On the Use of General Control Samples for Genome-wide Association Studies: Genetic Matching Highlights Causal Variants

Abstract

Resources being amassed for genome-wide association (GWA) studies include "control databases" genotyped with a large-scale SNP array. How to use these databases effectively is an open question. We develop a method to match, by genetic ancestry, controls to affected individuals (cases). The impact of this method, especially for heterogeneous human populations, is to reduce the false-positive rate, inflate other spuriously small p values, and have little impact on the p values associated with true positive loci. Thus, it highlights true positives by downplaying false positives. We perform a GWA by matching Americans with type 1 diabetes (T1D) to controls from Germany. Despite the complex study design, these analyses identify numerous loci known to confer risk for T1D.

Overview of techniques to account for confounding due to population stratification and cryptic relatedness in genomic data association analyses

“…Special procedures for genetic ancestry matching that are applicable to existing data sets have been proposed lately on the basis of the information on non-genetic variables (Lee, 2004) and marker genotypes (Hinds et al, 2004;Luca et al, 2008;Guan et al, 2009). In Hinds et al (2004), ancestry was estimated by population assignment methods, and in Luca et al (2008) by principal components.…”

“…In Hinds et al (2004), ancestry was estimated by population assignment methods, and in Luca et al (2008) by principal components. These methods considered different strategies for genetic ancestry group matching and removing 'unmatchable outlier individuals'.…”

“…Guan et al (2009) proposed the use of identity-by-state-based simple (dis)similarity measures as a tool for individual genetic ancestry matching. Luca et al (2008) emphasized the use of 'control databases' in genome-wide association studies and considered a problem wherein cases are sampled in a quite different region from that of the controls.…”

Genetic Polymorphisms of Infectious Diseases in Case-Control Studies

“…Briefly, several randomly selected SNPs are used to normalize the differences between cases and controls. Other methods try to use genetic ancestry based on "control databases" derived from genome-wide studies in different populations to match samples from cases and controls [38,39]. In general, important departures from HWE can be observed when different subpopulations are clustered in the same group and it is highly recommended that populations presenting several SNPs deviating from HWE be carefully reassigned or some genetic ancestry screening in the group be performed.…”

An evaluation of the genetic-matched pair study design using genome-wide SNP data from the European population

“…In genetic studies, however, including the large number of genome-wide association (GWA) studies that have recently been published, 1 -3 only so-called 'Mendelian' randomization has been employed to control for genetic confounders, whereas matching by genotype has not played an important role. 4 Nevertheless, there has always been some awareness among genetic epidemiologists that Mendelian randomization may fail, thereby leading to false positive reports of disease genes or to biased effect size estimates. 5 One possible cause of such failure may be systematic differences in terms of the rate at which individuals with a particular phenotype or genotype are sampled from genetically distinct populations.…”