Dataset Information

Demographic history mediates the effect of stratification on polygenic scores.

ABSTRACT: Population stratification continues to bias the results of genome-wide association studies (GWAS). When these results are used to construct polygenic scores, even subtle biases can cumulatively lead to large errors. To study the effect of residual stratification, we simulated GWAS under realistic models of demographic history. We show that when population structure is recent, it cannot be corrected using principal components of common variants because they are uninformative about recent history. Consequently, polygenic scores are biased in that they recapitulate environmental structure. Principal components calculated from rare variants or identity-by-descent segments can correct this stratification for some types of environmental effects. While family-based studies are immune to stratification, the hybrid approach of ascertaining variants in GWAS but reestimating effect sizes in siblings reduces but does not eliminate stratification. We show that the effect of population stratification depends not only on allele frequencies and environmental structure but also on demographic history.

SUBMITTER: Zaidi AA

PROVIDER: S-EPMC7758063 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Demographic history mediates the effect of stratification on polygenic scores.

Zaidi Arslan A AA Mathieson Iain I

eLife 20201117

Population stratification continues to bias the results of genome-wide association studies (GWAS). When these results are used to construct polygenic scores, even subtle biases can cumulatively lead to large errors. To study the effect of residual stratification, we simulated GWAS under realistic models of demographic history. We show that when population structure is recent, it cannot be corrected using principal components of common variants because they are uninformative about recent history. ...[more]

PMID: 33200985

Similar Datasets

Project description:BackgroundEarly identification of individuals at high risk for alcohol use disorder (AUD) coupled with prompt interventions could reduce the incidence of AUD. In this study, we investigated whether Polygenic Risk Scores (PRS) can be used to evaluate the risk for AUD and AUD severity (as measured by the number of DSM-5 AUD diagnostic criteria met) and compared their performance with a measure of family history of AUD.MethodsWe studied individuals of European ancestry from the Collaborative Study on the Genetics of Alcoholism (COGA). DSM-5 diagnostic criteria were available for 7203 individuals, of whom 3451 met criteria for DSM-IV alcohol dependence or DSM-5 AUD and 1616 were alcohol-exposed controls aged ≥21 years with no history of AUD or drug dependence. Further, 4842 individuals had a positive first-degree family history of AUD (FH+), 2722 had an unknown family history (FH?), and 336 had a negative family history (FH-). PRS were derived from a meta-analysis of a genome-wide association study of AUD from the Million Veteran Program and scores from the problem subscale of the Alcohol Use Disorders Identification Test in the UK Biobank. We used mixed models to test the association between PRS and risk for AUD and AUD severity.ResultsAUD cases had higher PRS than controls with PRS increasing as the number of DSM-5 diagnostic criteria increased (p-values ≤ 1.85E-05 ) in the full COGA sample, the FH+ subsample, and the FH? subsample. Individuals in the top decile of PRS had odds ratios (OR) for developing AUD of 1.96 (95% CI: 1.54 to 2.51, p-value = 7.57E-08 ) and 1.86 (95% CI: 1.35 to 2.56, p-value = 1.32E-04 ) in the full sample and the FH+ subsample, respectively. These values are comparable to previously reported ORs for a first-degree family history (1.91 to 2.38) estimated from national surveys. PRS were also significantly associated with the DSM-5 AUD diagnostic criterion count in the full sample, the FH+ subsample, and the FH? subsample (p-values ≤6.7E-11 ). PRS remained significantly associated with AUD and AUD severity after accounting for a family history of AUD (p-values ≤6.8E-10 ).ConclusionsBoth PRS and family history were associated with AUD and AUD severity, indicating that these risk measures assess distinct aspects of liability to AUD traits.

Project description:Background & aimsHepatocellular carcinoma (HCC) risk stratification in individuals with dysmetabolism is a major unmet need. Genetic predisposition contributes to non-alcoholic fatty liver disease (NAFLD). We aimed to exploit robust polygenic risk scores (PRS) that can be evaluated in the clinic to gain insight into the causal relationship between NAFLD and HCC, and to improve HCC risk stratification.MethodsWe examined at-risk individuals (NAFLD cohort, n = 2,566; 226 with HCC; and a replication cohort of 427 German patients with NAFLD) and the general population (UK Biobank [UKBB] cohort, n = 364,048; 202 with HCC). Variants in PNPLA3-TM6SF2-GCKR-MBOAT7 were combined in a hepatic fat PRS (PRS-HFC), and then adjusted for HSD17B13 (PRS-5).ResultsIn the NAFLD cohort, the adjusted impact of genetic risk variants on HCC was proportional to the predisposition to fatty liver (p = 0.002) with some heterogeneity in the effect. PRS predicted HCC more robustly than single variants (p <10-13). The association between PRS and HCC was mainly mediated through severe fibrosis, but was independent of fibrosis in clinically relevant subgroups, and was also observed in those without severe fibrosis (p <0.05). In the UKBB cohort, PRS predicted HCC independently of classical risk factors and cirrhosis (p <10-7). In the NAFLD cohort, we identified high PRS cut-offs (≥0.532/0.495 for PRS-HFC/PRS-5) that in the UKBB cohort detected HCC with ~90% specificity but limited sensitivity; PRS predicted HCC both in individuals with (p <10-5) and without cirrhosis (p <0.05).ConclusionsOur results are consistent with a causal relationship between hepatic fat and HCC. PRS improved the accuracy of HCC detection and may help stratify HCC risk in individuals with dysmetabolism, including those without severe liver fibrosis. Further studies are needed to validate our findings.Lay summaryBy analyzing variations in genes that contribute to fatty liver disease, we developed two risk scores to help predict liver cancer in individuals with obesity-related metabolic complications. These risk scores can be easily tested in the clinic. We showed that the risk scores helped to identify the risk of liver cancer both in high-risk individuals and in the general population.

Dataset Information

Demographic history mediates the effect of stratification on polygenic scores.

Publications

Demographic history mediates the effect of stratification on polygenic scores.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets