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Heritability estimation and differential analysis of count data with generalized linear mixed models in genomic sequencing studies
Author(s) -
Shiquan Sun,
Jiaqiang Zhu,
Sahar V. Mozaffari,
Carole Ober,
Mengjie Chen,
Xiang Zhou
Publication year - 2018
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/bty644
Subject(s) - heritability , computer science , data mining , mixed model , biology , computational biology , genetics , machine learning
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.

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