Quantitative trait analysis in sequencing studies under trait-dependent sampling

Abstract

It is not economically feasible to sequence all study subjects in a large cohort. A cost-effective strategy is to sequence only the subjects with the extreme values of a quantitative trait. In the National Heart, Lung, and Blood Institute Exome Sequencing Project, subjects with the highest or lowest values of body mass index, LDL, or blood pressure were selected for whole-exome sequencing. Failure to account for such trait-dependent sampling can cause severe inflation of type I error and substantial loss of power in quantitative trait analysis, especially when combining results from multiple studies with different selection criteria. We present valid and efficient statistical methods for association analysis of sequencing data under trait-dependent sampling. We pay special attention to gene-based analysis of rare variants. Our methods can be used to perform quantitative trait analysis not only for the trait that is used to select subjects for sequencing but for any other traits that are measured. For a particular trait of interest, our approach properly combines the association results from all studies with measurements of that trait. This meta-analysis is substantially more powerful than the analysis of any single study. By contrast, meta-analysis of standard linear regression results (ignoring trait-dependent sampling) can be less powerful than the analysis of a single study. The advantages of the proposed methods are demonstrated through simulation studies and the National Heart, Lung, and Blood Institute Exome Sequencing Project data. The methods are applicable to other types of genetic association studies and nongenetic studies. R ecent technological advances have made it possible to sequence genomic regions for association studies. At the present time, it is prohibitively expensive to perform large-scale wholeexome sequencing. In the near future, whole-exome sequencing on thousands of subjects will be economically feasible, but not whole-genome sequencing. If a quantitative trait is of primary interest in a large cohort study, a cost-effective strategy is to sequence those subjects with the extreme trait values preferentially. This strategy can substantially increase statistical power (relative to sequencing a random sample with the same number of subjects), as suggested by research in various contexts (1-9). Indeed, such trait-dependent sampling has been adopted in many sequencing projects, including the National Heart, Lung, and Blood Institute (NHLBI) Exome Sequencing Project (ESP) and the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) resequencing project. The NHLBI ESP consists of multiple studies, each of which is focused on one trait. For the body mass index (BMI) study, 267 subjects with BMI values >40 and 178 subjects with BMI values <25 were selected for sequencing out of a total of 11,468 subjects from Women's Health Initiative (WHI). Similar designs were used for the LDL and blood pressure (BP) studies, although the sampling was based on residuals (to adjust for age, sex,...

Integrative analysis of sequencing and array genotype data for discovering disease associations with rare mutations

“…We focused on the BMI values for the African American participants of the WHI cohort. Among the 8,142 African American participants who were genotyped by the Affymetrix 6.0 arrays, 360 with BMI values > 40 or < 25 were selected for whole-exome sequencing in the NHLBI ESP (2). The distribution of the BMI values is displayed in SI Appendix, Fig.…”

“…However, it is still economically infeasible to sequence all subjects in a large cohort, and, therefore, only a subset of cohort members can be selected for sequencing. A cost-effective sampling strategy is to preferentially select subjects in the extremes of a quantitative trait distribution or those with a specific disease (2,3). For case−control studies, an equal number of cases and controls provides more power than other case−control ratios.…”

“…For case−control studies, an equal number of cases and controls provides more power than other case−control ratios. For quantitative traits, the power increases as more extreme values are sampled (2).…”

“…The NHLBI ESP consists of three studies that sequenced subjects with the largest and smallest values of body mass index (BMI), low-density lipoprotein, and blood pressure, one case−control study on myocardial infarction, and one case-only study on stroke (2). The CHARGE resequencing project selected subjects with the highest values of 14 quantitative traits, as well as a random sample (4).…”

Meta-analysis for Discovering Rare-Variant Associations: Statistical Methods and Software Programs

“…Failure to account for trait-dependent sampling might increase type I error and reduce power. 27 Thus, we used SCORESeqTDS to generate summary statistics for the LDL, BMI, and BP phenotype groups and used RAREMETALWORKER for the EOMI, stroke, and DPR phenotype groups. Figure S3 shows the workflow for analyzing the six phenotype groups.…”