Population Stratification at the Phenotypic Variance level and Implication for the Analysis of Whole Genome Sequencing Data from Multiple Studies
Sofer, T.; Zheng, X.; Laurie, C. A.; Gogarten, S. M.; Brody, J. A.; Conomos, M. P.; Bis, J. C.; Thornton, T. A.; Szpiro, A.; O'Connell, J. R.; Lange, E. M.; Gao, Y.; Cupples, L. A.; Psaty, B. M.; Trans-Omics for Precision Medicine (TOPMed) Consortium, ; Rice, K. M.
Show abstract
In modern Whole Genome Sequencing (WGS) epidemiological studies, participant-level data from multiple studies are often pooled and results are obtained from a single analysis. We consider the impact of differential phenotype variances by study, which we term variance stratification. Unaccounted for, variance stratification can lead to both decreased statistical power, and increased false positives rates, depending on how allele frequencies, sample sizes, and phenotypic variances vary across the studies that are pooled. We describe a WGS-appropriate analysis approach, implemented in freely-available software, which allows study-specific variances and thereby improves performance in practice. We also illustrate the variance stratification problem, its solutions, and a corresponding diagnostic procedure in data from the Trans-Omics for Precision Medicine Whole Genome Sequencing Program (TOPMed), used in association tests for hemoglobin concentrations and BMI.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- Meta-MultiSKAT: Multiple phenotype meta-analysis for region-based association test 95%
- Identity-by-descent mapping using multi-individual IBD with genome-wide multiple testing adjustment 94%
- Rare variants association testing for a binary outcome when pooling individual level data from heterogeneous studies 94%
Similar papers in this journal
- Pitfalls in performing genome-wide association studies on ratio traits 95%
- BinomiRare: A carriers-only test for association of rare genetic variants with a binary outcome for mixed models and any case-control proportion 94%
- A simple approach for multiple observations improves power to detect genetic effects and genomic prediction accuracy. 94%
Similar papers in this journal
- Accounting for genetic effect heterogeneity in fine-mapping and improving power to detect gene-environment interactions with SharePro 95%
- Probabilistic inference of the genetic architecture underlying functional enrichment of complex traits 95%
- Simultaneous estimation of bi-directional causal effects and heritable confounding from GWAS summary statistics 95%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.