scholarly journals Polygenic adaptation on height is overestimated due to uncorrected stratification in genome-wide association studies

eLife ◽  
2019 ◽  
Vol 8 ◽  
Author(s):  
Mashaal Sohail ◽  
Robert M Maier ◽  
Andrea Ganna ◽  
Alex Bloemendal ◽  
Alicia R Martin ◽  
...  
Keyword(s):  
Population Stratification ◽  
Association Studies ◽  
Editorial Note ◽  
Human Populations ◽  
Genome Wide Association Studies ◽  
Multiple Traits ◽  
Large Numbers ◽  
Genome Wide ◽  
Polygenic Scores ◽  
Polygenic Adaptation

Genetic predictions of height differ among human populations and these differences have been interpreted as evidence of polygenic adaptation. These differences were first detected using SNPs genome-wide significantly associated with height, and shown to grow stronger when large numbers of sub-significant SNPs were included, leading to excitement about the prospect of analyzing large fractions of the genome to detect polygenic adaptation for multiple traits. Previous studies of height have been based on SNP effect size measurements in the GIANT Consortium meta-analysis. Here we repeat the analyses in the UK Biobank, a much more homogeneously designed study. We show that polygenic adaptation signals based on large numbers of SNPs below genome-wide significance are extremely sensitive to biases due to uncorrected population stratification. More generally, our results imply that typical constructions of polygenic scores are sensitive to population stratification and that population-level differences should be interpreted with caution.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>).

scholarly journals Signals of polygenic adaptation on height have been overestimated due to uncorrected population structure in genome-wide association studies

2018 ◽  
Author(s):  
Mashaal Sohail ◽  
Robert M. Maier ◽  
Andrea Ganna ◽  
Alex Bloemendal ◽  
Alicia R. Martin ◽  
...  
Keyword(s):  
Population Structure ◽  
Association Studies ◽  
Meta Analysis ◽  
Human Populations ◽  
Genome Wide Association Studies ◽  
Multiple Traits ◽  
Large Numbers ◽  
Genome Wide ◽  
Polygenic Adaptation ◽  
The Uk

AbstractGenetic predictions of height differ among human populations and these differences are too large to be explained by genetic drift. This observation has been interpreted as evidence of polygenic adaptation. Differences across populations were detected using SNPs genome-wide significantly associated with height, and many studies also found that the signals grew stronger when large numbers of subsignificant SNPs were analyzed. This has led to excitement about the prospect of analyzing large fractions of the genome to detect subtle signals of selection and claims of polygenic adaptation for multiple traits. Polygenic adaptation studies of height have been based on SNP effect size measurements in the GIANT Consortium meta-analysis. Here we repeat the height analyses in the UK Biobank, a much more homogeneously designed study. Our results show that polygenic adaptation signals based on large numbers of SNPs below genome-wide significance are extremely sensitive to biases due to uncorrected population structure.

scholarly journals Polygenic Adaptation has Impacted Multiple Anthropometric Traits

2017 ◽  
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.

scholarly journals A simple test identifies selection on complex traits in breeding and experimentally-evolved populations

2017 ◽  
Author(s):  
Tim Beissinger ◽  
Jochen Kruppa ◽  
David Cavero ◽  
Ngoc-Thuy Ha ◽  
Malena Erbe ◽  
...  
Keyword(s):  
Complex Traits ◽  
Association Studies ◽  
Human Populations ◽  
Frequency Change ◽  
Genome Wide Association Studies ◽  
Breeding Populations ◽  
Large Numbers ◽  
Genome Wide ◽  
Single Time Point ◽  
Time Point

AbstractImportant traits in agricultural, natural, and human populations are increasingly being shown to be under the control of many genes that individually contribute only a small proportion of genetic variation. However, the majority of modern tools in quantitative and population genetics, including genome wide association studies and selection mapping protocols, are designed to identify individual genes with large effects. We have developed an approach to identify traits that have been under selection and are controlled by large numbers of loci. In contrast to existing methods, our technique utilizes additive effects estimates from all available markers, and relates these estimates to allele frequency change over time. Using this information, we generate a composite statistic, denoted Ĝ, which can be used to test for significant evidence of selection on a trait. Our test requires pre- and post-selection genotypic data but only a single time point with phenotypic information. Simulations demonstrate that Ĝ is powerful for identifying selection, particularly in situations where the trait being tested is controlled by many genes, which is precisely the scenario where classical approaches for selection mapping are least powerful. We apply this test to breeding populations of maize and chickens, where we demonstrate the successful identification of selection on traits that are documented to have been under selection.

