publication . Preprint . 2020

Fine-mapping and QTL tissue-sharing information improve causal gene identification and transcriptome prediction performance

Heather E. Wheeler; Gao Wang; Rodrigo Bonazzola; Alvaro N. Barbeira; Owen J. Melia; Yanyu Liang; François Aguet; Hae Kyung Im; Kristin Ardlie; Xiaoquan Wen; ...
Open Access English
  • Published: 20 Mar 2020
  • Publisher: Cold Spring Harbor Laboratory
Abstract
<jats:title>Abstract</jats:title><jats:p>The integration of transcriptomic studies and GWAS (genome-wide association studies) via imputed expression has seen extensive application in recent years, enabling the functional characterization and causal gene prioritization of GWAS loci. However, the techniques for imputing transcriptomic traits from DNA variation remain underdeveloped. Furthermore, associations found when linking eQTL studies to complex traits through methods like PrediXcan can lead to false positives due to linkage disequilibrium between distinct causal variants. Therefore, the best prediction performance models may not necessarily lead to more reli...
Subjects
free text keywords: Imputation (statistics), Expression quantitative trait loci, Genetic association, Quantitative trait locus, Linkage disequilibrium, Genome-wide association study, Locus (genetics), Computational biology, False positive paradox, Biology
Funded by
NIH| Network-based algorithms for target identification and drug repositioning from genetic associations
Project
  • Funder: National Institutes of Health (NIH)
  • Project Code: 1R01HG010067-01
  • Funding stream: NATIONAL HUMAN GENOME RESEARCH INSTITUTE
,
NIH| National Cell Repository for Alzheimers Disease
Project
  • Funder: National Institutes of Health (NIH)
  • Project Code: 5U24AG021886-09
  • Funding stream: NATIONAL INSTITUTE ON AGING
,
NIH| Alzheimer's Disease Genetics Consortium
Project
  • Funder: National Institutes of Health (NIH)
  • Project Code: 5U01AG032984-07
  • Funding stream: NATIONAL INSTITUTE ON AGING
,
NIH| Synaptic Plasticity In Aging And Neurodegenerative Disor
Project
  • Funder: National Institutes of Health (NIH)
  • Project Code: 1Z01AG000317-06
  • Funding stream: NATIONAL INSTITUTE ON AGING
,
NIH| Mechanisms of necrosis regulation of hematopoietic stem cell function
Project
  • Funder: National Institutes of Health (NIH)
  • Project Code: 1R01HL133559-01A1
  • Funding stream: NATIONAL HEART, LUNG, AND BLOOD INSTITUTE
42 references, page 1 of 3

1. Albert FW, Kruglyak L. The role of regulatory variation in complex traits and disease. Nature Reviews Genetics. 2015;16(4):197{212. doi:10.1038/nrg3891.

2. Aguet F, Barbeira AN, Bonazzola R, Brown A, Castel SE, Jo B, et al. The GTEx Consortium atlas of genetic regulatory effects across human tissues. bioRxiv. 2019;doi:10.1101/787903.

3. Huckins LM, Dobbyn A, Ruderfer DM, Hoffman G, Wang W, Pardin~as AF, et al. Gene expression imputation across multiple brain regions provides insights into schizophrenia risk. Nature Genetics. 2019;51(4):1{20. doi:10.1038/s41588-019- 0364-4. [OpenAIRE]

4. Mancuso N, Gayther S, Gusev A, Zheng W, Penney KL, Kote-Jarai Z, et al. Large-scale transcriptome-wide association study identifies new prostate cancer risk regions. Nature Communications. 2018; p. 1{11. doi:10.1038/s41467-018- 06302-1.

5. Gusev A, Mancuso N, Won H, Kousi M, Finucane HK, Reshef Y, et al. Transcriptome-wide association study of schizophrenia and chromatin activity yields mechanistic disease insights. Nature Genetics. 2018;50(4):538{548. doi:10.1038/s41588-018-0092-1.

6. So HC, Chau CKL, Chiu WT, Ho KS, Lo CP, Yim SHY, et al. Analysis of genomewide association data highlights candidates for drug repositioning in psychiatry. Nature Neuroscience. 2017;doi:10.1038/nn.4618.

