TY - JOUR
T1 - A multi-dimensional integrative scoring framework for predicting functional variants in the human genome
AU - Li, Xihao
AU - Yung, Godwin
AU - Zhou, Hufeng
AU - Sun, Ryan
AU - Li, Zilin
AU - Hou, Kangcheng
AU - Zhang, Martin Jinye
AU - Liu, Yaowu
AU - Arapoglou, Theodore
AU - Wang, Chen
AU - Ionita-Laza, Iuliana
AU - Lin, Xihong
N1 - Publisher Copyright:
© 2022 American Society of Human Genetics
PY - 2022/3/3
Y1 - 2022/3/3
N2 - Attempts to identify and prioritize functional DNA elements in coding and non-coding regions, particularly through use of in silico functional annotation data, continue to increase in popularity. However, specific functional roles can vary widely from one variant to another, making it challenging to summarize different aspects of variant function with a one-dimensional rating. Here we propose multi-dimensional annotation-class integrative estimation (MACIE), an unsupervised multivariate mixed-model framework capable of integrating annotations of diverse origin to assess multi-dimensional functional roles for both coding and non-coding variants. Unlike existing one-dimensional scoring methods, MACIE views variant functionality as a composite attribute encompassing multiple characteristics and estimates the joint posterior functional probabilities of each genomic position. This estimate offers more comprehensive and interpretable information in the presence of multiple aspects of functionality. Applied to a variety of independent coding and non-coding datasets, MACIE demonstrates powerful and robust performance in discriminating between functional and non-functional variants. We also show an application of MACIE to fine-mapping and heritability enrichment analysis by using the lipids GWAS summary statistics data from the European Network for Genetic and Genomic Epidemiology Consortium.
AB - Attempts to identify and prioritize functional DNA elements in coding and non-coding regions, particularly through use of in silico functional annotation data, continue to increase in popularity. However, specific functional roles can vary widely from one variant to another, making it challenging to summarize different aspects of variant function with a one-dimensional rating. Here we propose multi-dimensional annotation-class integrative estimation (MACIE), an unsupervised multivariate mixed-model framework capable of integrating annotations of diverse origin to assess multi-dimensional functional roles for both coding and non-coding variants. Unlike existing one-dimensional scoring methods, MACIE views variant functionality as a composite attribute encompassing multiple characteristics and estimates the joint posterior functional probabilities of each genomic position. This estimate offers more comprehensive and interpretable information in the presence of multiple aspects of functionality. Applied to a variety of independent coding and non-coding datasets, MACIE demonstrates powerful and robust performance in discriminating between functional and non-functional variants. We also show an application of MACIE to fine-mapping and heritability enrichment analysis by using the lipids GWAS summary statistics data from the European Network for Genetic and Genomic Epidemiology Consortium.
KW - EM algorithm
KW - functional annotations
KW - generalized linear mixed model
KW - multi-dimensional integrated scores
KW - prediction of functional effect
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U2 - 10.1016/j.ajhg.2022.01.017
DO - 10.1016/j.ajhg.2022.01.017
M3 - Article
C2 - 35216679
AN - SCOPUS:85125225480
SN - 0002-9297
VL - 109
SP - 446
EP - 456
JO - American journal of human genetics
JF - American journal of human genetics
IS - 3
ER -