The impact of a fine-scale population stratification on rare variant association test results

Persyn, Elodie; Redon, Richard; Bellanger, Lise; Dina, Christian

doi:10.1371/journal.pone.0207677

Cited by 36 publications

(32 citation statements)

References 43 publications

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“…For a given setting, power was similar in the different stratification situations, indicating that the correction method could maintain the power it would have in the absence of stratification. These results are in partial agreement with several studies reporting a small loss of power for PC-adjusted logistic regression in the presence of stratification relative to an absence of stratification [13, 20].…”

Section: Discussionsupporting

confidence: 91%

“…Additional studies are required to investigate more complex genetic models, such as the presence of both risk and protective variants of a given gene, for which other association tests, such as variant-component approaches, may be more appropriate. Different results can be expected, as the effect of population stratification differs between testing strategies [17, 20]. In addition, the novel LocPerm strategy has not been evaluated in combination with other association tests.…”

Section: Discussionmentioning

confidence: 99%

“…The performance of the correction method depends on the study setting and the method used to analyze the variants [11, 12, 18-21]. PC has been widely investigated [5, 6, 22-25] and shown to yield satisfactory correction at large geographic scales, but not at finer scales [20]. LMM have also been studied [19, 26] and shown to account for stratification well if variance-component approaches are used to test for association [19].…”

Section: Introductionmentioning

confidence: 99%

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Controlling for Human Population Stratification in Rare Variant Association Studies

Bouaziz

Mullaert

Bigio

et al. 2020

Preprint

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22 Population stratification is a strong confounding factor in human genetic association studies. In 23 analyses of rare variants, the main correction strategies based on principal components (PC) and linear 24 mixed models (LMM), may yield conflicting conclusions, due to both the specific type of structure 25 induced by rare variants and the particular statistical features of association tests. Studies evaluating 26 these approaches generally focused on specific situations with limited types of simulated structure 27 and large sample sizes. We investigated the properties of several correction methods in the context 28 of a large simulation study using real exome data, and several within-and between-continent 29 stratification scenarios. We also considered different sample sizes, with situations including as few 30 as 50 cases, to account for the analysis of rare disorders. In this context, we focused on a genetic 31 model with a phenotype driven by rare deleterious variants well suited for a burden test. For analyses 32 of large samples, we found that accounting for stratification was more difficult with a continental 33 structure than with a worldwide structure. LMM failed to maintain a correct type I error in many 34 scenarios, whereas PCs based on common variants failed only in the presence of extreme continental 35 stratification. When a sample of 50 cases was considered, an inflation of type I errors was observed 36 with PC for small numbers of controls (≤100), and with LMM for large numbers of controls (≥1000).37 We also tested a promising novel adapted local permutation method (LocPerm), which maintained a 38 correct type I error in all situations. All approaches capable of correcting for stratification properly 39 had similar powers for detecting actual associations pointing out that the key issue is to properly 40 control type I errors. Finally, we found that adding a large panel of external controls (e.g. extracted 41 from publicly available databases) was an efficient way to increase the power of analyses including 42 small numbers of cases, provided an appropriate stratification correction was used. 43 44 3 45 Author Summary 4647 Genetic association studies focusing on rare variants using next generation sequencing (NGS) data 48 have become a common strategy to overcome the shortcomings of classical genome-wide association 49 studies for the analysis of rare and common diseases. The issue of population stratification remains 50 however a substantial question that has not been fully resolved when analyzing NGS data. In this 51 work, we propose a comprehensive evaluation of the main strategies to account for stratification, that 52 are principal components and linear mixed model, along with a novel approach based on local 53 permutations (LocPerm). We compared these correction methods in many different settings, 54 considering several types of population structures, sample sizes or types of variants. Our results 55 highlighted important limitations of some classical methods as those using principal com...

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Section: Discussionsupporting

confidence: 91%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Controlling for Human Population Stratification in Rare Variant Association Studies

Bouaziz

Mullaert

Bigio

et al. 2020

Preprint

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“…The odds of disease conditional on covariates were estimated under a null model of no genetic association, and individual phenotypes were resampled, using these disease probabilities as individual weights, to obtain permuted data with a similar PS. However, subsequent studies showed that this procedure was less efficient than regular PC correction for dealing with fine-scale population structure (15). We show here that LocPerm , which uses the first 10 PCs weighted by their eigenvalues to compute a genetic distance matrix, handles complex and extreme PS more effectively than the standard PC-based correction approach, particularly in the context of small sample size.…”

Section: Discussionmentioning

confidence: 99%

Taking population stratification into account by local permutations in rare-variant association studies on small samples

Mullaert

Bouaziz

Seeleuthner

et al. 2020

Preprint

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1Many methods for rare variant association studies require permutations to assess the significance of 2 tests. Standard permutations assume that all individuals are exchangeable and do not take population 3 stratification (PS), a known confounding factor in genetic studies, into account. We propose a novel 4 strategy, LocPerm, in which individuals are permuted only with their closest ancestry-based 5 neighbors. We performed a simulation study, focusing on small samples, to evaluate and compare 6LocPerm with standard permutations and classical adjustment on first principal components. Under 7 the null hypothesis, LocPerm was the only method providing an acceptable type I error, regardless of 8 * N protein coding genes with at least 10 carriers of rare variants over 15 replicates 1 7 8

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“…Identification of genetic structure is important to guide future studies of association both for common, but more importantly, for rare variants 40 . In the near future, interrogating the demographical history of France from genetic data will bring more precise results thanks to whole genome sequencing that, along with new methods, could allow testing formal models of demographic inference.…”

Section: Discussionmentioning

confidence: 99%

The Genetic History of France

Pierre

Giemza

Karakachoff

et al. 2019

Preprint

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24The study of the genetic structure of different countries within Europe has provided significant 25 insights into their demographic history and their actual stratification. Although France occupies 26 a particular location at the end of the European peninsula and at the crossroads of migration 27 routes, few population genetic studies have been conducted so far with genome-wide data. In 28 this study, we analyzed SNP-chip genetic data from 2 184 individuals born in France who were 29 enrolled in two independent population cohorts. Using FineStructure, six different genetic 30 clusters of individuals were found that were very consistent between the two cohorts. These 31 clusters match extremely well the geography and overlap with historical and linguistic divisions 32 of France. By modeling the relationship between genetics and geography using EEMS software, 33 we were able to detect gene flow barriers that are similar in the two cohorts and corresponds to 34 major French rivers or mountains. Estimations of effective population sizes using IBDNe 35 program also revealed very similar patterns in both cohorts with a rapid increase of effective 36 population sizes over the last 150 generations similar to what was observed in other European 37 countries. A marked bottleneck is also consistently seen in the two datasets starting in the 38 fourteenth century when the Black Death raged in Europe. In conclusion, by performing the 39 first exhaustive study of the genetic structure of France, we fill a gap in the genetic studies in 40 Europe that would be useful to medical geneticists but also historians and archeologists. 41 42 43 44Gallia est omnis divisa in partes tres [commentarii de bello gallico 1 ] was one of the earliest 45 demographic description of antique France (known as Gaul). These three parts were Aquitania, 46

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The impact of a fine-scale population stratification on rare variant association test results

Cited by 36 publications

References 43 publications

Controlling for Human Population Stratification in Rare Variant Association Studies

Controlling for Human Population Stratification in Rare Variant Association Studies

Taking population stratification into account by local permutations in rare-variant association studies on small samples

The Genetic History of France

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