PMID- 35977827
OWN - NLM
STAT- MEDLINE
DCOM- 20220819
LR  - 20240409
IS  - 2575-1077 (Electronic)
IS  - 2575-1077 (Linking)
VI  - 5
IP  - 12
DP  - 2022 Aug 17
TI  - Telescoping bimodal latent Dirichlet allocation to identify expression QTLs 
      across tissues.
LID - 10.26508/lsa.202101297 [doi]
LID - e202101297
AB  - Expression quantitative trait loci (eQTLs), or single-nucleotide polymorphisms 
      that affect average gene expression levels, provide important insights into 
      context-specific gene regulation. Classic eQTL analyses use one-to-one 
      association tests, which test gene-variant pairs individually and ignore 
      correlations induced by gene regulatory networks and linkage disequilibrium. 
      Probabilistic topic models, such as latent Dirichlet allocation, estimate latent 
      topics for a collection of count observations. Prior multimodal frameworks that 
      bridge genotype and expression data assume matched sample numbers between 
      modalities. However, many data sets have a nested structure where one individual 
      has several associated gene expression samples and a single germline genotype 
      vector. Here, we build a telescoping bimodal latent Dirichlet allocation (TBLDA) 
      framework to learn shared topics across gene expression and genotype data that 
      allows multiple RNA sequencing samples to correspond to a single individual's 
      genotype. By using raw count data, our model avoids possible adulteration via 
      normalization procedures. Ancestral structure is captured in a genotype-specific 
      latent space, effectively removing it from shared components. Using GTEx v8 
      expression data across 10 tissues and genotype data, we show that the estimated 
      topics capture meaningful and robust biological signal in both modalities and 
      identify associations within and across tissue types. We identify 4,645 cis-eQTLs 
      and 995 trans-eQTLs by conducting eQTL mapping between the most informative 
      features in each topic. Our TBLDA model is able to identify associations using 
      raw sequencing count data when the samples in two separate data modalities are 
      matched one-to-many, as is often the case in biological data. Our code is freely 
      available at https://github.com/gewirtz/TBLDA.
CI  - (c) 2022 Gewirtz et al.
FAU - Gewirtz, Ariel Dh
AU  - Gewirtz AD
AUID- ORCID: 0000-0001-9801-1354
AD  - Lewis-Sigler Institute of Integrative Genomics, Princeton University, Princeton, 
      NJ, USA.
FAU - Townes, F William
AU  - Townes FW
AD  - Department of Computer Science, Princeton University, Princeton, NJ, USA.
FAU - Engelhardt, Barbara E
AU  - Engelhardt BE
AUID- ORCID: 0000-0002-6139-7334
AD  - Department of Computer Science, Princeton University, Princeton, NJ, USA.
AD  - Gladstone Institutes, San Francisco, CA, USA.
LA  - eng
GR  - R01 HL133218/HL/NHLBI NIH HHS/United States
GR  - U2C CA233195/CA/NCI NIH HHS/United States
PT  - Journal Article
PT  - Research Support, N.I.H., Extramural
PT  - Research Support, Non-U.S. Gov't
PT  - Research Support, U.S. Gov't, Non-P.H.S.
DEP - 20220817
PL  - United States
TA  - Life Sci Alliance
JT  - Life science alliance
JID - 101728869
SB  - IM
MH  - Gene Expression Regulation
MH  - Gene Regulatory Networks
MH  - Genotype
MH  - *Polymorphism, Single Nucleotide/genetics
MH  - *Quantitative Trait Loci/genetics
PMC - PMC9387650
COIS- BE Engelhardt is on the SAB of Creyon Bio, ArrePath, and Freenome.
EDAT- 2022/08/18 06:00
MHDA- 2022/08/20 06:00
PMCR- 2022/08/17
CRDT- 2022/08/17 21:41
PHST- 2021/11/11 00:00 [received]
PHST- 2022/07/15 00:00 [revised]
PHST- 2022/07/18 00:00 [accepted]
PHST- 2022/08/17 21:41 [entrez]
PHST- 2022/08/18 06:00 [pubmed]
PHST- 2022/08/20 06:00 [medline]
PHST- 2022/08/17 00:00 [pmc-release]
AID - 5/12/e202101297 [pii]
AID - LSA-2021-01297 [pii]
AID - 10.26508/lsa.202101297 [doi]
PST - epublish
SO  - Life Sci Alliance. 2022 Aug 17;5(12):e202101297. doi: 10.26508/lsa.202101297.