◽  
Risk Scores ◽  
Polygenic Inheritance ◽  
Polygenic Risk ◽  
Functional Variants

The reconciliation between Mendelian inheritance of discrete traits and the genetically based correlation between relatives for quantitative traits was Fisher’s infinitesimal model of a large number of genetic variants, each with very small effects, whose causal effects could not be individually identified. The development of genome-wide genetic association studies (GWAS) raised the hope that it would be possible to identify single polymorphic variants with identifiable functional effects on complex traits. It soon became clear that, with larger and larger GWAS on more and more complex traits, most of the significant associations had such small effects, that identifying their individual functional effects was essentially hopeless. Polygenic risk scores that provide an overall estimate of the genetic propensity to a trait at the individual level have been developed using GWAS data. These provide useful identification of groups of individuals with substantially increased risks, which can lead to recommendations of medical treatments or behavioral modifications to reduce risks. However, each such claim will require extensive investigation to justify its practical application. The challenge now is to use limited genetic association studies to find individually identifiable variants of significant functional effect that can help to understand the molecular basis of complex diseases and traits, and so lead to improved disease prevention and treatment. This can best be achieved by 1) the study of rare variants, often chosen by careful candidate assessment, and 2) the careful choice of phenotypes, often extremes of a quantitative variable, or traits with relatively high heritability.

scholarly journals Explicit modeling of ancestry improves polygenic risk scores and BLUP prediction

2014 ◽  
Author(s):  
Chia-Yen Chen ◽  
Jiali Han ◽  
David J. Hunter ◽  
Peter Kraft ◽  
Alkes L. Price
Keyword(s):  
Complex Traits ◽  
Prediction Accuracy ◽  
Structured Populations ◽  
Genetic Ancestry ◽  
Risk Scores ◽  
Hair Color ◽  
Polygenic Risk ◽  
European Americans ◽  
Genome Wide ◽  
High Prediction

Polygenic prediction using genome-wide SNPs can provide high prediction accuracy for complex traits. Here, we investigate the question of how to account for genetic ancestry when conducting polygenic prediction. We show that the accuracy of polygenic prediction in structured populations may be partly due to genetic ancestry. However, we hypothesized that explicitly modeling ancestry could improve polygenic prediction accuracy. We analyzed three GWAS of hair color, tanning ability and basal cell carcinoma (BCC) in European Americans (sample size from 7,440 to 9,822) and considered two widely used polygenic prediction approaches: polygenic risk scores (PRS) and Best Linear Unbiased Prediction (BLUP). We compared polygenic prediction without correction for ancestry to polygenic prediction with ancestry as a separate component in the model. In 10-fold cross-validation using the PRS approach, the R2for hair color increased by 66% (0.0456 to 0.0755; p<10-16), the R2for tanning ability increased by 123% (0.0154 to 0.0344; p<10-16) and the liability-scale R2for BCC increased by 68% (0.0138 to 0.0232; p<10-16) when explicitly modeling ancestry, which prevents ancestry effects from entering into each SNP effect and being over-weighted. Surprisingly, explicitly modeling ancestry produces a similar improvement when using the BLUP approach, which fits all SNPs simultaneously in a single variance component and causes ancestry to be under-weighted. We validate our findings via simulations, which show that the differences in prediction accuracy will increase in magnitude as sample sizes increase. In summary, our results show that explicitly modeling ancestry can be important in both PRS and BLUP prediction.

scholarly journals Leveraging fine-mapping and non-European training data to improve trans-ethnic polygenic risk scores

2021 ◽  
Author(s):  
Omer Weissbrod ◽  
Masahiro Kanai ◽  
Huwenbo Shi ◽  
Steven Gazal ◽  
Wouter Peyrot ◽  
...  
Keyword(s):  
Health Disparities ◽  
Fine Mapping ◽  
Complex Traits ◽  
Prediction Accuracy ◽  
Causal Effect ◽  
Training Data ◽  
Risk Scores ◽  
Uk Biobank ◽  
Polygenic Risk ◽  
European Training

AbstractPolygenic risk scores (PRS) based on European training data suffer reduced accuracy in non-European target populations, exacerbating health disparities. This loss of accuracy predominantly stems from LD differences, MAF differences (including population-specific SNPs), and/or causal effect size differences. Here, we propose PolyPred, a method that improves trans-ethnic polygenic prediction by combining two complementary predictors: a new predictor that leverages functionally informed fine-mapping to estimate causal effects (instead of tagging effects), addressing LD differences; and BOLT-LMM, a published predictor. In the special case where a large training sample is available in the non-European target population (or a closely related population), we propose PolyPred+, which further incorporates the non-European training data, addressing MAF differences and causal effect size differences. We applied PolyPred to 49 diseases and complex traits in 4 UK Biobank populations using UK Biobank British training data (average N=325K), and observed statistically significant average relative improvements in prediction accuracy vs. BOLT-LMM ranging from +7% in South Asians to +32% in Africans (and vs. LD-pruning + P-value thresholding (P+T) ranging from +77% to +164%), consistent with simulations. We applied PolyPred+ to 23 diseases and complex traits in UK Biobank East Asians using both UK Biobank British (average N=325K) and Biobank Japan (average N=124K) training data, and observed statistically significant average relative improvements in prediction accuracy of +24% vs. BOLT-LMM and +12% vs. PolyPred. In conclusion, PolyPred and PolyPred+ improve trans-ethnic polygenic prediction accuracy, ameliorating health disparities.

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