Heritability estimation and differential analysis of count data with generalized linear mixed models in genomic sequencing studies.

ABSTRACT:

Motivation

Genomic sequencing studies, including RNA sequencing and bisulfite sequencing studies, are becoming increasingly common and increasingly large. Large genomic sequencing studies open doors for accurate molecular trait heritability estimation and powerful differential analysis. Heritability estimation and differential analysis in sequencing studies requires the development of statistical methods that can properly account for the count nature of the sequencing data and that are computationally efficient for large datasets.

Results

Here, we develop such a method, PQLseq (Penalized Quasi-Likelihood for sequencing count data), to enable effective and efficient heritability estimation and differential analysis using the generalized linear mixed model framework. With exte

SUBMITTER: Sun S 

PROVIDER: S-EPMC6361238 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature