Reduced signal for polygenic adaptation of height in UK Biobank

AbstractSeveral recent papers have reported strong signals of selection on European polygenic height scores. These analyses used height effect estimates from the GIANT consortium and replication studies. Here, we describe a new analysis based on the the UK Biobank (UKB), a large, independent dataset. We find that the signals of selection using UKB effect-size estimates for height are strongly attenuated or absent. We also provide evidence that previous analyses were confounded by population stratification Therefore, the conclusion of strong polygenic adaptation now lacks support. Moreover, these discrepancies highlight (1) that methods for correcting for population stratification in GWAS may not always be sufficient for polygenic trait analyses, and (2) that claims of differences in polygenic scores between populations should be treated with caution until these issues are better understood.

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Reduced signal for polygenic adaptation of height in UK Biobank

eLife ◽

10.7554/elife.39725 ◽

2019 ◽

Vol 8 ◽

Cited By ~ 99

Author(s):

Jeremy J Berg ◽

Arbel Harpak ◽

Nasa Sinnott-Armstrong ◽

Anja Moltke Joergensen ◽

Hakhamanesh Mostafavi ◽

...

Keyword(s):

Peer Review ◽

Population Stratification ◽

Editorial Note ◽

Editorial Process ◽

Uk Biobank ◽

Independent Dataset ◽

Replication Studies ◽

Polygenic Scores ◽

Polygenic Adaptation ◽

The Uk

Several recent papers have reported strong signals of selection on European polygenic height scores. These analyses used height effect estimates from the GIANT consortium and replication studies. Here, we describe a new analysis based on the the UK Biobank (UKB), a large, independent dataset. We find that the signals of selection using UKB effect estimates are strongly attenuated or absent. We also provide evidence that previous analyses were confounded by population stratification. Therefore, the conclusion of strong polygenic adaptation now lacks support. Moreover, these discrepancies highlight (1) that methods for correcting for population stratification in GWAS may not always be sufficient for polygenic trait analyses, and (2) that claims of differences in polygenic scores between populations should be treated with caution until these issues are better understood.Editorial note: This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that all the issues have been addressed (<xref ref-type="decision-letter" rid="SA1">see decision letter</xref>).

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How robust are cross-population signatures of polygenic adaptation in humans?

10.1101/2020.07.13.200030 ◽

2020 ◽

Author(s):

Alba Refoyo-Martínez ◽

Siyang Liu ◽

Anja Moltke Jørgensen ◽

Xin Jin ◽

Anders Albrechtsen ◽

...

Keyword(s):

Effect Size ◽

Association Studies ◽

Gwas Data ◽

Genome Wide Association Studies ◽

Summary Statistics ◽

Uk Biobank ◽

Polygenic Adaptation ◽

The Uk ◽

Meta Analyses ◽

Size Estimates

AbstractOver the past decade, summary statistics from genome-wide association studies (GWAS) have been used to detect and quantify polygenic adaptation in humans. Several studies have reported signatures of natural selection at sets of SNPs associated with complex traits, like height and body mass index. However, more recent studies suggest that some of these signals may be caused by biases from uncorrected population stratification in the GWAS data with which these tests are performed. Moreover, past studies have predominantly relied on SNP effect size estimates obtained from GWAS panels of European ancestries, which are known to be poor predictors of phenotypes in non-European populations. Here, we collated GWAS data from multiple anthropometric and metabolic traits that have been measured in more than one cohort around the world, including the UK Biobank, FINRISK, Chinese NIPT, Biobank Japan, APCDR and PAGE. We then evaluated how robust signals of polygenic adaptation are to the choice of GWAS cohort used to identify associated variants and their effect size estimates, while using the same panel to obtain population allele frequencies (The 1000 Genomes Project). We observe many discrepancies across tests performed on the same phenotype and find that association studies performed using multiple different cohorts, like meta-analyses, tend to produce scores with strong overdispersion across populations. This results in apparent signatures of polygenic adaptation which are not observed when using effect size estimates from biobank-based GWAS of homogeneous ancestries. Indeed, we were able to artificially create score overdispersion when taking the UK Biobank cohort and simulating a meta-analysis on multiple subsets of the cohort. This suggests that extreme caution should be taken in the execution and interpretation of future tests of polygenic adaptation based on population differentiation, especially when using summary statistics from GWAS meta-analyses.

