Proper analysis of secondary phenotype data in case‐control association studies

Abstract

Case-control association studies often collect extensive information on secondary phenotypes, which are quantitative or qualitative traits other than the case-control status. Exploring secondary phenotypes can yield valuable insights into biological pathways and identify genetic variants influencing phenotypes of direct interest. All publications on secondary phenotypes have used standard statistical methods, such as least-squares regression for quantitative traits. Because of unequal selection probabilities between cases and controls, the case-control sample is not a random sample from the general population. As a result, standard statistical analysis of secondary phenotype data can be extremely misleading. Although one may avoid the sampling bias by analyzing cases and controls separately or by including the case-control status as a covariate in the model, the associations between a secondary phenotype and a genetic variant in the case and control groups can be quite different from the association in the general population. In this article, we present novel statistical methods that properly reflect the case-control sampling in the analysis of secondary phenotype data. The new methods provide unbiased estimation of genetic effects and accurate control of false-positive rates while maximizing statistical power. We demonstrate the pitfalls of the standard methods and the advantages of the new methods both analytically and numerically. The relevant software is available at our website.

A flexible likelihood framework for detecting associations with secondary phenotypes in genetic studies using selected samples: application to sequence data

“…There have been methods developed for detecting associations with multiple phenotypes in selected samples. 9,10 However, these methods are limited to case-control studies. They are not applicable to more complicated study designs, for example, studies that sequence individuals with extreme primary traits (extreme-trait study), or studies where secondary phenotypes are also involved in sample selection.…”

“…[11][12][13] The results for detecting associations with secondary traits can be seriously biased if the secondary traits are not properly analyzed. 9 It is desirable to have a unified approach for analyzing secondary phenotypes from all available data sets.…”

From exomes to genomes: challenges and solutions in population-based genetic association studies

“…15 Second, unless specialized analytical methods are used that specifically acknowledge the sampling design, the analysis of secondary traits from an extreme trait design can lead to biased-or false-positive findings. 16 In addition to sample selection, large-scale sequencing projects need to weigh the trade-offs associated with sequencing depth and sample size. High-quality genotypes may be obtained from low-coverage sequencing (defined here as o10 × ) of whole genomes by using haplotype aware genotype callers.…”

A General and Robust Framework for Secondary Traits Analysis

“…The second group explicitly accounts for the case-control sampling scheme by maximizing the retrospective likelihood function conditional on the primary disease or joint modeling of the primary and secondary traits (Jiang et al 2006;Lin and Zeng 2009;He et al 2011;Shete 2011, 2012;Li and Gail 2012;Ghosh et al 2013;Wei et al 2013). The semiparametric maximum likelihood (SPML) proposed by Lin and Zeng (2009) is the most widely recognized approach in this group as it largely improves the efficiency over IPW.…”

“…The semiparametric maximum likelihood (SPML) proposed by Lin and Zeng (2009) is the most widely recognized approach in this group as it largely improves the efficiency over IPW. This approach makes the linear logit assumption for the probability of primary disease with respect to genotypic score and secondary trait.…”