Integrative approaches for large-scale transcriptome-wide association studies
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Abstract
Many genetic variants influence complex traits by modulating gene expression, thus altering the abundance of one or multiple proteins. Here we introduce a powerful strategy that integrates gene expression measurements with summary association statistics from large-scale genome-wide association studies (GWAS) to identify genes whose cis-regulated expression is associated with complex traits. We leverage expression imputation from genetic data to perform a transcriptome-wide association study (TWAS) to identify significant expression-trait associations. We applied our approaches to expression data from blood and adipose tissue measured in ∼ 3,000 individuals overall. We imputed gene expression into GWAS data from over 900,000 phenotype measurements to identify 69 new genes significantly associated with obesity-related traits (BMI, lipids and height). Many of these genes are associated with relevant phenotypes in the Hybrid Mouse Diversity Panel. Our results showcase the power of integrating genotype, gene expression and phenotype to gain insights into the genetic basis of complex traits.
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Identifying causal genes and mechanisms underlying disease-associated loci requires integrative analyses of expression and epigenetic datasets in disease-relevant cell types.
“Identifying causal genes and mechanisms underlying disease-associated loci requires integrative analyses of expression and epigenetic datasets in disease-relevant cell types14.”
From A common haplotype lowers PU.1 expression in myeloid cells and delays onset of Alzheimer's disease (2017) · cites this paper as [14]
The Discussion (page unavailable) describes integrating genetic and expression data to identify genes associated with complex traits, and the Results report tissue-specific findings. The paper does not say that identifying causal genes requires epigenetic datasets; its Discussion also cautions that some associations may be indistinguishable from confounding without further analysis. (LLM, conf=0.88)
Experimental — accuracy not yet measuredThe study performed a PWAS using FUSION. After FDR correction and pleiotropy filtering, 125 pQTL for 108 proteins were significantly associated with AD.
“We performed a PWAS using FUSION45. After FDR correction and pleiotropy filtering50,51, 125 pQTL for 108 proteins were significantly associated with AD.”
From Proteogenomic analysis of human cerebrospinal fluid identifies neurologically relevant regulation and implicates causal proteins for Alzheimer’s disease (2024) · cites this paper as [45]
H-MAGMA and two eQTL-based gene mapping tools, coloc and TWAS, show significant overlap.
“We also found a significant overlap between H-MAGMA and two widely used expression quantitative trait loci (eQTL)-based gene mapping tools, coloc9 and TWAS10.”
From A computational tool (H-MAGMA) for improved prediction of brain-disorder risk genes by incorporating brain chromatin interaction profiles (2020) · cites this paper as [10]
It is possible to infer genetic signatures (eQTL) or phenotypes based on genotypes when datasets are collected from different subjects.
“In such cases, it is possible to infer genetic signatures (eQTL) or phenotypes based on genotypes [48–50].”
From Multi-omics approaches to disease (2017) · cites this paper as [48]
For miRWAS, we restricted the SNPs to the set of 1,190,321 SNPs in the linkage disequilibrium reference panel provided by FUSION.
“For the miRWAS analysis, we restricted the SNPs to the set of 1,190,321 SNPs in the linkage disequilibrium reference panel provided by FUSION35.”
From Mapping the microRNA landscape in the older adult brain and its genetic contribution to neuropsychiatric conditions (2024) · cites this paper as [35]
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Cites
STAR: ultrafast universal RNA-seq aligner
Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., & Gingeras, T. R. (2013) · Bioinformatics · doi:10.1093/bioinformatics/bts635 · Cited by 51,709 · see citing works
GCTA: a tool for genome-wide complex trait analysis
Yang, J., Lee, S. H., Goddard, M. E., & Visscher, P. M. (2011) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2010.11.011 · Cited by 8,279 · see citing works
Davey Smith, G., & Ebrahim, S. (2003) · International Journal of Epidemiology · doi:10.1093/ije/dyg070 · Cited by 5,882 · see citing works
LD Score regression distinguishes confounding from polygenicity in genome-wide association studies
Schizophrenia Working Group of the Psychiatric Genomics Consortium, Bulik-Sullivan, B. K., Loh, P.-R., Finucane, H. K., Ripke, S., Yang, J., Patterson, N., Daly, M. J., Price, A. L., & Neale, B. M. (2015) · Nature Genetics · doi:10.1038/ng.3211 · Cited by 5,559 · see citing works
Self-citationCommon polygenic variation contributes to risk of schizophrenia and bipolar disorder
