Analysis of the Genetic Basis of Disease in the Context of Worldwide Human Relationships and Migration

Corona, Erik; Chen, Rong; Sikora, Martin; Morgan, Alexander A.; Patel, Chirag; Ramesh, A.; Bustamante, Carlos D.; Butte, Atul J.

doi:10.1371/journal.pgen.1003447

Cited by 70 publications

(72 citation statements)

References 37 publications

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“…Although adding an ancestry predictor to LAT produced a substantial improvement (LAT+ANC vs. LAT), adding an ancestry predictor to EUR+LAT produced an insignificant change in accuracy for EUR+LAT+ANC compared to EUR+LAT; this can be explained by the large negative correlation between the European PRS (EUR) and the proportion of European ancestry within Latino samples ( R = -0.75; S9 Table), such that any predictor that includes EUR already includes effects of genetic ancestry. This correlation is far larger than analogous correlations due to random genetic drift in our simulations (S3 Table), suggesting that this systematically lower load of T2D risk alleles in Latino individuals with more European ancestry could be due to polygenic selection (Robinson et al, 2015; Turchin et al, 2012) in ancestral European and/or Native American populations; previous studies using top GWAS-associated SNPs have also reported continental differences in genetic risk for T2D (R. Chen et al, 2012; Corona et al, 2013). We observed a similar correlation ( R =−0.77) when using British UK Biobank type 2 diabetes samples as European training data (row 4 of Table 1; see Methods), confirming that this negative correlation is not caused by population stratification in DIAGRAM.…”

Section: Resultscontrasting

confidence: 51%

Multiethnic polygenic risk scores improve risk prediction in diverse populations

Márquez-Luna¹,

Loh

Price

2017

Genetic Epidemiology

277

225

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Methods for genetic risk prediction have been widely investigated in recent years. However, most available training data involves European samples, and it is currently unclear how to accurately predict disease risk in other populations. Previous studies have used either training data from European samples in large sample size or training data from the target population in small sample size, but not both. Here, we introduce a multi-ethnic polygenic risk score that combines training data from European samples and training data from the target population. We applied this approach to predict type 2 diabetes (T2D) in a Latino cohort using both publicly available European summary statistics in large sample size (Neff=40k) and Latino training data in small sample size (Neff=8k). Here, we attained a >70% relative improvement in prediction accuracy (from R2=0.027 to R2=0.047) compared to methods that use only one source of training data, consistent with large relative improvements in simulations. We observed a systematically lower load of T2D risk alleles in Latino individuals with more European ancestry, which could be explained by polygenic selection in ancestral European and/or Native American populations. We predict T2D in a South Asian UK Biobank cohort using European (Neff=40k) and South Asian (Neff=16k) training data and attained a >70% relative improvement in prediction accuracy, and application to predict height in an African UK Biobank cohort using European (N=113k) and African (N=2k) training data attained a 30% relative improvement. Our work reduces the gap in polygenic risk prediction accuracy between European and non-European target populations.

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Section: Resultscontrasting

confidence: 51%

Multiethnic polygenic risk scores improve risk prediction in diverse populations

Márquez-Luna¹,

Loh

Price

2017

Genetic Epidemiology

277

225

View full text Add to dashboard Cite

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“…Our work is most closely related to the recent work of Turchin et al [28], Fraser [29] and Corona et al [30], who look for co-ordinated shifts in allele frequencies of GWAS alleles for particular traits. Our approach constitutes an improvement over the methods implemented in these studies as it provides a high powered and theoretically grounded approach to investigate selection in an arbitrary number of populations with an arbitrary relatedness structure.…”