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Polygenic Adaptation has Impacted Multiple Anthropometric Traits

10.1101/167551 ◽

2017 ◽

Cited By ~ 30

Author(s):

Jeremy J. Berg ◽

Xinjun Zhang ◽

Graham Coop

Keyword(s):

Complex Traits ◽

Association Studies ◽

Gwas Data ◽

Human Populations ◽

Genome Wide Association Studies ◽

Uk Biobank ◽

Link Type ◽

Polygenic Scores ◽

Polygenic Adaptation ◽

The Uk

AbstractOur understanding of the genetic basis of human adaptation is biased toward loci of large pheno-typic effect. Genome wide association studies (GWAS) now enable the study of genetic adaptation in polygenic phenotypes. We test for polygenic adaptation among 187 world-wide human populations using polygenic scores constructed from GWAS of 34 complex traits. We identify signals of polygenic adaptation for anthropometric traits including height, infant head circumference (IHC), hip circumference and waist-to-hip ratio (WHR). Analysis of ancient DNA samples indicates that a north-south cline of height within Europe and and a west-east cline across Eurasia can be traced to selection for increased height in two late Pleistocene hunter gatherer populations living in western and west-central Eurasia. Our observation that IHC and WHR follow a latitudinal cline in Western Eurasia support the role of natural selection driving Bergmann’s Rule in humans, consistent with thermoregulatory adaptation in response to latitudinal temperature variation.Author’s Note on Failure to ReplicateAfter this preprint was posted, the UK Biobank dataset was released, providing a new and open GWAS resource. When attempting to replicate the height selection results from this preprint using GWAS data from the UK Biobank, we discovered that we could not. In subsequent analyses, we determined that both the GIANT consortium height GWAS data, as well as another dataset that was used for replication, were impacted by stratification issues that created or at a minimum substantially inflated the height selection signals reported here. The results of this second investigation, written together with additional coauthors, have now been published (https://elifesciences.org/articles/39725 along with another paper by a separate group of authors, showing similar issues https://elifesciences.org/articles/39702). A preliminary investigation shows that the other non-height based results may suffer from similar issues. We stand by the theory and statistical methods reported in this paper, and the paper can be cited for these results. However, we have shown that the data on which the major empirical results were based are not sound, and so should be treated with caution until replicated.

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Contrasting Broad- and Clinically- defined Polygenic Indicators of Depression and Depression-related Phenotypes in Adults and Children

10.31234/osf.io/pn9vb ◽

2020 ◽

Author(s):

John E. McGeary ◽

Chelsie Benca-Bachman ◽

Victoria Risner ◽

Christopher G Beevers ◽

Brandon Gibb ◽

...

Keyword(s):

Suicidal Ideation ◽

Cognitive Reappraisal ◽

Twin Studies ◽

European Ancestry ◽

Summary Statistics ◽

Depression Severity ◽

Uk Biobank ◽

Polygenic Scores ◽

Adults And Children ◽

The Uk

Twin studies indicate that 30-40% of the disease liability for depression can be attributed to genetic differences. Here, we assess the explanatory ability of polygenic scores (PGS) based on broad- (PGSBD) and clinical- (PGSMDD) depression summary statistics from the UK Biobank using independent cohorts of adults (N=210; 100% European Ancestry) and children (N=728; 70% European Ancestry) who have been extensively phenotyped for depression and related neurocognitive phenotypes. PGS associations with depression severity and diagnosis were generally modest, and larger in adults than children. Polygenic prediction of depression-related phenotypes was mixed and varied by PGS. Higher PGSBD, in adults, was associated with a higher likelihood of having suicidal ideation, increased brooding and anhedonia, and lower levels of cognitive reappraisal; PGSMDD was positively associated with brooding and negatively related to cognitive reappraisal. Overall, PGS based on both broad and clinical depression phenotypes have modest utility in adult and child samples of depression.