The International Schizophrenia Consortium (2009) · Nature · doi:10.1038/nature08185 · Cited by 4,821 · see citing works
Genetic studies of body mass index yield new insights for obesity biology
Study, T. L. C., Locke, A. E., The ADIPOGen Consortium, The AGEN-BMI Working Group, The CARDIOGRAMplusC4D Consortium, The CKDGen Consortium, The, G. L. G. C., The, I. C. B. P., The MAGIC Investigators, The MuTHER Consortium, et al. (2015) · Nature · doi:10.1038/nature14177 · Cited by 4,534 · see citing works
Self-citationCommon SNPs explain a large proportion of the heritability for human height
Yang, J., Benyamin, B., McEvoy, B. P., Gordon, S., Henders, A. K., Nyholt, D. R., Madden, P. A., Heath, A. C., Martin, N. G., Montgomery, G. W., et al. (2010) · Nature Genetics · doi:10.1038/ng.608 · Cited by 4,446 · see citing works
Giambartolomei, C., Vukcevic, D., Schadt, E. E., Franke, L., Hingorani, A. D., Wallace, C., & Plagnol, V. (2014) · PLoS Genetics · doi:10.1371/journal.pgen.1004383 · Cited by 4,261 · see citing works
An atlas of genetic correlations across human diseases and traits
ReproGen Consortium, Bulik-Sullivan, B., Psychiatric Genomics Consortium, Genetic Consortium for Anorexia Nervosa of the Wellcome Trust Case Control Consortium 3, Finucane, H. K., Anttila, V., Gusev, A., Day, F. R., Loh, P.-R., Duncan, L., et al. (2015) · Nature Genetics · doi:10.1038/ng.3406 · Cited by 4,255 · see citing works
Self-citationBiological, clinical and population relevance of 95 loci for blood lipids
Teslovich, T. M., Musunuru, K., Smith, A. V., Edmondson, A. C., Stylianou, I. M., Koseki, M., Pirruccello, J. P., Ripatti, S., Chasman, D. I., Willer, C. J., et al. (2010) · Nature · doi:10.1038/nature09270 · Cited by 3,487 · see citing works
Self-citationWiller (2013) · doi:10.1038/ng.2797 · Cited by 2,983 · see citing works
Partitioning heritability by functional annotation using genome-wide association summary statistics
ReproGen Consortium, Finucane, H. K., Schizophrenia Working Group of the Psychiatric Genomics Consortium, The RACI Consortium, Bulik-Sullivan, B., Gusev, A., Trynka, G., Reshef, Y., Loh, P.-R., Anttila, V., et al. (2015) · Nature Genetics · doi:10.1038/ng.3404 · Cited by 2,604 · see citing works
Self-citationVisscher, P. M., Brown, M. A., McCarthy, M. I., & Yang, J. (2012) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2011.11.029 · Cited by 2,425 · see citing works
Hundreds of variants clustered in genomic loci and biological pathways affect human height
Lango Allen, H., Estrada, K., Lettre, G., Berndt, S. I., Weedon, M. N., Rivadeneira, F., Willer, C. J., Jackson, A. U., Vedantam, S., Raychaudhuri, S., et al. (2010) · Nature · doi:10.1038/nature09410 · Cited by 1,945 · see citing works
Self-citationTranscriptome and genome sequencing uncovers functional variation in humans
The Geuvadis Consortium, Lappalainen, T., Sammeth, M., Friedländer, M. R., ‘t Hoen, P. A. C., Monlong, J., Rivas, M. A., Gonzàlez-Porta, M., Kurbatova, N., Griebel, T., et al. (2013) · Nature · doi:10.1038/nature12531 · Cited by 1,888 · see citing works
The Electronic Medical Records and Genomics (eMERGE) Consortium, Wood, A. R., The MIGen Consortium, The PAGE Consortium, Study, T. L. C., Esko, T., Yang, J., Vedantam, S., Pers, T. H., Gustafsson, S., et al. (2014) · Nature Genetics · doi:10.1038/ng.3097 · Cited by 1,853 · see citing works
Self-citationA gene-based association method for mapping traits using reference transcriptome data
Gamazon, E. R., Wheeler, H. E., Shah, K. P., Mozaffari, S. V., Aquino-Michaels, K., Carroll, R. J., Eyler, A. E., Denny, J. C., Nicolae, D. L., Cox, N. J., et al. (2015) · Nature Genetics · doi:10.1038/ng.3367 · Cited by 1,762 · see citing works
Genetic Investigation of ANthropometric Traits (GIANT) Consortium, Yang, J., DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium, Ferreira, T., Morris, A. P., Medland, S. E., Madden, P. A. F., Heath, A. C., Martin, N. G., Montgomery, G. W., et al. (2012) · Nature Genetics · doi:10.1038/ng.2213 · Cited by 1,712 · see citing works
That BLUP is a Good Thing: The Estimation of Random Effects
Robinson, G. K. (1991) · Statistical Science · doi:10.1214/ss/1177011926 · Cited by 1,694 · see citing works
Systematic identification of trans eQTLs as putative drivers of known disease associations
Westra, H.-J., Peters, M. J., Esko, T., Yaghootkar, H., Schurmann, C., Kettunen, J., Christiansen, M. W., Fairfax, B. P., Schramm, K., Powell, J. E., et al. (2013) · Nature Genetics · doi:10.1038/ng.2756 · Cited by 1,658 · see citing works
Self-citationPower and predictive accuracy of polygenic risk scores
Dudbridge, F. (2013) · PLoS Genetics · doi:10.1371/journal.pgen.1003348 · Cited by 1,509 · see citing works
Pierce, B. L., & Burgess, S. (2013) · American Journal of Epidemiology · doi:10.1093/aje/kwt084 · Cited by 1,389 · see citing works
Linkage disequilibrium in humans: models and data.
