Using probabilistic estimation of expression residuals (PEER) to obtain increased power and interpretability of gene expression analyses

PLINK: a tool set for whole-genome association and population-based linkage analyses.

Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M. A. R., Bender, D., Maller, J., Sklar, P., de Bakker, P. I. W., Daly, M. J., et al. (2007) · The American Journal of Human Genetics · doi:10.1086/519795 · Cited by 34,089 · see citing works

Differential expression analysis for sequence count data

Anders, S., & Huber, W. (2010) · Genome Biology · doi:10.1186/gb-2010-11-10-r106 · Cited by 15,581 · see citing works

Statistical significance for genomewide studies

Storey, J. D., & Tibshirani, R. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1530509100 · Cited by 9,692 · see citing works

A map of human genome variation from population-scale sequencing.

The 1000 Genomes Project Consortium (2010) · Nature · doi:10.1038/nature09534 · Cited by 7,877 · see citing works

R/qtl: QTL mapping in experimental crosses

Broman, K. W., Wu, H., Sen, Ś., & Churchill, G. A. (2003) · Bioinformatics · doi:10.1093/bioinformatics/btg112 · Cited by 3,481 · see citing works

Variance stabilization applied to microarray data calibration and to the quantification of differential expression

Huber, W., von Heydebreck, A., Sültmann, H., Poustka, A., & Vingron, M. (2002) · Bioinformatics · doi:10.1093/bioinformatics/18.suppl_1.s96 · Cited by 2,440 · see citing works

Capturing Heterogeneity in Gene Expression Studies by Surrogate Variable Analysis

Leek, J. T., & Storey, J. D. (2007) · PLoS Genetics · doi:10.1371/journal.pgen.0030161 · Cited by 1,930 · see citing works

Genetic Dissection of Transcriptional Regulation in Budding Yeast

Brem, R. B., Yvert, G., Clinton, R., & Kruglyak, L. (2002) · Science · doi:10.1126/science.1069516 · Cited by 1,392 · see citing works

Understanding mechanisms underlying human gene expression variation with RNA sequencing

Pickrell, J. K., Marioni, J. C., Pai, A. A., Degner, J. F., Engelhardt, B. E., Nkadori, E., Veyrieras, J.-B., Stephens, M., Gilad, Y., & Pritchard, J. K. (2010) · Nature · doi:10.1038/nature08872 · Cited by 1,256 · see citing works

Population genomics of human gene expression

Stranger, B. E., Nica, A. C., Forrest, M. S., Dimas, A. S., Bird, C., Beazley, C., Ingle, C., Dunning, M., Flicek, P., Koller, D., et al. (2007) · Nature Genetics · doi:10.1038/ng2142 · Cited by 1,132 · see citing works

An integrative genomics approach to infer causal associations between gene expression and disease

Schadt, E. E., Lamb, J., Yang, X., Zhu, J., Edwards, S., GuhaThakurta, D., Sieberts, S. K., Monks, S., Reitman, M., Zhang, C., et al. (2005) · Nature Genetics · doi:10.1038/ng1589 · Cited by 1,043 · see citing works

Probable networks and plausible predictions — a review of practical Bayesian methods for supervised neural networks

Mackay, D. J. C. (1995) · Network Computation in Neural Systems · doi:10.1088/0954-898x/6/3/011 · Cited by 915 · 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

Genetics of global gene expression

Rockman, M. V., & Kruglyak, L. (2006) · Nature Reviews Genetics · doi:10.1038/nrg1964 · Cited by 666 · cited 2× · see citing works

Handbook of Statistical Genetics

Balding, D., Bishop, M., & Cannings, C. (2004) · Wiley eBooks · doi:10.1002/0470022620 · Cited by 619 · see citing works

Integrating large-scale functional genomic data to dissect the complexity of yeast regulatory networks

Zhu, J., Zhang, B., Smith, E. N., Drees, B., Brem, R. B., Kruglyak, L., Bumgarner, R. E., & Schadt, E. E. (2008) · Nature Genetics · doi:10.1038/ng.167 · Cited by 549 · see citing works

Genome-wide genetic association of complex traits in heterogeneous stock mice

Valdar, W. S. J., Solberg, L. C., Gauguier, D., Heyes, S. B., Klenerman, P., Cookson, W., Taylor, M. S., Rawlins, J. N. P., Mott, R., & Flint, J. (2006) · Nature Genetics · doi:10.1038/ng1840 · Cited by 548 · see citing works

A Bayesian Framework to Account for Complex Non-Genetic Factors in Gene Expression Levels Greatly Increases Power in eQTL Studies

Stegle, O., Parts, L., Durbin, R., & Winn, J. (2010) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000770 · Cited by 497 · see citing works

Gene–Environment Interaction in Yeast Gene Expression

Smith, E. N., & Kruglyak, L. (2008) · PLoS Biology · doi:10.1371/journal.pbio.0060083 · Cited by 421 · see citing works

The Architecture of Gene Regulatory Variation across Multiple Human Tissues: The MuTHER Study

Nica, A. C., Parts, L., Glass, D., Nisbet, J. P., Barrett, A., Sekowska, M., Travers, M. E., Potter, S., Grundberg, E., Small, K. S., et al. (2011) · PLoS Genetics · doi:10.1371/journal.pgen.1002003 · Cited by 395 · see citing works

Identification of an imprinted master trans regulator at the KLF14 locus related to multiple metabolic phenotypes

Small, K. S., Grundberg, E., Soranzo, N., Ahmadi, K. R., Spector, T. D., Shin, S., Deloukas, P., Hedman, Å. K., Lindgren, C. M., McCarthy, M. I., et al. (2011) · Nature Genetics · doi:10.1038/ng.833 · Cited by 319 · see citing works

Genetic interactions between polymorphisms that affect gene expression in yeast

Brem, R. B., Storey, J. D., Whittle, J., & Kruglyak, L. (2005) · Nature · doi:10.1038/nature03865 · Cited by 298 · see citing works

<i>Cis</i> -acting expression quantitative trait loci in mice

Doss, S., Schadt, E. E., Drake, T. A., & Lusis, A. J. (2005) · Genome Research · doi:10.1101/gr.3216905 · Cited by 273 · see citing works

Genetics of human gene expression: mapping DNA variants that influence gene expression

Cheung, V. G., & Spielman, R. S. (2009) · Nature Reviews Genetics · doi:10.1038/nrg2630 · Cited by 233 · see citing works

Genetical Genomics: Spotlight on QTL Hotspots

Rainer Breitling, A., Li, Y., Tesson, B. M., Fu, J., Wu, C., Wiltshire, T., Gerrits, A., Bystrykh, L. V., de Haan, G., Su, A. I., et al. (2008) · PLoS Genetics · doi:10.1371/journal.pgen.1000232 · Cited by 191 · see citing works

Using genetic markers to orient the edges in quantitative trait networks: The NEO software

Aten, J. E., Fuller, T. F., Lusis, A. J., & Horvath, S. (2008) · BMC Systems Biology · doi:10.1186/1752-0509-2-34 · Cited by 147 · see citing works

Accurate Discovery of Expression Quantitative Trait Loci Under Confounding From Spurious and Genuine Regulatory Hotspots

Kang, H. M., Ye, C., & Eskin, E. (2008) · Genetics · doi:10.1534/genetics.108.094201 · Cited by 142 · see citing works

Correction for hidden confounders in the genetic analysis of gene expression

Listgarten, J., Kadie, C. M., Schadt, E. E., & Heckerman, D. E. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1002425107 · Cited by 141 · see citing works

Identifying regulatory mechanisms using individual variation reveals key role for chromatin modification

Lee, S., Pe’er, D., Dudley, A. M., Church, G. M., & Koller, D. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0601852103 · Cited by 132 · see citing works

Handbook of Statistical Genetics

(2007) · Wiley eBooks · doi:10.1002/9780470061619 · Cited by 129 · see citing works

Joint Genetic Analysis of Gene Expression Data with Inferred Cellular Phenotypes

Parts, L., Stegle, O., Winn, J. M., & Durbin, R. M. (2011) · PLoS Genetics · doi:10.1371/journal.pgen.1001276 · Cited by 82 · see citing works

eQTL Analysis in Humans

Franke, L., & Jansen, R. C. (2009) · Methods in molecular biology · doi:10.1007/978-1-60761-247-6_17 · Cited by 70 · see citing works