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Reproducibility in the UK Biobank of Genome-Wide Significant Signals Discovered in Earlier Genome-wide Association Studies

10.1101/2020.06.24.20139576 ◽

2020 ◽

Cited By ~ 1

Author(s):

Jack W. O’Sullivan ◽

John P. A. Ioannidis

Keyword(s):

Effect Size ◽

Association Studies ◽

Genome Wide Association ◽

P Value ◽

Genome Wide Association Studies ◽

Uk Biobank ◽

Single Nucleotide ◽

Genome Wide ◽

The Uk ◽

Open Question

AbstractWith the establishment of large biobanks, discovery of single nucleotide polymorphism (SNPs) that are associated with various phenotypes has been accelerated. An open question is whether SNPs identified with genome-wide significance in earlier genome-wide association studies (GWAS) are replicated also in later GWAS conducted in biobanks. To address this question, the authors examined a publicly available GWAS database and identified two, independent GWAS on the same phenotype (an earlier, “discovery” GWAS and a later, replication GWAS done in the UK biobank). The analysis evaluated 136,318,924 SNPs (of which 6,289 had reached p<5e-8 in the discovery GWAS) from 4,397,962 participants across nine phenotypes. The overall replication rate was 85.0% and it was lower for binary than for quantitative phenotypes (58.1% versus 94.8% respectively). There was a18.0% decrease in SNP effect size for binary phenotypes, but a 12.0% increase for quantitative phenotypes. Using the discovery SNP effect size, phenotype trait (binary or quantitative), and discovery p-value, we built and validated a model that predicted SNP replication with area under the Receiver Operator Curve = 0.90. While non-replication may often reflect lack of power rather than genuine false-positive findings, these results provide insights about which discovered associations are likely to be seen again across subsequent GWAS.

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Polygenic scores for UK Biobank scale data

10.1101/252270 ◽

2018 ◽

Cited By ~ 5

Author(s):

Timothy Shin Heng Mak ◽

Robert Milan Porsch ◽

Shing Wan Choi ◽

Pak Chung Sham

Keyword(s):

External Source ◽

Summary Statistics ◽

Uk Biobank ◽

Validation Data ◽

Raw Data ◽

Cross Prediction ◽

Polygenic Scores ◽

The Difference ◽

The Uk ◽

Meta Analyses

AbstractPolygenic scores (PGS) are estimated scores representing the genetic tendency of an individual for a disease or trait and have become an indispensible tool in a variety of analyses. Typically they are linear combination of the genotypes of a large number of SNPs, with the weights calculated from an external source, such as summary statistics from large meta-analyses. Recently cohorts with genetic data have become very large, such that it would be a waste if the raw data were not made use of in constructing PGS. Making use of raw data in calculating PGS, however, presents us with problems of overfitting. Here we discuss the essence of overfitting as applied in PGS calculations and highlight the difference between overfitting due to the overlap between the target and the discovery data (OTD), and overfitting due to the overlap between the target the the validation data (OTV). We propose two methods — cross prediction and split validation — to overcome OTD and OTV respectively. Using these two methods, PGS can be calculated using raw data without overfitting. We show that PGSs thus calculated have better predictive power than those using summary statistics alone for six phenotypes in the UK Biobank data.

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Population stratification in GWAS meta-analysis should be standardized to the best available reference datasets

10.1101/2020.09.03.281568 ◽

2020 ◽

Author(s):

Aliya Sarmanova ◽

Tim Morris ◽

Daniel John Lawson

Keyword(s):

Population Stratification ◽

Association Studies ◽

Meta Analysis ◽

Principal Component ◽

Underlying Structure ◽

Genome Wide Association Studies ◽

Uk Biobank ◽

External Reference ◽

Major Disadvantage ◽

The Uk

AbstractPopulation stratification has recently been demonstrated to bias genetic studies even in relatively homogeneous populations such as within the British Isles. A key component to correcting for stratification in genome-wide association studies (GWAS) is accurately identifying and controlling for the underlying structure present in the sample. Meta-analysis across cohorts is increasingly important for achieving very large sample sizes, but comes with the major disadvantage that each individual cohort corrects for different population stratification. Here we demonstrate that correcting for structure against an external reference adds significant value to meta-analysis. We treat the UK Biobank as a collection of smaller studies, each of which is geographically localised. We provide software to standardize an external dataset against a reference, provide the UK Biobank principal component loadings for this purpose, and demonstrate the value of this with an analysis of the geographically sampled ALSPAC cohort.

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Within-family studies for Mendelian randomization: avoiding dynastic, assortative mating, and population stratification biases

10.1101/602516 ◽

2019 ◽

Cited By ~ 30

Author(s):

Ben Brumpton ◽

Eleanor Sanderson ◽

Fernando Pires Hartwig ◽

Sean Harrison ◽

Gunnhild Åberge Vie ◽

...