Pritchard, J., & Przeworski, M. (2001) · American Journal of Human Genetics · doi:10.1086/321275 · Cited by 1,340 · see citing works
Trait-associated SNPs are more likely to be eQTLs: annotation to enhance discovery from GWAS
Nicolae, D. L., Gamazon, E., Zhang, W., Duan, S., Dolan, M. E., & Cox, N. J. (2010) · PLoS Genetics · doi:10.1371/journal.pgen.1000888 · Cited by 1,292 · see citing works
From noncoding variant to phenotype via SORT1 at the 1p13 cholesterol locus
Musunuru, K., Strong, A., Frank-Kamenetsky, M., Lee, N. E., Ahfeldt, T., Sachs, K. V., Li, X., Li, H., Kuperwasser, N., Ruda, V. M., et al. (2010) · Nature · doi:10.1038/nature09266 · Cited by 1,140 · see citing works
Stegle, O., Parts, L., Piipari, M., Winn, J., & Durbin, R. (2012) · Nature Protocols · doi:10.1038/nprot.2011.457 · Cited by 1,049 · see citing works
The role of regulatory variation in complex traits and disease
Albert, F. W., & Kruglyak, L. (2015) · Nature Reviews Genetics · doi:10.1038/nrg3891 · Cited by 1,047 · see citing works
Polygenic Modeling with Bayesian Sparse Linear Mixed Models
Zhou, X., Carbonetto, P. S., & Stephens, M. (2013) · PLoS Genetics · doi:10.1371/journal.pgen.1003264 · Cited by 801 · see citing works
Transcriptome genetics using second generation sequencing in a Caucasian population
Montgomery, S. B., Sammeth, M., Gutierrez-Arcelus, M., Lach, R. P., Ingle, C., Nisbett, J., Guigo, R., & Dermitzakis, E. T. (2010) · Nature · doi:10.1038/nature08903 · Cited by 797 · see citing works
Biological interpretation of genome-wide association studies using predicted gene functions
Pers, T. H., Karjalainen, J. M., Chan, Y., Westra, H.-J., Wood, A. R., Yang, J., Lui, J. C., Vedantam, S., Gustafsson, S., Esko, T., et al. (2015) · Nature Communications · doi:10.1038/ncomms6890 · Cited by 787 · see citing works
Cohort Profile: The Cardiovascular Risk in Young Finns Study
Raitakari, O. T., Juonala, M., Rönnemaa, T., Keltikangas‐Järvinen, L., Räsänen, L., Pietikainen, M., Hutri‐Kähönen, N., Taittonen, L. M., Jokinen, E. V., Marniemi, J., et al. (2008) · International Journal of Epidemiology · doi:10.1093/ije/dym225 · Cited by 754 · see citing works
Self-citationCommon Regulatory Variation Impacts Gene Expression in a Cell Type–Dependent Manner
Dimas, A. S., Deutsch, S., Stranger, B. E., Montgomery, S. B., Borel, C., Attar, H., Ingle, C., Beazley, C., Arcelus, M. G., Sekowska, M., et al. (2009) · Science · doi:10.1126/science.1174148 · Cited by 746 · see citing works
Mapping cis- and trans-regulatory effects across multiple tissues in twins
The Multiple Tissue Human Expression Resource (MuTHER) Consortium, Grundberg, E., Small, K. S., Hedman, Å. K., Nica, A. C., Buil, A., Keildson, S., Bell, J. T., Yang, T.-P., Meduri, E., et al. (2012) · Nature Genetics · doi:10.1038/ng.2394 · Cited by 729 · see citing works
The allelic architecture of human disease genes: common disease-common variant... or not?
Pritchard, J. K. (2002) · Human Molecular Genetics · doi:10.1093/hmg/11.20.2417 · Cited by 711 · see citing works
Pitfalls of predicting complex traits from SNPs
Wray, N. R., Yang, J., Hayes, B. J., Price, A. L., Goddard, M. E., & Visscher, P. M. (2013) · Nature Reviews Genetics · doi:10.1038/nrg3457 · Cited by 672 · see citing works
Self-citationNetherlands Twin Register: From Twins to Twin Families
Boomsma, D., de Geus, E. D., Vink, J., Stubbe, J., Distel, M., Hottenga, J., Posthuma, D., van Beijsterveldt, T. V., Hudziak, J., Bartels, M., et al. (2006) · Twin Research and Human Genetics · doi:10.1375/twin.9.6.849 · Cited by 551 · see citing works
Self-citationPartitioning Heritability of Regulatory and Cell-Type-Specific Variants across 11 Common Diseases
Gusev, A., Lee, S. H., Trynka, G., Finucane, H., Vilhjálmsson, B. J., Xu, H., Zang, C., Ripke, S., Bulik-Sullivan, B., Stahl, E., et al. (2014) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2014.10.004 · Cited by 544 · see citing works
Self-citationJoint Analysis of Functional Genomic Data and Genome-wide Association Studies of 18 Human Traits
Pickrell, J. K. (2014) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2014.03.004 · Cited by 520 · see citing works
Polarization of the Effects of Autoimmune and Neurodegenerative Risk Alleles in Leukocytes
Raj, T., Rothamel, K., Mostafavi, S., Ye, C., Lee, M. N., Replogle, J. M., Feng, T., Lee, M., Asinovski, N., Frohlich, I., et al. (2014) · Science · doi:10.1126/science.1249547 · Cited by 506 · see citing works
Integrating Functional Data to Prioritize Causal Variants in Statistical Fine-Mapping Studies
Kichaev, G., Yang, W.-Y., Lindström, S., Hormozdiari, F., Eskin, E., Price, A. L., Kraft, P. L., & Paşaniuc, B. (2014) · PLoS Genetics · doi:10.1371/journal.pgen.1004722 · Cited by 476 · see citing works
Self-citationNica, A. C., Montgomery, S. B., Dimas, A. S., Stranger, B. E., Beazley, C., Barroso, I., & Dermitzakis, E. T. (2010) · PLoS Genetics · doi:10.1371/journal.pgen.1000895 · Cited by 423 · see citing works
Zhu, X., Feng, T., Tayo, B. O., Liang, J., Young, J. H., Franceschini, N., Smith, J. A., Yanek, L. R., Sun, Y. V., Edwards, T. L., et al. (2014) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2014.11.011 · Cited by 421 · see citing works
