A Method to Exploit the Structure of Genetic Ancestry Space to Enhance Case-Control Studies.

Am J Hum Genet
Authors
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 case subjects are contrasted with those from population-based samples used as control subjects. In an ideal world we would know population-level allele frequencies, releasing 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 control subjects from studies of Crohn 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.

Year of Publication
2016
Journal
Am J Hum Genet
Volume
98
Issue
5
Pages
857-68
Date Published
2016 May 05
ISSN
1537-6605
URL
DOI
10.1016/j.ajhg.2016.02.025
PubMed ID
27087321
PubMed Central ID
PMC4864319
Links
Grant list
R37 MH057881 / MH / NIMH NIH HHS / United States
R01 MH057881 / MH / NIMH NIH HHS / United States
G0800759 / Medical Research Council / United Kingdom
ETM/75 / Chief Scientist Office / United Kingdom
MC_UU_12013/4 / Medical Research Council / United Kingdom
CZB/4/540 / Chief Scientist Office / United Kingdom
ETM/137 / Chief Scientist Office / United Kingdom
R01 MH101244 / MH / NIMH NIH HHS / United States
G0800675 / Medical Research Council / United Kingdom
U01 DK062420 / DK / NIDDK NIH HHS / United States
G0600329 / Medical Research Council / United Kingdom