Keyword(s):

Population Stratification ◽

Mendelian Randomization ◽

Genetic Data ◽

Population Based ◽

Health Study ◽

Uk Biobank ◽

Family Effects ◽

Family Based ◽

Using Data ◽

The Uk

AbstractMendelian randomization (MR) is a widely-used method for causal inference using genetic data. Mendelian randomization studies of unrelated individuals may be susceptible to bias from family structure, for example, through dynastic effects which occur when parental genotypes directly affect offspring phenotypes. Here we describe methods for within-family Mendelian randomization and through simulations show that family-based methods can overcome bias due to dynastic effects. We illustrate these issues empirically using data from 61,008 siblings from the UK Biobank and Nord-Trøndelag Health Study. Both within-family and population-based Mendelian randomization analyses reproduced established effects of lower BMI reducing risk of diabetes and high blood pressure. However, while MR estimates from population-based samples of unrelated individuals suggested that taller height and lower BMI increase educational attainment, these effects largely disappeared in within-family MR analyses. We found differences between population-based and within-family based estimates, indicating the importance of controlling for family effects and population structure in Mendelian randomization studies.

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Variable prediction accuracy of polygenic scores within an ancestry group

10.1101/629949 ◽

2019 ◽

Cited By ~ 14

Author(s):

Hakhamanesh Mostafavi ◽

Arbel Harpak ◽

Dalton Conley ◽

Jonathan K Pritchard ◽

Molly Przeworski

Keyword(s):

Prediction Accuracy ◽

Human Genetics ◽

Association Studies ◽

Genome Wide Association Studies ◽

Uk Biobank ◽

Gwas Study ◽

Ancestry Group ◽

Genome Wide ◽

Polygenic Scores ◽

The Uk

AbstractFields as diverse as human genetics and sociology are increasingly using polygenic scores based on genome-wide association studies (GWAS) for phenotypic prediction. However, recent work has shown that polygenic scores have limited portability across groups of different genetic ancestries, restricting the contexts in which they can be used reliably and potentially creating serious inequities in future clinical applications. Using the UK Biobank data, we demonstrate that even within a single ancestry group, the prediction accuracy of polygenic scores depends on characteristics such as the age or sex composition of the individuals in which the GWAS and the prediction were conducted, and on the GWAS study design. Our findings highlight both the complexities of interpreting polygenic scores and underappreciated obstacles to their broad use.

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Comparison of adopted and non-adopted individuals reveals gene-environment interplay for education in the UK Biobank

10.1101/707695 ◽

2019 ◽

Cited By ~ 12

Author(s):

Rosa Cheesman ◽

Avina Hunjan ◽

Jonathan R. I. Coleman ◽

Yasmin Ahmadzadeh ◽

Robert Plomin ◽

...

Keyword(s):

Home Environment ◽

Adoptive Parents ◽

Genetic Influences ◽

Uk Biobank ◽

Rearing Environment ◽

Individual Level ◽

Gene Environment ◽

Polygenic Scores ◽

The Uk ◽

Difference Test

AbstractIndividual-level polygenic scores can now explain ∼10% of the variation in number of years of completed education. However, associations between polygenic scores and education capture not only genetic propensity but information about the environment that individuals are exposed to. This is because individuals passively inherit effects of parental genotypes, since their parents typically also provide the rearing environment. In other words, the strong correlation between offspring and parent genotypes results in an association between the offspring genotypes and the rearing environment. This is termed passive gene-environment correlation. We present an approach to test for the extent of passive gene-environment correlation for education without requiring intergenerational data. Specifically, we use information from 6311 individuals in the UK Biobank who were adopted in childhood to compare genetic influence on education between adoptees and non-adopted individuals. Adoptees’ rearing environments are less correlated with their genotypes, because they do not share genes with their adoptive parents. We find that polygenic scores are twice as predictive of years of education in non-adopted individuals compared to adoptees (R2= 0.074 vs 0.037, difference test p= 8.23 × 10−24). We provide another kind of evidence for the influence of parental behaviour on offspring education: individuals in the lowest decile of education polygenic score attain significantly more education if they are adopted, possibly due to educationally supportive adoptive environments. Overall, these results suggest that genetic influences on education are mediated via the home environment. As such, polygenic prediction of educational attainment represents gene-environment correlations just as much as it represents direct genetic effects.

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