A Bayesian Partition Method for Detecting Pleiotropic and Epistatic eQTL Modules

Zhang, W., Zhu, J., Schadt, E. E., & Liu, J. S. (2010) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000642 · Cited by 69 · see citing works

puma: a Bioconductor package for propagating uncertainty in microarray analysis

Pearson, R. D., Liu, X., Sanguinetti, G., Milo, M., Lawrence, N. D., & Rattray, M. (2009) · BMC Bioinformatics · doi:10.1186/1471-2105-10-211 · Cited by 69 · see citing works

Extreme Clonality in Lymphoblastoid Cell Lines with Implications for Allele Specific Expression Analyses

Plagnol, V., Uz, E., Wallace, C., Stevens, H. E., Clayton, D. G., Özçelık, T., & Todd, J. A. (2008) · PLoS ONE · doi:10.1371/journal.pone.0002966 · Cited by 53 · see citing works

Mapping gene expression quantitative trait loci by singular value decomposition and independent component analysis

Biswas, S., Storey, J. D., & Akey, J. M. (2008) · BMC Bioinformatics · doi:10.1186/1471-2105-9-244 · Cited by 42 · see citing works

Inference algorithms and learning theory for Bayesian sparse factor analysis

Rattray, M., Stegle, O., Sharp, K., & Winn, J. M. (2009) · Journal of Physics Conference Series · doi:10.1088/1742-6596/197/1/012002 · Cited by 19 · see citing works

Erratum: Identification of an imprinted master trans regulator at the KLF14 locus related to multiple metabolic phenotypes (Nature Genetics (2011) 43 (561-564))

Small, K. S., Hedman, Å. K., Grundberg, E., Nica, A. C., Þorleifsson, G., Kong, A., Thorsteindottir, U., Shin, S., Richards, H. B., Soranzo, N., et al. (2011) · Surrey Research Insight Open Access (The University of Surrey) · doi:10.1038/ng1011-1040c · Cited by 7 · see citing works

Fast, sensitive and accurate integration of single-cell data with Harmony

Korsunsky, I., Millard, N., Fan, J., Slowikowski, K., Zhang, F., Wei, K., Baglaenko, Y., Brenner, M., Loh, P.-R., & Raychaudhuri, S. (2019) · Nature Methods · doi:10.1038/s41592-019-0619-0 · Cited by 10,488 · see citing works

The Genotype-Tissue Expression (GTEx) pilot analysis: Multitissue gene regulation in humans

The GTEx Consortium, Ardlie, K. G., Deluca, D. S., Segrè, A. V., Sullivan, T. J., Young, T. R., Gelfand, E. T., Trowbridge, C. A., Maller, J. B., Tukiainen, T., et al. (2015) · Science · doi:10.1126/science.1262110 · Cited by 5,062 · see citing works

Genetic effects on gene expression across human tissues

GTEx Consortium (2017) · Nature · doi:10.1038/nature24277 · Cited by 4,036 · see citing works

Integrative approaches for large-scale transcriptome-wide association studies

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., et al. (2016) · Nature Genetics · doi:10.1038/ng.3506 · Cited by 2,479 · see citing works

Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps

Mahajan, A., Taliun, D., Thurner, M., Robertson, N. R., Torres, J. M., Rayner, N. W., Payne, A. J., Steinthorsdottir, V., Scott, R. A., Grarup, N., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0241-6 · Cited by 1,744 · see citing works

Scater: Pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R

McCarthy, D. J., Campbell, K. R., Lun, A. T. L., & Wills, Q. F. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btw777 · Cited by 1,852 · see citing works

A 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

Computational and analytical challenges in single-cell transcriptomics

Stegle, O., Teichmann, S. A., & Marioni, J. C. (2015) · Nature Reviews Genetics · doi:10.1038/nrg3833 · Cited by 1,170 · see citing works

Gene expression elucidates functional impact of polygenic risk for schizophrenia

Fromer, M., Roussos, P., Sieberts, S. K., Johnson, J. S., Kavanagh, D. H., Perumal, T. M., Ruderfer, D. M., Oh, E. C., Topol, A., Shah, H. R., et al. (2016) · Nature Neuroscience · doi:10.1038/nn.4399 · Cited by 1,091 · 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

Rewiring of the Fruit Metabolome in Tomato Breeding

Zhu, G., Wang, S., Huang, Z., Zhang, S., Liao, Q., Zhang, C., Lin, T., Mao, Q., Meng, P., Yang, C., et al. (2018) · Cell · doi:10.1016/j.cell.2017.12.019 · Cited by 952 · see citing works

