A method to exploit the structure of genetic ancestry space to enhance case-control studies

Abstract

One goal of human genetics is to understand the genetic basis of disease, a challenge for diseases of complex inheritance because risk alleles are few relative to the vast set of benign variants. Risk variants are often sought by association studies in which allele frequencies in cases are contrasted with those from population-based samples used as controls. In an ideal world we would know population-level allele frequencies, releasing * Correspondence: roeder@andrew.cmu.edu 1 peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission.The copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/043166 doi: bioRxiv preprint first posted online Mar. 10, 2016; researchers to focus on case subjects. We argue this ideal is possible, at least theoretically, and we outline a path to achieving it in reality. If such a resource were to exist, it would yield ample savings and would facilitate the effective use of data repositories by removing administrative and technical barriers. We call this concept the Universal Control Repository Network (UNICORN), a means to perform association analyses without necessitating direct access to individual-level control data. Our approach to UNICORN uses existing genetic resources and various statistical tools to analyze these data, including hierarchical clustering with spectral analysis of ancestry; and empirical Bayesian analysis along with Gaussian spatial processes to estimate ancestry-specific allele frequencies. We demonstrate our approach using tens of thousands of controls from studies of Crohn's disease, showing how it controls false positives, provides power similar to that achieved when all control data are directly accessible, and enhances power when control data are limiting or even imperfectly matched ancestrally. These results highlight how UNICORN can enable reliable, powerful and convenient genetic association analyses without access to the individual level data.

Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program

“…Advancing DNA-sequencing technologies and decreasing costs are enabling researchers to explore human genetic variation at an unprecedented scale 2,3 . For these advances to improve our understanding of human health, they must be deployed in well-phenotyped human samples and used to build resources such as variation catalogues 3,4 , control collections 5,6 and imputation reference panels 7-9 . Here we describe high-coverage whole-genome sequencing (WGS) analyses of the first 53,831 TOPMed samples (Box 1 and Extended Data Tables 1, 2); additional data are being made available as quality control, variant calling and dbGaP curation are completed (altogether more than 130,000 TOPMed samples are now available in dbGaP).…”

How rare and common risk variation jointly affect liability for autism spectrum disorder

“…Using unaffected subjects not matched to ASD subjects, we obtained within-cluster and overall allele frequency estimates. Then, from these two estimates, we used empirical Bayes methods, as described in Bodea et al(31), to determine final cluster-specific allele-frequency estimates. These frequencies were then used to standardize cluster-specific genotypes and compute a cluster-specific GRM.…”

The genetic architecture of obsessive-compulsive disorder: alleles across the frequency spectrum contribute liability to OCD

“…The sampling in (1) is consistent with our previouslypublished, family-based analyses and will be our primary analytical approach. (5) and (6), is a common epidemiological approach for controlling confounding (here, differences in ancestry in cases versus controls) and has been shown to be useful for genetic studies (26)(27)(28).…”

“…(6) As in (5) , using only individuals of European ancestry. Pair matching, as done in (5) and (6) , is a common epidemiological approach for controlling confounding (here, differences in ancestry in cases versus controls) and has been shown to be useful for genetic studies (26–28). Note (3)-(6) use all high-quality SNPs.…”