Netherlands Twin Register: From Twins to Twin Families
Boomsma, D. I., de Geus, E. J. C., Vink, J. M., Stubbe, J. H., Distel, M. A., Hottenga, J., Danielle Posthuma, B., van Beijsterveldt, T. C. E. M., Hudziak, J. J., Bartels, M., et al. (2006) · Twin Research and Human Genetics · doi:10.1375/183242706779462426 · Cited by 401 · see citing works
Self-citationHeritability and genomics of gene expression in peripheral blood
Wright, F. A., Sullivan, P. F., Brooks, A. I., Zou, F., Sun, W., Xia, K., Madar, V., Jansen, R., Chung, W., Zhou, Y.-H., et al. (2014) · Nature Genetics · doi:10.1038/ng.2951 · Cited by 362 · see citing works
Self-citationHAPGEN2: simulation of multiple disease SNPs
Su, Z., Marchini, J., & Donnelly, P. (2011) · Bioinform. · doi:10.1093/bioinformatics/btr341 · Cited by 352 · see citing works
Chatterjee, N., Wheeler, B., Sampson, J. N., Hartge, P. A., Chanock, S. J., & Park, J.-H. (2013) · Nature Genetics · doi:10.1038/ng.2579 · Cited by 351 · see citing works
Stančáková, A., Javorský, M., Kuulasmaa, T., Haffner, S. M., Kuusisto, J., & Laakso, M. (2009) · Diabetes · doi:10.2337/db08-1607 · Cited by 348 · see citing works
Self-citationDavis, L. K., Yu, D., Keenan, C. L., Gamazon, E. R., Konkashbaev, A. I., Derks, E. M., Neale, B. M., Yang, J., Lee, S., Evans, P. D., et al. (2013) · PLoS Genetics · doi:10.1371/journal.pgen.1003864 · Cited by 331 · see citing works
Evidence of widespread selection on standing variation in Europe at height-associated SNPs
Turchin, M. C., Chiang, C. W., Palmer, C. D., Sankararaman, S., Reich, D., Genetic Investigation of ANthropometric Traits (GIANT) Consortium, & Hirschhorn, J. N. (2012) · Nature Genetics · doi:10.1038/ng.2368 · Cited by 329 · see citing works
Domains of genome-wide gene expression dysregulation in Down’s syndrome
Letourneau, A., Santoni, F. A., Bonilla, X., Sailani, M. R., Gonzalez, D., Kind, J., Chevalier, C., Thurman, R., Sandstrom, R. S., Hibaoui, Y., et al. (2014) · Nature · doi:10.1038/nature13200 · Cited by 319 · see citing works
Bolormaa, S., Pryce, J. E., Reverter, A., Zhang, Y., Barendse, W., Kemper, K. E., Tier, B., Savin, K. W., Hayes, B. J., & Goddard, M. E. (2014) · PLoS Genetics · doi:10.1371/journal.pgen.1004198 · Cited by 282 · see citing works
Stančáková, A., Civelek, M., Saleem, N. K., Soininen, P., Kangas, A. J., Cederberg, H., Paananen, J., Pihlajamäki, J., Bonnycastle, L. L., Morken, M. A., et al. (2012) · Diabetes · doi:10.2337/db11-1378 · Cited by 279 · see citing works
Self-citationOptimizing the noise versus bias trade-off for Illumina Whole Genome Expression Beadchips
Shi, W., Oshlack, A., & Smyth, G. K. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkq871 · Cited by 225 · see citing works
Gene-gene and gene-environment interactions detected by transcriptome sequence analysis in twins
Buil, A., Brown, A. A., Lappalainen, T., Viñuela, A., Davies, M. N., Zheng, H.-F., Richards, J. B., Glass, D., Small, K. S., Durbin, R. M., et al. (2014) · Nature Genetics · doi:10.1038/ng.3162 · Cited by 210 · see citing works
Wen, W., Zheng, W., Okada, Y., Takeuchi, F., Tabara, Y., Hwang, J., Dorajoo, R., Li, H., Tsai, F., Yang, X., et al. (2014) · Human Molecular Genetics · doi:10.1093/hmg/ddu248 · Cited by 195 · see citing works
Fast and accurate imputation of summary statistics enhances evidence of functional enrichment
Paşaniuc, B., Zaitlen, N., Shi, H., Bhatia, G., Gusev, A., Pickrell, J. K., Hirschhorn, J. N., Strachan, D. P., Patterson, N. J., & Price, A. L. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu416 · Cited by 180 · see citing works
Self-citationHan, B., Kang, H. M., & Eskin, E. (2009) · PLoS Genetics · doi:10.1371/journal.pgen.1000456 · Cited by 175 · see citing works
Brown, C. D., Mangravite, L. M., & Engelhardt, B. E. (2013) · PLoS Genetics · doi:10.1371/journal.pgen.1003649 · Cited by 173 · see citing works
Leveraging Genetic Variability across Populations for the Identification of Causal Variants
Zaitlen, N., Paşaniuc, B., Gur, T., Ziv, E., & Halperin, E. (2010) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2009.11.016 · Cited by 146 · see citing works
An Atlas of Genetic Correlations across Human Diseases and TraitsPreprint
Bulik-Sullivan, B. K., Finucane, H. K., Anttila, V., et al. (2015) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/014498 · Cited by 143 · see citing works
Quantifying Missing Heritability at Known GWAS Loci
Gusev, A., Bhatia, G., Zaitlen, N., Vilhjálmsson, B. J., Diogo, D., Stahl, E. A., Gregersen, P. K., Worthington, J., Klareskog, L., Raychaudhuri, S., et al. (2013) · PLoS Genetics · doi:10.1371/journal.pgen.1003993 · Cited by 123 · see citing works
Self-citationAnother explanation for apparent epistasis
Wood, A. R., Tuke, M. A., Nalls, M. A., Hernandez, D. G., Bandinelli, S., Singleton, A., Melzer, D. R., Ferrucci, L., Frayling, T. M., & Weedon, M. N. (2014) · Nature · doi:10.1038/nature13691 · Cited by 115 · see citing works
Zhang, X., Joehanes, R., Chen, B. H., Huan, T., Ying, S., Munson, P. J., Johnson, A. D., Levy, D. A., & O’Donnell, C. J. (2015) · Nature Genetics · doi:10.1038/ng.3220 · Cited by 96 · see citing works
Cross-Population Joint Analysis of eQTLs: Fine Mapping and Functional Annotation