RNA Sequencing and Analysis

Kukurba, K. R., & Montgomery, S. B. (2015) · Cold Spring Harbor Protocols · doi:10.1101/pdb.top084970 · Cited by 924 · see citing works

Opportunities and challenges for transcriptome-wide association studies

Wainberg, M., Sinnott-Armstrong, N., Mancuso, N., Barbeira, A., Knowles, D. A., Golan, D., Ermel, R., Ruusalepp, A., Quertermous, T., Hao, K., et al. (2019) · Nature Genetics · doi:10.1038/s41588-019-0385-z · Cited by 929 · see citing works

The impact of sex on gene expression across human tissues

Oliva, M., Muñoz-Aguirre, M., Kim-Hellmuth, S., Wucher, V., Gewirtz, A. D., Cotter, D. J., Parsana, P., Kasela, S., Balliu, B., Viñuela, A., et al. (2020) · Science · doi:10.1126/science.aba3066 · Cited by 679 · see citing works

Genetic Drivers of Epigenetic and Transcriptional Variation in Human Immune Cells

Chen, L., Ge, B., Casale, F. P., Vasquez, L., Kwan, T., Garrido-Martín, D., Watt, S., Yan, Y., Kundu, K., Ecker, S., et al. (2016) · Cell · doi:10.1016/j.cell.2016.10.026 · Cited by 679 · see citing works

Fast and efficient QTL mapper for thousands of molecular phenotypes

Ongen, H., Buil, A., Brown, A. A., Dermitzakis, E. T., & Delaneau, O. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv722 · Cited by 571 · see citing works

Graph pangenome captures missing heritability and empowers tomato breeding

Zhou, Y., Zhang, Z., Bao, Z., Li, H., Lyu, Y., Zan, Y., Wu, Y., Cheng, L. Z., Fang, Y., Wu, K., et al. (2022) · Nature · doi:10.1038/s41586-022-04808-9 · Cited by 527 · see citing works

A test metric for assessing single-cell RNA-seq batch correction

Büttner, M., Miao, Z., Wolf, F. A., Teichmann, S. A., & Theis, F. J. (2019) · Nature Methods · doi:10.1038/s41592-018-0254-1 · Cited by 511 · see citing works

The impact of structural variation on human gene expression

GTEx Consortium, Chiang, C., Scott, A. J., Davis, J. R., Tsang, E. K., Li, X., Kim, Y., Hadzic, T., Damani, F. N., Ganel, L., et al. (2017) · Nature Genetics · doi:10.1038/ng.3834 · Cited by 495 · see citing works

Genetics meets proteomics: perspectives for large population-based studies

Suhre, K., McCarthy, M. I., & Schwenk, J. M. (2021) · Nature Reviews Genetics · doi:10.1038/s41576-020-0268-2 · Cited by 478 · see citing works

Computational principles and challenges in single-cell data integration

Argelaguet, R., Cuomo, A. S. E., Stegle, O., & Marioni, J. C. (2021) · Nature Biotechnology · doi:10.1038/s41587-021-00895-7 · Cited by 472 · see citing works

Genome‐wide mapping of plasma protein QTLs identifies putatively causal genes and pathways for cardiovascular disease

Yao, C., Chen, G., Song, C., Keefe, J., Mendelson, M., Huan, T., Sun, B. B., Laser, A., Maranville, J. C., Wu, H., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-05512-x · Cited by 431 · see citing works

Genomic basis for RNA alterations in cancer

PCAWG Transcriptome Core Group, Calabrese, C., Davidson, N. R., Demircioğlu, D., Fonseca, N. A., He, Y., Kahles, A., Lehmann, K.-V., Liu, F., Shiraishi, Y., et al. (2020) · Nature · doi:10.1038/s41586-020-1970-0 · Cited by 396 · see citing works

Plasma proteome analyses in individuals of European and African ancestry identify cis-pQTLs and models for proteome-wide association studies

Zhang, J., Dutta, D., Köttgen, A., Tin, A., Schlosser, P., Grams, M. E., Harvey, B., CKDGen Consortium, Yu, B., Boerwinkle, E., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01051-w · Cited by 373 · see citing works