scholarly journals The Genetic Basis of Hypertriglyceridemia

2021 ◽  
Vol 23 (8) ◽  
Author(s):  
Germán D. Carrasquilla ◽  
Malene Revsbech Christiansen ◽  
Tuomas O. Kilpeläinen
Keyword(s):  
Genetic Basis ◽  
Association Studies ◽  
Cumulative Effect ◽  
Genome Wide Association Studies ◽  
Genetic Loci ◽  
Risk Variants ◽  
Genome Wide ◽  
Severe Hypertriglyceridemia ◽  
Increased Risk ◽  
Polygenic Scores

Abstract Purpose of Review Hypertriglyceridemia is a common dyslipidemia associated with an increased risk of cardiovascular disease and pancreatitis. Severe hypertriglyceridemia may sometimes be a monogenic condition. However, in the vast majority of patients, hypertriglyceridemia is due to the cumulative effect of multiple genetic risk variants along with lifestyle factors, medications, and disease conditions that elevate triglyceride levels. In this review, we will summarize recent progress in the understanding of the genetic basis of hypertriglyceridemia. Recent Findings More than 300 genetic loci have been identified for association with triglyceride levels in large genome-wide association studies. Studies combining the loci into polygenic scores have demonstrated that some hypertriglyceridemia phenotypes previously attributed to monogenic inheritance have a polygenic basis. The new genetic discoveries have opened avenues for the development of more effective triglyceride-lowering treatments and raised interest towards genetic screening and tailored treatments against hypertriglyceridemia. Summary The discovery of multiple genetic loci associated with elevated triglyceride levels has led to improved understanding of the genetic basis of hypertriglyceridemia and opened new translational opportunities.

scholarly journals A practical approach to adjusting for population stratification in genome-wide association studies: principal components and propensity scores (PCAPS)

2018 ◽  
Vol 17 (6) ◽  
Author(s):  
Huaqing Zhao ◽  
Nandita Mitra ◽  
Peter A. Kanetsky ◽  
Katherine L. Nathanson ◽  
Timothy R. Rebbeck
Keyword(s):  
Principal Components ◽  
Population Stratification ◽  
Propensity Scores ◽  
Association Studies ◽  
Germ Cell Tumors ◽  
Gwas Data ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Testicular Germ Cell ◽  
Genome Wide

Abstract Genome-wide association studies (GWAS) are susceptible to bias due to population stratification (PS). The most widely used method to correct bias due to PS is principal components (PCs) analysis (PCA), but there is no objective method to guide which PCs to include as covariates. Often, the ten PCs with the highest eigenvalues are included to adjust for PS. This selection is arbitrary, and patterns of local linkage disequilibrium may affect PCA corrections. To address these limitations, we estimate genomic propensity scores based on all statistically significant PCs selected by the Tracy-Widom (TW) statistic. We compare a principal components and propensity scores (PCAPS) approach to PCA and EMMAX using simulated GWAS data under no, moderate, and severe PS. PCAPS reduced spurious genetic associations regardless of the degree of PS, resulting in odds ratio (OR) estimates closer to the true OR. We illustrate our PCAPS method using GWAS data from a study of testicular germ cell tumors. PCAPS provided a more conservative adjustment than PCA. Advantages of the PCAPS approach include reduction of bias compared to PCA, consistent selection of propensity scores to adjust for PS, the potential ability to handle outliers, and ease of implementation using existing software packages.

scholarly journals Phenotypic Annotation: Using Polygenic Scores to Translate Discoveries From Genome-Wide Association Studies From the Top Down

2019 ◽  
Vol 28 (1) ◽  
pp. 82-90 ◽  
Author(s):  
Daniel W. Belsky ◽  
K. Paige Harden
Keyword(s):  
Educational Attainment ◽  
Association Studies ◽  
Science Research ◽  
Well Being ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Research Activities ◽  
Genome Wide ◽  
Polygenic Scores ◽  
Methodological Considerations