Wen, X., Luca, F., & Piqué-Regi, R. (2015) · PLoS Genetics · doi:10.1371/journal.pgen.1005176 · Cited by 89 · see citing works
DIST: direct imputation of summary statistics for unmeasured SNPs
Lee, D., Bigdeli, T. B., Riley, B. P., Fanous, A. H., & Bacanu, S. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt500 · Cited by 74 · see citing works
Cross-Tissue and Tissue-Specific eQTLs: Partitioning the Heritability of a Complex Trait
Torres, J., Gamazon, E. R., Parra, E. J., Below, J. E., Valladares‐Salgado, A., Wacher, N. H., Cruz, M., Hanis, C. L., & Cox, N. J. (2014) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2014.10.001 · Cited by 73 · see citing works
Nuotio, J., Oikonen, M., Magnussen, C. G., Jokinen, E. V., Laitinen, T. T., Hutri‐Kähönen, N., Kähönen, M. A. P., Lehtimäki, T. J., Taittonen, L. M., Tossavainen, P. H., et al. (2014) · Scandinavian Journal of Public Health · doi:10.1177/1403494814541597 · Cited by 65 · see citing works
Self-citationPartitioning heritability by functional category using GWAS summary statisticsPreprint
Finucane, H. K., Bulik-Sullivan, B. K., Gusev, A., et al. (2015) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/014241 · Cited by 33 · see citing works
Self-citationFulfilling the promise of Mendelian randomizationPreprint
Pickrell, J. (2015) · bioRxiv · doi:10.1101/018150 · Cited by 26 · see citing works
JEPEG: a summary statistics based tool for gene-level joint testing of functional variants
Lee, D., Williamson, V. S., Bigdeli, T. B., Riley, B. P., Fanous, A. H., Vladimirov, V. I., & Bacanu, S. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu816 · Cited by 26 · see citing works
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Visscher, P. M., Wray, N. R., Zhang, Q., Sklar, P., McCarthy, M. I., Brown, M. A., & Yang, J. (2017) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2017.06.005 · Cited by 3,719 · see citing works
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Zhu, Z., Zhang, F., Hu, H., Bakshi, A., Robinson, M. R., Powell, J. E., Montgomery, G. W., Goddard, M. E., Wray, N. R., Visscher, P. M., et al. (2016) · Nature Genetics · doi:10.1038/ng.3538 · Cited by 3,352 · see citing works
eQTLGen, Wray, N. R., 23andMe, the Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium, Ripke, S., Mattheisen, M., Trzaskowski, M., Byrne, E. M., Abdellaoui, A., Adams, M. J., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0090-3 · Cited by 3,005 · see citing works
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Hasin, Y., Seldin, M., & Lusis, A. (2017) · Genome Biology · doi:10.1186/s13059-017-1215-1 · Cited by 2,953 · see citing works
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Trubetskoy, V., Pardiñas, A. F., Qi, T., Panagiotaropoulou, G., Awasthi, S., Bigdeli, T. B., Bryois, J., Chen, C.-Y., Dennison, C. A., Hall, L. S., et al. (2022) · Nature · doi:10.1038/s41586-022-04434-5 · Cited by 2,964 · see citing works
Self-citationGenome-wide association studies
Uffelmann, E., Huang, Q. Q., Munung, N. S., de Vries, J., Okada, Y., Martin, A. R., Martin, H. C., Lappalainen, T., & Posthuma, D. (2021) · Nature Reviews Methods Primers · doi:10.1038/s43586-021-00056-9 · Cited by 1,849 · see citing works
Evaluating the potential role of pleiotropy in Mendelian randomization studies
Hemani, G., Bowden, J., & Davey Smith, G. (2018) · Human Molecular Genetics · doi:10.1093/hmg/ddy163 · Cited by 1,496 · see citing works
Transcriptome-scale spatial gene expression in the human dorsolateral prefrontal cortex
Maynard, K. R., Collado-Torres, L., Weber, L. M., Uytingco, C., Barry, B. K., Williams, S. R., Catallini, J. L., Tran, M. N., Besich, Z., Tippani, M., et al. (2021) · Nature Neuroscience · doi:10.1038/s41593-020-00787-0 · Cited by 1,222 · see citing works
Finucane, H. K., Reshef, Y. A., Anttila, V., Slowikowski, K., Gusev, A., Byrnes, A., Gazal, S., Loh, P.-R., Lareau, C., Shoresh, N., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0081-4 · Cited by 1,097 · see citing works
Self-citationTranscriptome-wide isoform-level dysregulation in ASD, schizophrenia, and bipolar disorder
Gandal, M. J., Zhang, P., Hadjimichael, E., Walker, R. L., Chen, C., Liu, S., Won, H., van Bakel, H., Varghese, M., Wang, Y., et al. (2018) · Science · doi:10.1126/science.aat8127 · Cited by 1,247 · see citing works
Self-citationZheng, J., Haberland, V., Baird, D., Walker, V., Haycock, P., Hurle, M., Gutteridge, A., Erola, P., Liu, Y., Luo, S., et al. (2019) · doi:10.1101/627398 · Cited by 812 · see citing works
Integrative analysis of omics summary data reveals putative mechanisms underlying complex traits
Wu, Y., Zeng, J., Zhang, F., Zhu, Z., Qi, T., Zheng, Z., Lloyd-Jones, L. R., Marioni, R. E., Martin, N. G., Montgomery, G. W., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-03371-0 · Cited by 685 · see citing works
Colocalization of GWAS and eQTL Signals Detects Target Genes
Hormozdiari, F., van de Bunt, M., Segrè, A. V., Li, X., Joo, J. W. J., Bilow, M., Sul, J. H., Sankararaman, S., Pasaniuc, B., & Eskin, E. (2016) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2016.10.003 · Cited by 697 · see citing works