A super pan-genomic landscape of rice

Shang, L., Li, X., He, H., Yuan, Q., Song, Y., Wei, Z., Lin, H., Hu, M., Zhao, F., Zhang, C., et al. (2022) · Cell Research · doi:10.1038/s41422-022-00685-z · Cited by 375 · see citing works

A guide for the diagnosis of rare and undiagnosed disease: beyond the exome

Marwaha, S., Knowles, J. W., & Ashley, E. A. (2022) · Genome Medicine · doi:10.1186/s13073-022-01026-w · Cited by 365 · see citing works

Quantitative variability of 342 plasma proteins in a human twin population

Liu, Y., Buil, A., Collins, B. C., Gillet, L. C., Blum, L. C., Cheng, L., Vitek, O., Mouritsen, J., Lachance, G., Spector, T. D., et al. (2015) · Molecular Systems Biology · doi:10.15252/msb.20145728 · Cited by 328 · see citing works

NOREVA: normalization and evaluation of MS-based metabolomics data

Li, B., Tang, J., Yang, Q., Li, S., Cui, X., Li, Y., Chen, Y., Xue, W., Li, X., & Zhu, F. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx449 · Cited by 357 · see citing works

Computational solutions for omics data

Berger, B., Peng, J., & Singh, M. (2013) · Nature Reviews Genetics · doi:10.1038/nrg3433 · Cited by 341 · see citing works

Shared genetic effects on chromatin and gene expression indicate a role for enhancer priming in immune response

HIPSCI Consortium, Alasoo, K., Rodrigues, J., Mukhopadhyay, S., Knights, A. J., Mann, A. L., Kundu, K., Hale, C., Dougan, G., & Gaffney, D. J. (2018) · Nature Genetics · doi:10.1038/s41588-018-0046-7 · Cited by 338 · see citing works

Dynamic landscape of immune cell-specific gene regulation in immune-mediated diseases

Ota, M., Nagafuchi, Y., Hatano, H., Ishigaki, K., Terao, C. C., Takeshima, Y., Yanaoka, H., Kobayashi, S., Okubo, M., Shirai, H., et al. (2021) · Cell · doi:10.1016/j.cell.2021.03.056 · Cited by 322 · see citing works

Identification of rare-disease genes using blood transcriptome sequencing and large control cohorts

Undiagnosed Diseases Network, Frésard, L., Care4Rare Canada Consortium, Smail, C., Ferraro, N. M., Teran, N. A., Li, X., Smith, K. S., Bonner, D., Kernohan, K. D., et al. (2019) · Nature Medicine · doi:10.1038/s41591-019-0457-8 · Cited by 321 · see citing works

Effect of predicted protein-truncating genetic variants on the human transcriptome

Rivas, M. A., Pirinen, M., Conrad, D. F., Lek, M., Tsang, E. K., Karczewski, K. J., Maller, J. B., Kukurba, K. R., DeLuca, D. S., Fromer, M., et al. (2015) · Science · doi:10.1126/science.1261877 · Cited by 313 · see citing works

A multi-tissue atlas of regulatory variants in cattle

Liu, S., Gao, Y., Canela‐Xandri, O., Wang, S., Yu, Y., Cai, W., Li, B., Xiang, R., Chamberlain, A. J., Pairo‐Castineira, E., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01153-5 · Cited by 311 · see citing works

RNA sequencing reveals the complex regulatory network in the maize kernel

Fu, J., Cheng, Y., Linghu, J., Yang, X., Kang, L., Zhang, Z., Zhang, J., He, C., Du, X., Peng, Z., et al. (2013) · Nature Communications · doi:10.1038/ncomms3832 · Cited by 298 · see citing works

Genome assembly of a tropical maize inbred line provides insights into structural variation and crop improvement

Yang, N., Liu, J., Gao, Q., Gui, S., Chen, L., Yang, L., Huang, J., Deng, T., Luo, J., He, L., et al. (2019) · Nature Genetics · doi:10.1038/s41588-019-0427-6 · Cited by 304 · see citing works

Quantifying genetic effects on disease mediated by assayed gene expression levels

Yao, D. W., O’Connor, L. J., Price, A. L., & Gusev, A. (2020) · Nature Genetics · doi:10.1038/s41588-020-0625-2 · Cited by 274 · see citing works

The impact of short tandem repeat variation on gene expression

Feupe, S. F., Margoliash, J., Wang, C., Saini, S., Yanicky, R., Shleizer-Burko, S., Goren, A., & Gymrek, M. (2019) · Nature Genetics · doi:10.1038/s41588-019-0521-9 · Cited by 271 · see citing works