Genome-wide association studies (GWASs) have identified specific genetic variants associated with complex human traits and behaviors, such as educational attainment, mental disorders, and personality. However, small effect sizes for individual variants, uncertainty regarding the biological function of discovered genotypes, and potential “outside-the-skin” environmental mechanisms leave a translational gulf between GWAS results and scientific understanding that will improve human health and well-being. We propose a set of social, behavioral, and brain-science research activities that map discovered genotypes to neural, developmental, and social mechanisms and call this research program phenotypic annotation. Phenotypic annotation involves (a) elaborating the nomological network surrounding discovered genotypes, (b) shifting focus from individual genes to whole genomes, and (c) testing how discovered genotypes affect life-span development. Phenotypic-annotation research is already advancing the understanding of GWAS discoveries for educational attainment and schizophrenia. We review examples and discuss methodological considerations for psychologists taking up the phenotypic-annotation approach.

scholarly journals Contributions of common genetic variants to risk of schizophrenia among individuals of African and Latino ancestry

2019 ◽  
Vol 25 (10) ◽  
pp. 2455-2467 ◽  
Author(s):  
Tim B. Bigdeli ◽  
◽  
Giulio Genovese ◽  
Penelope Georgakopoulos ◽  
Jacquelyn L. Meyers ◽  
...  
Keyword(s):  
Genetic Variants ◽  
Association Studies ◽  
Large Fraction ◽  
African Ancestry ◽  
Common Variant ◽  
Human Populations ◽  
Polygenic Risk Score ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Common Genetic Variants

Abstract Schizophrenia is a common, chronic and debilitating neuropsychiatric syndrome affecting tens of millions of individuals worldwide. While rare genetic variants play a role in the etiology of schizophrenia, most of the currently explained liability is within common variation, suggesting that variation predating the human diaspora out of Africa harbors a large fraction of the common variant attributable heritability. However, common variant association studies in schizophrenia have concentrated mainly on cohorts of European descent. We describe genome-wide association studies of 6152 cases and 3918 controls of admixed African ancestry, and of 1234 cases and 3090 controls of Latino ancestry, representing the largest such study in these populations to date. Combining results from the samples with African ancestry with summary statistics from the Psychiatric Genomics Consortium (PGC) study of schizophrenia yielded seven newly genome-wide significant loci, and we identified an additional eight loci by incorporating the results from samples with Latino ancestry. Leveraging population differences in patterns of linkage disequilibrium, we achieve improved fine-mapping resolution at 22 previously reported and 4 newly significant loci. Polygenic risk score profiling revealed improved prediction based on trans-ancestry meta-analysis results for admixed African (Nagelkerke’s R2 = 0.032; liability R2 = 0.017; P < 10−52), Latino (Nagelkerke’s R2 = 0.089; liability R2 = 0.021; P < 10−58), and European individuals (Nagelkerke’s R2 = 0.089; liability R2 = 0.037; P < 10−113), further highlighting the advantages of incorporating data from diverse human populations.

scholarly journals Genetics of hypertension: discoveries from the bench to human populations

2014 ◽  
Vol 306 (1) ◽  
pp. F1-F11 ◽  
Author(s):  
Nora Franceschini ◽  
Thu H. Le
Keyword(s):  
Association Studies ◽  
Complex Trait ◽  
Experimental Models ◽  
Sodium Reabsorption ◽  
Human Populations ◽  
Genome Wide Association Studies ◽  
Blood Pressure Homeostasis ◽  
Genome Wide ◽  
Clinical Hypertension ◽  
Renal Sodium Reabsorption

Hypertension is a complex trait that is influenced by both heritable and environmental factors. The search for genes accounting for the susceptibility to hypertension has driven parallel efforts in human research and in research using experimental animals in controlled environmental settings. Evidence from rodent models of genetic hypertension and human Mendelian forms of hypertension and hypotension have yielded mechanistic insights into the pathways that are perturbed in blood pressure homeostasis, most of which converge at the level of renal sodium reabsorption. However, the bridging of evidence from these very diverse approaches to identify mechanisms underlying hypertension susceptibility and the translation of these findings to human populations and public health remain a challenge. Furthermore, findings from genome-wide association studies still require functional validation in experimental models. In this review, we highlight results and implications from key studies in experimental and clinical hypertension to date.

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