A framework for detecting noncoding rare-variant associations of large-scale whole-genome sequencing studies

IMMerge: merging imputation data at scale

“…While sample size limitations prohibit combined imputation in very large datasets, IMMerge enables researchers to combine genetic data in a computationally efficient manner. This facilitates joint analysis of datasets which increases statistical power, allowing for modeling relationships and population structure across the entire sample such as SAIGE ( Zheng and Davis, 2021 ; Zhou et al , 2018 ), GENESIS ( Gogarten et al , 2019 ), STAAR ( Gaynor et al , 2022 ; Li et al , 2020 ) and STAARpipeline ( Li et al , 2022 ).…”

TIVAN-indel: a computational framework for annotating and predicting non-coding regulatory small insertions and deletions

“…To make TIVAN-indel widely accessible to the research community, we provide the precomputed functional scores for ∼35.2 million nc-sindels in the 1000 Genomes Project across 44 tissues. The functional scores can be potentially used as the functional weights in the rare variant association analysis of nc-sindels in whole-genome sequencing studies such as the STAAR framework ( Gaynor et al , 2022 ; Li et al , 2020 , 2022b ). The functional scores can be used to prioritize regulatory nc-sindels, which can help narrow down the candidates for experimental validation by using Massively Parallel Reporter Assay or CRISPR-Cas9.…”

The hidden factor: accounting for covariate effects in power and sample size computation for a binary trait

“…Another direction of extension would be to account for mis-classification (particularly the control data), which affects the power of an association study ( Rekaya et al 2016 ; Lin et al 2020 ; Zhang and Yi 2020 ). Additionally, the proposed framework can be further generalized to accommodate the simultaneous analysis of multiple rare variants ( Derkach et al 2014 ; Li et al 2019 , 2020 , 2022 ). Finally, the proposed method assumes a random sample of unrelated individuals.…”