Navigating the pitfalls of applying machine learning in genomics

Whalen, S., Schreiber, J. M., Noble, W. S., & Pollard, K. S. (2021) · Nature Reviews Genetics · doi:10.1038/s41576-021-00434-9 · Cited by 278 · see citing works

Genetic analysis of the human microglial transcriptome across brain regions, aging and disease pathologies

Lopes, K. D. P., Snijders, G. J. L., Humphrey, J., Allan, A., Sneeboer, M. A. M., Navarro, E., Schilder, B. M., Vialle, R. A., Parks, M., Missall, R., et al. (2022) · Nature Genetics · doi:10.1038/s41588-021-00976-y · Cited by 273 · see citing works

Explaining the unique nature of individual gait patterns with deep learning

Horst, F., Lapuschkin, S., Samek, W., Müller, K.-R., & Schöllhorn, W. I. (2019) · Scientific Reports · doi:10.1038/s41598-019-38748-8 · Cited by 273 · see citing works

A compendium of genetic regulatory effects across pig tissues

Teng, J., Gao, Y., Yin, H. H., Bai, Z., Liu, S., Zeng, H., Bai, L., Cai, Z., Zhao, B., Li, X., et al. (2024) · Nature Genetics · doi:10.1038/s41588-023-01585-7 · Cited by 276 · see citing works

A Statistical Framework for Joint eQTL Analysis in Multiple Tissues

Flutre, T., Wen, X., Pritchard, J. K., & Stephens, M. (2013) · PLoS Genetics · doi:10.1371/journal.pgen.1003486 · Cited by 252 · see citing works

The impact of rare variation on gene expression across tissues

Li, X., Kim, Y., Tsang, E. K., Davis, J. R., Damani, F. N., Chiang, C., Hess, G. T., Zappala, Z., Strober, B. J., Scott, A. J., et al. (2017) · Nature · doi:10.1038/nature24267 · Cited by 247 · see citing works

Genetic control of RNA splicing and its distinct role in complex trait variation

Qi, T., Wu, Y., Fang, H., Zhang, F., Liu, S., Zeng, J., & Yang, J. (2022) · Nature Genetics · doi:10.1038/s41588-022-01154-4 · Cited by 237 · see citing works

Reproducibility of high-throughput mRNA and small RNA sequencing across laboratories

The GEUVADIS Consortium, 't Hoen, P. A. C., Friedländer, M. R., Almlöf, J., Sammeth, M., Pulyakhina, I., Anvar, S. Y., Laros, J. F. J., Buermans, H. P. J., Karlberg, O., et al. (2013) · Nature Biotechnology · doi:10.1038/nbt.2702 · Cited by 250 · see citing works

The Genetic Architecture of Gene Expression in Peripheral Blood

Lloyd‐Jones, L. R., Holloway, A., McRae, A. F., Yang, J., Small, K. S., Zhao, J., Zeng, B., Bakshi, A., Metspalu, A., Dermitzakis, M., et al. (2017) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2016.12.008 · Cited by 243 · see citing works

Deep learning predicts hip fracture using confounding patient and healthcare variables

Badgeley, M. A., Zech, J. R., Oakden-Rayner, L., Glicksberg, B. S., Liu, M., Gale, W., McConnell, M. V., Percha, B., Snyder, T. M., & Dudley, J. T. (2019) · npj Digital Medicine · doi:10.1038/s41746-019-0105-1 · Cited by 290 · see citing works

Deciphering colorectal cancer genetics through multi-omic analysis of 100,204 cases and 154,587 controls of European and east Asian ancestries

Fernández–Rozadilla, C., Timofeeva, M. N., Chen, Z., Law, P., Thomas, M., Schmit, S. L., Díez‐Obrero, V., Hsu, L., Fernandez-Tajes, J. J., Palles, C., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01222-9 · Cited by 230 · see citing works

Renal compartment–specific genetic variation analyses identify new pathways in chronic kidney disease

Qiu, C., Huang, S., Park, J., Park, Y., Ko, Y.-A., Seasock, M. J., Bryer, J. S., Xu, X., Song, W., Palmer, M., et al. (2018) · Nature Medicine · doi:10.1038/s41591-018-0194-4 · Cited by 230 · see citing works

