Homogeneous case subgroups increase power in genetic association studies

Abstract

Genome-wide association studies of clinically defined cases against controls have transformed our understanding of the genetic causes of many diseases. However, there are limitations to the simple clinical definitions used in these studies, and GWAS analyses are beginning to explore more refined phenotypes in subgroups of the existing data sets. These analyses are often performed ad hoc without considering the power requirements to justify such analyses. Here we derive expressions for the relative power of such subgroup analyses and determine the genotypic relative risks (GRRs) required to achieve equivalent power to a full analysis for relevant scenarios. We show that only modest increases in GRRs may be required to offset the reduction in power from analysing fewer cases, implying that analyses of more genetically homogenous case subgroups may have the potential to identify further associations. We find that, for lower genotypic relative risks in the full sample, subgroup analyses of more homogeneous cases have relatively more power than for higher index genotypic relative risks and that this effect is stronger for rare as opposed to common variants. As GWA studies are likely to have now identified the majority of SNPs with stronger effects, these results strongly advocate a renewed effort to identify phenotypically homogeneous disease groups, in which power to detect genetic variants with small effects will be greater. These results suggest that analysis of case subsets could be a powerful strategy to uncover some of the hidden heritability for common complex disorders, particularly in identifying rarer variants of modest effect.

Clustering by phenotype and genome-wide association study in autism

“…At least three reasons could reduce the possibility of false positives of the present results of statistically significant SNPs in cluster-based GWAS. First, the present study validated the usefulness and feasibility of the concept of a previous simulation study (8), which indicated that homogeneous case subgroups increase power in genetic association studies by Traylor and colleagues, using measurement data in the real world. Second, a substantial number of statistically significant SNPs in cluster-based GWAS observed in the present study were located within or near previously reported candidate genes for ASD (6,(38)(39)(40)(41)(42)(43).…”

“…If the heterogeneous phenotypes and responses to treatment in some way correspond to differences in genotype, grouping persons with ASD according to phenotype and responses to treatment variables may increase the chances of identifying genetic susceptibility factors. Traylor and colleagues (8) demonstrated that attempts to categorize patients with a complex disease into more homogeneous subgroups could have more power to elucidate the hidden heritability in a simulation study. Several studies on Alzheimer's disease, neuroticism, or asthma indicated that items or symptoms were to some degree more useful for identifying high-impact genetic factors than broadly defined diagnoses (9)(10)(11), although a study of ASD demonstrated modest effects of two-way stratification by individual symptoms (12).…”

“…We therefore explored whether grouping persons with ASD using a clustering algorithm with phenotype and responses to treatment variables can be used to discriminate more genetically homogeneous persons with ASD. In the present study, we conducted cluster-based genome-wide association studies (named cluster-based GWASs) using real data based on the concept of a previous simulation study (8) adopting a machine learning k-means (14) algorithm for cluster analysis.…”

Genome-wide Association for Major Depression Through Age at Onset Stratification: Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium

“…The other disadvantage of looking at more homogenous groups is the reduction in sample size. However, it has previously been shown that only modest increases in effect size may be required to offset the reduction in power from analyzing fewer cases, implying that analyses of more homogenous subgroups have the potential to identify novel associations (56). …”

Identification of clinically relevant phenotypes in patients with Ebstein anomaly

“…However, a strategy based on the identification of genetic variants in phenotypically defined subgroups (or endophenotypes) has the potential to empower genetic research into rare conditions like EA. 6 This study describes the prevalence of cardiovascular (CV) and non-CV comorbidities in EA patients from a single CV center during a 20-year period. The results highlight some unexpected associations between different comorbidities and their relationship to disease severity and survival.…”