7. Wu L, Shi W, Long J, Guo X, Michailidou K, Beesley J, et al. A transcriptomewide association study of 229,000 women identifies new candidate susceptibility genes for breast cancer. Nature Genetics. 2018;doi:10.1038/s41588-018-0132-x.

8. Gamazon ER, Wheeler HE, Shah KP, Mozaffari SV, Aquino-Michaels K, Carroll RJ, et al. A gene-based association method for mapping traits using reference transcriptome data. Nature genetics. 2015;47(9):1091{1098. doi:10.1038/ng.3367. [OpenAIRE]

9. Barbeira AN, Dickinson SP, Bonazzola R, Zheng J, Wheeler HE, Torres JM, et al. Exploring the phenotypic consequences of tissue specific gene expression variation inferred from GWAS summary statistics. Nature Communications. 2018;doi:10.1038/s41467-018-03621-1.

10. Gusev A, Ko A, Shi H, Bhatia G, Chung W, Penninx BWJH, et al. Integrative approaches for large-scale transcriptome-wide association studies. Nature Genetics. 2016;48(3):245{252. doi:10.1038/ng.3506.

11. Hu Y, Li M, Lu Q, Weng H, Wang J, Zekavat SM, et al. A statistical framework for cross-tissue transcriptome-wide association analysis. Nature Genetics. 2019;51:568 { 576. doi:10.1038/s41588-019-0345-7.

12. Wheeler HE, Shah KP, Brenner J, Garcia T, Aquino-Michaels K, Cox NJ, et al. Survey of the Heritability and Sparse Architecture of Gene Expression Traits across Human Tissues. PLoS Genetics. 2016;12(11). doi:10.1371/journal.pgen.1006423.

13. Fryett JJ, Inshaw J, Morris AP, Cordell HJ. Comparison of methods for transcriptome imputation through application to two common complex diseases. European Journal of Human Genetics. 2018; p. 1{10. doi:10.1038/s41431-018-0176-5.

14. Friedman J, Hastie T, Tibshirani R. Regularization Paths for Generalized Linear Models via Coordinate Descent. Journal of Statistical Software. 2010;33(1):1{22.

15. Aguet F, Brown AA, Castel SE, Davis JR, He Y, Jo B, et al. Genetic effects on gene expression across human tissues. Nature. 2017;doi:10.1038/nature24277.

42 references, page 1 of 3
Abstract
<jats:title>Abstract</jats:title><jats:p>The integration of transcriptomic studies and GWAS (genome-wide association studies) via imputed expression has seen extensive application in recent years, enabling the functional characterization and causal gene prioritization of GWAS loci. However, the techniques for imputing transcriptomic traits from DNA variation remain underdeveloped. Furthermore, associations found when linking eQTL studies to complex traits through methods like PrediXcan can lead to false positives due to linkage disequilibrium between distinct causal variants. Therefore, the best prediction performance models may not necessarily lead to more reli...
Subjects
free text keywords: Imputation (statistics), Expression quantitative trait loci, Genetic association, Quantitative trait locus, Linkage disequilibrium, Genome-wide association study, Locus (genetics), Computational biology, False positive paradox, Biology
Funded by
NIH| Network-based algorithms for target identification and drug repositioning from genetic associations
Project
  • Funder: National Institutes of Health (NIH)
  • Project Code: 1R01HG010067-01
  • Funding stream: NATIONAL HUMAN GENOME RESEARCH INSTITUTE
,
NIH| National Cell Repository for Alzheimers Disease
Project
  • Funder: National Institutes of Health (NIH)
  • Project Code: 5U24AG021886-09
  • Funding stream: NATIONAL INSTITUTE ON AGING
,
NIH| Alzheimer's Disease Genetics Consortium
Project
  • Funder: National Institutes of Health (NIH)
  • Project Code: 5U01AG032984-07
  • Funding stream: NATIONAL INSTITUTE ON AGING
,
NIH| Synaptic Plasticity In Aging And Neurodegenerative Disor
Project
  • Funder: National Institutes of Health (NIH)
  • Project Code: 1Z01AG000317-06
  • Funding stream: NATIONAL INSTITUTE ON AGING
,
NIH| Mechanisms of necrosis regulation of hematopoietic stem cell function
Project
  • Funder: National Institutes of Health (NIH)
  • Project Code: 1R01HL133559-01A1
  • Funding stream: NATIONAL HEART, LUNG, AND BLOOD INSTITUTE
42 references, page 1 of 3

1. Albert FW, Kruglyak L. The role of regulatory variation in complex traits and disease. Nature Reviews Genetics. 2015;16(4):197{212. doi:10.1038/nrg3891.

