DISTMIX2: robust imputation of summary statistics for cosmopolitan cohorts using a large and diverse reference panel

Methods for direct imputation of summary statistics, e.g. our group’s DISTMIX tool, were shown to be practically as accurate as genotype imputation method, while incurring orders of magnitude lower computational burden. Given that such imputation needs a precise estimation of linkage disequilibrium (LD) for mixed ethnicity (cosmopolitan) cohorts, there is a great need i) for much larger and diverse panels and ii) to estimate the ethnic composition of the cohort, e.g. the weights for subpopulations in the diverse panel. Unfortunately, DISTMIX and its main competitors are largely using a very small reference panel of ~2,500 subject coming from the 1000 Genome (1KG) Project. DISTMIX computed the ethnic weights of a cohort based on in-cohort allele frequency (AF) estimates. Unfortunately, due to privacy issues, most genome wide association studies (GWAS) largely stopped providing cohort AFs. Thus, to accurately estimate the LD needed for an exhaustive analysis of cosmopolitan cohorts, we propose DISTMIX2. When compared to DISTMIX and its competitors, the proposed method adds a i) much larger and diverse reference panel and ii) novel estimation for weights of ethnic mixture based solely on Z-scores (when AFs not available). To build a larger and more diverse reference panel, we use the publicly and privately available data to obtain a 33,000 (33K) panel which includes ~11K Han Chinese. The proposed method of estimating ethnic weights adequately controls the Type I error rates, especially when the subpopulations in the study are well represented in the reference panel. However, using naive pre-estimated weights incurs a much higher false positive rate. We apply DISTMIX2 to the GWAS summary statistics from the Psychiatric Genetic Consortium (PGC). Our method which uncover signals in numerous new regions, with most of these findings coming from the rarer variants. Author summary By predicting summary statistics at unmeasured genetic variants, direct imputation is a promising method for enhancing the resolution of genetic studies. However, for a better prediction of statistics at unmeasured variants, there is a need to address two urgent issues. First, there is a need for very large and diverse reference panels that greatly improve on the mostly European ones having ~2,500 (2.5K) subjects. We address this shortcoming by building a large and diverse reference panel (33K subjects, ~20K Europeans and ~11K Asians). Second, there is a need to estimate the ethnic composition of the study cohort, even when they do not report in-cohort allele frequency for genetic variants. We solve this issue by using a novel method that uses only Z-scores, which are easily computed from reported summary statistics. Our method that implements the two above solutions i) adequately controls the false positive rate and ii) provides much improved resolution when compared to methods based on older reference panels. Practical application to reported summary statistics from studies of psychiatric disorders greatly increase the number of regions harboring signals. Most of these findings are associated with rarer variants that could not be robustly assessed using smaller panels.