Gusev, A., Mancuso, N., Won, H., Kousi, M., Finucane, H. K., Reshef, Y., Song, L., Safi, A., Schizophrenia Working Group of the Psychiatric Genomics Consortium, McCarroll, S., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0092-1 · Cited by 491 · see citing works
Self-citationThe International League Against Epilepsy Consortium on Complex Epilepsies, Abou-Khalil, B., Auce, P., Avbersek, A., Bahlo, M., Balding, D. J., Bast, T., Baum, L., Becker, A. J., Becker, F., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-07524-z · Cited by 482 · see citing works
A common haplotype lowers PU.1 expression in myeloid cells and delays onset of Alzheimer's disease
The International Genomics of Alzheimer's Project, Huang, K.-L., Initiative, T. A. D. N., Marcora, E., Pimenova, A. A., Di Narzo, A. F., Kapoor, M., Jin, S. C., Harari, O., Bertelsen, S., et al. (2017) · Nature Neuroscience · doi:10.1038/nn.4587 · Cited by 423 · see citing works
Distinguishing genetic correlation from causation across 52 diseases and complex traits
O’Connor, L. J., & Price, A. L. (2018) · Nature Genetics · doi:10.1038/s41588-018-0255-0 · Cited by 394 · see citing works
Self-citationEliciting priors and relaxing the single causal variant assumption in colocalisation analyses
Wallace, C. (2020) · PLOS Genetics · doi:10.1371/journal.pgen.1008720 · Cited by 441 · see citing works
Qi, T., Wu, Y., Zeng, J., Zhang, F., Xue, A., Jiang, L., Zhu, Z., Kemper, K., Yengo, L., Zheng, Z., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-04558-1 · Cited by 427 · see citing works
Self-citationLarge-scale GWAS reveals insights into the genetic architecture of same-sex sexual behavior
Ganna, A., Verweij, K. J. H., Nivard, M. G., Maier, R., Wedow, R., Busch, A. S., Abdellaoui, A., Guo, S., Sathirapongsasuti, J. F., 23andMe Research Team, et al. (2019) · Science · doi:10.1126/science.aat7693 · Cited by 406 · see citing works
Ratnapriya, R., Sosina, O. A., Starostik, M. R., Kwicklis, M., Kapphahn, R. J., Fritsche, L. G., Walton, A., Arvanitis, M., Gieser, L., Pietraszkiewicz, A., et al. (2019) · Nature Genetics · doi:10.1038/s41588-019-0351-9 · Cited by 312 · see citing works
RICOPILI: Rapid Imputation for COnsortias PIpeLIne
Lam, M., Awasthi, S., Watson, H. J., Goldstein, J., Panagiotaropoulou, G., Trubetskoy, V., Karlsson, R., Frei, O., Fan, C.-C., De Witte, W., et al. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btz633 · Cited by 315 · see citing works
Systematic differences in discovery of genetic effects on gene expression and complex traits
Mostafavi, H., Spence, J. P., Naqvi, S., & Pritchard, J. K. (2023) · Nature Genetics · doi:10.1038/s41588-023-01529-1 · Cited by 336 · see citing works
Porcu, E., Rüeger, S., Lepik, K., eQTLGen Consortium, Agbessi, M., Ahsan, H., Alves, I., Andiappan, A., Arindrarto, W., Awadalla, P., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-10936-0 · Cited by 322 · see citing works
Self-citationMultivariate genome-wide analyses of the well-being spectrum
BIOS consortium, Baselmans, B. M. L., Social Science Genetic Association Consortium, Jansen, R., Ip, H. F., van Dongen, J., Abdellaoui, A., van de Weijer, M. P., Bao, Y., Smart, M., et al. (2019) · Nature Genetics · doi:10.1038/s41588-018-0320-8 · Cited by 320 · see citing works
Self-citationWhere Are the Disease-Associated eQTLs?
Umans, B. D., Battle, A., & Gilad, Y. (2021) · Trends in Genetics · doi:10.1016/j.tig.2020.08.009 · Cited by 296 · see citing works
Sey, N. Y. A., Hu, B., Mah, W., Fauni, H., McAfee, J. C., Rajarajan, P., Brennand, K. J., Akbarian, S., & Won, H. (2020) · Nature Neuroscience · doi:10.1038/s41593-020-0603-0 · Cited by 297 · see citing works
Extreme Polygenicity of Complex Traits Is Explained by Negative Selection
O'Connor, L. J., Schoech, A. P., Hormozdiari, F., Gazal, S., Patterson, N., & Price, A. L. (2019) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2019.07.003 · Cited by 268 · see citing works
Self-citationPirruccello, J. P., Bick, A., Wang, M., Chaffin, M., Friedman, S., Yao, J., Guo, X., Venkatesh, B. A., Taylor, K. D., Post, W. S., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-15823-7 · Cited by 267 · see citing works
Locus for severity implicates CNS resilience in progression of multiple sclerosis
International Multiple Sclerosis Genetics Consortium, Harroud, A., Stridh, P., McCauley, J. L., Saarela, J., van den Bosch, A. M. R., Engelenburg, H. J., Beecham, A. H., Alfredsson, L., Alikhani, K., et al. (2023) · Nature · doi:10.1038/s41586-023-06250-x · Cited by 243 · see citing works
The road to precision psychiatry: translating genetics into disease mechanisms
Gandal, M. J., Leppa, V., Won, H., Parikshak, N. N., & Geschwind, D. H. (2016) · Nature Neuroscience · doi:10.1038/nn.4409 · Cited by 223 · see citing works
Lindström, S., Wang, L., Smith, E. N., Gordon, W., van Hylckama Vlieg, A., de Andrade, M., Brody, J. A., Pattee, J. W., Haessler, J., Brumpton, B. M., et al. (2019) · Blood · doi:10.1182/blood.2019000435 · Cited by 223 · see citing works
Deep learning enables genetic analysis of the human thoracic aorta
Pirruccello, J. P., Chaffin, M. D., Chou, E. L., Fleming, S. J., Lin, H., Nekoui, M., Khurshid, S., Friedman, S. F., Bick, A. G., Arduini, A., et al. (2022) · Nature Genetics · doi:10.1038/s41588-021-00962-4 · Cited by 210 · see citing works
