Efficiently controlling for case-control imbalance and sample relatedness in large-scale genetic association studies

Abstract

In genome-wide association studies (GWAS) for thousands of phenotypes in large biobanks, most binary traits have substantially fewer cases than controls. Both of the widely used approaches, the linear mixed model and the recently proposed logistic mixed model, perform poorly; they produce large type I error rates when used to analyze unbalanced case-control phenotypes. Here we propose a scalable and accurate generalized mixed model association test that uses the saddlepoint approximation to calibrate the distribution of score test statistics. This method, SAIGE (Scalable and Accurate Implementation of GEneralized mixed model), provides accurate P values even when case-control ratios are extremely unbalanced. SAIGE uses state-of-art optimization strategies to reduce computational costs; hence, it is applicable to GWAS for thousands of phenotypes by large biobanks. Through the analysis of UK Biobank data of 408,961 samples from white British participants with European ancestry for > 1,400 binary phenotypes, we show that SAIGE can efficiently analyze large sample data, controlling for unbalanced case-control ratios and sample relatedness.

Heritability estimation of dichotomous phenotypes using a liability threshold model on ascertained family‐based samples

“…Most of the methods are based on the liability threshold model (Blangero et al, 2001;Golan et al, 2014;Loh et al, 2015;Weissbrod et al, 2018;Yang et al, 2011) and GLMM based on the logit link is a possible alternative of a disease model (Wang et al, 2015). However, the relative proportion of variances attributable to the polygenic effects cannot be defined for GLMM using the logit link (Chen et al, 2016;Papachristou et al, 2011;Zhou et al, 2018). Heritability shows an important utility for genetic epidemiology; however, heritability estimation of dichotomous phenotypes can be extremely complicated due to ascertainment bias.…”

“…Haseman-Elston regression is a well-known method for estimating variance components, and by restricting the phenotypic variance to 1, the heritability can be estimated as the sum of the coefficient estimates of the kinship matrix (Golan, Lander, & Rosset, 2014;Haseman & Elston, 1972). For dichotomous traits, generalized linear mixed models (GLMM) or Liability Threshold Models have often been utilized (Burton et al, 1999;Chen et al, 2016;Papachristou, Ober, & Abney, 2011;Zhou et al, 2018). However, the variance estimates from GLMM are biased for ascertained samples, and it is not easy to define the proportion of variances attributable to the polygenic effect.…”

A comparison of popular TDT‐generalizations for family‐based association analysis

“…In general, mixed model approaches treat the phenotype as random, whereas the TDT and the TDT generalizations consider the phenotypes as fixed. SAIGE utilizes a saddle point approximation of the score statistic to deal with data sets with the imbalanced case-control ratio (W. Zhou et al, 2018). Therefore, the standard scenario assumes the random sampling of a quantitative trait from the population.…”

“…Very recently, a similar approach to GMMAT was proposed. SAIGE utilizes a saddle point approximation of the score statistic to deal with data sets with the imbalanced case-control ratio (W. Zhou et al, 2018). SAIGE is very flexible as it can incorporate unrelated and related samples and only requires the GRM information to correct for the corresponding structure.…”

“…The first two principal components for both scenarios, U and ADM, are plotted against each other in Supporting Information Figures 1 and 2 (Section C). Then, we tested the remaining 95,000 variants for each MAF using the null model and the score statistic-based SAIGE approach, implemented in the SAIGE R package (W. Zhou et al, 2018). First, we chose a phenotype structure where we randomly assigned 2,000 samples as affected and 1,000 as unaffected.…”