VBASS enables integration of single cell gene expression data in Bayesian association analysis of rare variants.
VBASS enables integration of single cell gene expression data in Bayesian association analysis of rare variants.
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DOI：
10.1038/s42003-023-05155-9
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发表时间：
2023-07-25
影响因子：
5.9
通讯作者：
Shen, Yufeng
中科院分区：
文献类型：
--
作者：
Zhong, Guojie;Choi, Yoolim A.;Shen, Yufeng
Rare or de novo variants have substantial contribution to human diseases, but the statistical power to identify risk genes by rare variants is generally low due to rarity of genotype data. Previous studies have shown that risk genes usually have high expression in relevant cell types, although for many conditions the identity of these cell types are largely unknown. Recent efforts in single cell atlas in human and model organisms produced large amount of gene expression data. Here we present VBASS, a Bayesian method that integrates single-cell expression and de novo variant (DNV) data to improve power of disease risk gene discovery. VBASS models disease risk prior as a function of expression profiles, approximated by deep neural networks. It learns the weights of neural networks and parameters of Gamma-Poisson likelihood models of DNV counts jointly from expression and genetics data. On simulated data, VBASS shows proper error rate control and better power than state-of-the-art methods. We applied VBASS to published datasets and identified more candidate risk genes with supports from literature or data from independent cohorts. VBASS can be generalized to integrate other types of functional genomics data in statistical genetics analysis. VBASS is a Bayesian method that integrates gene expression in association analysis of rare de novo variants to improve power in finding disease risk genes.
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影响因子：
5.4
作者：
Hinton RB;Prakash A;Romp RL;Krueger DA;Knilans TK;International Tuberous Sclerosis Consensus Group
通讯作者：
International Tuberous Sclerosis Consensus Group
影响因子：
64.8
作者：
Iossifov, Ivan;O'Roak, Brian J.;Sanders, Stephan J.;Ronemus, Michael;Krumm, Niklas;Levy, Dan;Stessman, Holly A.;Witherspoon, Kali T.;Vives, Laura;Patterson, Karynne E.;Smith, Joshua D.;Paeper, Bryan;Nickerson, Deborah A.;Dea, Jeanselle;Dong, Shan;Gonzalez, Luis E.;Mandell, Jeffrey D.;Mane, Shrikant M.;Murtha, Michael T.;Sullivan, Catherine A.;Walker, Michael F.;Waqar, Zainulabedin;Wei, Liping;Willsey, A. Jeremy;Yamrom, Boris;Lee, Yoon-ha;Grabowska, Ewa;Dalkic, Ertugrul;Wang, Zihua;Marks, Steven;Andrews, Peter;Leotta, Anthony;Kendall, Jude;Hakker, Inessa;Rosenbaum, Julie;Ma, Beicong;Rodgers, Linda;Troge, Jennifer;Narzisi, Giuseppe;Yoon, Seungtai;Schatz, Michael C.;Ye, Kenny;McCombie, W. Richard;Shendure, Jay;Eichler, Evan E.;State, Matthew W.;Wigler, Michael
通讯作者：
Wigler, Michael
影响因子：
30.8
作者：
Coe, Bradley P.;Stessman, Holly A. F.;Eichler, Evan E.
通讯作者：
Eichler, Evan E.
影响因子：
64.8
作者：
Karczewski, Konrad J;Francioli, Laurent C;MacArthur, Daniel G
通讯作者：
MacArthur, Daniel G
影响因子：
20.1
作者：
Pediatric Cardiac Genomics Consortium;Gelb B;Brueckner M;Chung W;Goldmuntz E;Kaltman J;Kaski JP;Kim R;Kline J;Mercer-Rosa L;Porter G;Roberts A;Rosenberg E;Seiden H;Seidman C;Sleeper L;Tennstedt S;Kaltman J;Schramm C;Burns K;Pearson G;Rosenberg E
通讯作者：
Rosenberg E