Interrogation of human hematopoiesis at single-cell and single-variant resolution
Ulirsch, J. C., Lareau, C. A., Bao, E. L., Ludwig, L. S., Guo, M. H., Benner, C., Satpathy, A. T., Kartha, V. K., Salem, R. M., Hirschhorn, J. N., et al. (2019) · Nature Genetics · doi:10.1038/s41588-019-0362-6 · Cited by 208 · see citing works
Combining SNP-to-gene linking strategies to identify disease genes and assess disease omnigenicity
Gazal, S., Weissbrod, O., Hormozdiari, F., Dey, K. K., Nasser, J., Jagadeesh, K. A., Weiner, D. J., Shi, H., Fulco, C. P., O’Connor, L. J., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01087-y · Cited by 181 · see citing works
Self-citationConditional eQTL analysis reveals allelic heterogeneity of gene expression
Jansen, R., Hottenga, J.-J., Nivard, M. G., Abdellaoui, A., Laport, B., de Geus, E. J., Wright, F. A., Penninx, B. W. J. H., & Boomsma, D. I. (2017) · Human Molecular Genetics · doi:10.1093/hmg/ddx043 · Cited by 141 · see citing works
Single-cell genomics meets human genetics
Cuomo, A. S. E., Nathan, A., Raychaudhuri, S., MacArthur, D. G., & Powell, J. E. (2023) · Nature Reviews Genetics · doi:10.1038/s41576-023-00599-5 · Cited by 124 · see citing works
Thériault, S., Gaudreault, N., Lamontagne, M., Rosa, M., Boulanger, M.-C., Messika-Zeitoun, D., Clavel, M.-A., Capoulade, R., Dagenais, F., Pibarot, P., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-03260-6 · Cited by 135 · see citing works
Humphrey, J., Venkatesh, S., Hasan, R., Herb, J. T., de Paiva Lopes, K., Küçükali, F., Byrska-Bishop, M., Evani, U. S., Narzisi, G., Fagegaltier, D., et al. (2023) · Nature Neuroscience · doi:10.1038/s41593-022-01205-3 · Cited by 138 · see citing works
A Review of Integrative Imputation for Multi-Omics Datasets
Song, M., Greenbaum, J., Luttrell, J., Zhou, W., Wu, C., Shen, H., Gong, P., Zhang, C., & Deng, H.-W. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2020.570255 · Cited by 127 · see citing works
Phenotype-Specific Enrichment of Mendelian Disorder Genes near GWAS Regions across 62 Complex Traits
Freund, M. K., Burch, K. S., Shi, H., Mancuso, N., Kichaev, G., Garske, K. M., Pan, D. Z., Miao, Z., Mohlke, K. L., Laakso, M., et al. (2018) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2018.08.017 · Cited by 102 · see citing works
Self-citationWestern, D., Timsina, J., Wang, L., Wang, C., Yang, C., Phillips, B., Wang, Y., Liu, M., Ali, M., Beric, A., et al. (2024) · Nature Genetics · doi:10.1038/s41588-024-01972-8 · Cited by 111 · see citing works
Pavlides, J. M. W., Zhu, Z., Gratten, J., McRae, A. F., Wray, N. R., & Yang, J. (2016) · Genome Medicine · doi:10.1186/s13073-016-0338-4 · Cited by 109 · see citing works
Lu, Q., Powles, R. L., Abdallah, S., Ou, D., Wang, Q., Hu, Y., Lu, Y., Liu, W., Li, B., Mukherjee, S., et al. (2017) · PLOS Genetics · doi:10.1371/journal.pgen.1006933 · Cited by 97 · see citing works
Sex differences in brain protein expression and disease
Wingo, A. P., Liu, Y., Gerasimov, E. S., Vattathil, S. M., Liu, J., Cutler, D. J., Epstein, M. P., Blokland, G. A. M., Thambisetty, M., Troncoso, J. C., et al. (2023) · Nature Medicine · doi:10.1038/s41591-023-02509-y · Cited by 94 · see citing works
Thériault, S., Dina, C., Messika-Zeitoun, D., Le Scouarnec, S., Capoulade, R., Gaudreault, N., Rigade, S., Li, Z., Simonet, F., Lamontagne, M., et al. (2019) · Circulation: Genomic and Precision Medicine · doi:10.1161/circgen.119.002617 · Cited by 90 · see citing works
TWAS results are complementary to and less affected by linkage disequilibrium than GWAS
Li, D., Liu, Q., & Schnable, P. S. (2021) · Plant Physiology · doi:10.1093/plphys/kiab161 · Cited by 71 · see citing works
Genome-wide association identifies the first risk loci for psychosis in Alzheimer disease
DeMichele-Sweet, M. A. A., Klei, L., Creese, B., Harwood, J. C., Weamer, E. A., McClain, L., Sims, R., Hernandez, I., Moreno-Grau, S., Tárraga, L., et al. (2021) · Molecular Psychiatry · doi:10.1038/s41380-021-01152-8 · Cited by 64 · see citing works
Strunz, T., Lauwen, S., Kiel, C., International AMD Genomics Consortium (IAMDGC), Fritsche, L. G., Igl, W., Bailey, J. N. C., Grassmann, F., Sengupta, S., Bragg-Gresham, J. L., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-58510-9 · Cited by 60 · see citing works
Detecting genome-wide directional effects of transcription factor binding on polygenic disease risk
Reshef, Y. A., Finucane, H. K., Kelley, D. R., Gusev, A., Kotliar, D., Ulirsch, J. C., Hormozdiari, F., Nasser, J., O’Connor, L., van de Geijn, B., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0196-7 · Cited by 68 · see citing works
Self-citationZhao, T., Zeng, J., & Cheng, H. (2022) · Genetics · doi:10.1093/genetics/iyac034 · Cited by 52 · see citing works
Optimized design of single-cell RNA sequencing experiments for cell-type-specific eQTL analysis
Mandric, I., Schwarz, T., Majumdar, A., Hou, K., Briscoe, L., Perez, R., Subramaniam, M., Hafemeister, C., Satija, R., Ye, C. J., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-19365-w · Cited by 62 · see citing works
Imaging-wide association study: integrating imaging endophenotypes in GWAS
Xu, Z., Wu, C., & Pan, W. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.07.036 · Cited by 56 · see citing works
