Controlling for human population stratification in rare variant association studies

Abstract

Population stratification is a confounder of genetic association studies. In analyses of rare variants, corrections based on principal components (PCs) and linear mixed models (LMMs) yield conflicting conclusions. Studies evaluating these approaches generally focused on limited types of structure and large sample sizes. We investigated the properties of several correction methods through a large simulation study using real exome data, and several within- and between-continent stratification scenarios. We considered different sample sizes, with situations including as few as 50 cases, to account for the analysis of rare disorders. Large samples showed that accounting for stratification was more difficult with a continental than with a worldwide structure. When considering a sample of 50 cases, an inflation of type-I-errors was observed with PCs for small numbers of controls (≤ 100), and with LMMs for large numbers of controls (≥ 1000). We also tested a novel local permutation method (LocPerm), which maintained a correct type-I-error in all situations. Powers were equivalent for all approaches pointing out that the key issue is to properly control type-I-errors. Finally, we found that power of analyses including small numbers of cases can be increased, by adding a large panel of external controls, provided an appropriate stratification correction was used.

Comprehensive Molecular Analysis of Disease-Related Genes as First-Tier Test for Early Diagnosis, Classification, and Management of Patients Affected by Nonsyndromic Ichthyosis

“…In this group of subjects, we identified 357 rare alleles (gnomAD MAF<0.01, in the European descent) that carried 246 different SNVs, including 11 pathogenic/likely pathogenic variants in ABHD5, ALOX12B, ALOXE3, LIPN, NIPAL4, PHYH, SLC27A4, ST14, and numerous variants of uncertain significance (Class 3). Despite the fact that population stratification could act as a confounder in our genetic analysis [55], the relatively high frequency of Class 3 variants in our homogeneous group of Italian subjects strongly suggests that inherited ichthyoses are more frequent than that reported; however, due to the wide clinical heterogeneity that can vary even over the years, also as a consequence of environmental factors or mosaicism [1], patients with ichthyosis could often escape clinical attention or be misclassified. This latter consideration is also supported by the high prevalence (90%) of compound heterozygotes identified in our cohort of ichthyosis patients compared to homozygotes [4,16].…”

Clarifying the causes of consistent and inconsistent findings in genetics

“…Residual confounding refers to confounding that remains in an analysis, and can result from confounders that were unknown or unmeasured, confounders that were not adequately controlled for, and measurement error in the confounders that were adjusted (Kaufman et al, 1997). Rare variant analysis is particularly prone to residual confounding because individuals who share ultra-rare variants are likely to have a recent common ancestor, and adjustment for principal components is insufficient to control for close relatedness (Bhatia et al, 2016;Bouaziz et al, 2021;Conomos et al, 2015;Persyn et al, 2018;Young, 2019).…”

An exploration of linkage fine‐mapping on sequences from case‐control studies

“…In fine‐mapping, fine‐scale population structure is a confounding variable of particular concern because rare variants tend to cluster geographically due to their recent origin (Mathieson & McVean, 2012 ). Adjusting for fine‐scale population structure when fine‐mapping rare variants is challenging, though recently proposed permutation approaches offer a potential way forward (Bouaziz et al, 2021 ).…”