2. Aguet F, Barbeira AN, Bonazzola R, Brown A, Castel SE, Jo B, et al. The GTEx Consortium atlas of genetic regulatory effects across human tissues. bioRxiv. 2019;doi:10.1101/787903.

3. Huckins LM, Dobbyn A, Ruderfer DM, Hoffman G, Wang W, Pardin~as AF, et al. Gene expression imputation across multiple brain regions provides insights into schizophrenia risk. Nature Genetics. 2019;51(4):1{20. doi:10.1038/s41588-019- 0364-4. [OpenAIRE]

4. Mancuso N, Gayther S, Gusev A, Zheng W, Penney KL, Kote-Jarai Z, et al. Large-scale transcriptome-wide association study identifies new prostate cancer risk regions. Nature Communications. 2018; p. 1{11. doi:10.1038/s41467-018- 06302-1.

5. Gusev A, Mancuso N, Won H, Kousi M, Finucane HK, Reshef Y, et al. Transcriptome-wide association study of schizophrenia and chromatin activity yields mechanistic disease insights. Nature Genetics. 2018;50(4):538{548. doi:10.1038/s41588-018-0092-1.

6. So HC, Chau CKL, Chiu WT, Ho KS, Lo CP, Yim SHY, et al. Analysis of genomewide association data highlights candidates for drug repositioning in psychiatry. Nature Neuroscience. 2017;doi:10.1038/nn.4618.

7. Wu L, Shi W, Long J, Guo X, Michailidou K, Beesley J, et al. A transcriptomewide association study of 229,000 women identifies new candidate susceptibility genes for breast cancer. Nature Genetics. 2018;doi:10.1038/s41588-018-0132-x.

8. Gamazon ER, Wheeler HE, Shah KP, Mozaffari SV, Aquino-Michaels K, Carroll RJ, et al. A gene-based association method for mapping traits using reference transcriptome data. Nature genetics. 2015;47(9):1091{1098. doi:10.1038/ng.3367. [OpenAIRE]

9. Barbeira AN, Dickinson SP, Bonazzola R, Zheng J, Wheeler HE, Torres JM, et al. Exploring the phenotypic consequences of tissue specific gene expression variation inferred from GWAS summary statistics. Nature Communications. 2018;doi:10.1038/s41467-018-03621-1.

10. Gusev A, Ko A, Shi H, Bhatia G, Chung W, Penninx BWJH, et al. Integrative approaches for large-scale transcriptome-wide association studies. Nature Genetics. 2016;48(3):245{252. doi:10.1038/ng.3506.

11. Hu Y, Li M, Lu Q, Weng H, Wang J, Zekavat SM, et al. A statistical framework for cross-tissue transcriptome-wide association analysis. Nature Genetics. 2019;51:568 { 576. doi:10.1038/s41588-019-0345-7.

12. Wheeler HE, Shah KP, Brenner J, Garcia T, Aquino-Michaels K, Cox NJ, et al. Survey of the Heritability and Sparse Architecture of Gene Expression Traits across Human Tissues. PLoS Genetics. 2016;12(11). doi:10.1371/journal.pgen.1006423.

13. Fryett JJ, Inshaw J, Morris AP, Cordell HJ. Comparison of methods for transcriptome imputation through application to two common complex diseases. European Journal of Human Genetics. 2018; p. 1{10. doi:10.1038/s41431-018-0176-5.

14. Friedman J, Hastie T, Tibshirani R. Regularization Paths for Generalized Linear Models via Coordinate Descent. Journal of Statistical Software. 2010;33(1):1{22.

15. Aguet F, Brown AA, Castel SE, Davis JR, He Y, Jo B, et al. Genetic effects on gene expression across human tissues. Nature. 2017;doi:10.1038/nature24277.

42 references, page 1 of 3
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