Section: Introductionsupporting

confidence: 56%

A Population Genetic Signal of Polygenic Adaptation

2014

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Adaptation in response to selection on polygenic phenotypes may occur via subtle allele frequencies shifts at many loci. Current population genomic techniques are not well posed to identify such signals. In the past decade, detailed knowledge about the specific loci underlying polygenic traits has begun to emerge from genome-wide association studies (GWAS). Here we combine this knowledge from GWAS with robust population genetic modeling to identify traits that may have been influenced by local adaptation. We exploit the fact that GWAS provide an estimate of the additive effect size of many loci to estimate the mean additive genetic value for a given phenotype across many populations as simple weighted sums of allele frequencies. We use a general model of neutral genetic value drift for an arbitrary number of populations with an arbitrary relatedness structure. Based on this model, we develop methods for detecting unusually strong correlations between genetic values and specific environmental variables, as well as a generalization of comparisons to test for over-dispersion of genetic values among populations. Finally we lay out a framework to identify the individual populations or groups of populations that contribute to the signal of overdispersion. These tests have considerably greater power than their single locus equivalents due to the fact that they look for positive covariance between like effect alleles, and also significantly outperform methods that do not account for population structure. We apply our tests to the Human Genome Diversity Panel (HGDP) dataset using GWAS data for height, skin pigmentation, type 2 diabetes, body mass index, and two inflammatory bowel disease datasets. This analysis uncovers a number of putative signals of local adaptation, and we discuss the biological interpretation and caveats of these results.

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“…In a similar study of the role of established T2D and BMI loci on metabolic traits measured in an island population from Croatia, a significant association of TCF7L2 variants with fasting plasma glucose and HbA1c levels was reported [132]. (Figures 2 and 3) Several studies have investigated whether the difference in the prevalence of T2D among different populations is attributable to population differences in the frequencies of T2D risk alleles [133,134]. Recent reports suggest that the difference in allele frequency at established T2D loci between major continental populations is greater than expected, given the genetic distance between the major continental populations.…”

Section: Analysis Of Established T2d Variants In Isolated Populationmentioning

confidence: 95%

“…Recent reports suggest that the difference in allele frequency at established T2D loci between major continental populations is greater than expected, given the genetic distance between the major continental populations. A gradient in genetic risk for T2D has also been proposed, with risk alleles having highest frequencies in Africans and those of lowest frequencies in East Asians [131,133,134]. Such divergence in allele frequency at disease-associated loci may represent an effect of natural selection along the course of the evolutionary history of these populations [131].…”

Section: Analysis Of Established T2d Variants In Isolated Populationmentioning

confidence: 99%

Complex Genetics of Type 2 Diabetes and Effect Size: What have We Learned from Isolated Populations?

Nair

Baier

2015

Rev Diabet Stud

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■ AbstractGenetic studies in large outbred populations have documented a complex, highly polygenic basis for type 2 diabetes (T2D). Most of the variants currently known to be associated with T2D risk have been identified in large studies that included tens of thousands of individuals who are representative of a single major ethnic group such as European, Asian, or African. However, most of these variants have only modest effects on the risk for T2D; identification of definitive 'causal variant' or 'causative loci' is typically lacking. Studies in isolated populations offer several advantages over outbred populations despite being, on average, much smaller in sample size. For example, reduced genetic variability, enrichment of rare variants, and a more uniform environment and lifestyle, which are hallmarks of isolated populations, can reduce the complexity of identifying disease-associated genes. To date, studies in isolated populations have provided valuable insight into the genetic basis of T2D by providing both a deeper understanding of previously identified T2D-associated variants (e.g. demonstrating that variants in KCNQ1 have a strong parent-of-origin effect) or providing novel variants (e.g. ABCC8 in Pima Indians, TBC1D4 in the Greenlandic population, HNF1A in Canadian Oji-Cree). This review summarizes advancements in genetic studies of T2D in outbred and isolated populations, and provides information on whether the difference in the prevalence of T2D in different populations (Pima Indians vs. non-Hispanic Whites and non-Hispanic Whites vs. non-Hispanic Blacks) can be explained by the difference in risk allele frequencies of established T2D variants.

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Analysis of the Genetic Basis of Disease in the Context of Worldwide Human Relationships and Migration

Cited by 70 publications

References 37 publications

Multiethnic polygenic risk scores improve risk prediction in diverse populations

Multiethnic polygenic risk scores improve risk prediction in diverse populations

A Population Genetic Signal of Polygenic Adaptation

Complex Genetics of Type 2 Diabetes and Effect Size: What have We Learned from Isolated Populations?

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