Barbeira, A. N., Dickinson, S. P., Bonazzola, R., Zheng, J., Wheeler, H. E., Torres, J. M., Torstenson, E. S., Shah, K. P., Garcia, T., Edwards, T. L., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-03621-1 · Cited by 69 · see citing works
Self-citationMah, W., & Won, H. (2020) · Schizophrenia Research · doi:10.1016/j.schres.2019.03.007 · Cited by 38 · see citing works
Zhang, Q., Linke, V., Overmyer, K. A., Traeger, L. L., Kasahara, K., Miller, I. J., Manson, D. E., Polaske, T. J., Kerby, R. L., Kemis, J. H., et al. (2023) · Nature Microbiology · doi:10.1038/s41564-023-01326-w · Cited by 43 · see citing works
Self-citationLi, Z., Gaudreault, N., Arsenault, B. J., Mathieu, P., Bossé, Y., & Thériault, S. (2020) · Communications Biology · doi:10.1038/s42003-020-01210-x · Cited by 23 · see citing works
Dashti, H. S., Jones, S. E., Wood, A. R., Lane, J. M., van Hees, V. T., Wang, H., Rhodes, J. A., Song, Y., Patel, K., Anderson, S. G., et al. (2018) · doi:10.1101/274977 · Cited by 614 · see citing works
Du, Y., Li, P., Wen, Y., Liang, X., Liu, L., Cheng, B., Ding, M., Zhao, Y., Ma, M., Zhang, L., et al. (2020) · Calcified Tissue International · doi:10.1007/s00223-019-00640-y · Cited by 14 · see citing works
Vattathil, S. M., Gerasimov, E. S., Canon, S. M., Lori, A., Tan, S. S. M., Kim, P. J., Liu, Y., Lai, E. C., Bennett, D. A., Wingo, T. S., et al. (2024) · Nature Aging · doi:10.1038/s43587-024-00778-x · Cited by 14 · see citing works
High false sign rates in transcriptome-wide association studies
Gerlach, P. A., Milind, N., Spence, J. P., & Pritchard, J. K. (2026) · Nature Genetics · doi:10.1038/s41588-026-02796-4 · Cited by 1 · see citing works
Western, D., Yang, C., Timsina, J., Liu, M., Wang, C., Campbell, M. C., Kotzbauer, P., Perlmutter, J. S., Schindler, S. E., Morris, J. C., et al. (2026) · Science Translational Medicine · doi:10.1126/scitranslmed.adz2016 · Cited by 1 · see citing works
TFXcan reveals transcription factor programs driving GWAS loci
Adeluwa, T., Salazar-Magaña, S., Sumner, S., Gona, S., Nyasimi, F., Carbonetto, P., Baca, S. C., Freedman, M., Gusev, A., Liu, B., et al. (2026) · Cell Genomics · doi:10.1016/j.xgen.2026.101398
Self-citation
- Authors
- Gusev, A., Ko, A., Shi, H., Bhatia, G., Chung, W., Penninx, B. W. J. H., Jansen, R., de Geus, E. J. C., Boomsma, D. I., Wright, F. A., Sullivan, P. F., Nikkola, E., Alvarez, M., Civelek, M., Lusis, A. J., Lehtimäki, T., Raitoharju, E., Kähönen, M., Seppälä, I., Raitakari, O. T., Kuusisto, J., Laakso, M., Price, A. L., Pajukanta, P., & Pasaniuc, B.
- Affiliations
- Broad Institute (US)
- University of California, Los Angeles (US)
- Harvard University (US)
- Amsterdam UMC Location Vrije Universiteit Amsterdam (NL)
- Vrije Universiteit Amsterdam (NL)
- North Carolina State University (US)
- University of North Carolina at Chapel Hill (US)
- Tampere University (FI)
- Pirkanmaa Hospital District (FI)
- University of Turku (FI)
- University of Eastern Finland (FI)
- Corresponding author
- Gusev, Alexander
- ORCID
- Gusev, Alexander
- Ko, Arthur
- Shi, Huwenbo
- Chung, Wonil
- Penninx, Brenda W J H
- Jansen, Rick
- de Geus, Eco J C
- Boomsma, Dorret I
- Wright, Fred A
- Sullivan, Patrick F
- Alvarez, Marcus
- Civelek, Mete
- Lusis, Aldons J
- Lehtimäki, Terho
- Raitoharju, Emma
- Kähönen, Mika
- Seppälä, Ilkka
- Raitakari, Olli T
- Kuusisto, Johanna
- Laakso, Markku
- Price, Alkes L
- Pajukanta, Päivi
- Venue
- Nature Genetics
- Published
- 2016-02-08
- Volume
- 48
- Issue
- 3
- Pages
- 245-252
- ISSN
- 1061-4036 (ISSN-L)
- 1546-1718
- PMC
- PMC4767558
- OpenAlex
- W2264585211
- Cited by
- 2,479
- References
- 70
- Citations by year
- 2026: 321 · 2025: 397 · 2024: 370 · 2023: 280 · 2022: 281 · 2021: 250 · 2020: 211 · 2019: 161 · 2018: 111 · 2017: 66 · 2016: 27 · 2014: 1
- FWCI
- 98.56
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Genetics (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed (publishedVersion)
- Amsterdam UMC (VU Amsterdam) - Institutional Repository (publishedVersion)
- PubMed Central (acceptedVersion)
- Carolina Digital Repository (University of North Carolina at Chapel Hill) (submittedVersion)
- Pure Amsterdam UMC (submittedVersion, other-oa)
- eScholarship (California Digital Library) (submittedVersion)
- VU Research Portal (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- UNC Libraries
- Funders
- National Institutes of Health (HL‐28481, HG002536)
- National Heart, Lung, and Blood Institute (HL28481, HL-095056, R01HL095056, F31HL127921, P01HL028481)
- National Human Genome Research Institute (T32HG002536)
- National Institute of General Medical Sciences (R01GM105857, F32GM106584, R25GM055052, R01 GM053725)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic and phenotypic traits in livestock
- Keywords
- transcriptome-wide association study, obesity-related traits, body mass index, lipids, height, complex traits, genetic variants, adipose tissue, blood, Hybrid Mouse Diversity Panel
- MeSH terms
- Animals, Gene Expression Regulation, Genotype, Humans, Obesity, Phenotype, Genetic Predisposition to Disease, Quantitative Trait Loci, Mice, Genome-Wide Association Study, Transcriptome
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