PancanQTL: systematic identification of cis-eQTLs and trans-eQTLs in 33 cancer types

Gong, J., Mei, S., Liu, C., Xiang, Y., Ye, Y., Zhang, Z., Feng, J., Renyan, L., Diao, L., Guo, A., et al. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx861 · Cited by 231 · see citing works

Large-scale plasma proteomic analysis identifies proteins and pathways associated with dementia risk

Walker, K. A., Chen, J., Zhang, J., Fornage, M., Yang, Y., Zhou, L., Grams, M. E., Tin, A., Daya, N., Hoogeveen, R. C., et al. (2021) · Nature Aging · doi:10.1038/s43587-021-00064-0 · Cited by 212 · see citing works

OUTRIDER: A Statistical Method for Detecting Aberrantly Expressed Genes in RNA Sequencing Data

Brechtmann, F., Mertes, C., Matusevičiūtė, A., Yépez, V. A., Avsec, Ž., Herzog, M., Bader, D. M., Prokisch, H., & Gagneur, J. (2018) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2018.10.025 · Cited by 223 · see citing works

Integration of eQTL and a Single-Cell Atlas in the Human Eye Identifies Causal Genes for Age-Related Macular Degeneration

Orozco, L. D., Chen, H.-H., Cox, C., Katschke, K. J., Arceo, R., Espiritu, C., Caplazi, P., Nghiem, S. S., Chen, Y., Modrusan, Z. D., et al. (2020) · Cell Reports · doi:10.1016/j.celrep.2019.12.082 · Cited by 212 · see citing works

A transcriptome-wide association study of 229,000 women identifies new candidate susceptibility genes for breast cancer

Collaborators, N., Wu, L., kConFab/AOCS Investigators, Shi, W., Long, J., Guo, X., Michailidou, K., Beesley, J., Bolla, M. K., Shu, X., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0132-x · Cited by 205 · 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

Genetic regulatory signatures underlying islet gene expression and type 2 diabetes

Varshney, A., Scott, L. J., Welch, R., Erdos, M. R., Chines, P. S., Narisu, N., Albanus, R. D., Orchard, P., Wolford, B. N., Kursawe, R., et al. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1621192114 · Cited by 212 · see citing works

Induced Pluripotent Stem Cell Differentiation Enables Functional Validation of GWAS Variants in Metabolic Disease

Warren, C. R., O’Sullivan, J. F., Friesen, M., Becker, C. E., Zhang, X., Liu, P., Wakabayashi, Y., Morningstar, J. E., Shi, X., Choi, J., et al. (2017) · Cell stem cell · doi:10.1016/j.stem.2017.01.010 · Cited by 191 · see citing works

Population-scale tissue transcriptomics maps long non-coding RNAs to complex disease

de Goede, O. M., Nachun, D. C., Ferraro, N. M., Gloudemans, M. J., Rao, A. S., Smail, C., Eulalio, T. Y., Aguet, F., Ng, B., Xu, J., et al. (2021) · Cell · doi:10.1016/j.cell.2021.03.050 · Cited by 194 · see citing works

RNA sequencing provides insights into the evolution of lettuce and the regulation of flavonoid biosynthesis

Zhang, L., Su, W., Tao, R., Zhang, W., Chen, J., Wu, P., Yan, C., Jia, Y., Larkin, R. M., Lavelle, D. O., et al. (2017) · Nature Communications · doi:10.1038/s41467-017-02445-9 · Cited by 191 · see citing works

DNA methylation QTL mapping across diverse human tissues provides molecular links between genetic variation and complex traits

Oliva, M., Demanelis, K., Lu, Y., Chernoff, M., Jasmine, F., Ahsan, H., Kibriya, M. G., Chen, L., & Pierce, B. L. (2022) · Nature Genetics · doi:10.1038/s41588-022-01248-z · Cited by 189 · see citing works

Modeling of RNA-seq fragment sequence bias reduces systematic errors in transcript abundance estimation

Love, M. I., Hogenesch, J. B., & Irizarry, R. A. (2016) · Nature Biotechnology · doi:10.1038/nbt.3682 · Cited by 186 · see citing works

Identifying temporal and spatial patterns of variation from multimodal data using MEFISTO

Velten, B., Braunger, J. M., Argelaguet, R., Arnol, D., Wirbel, J., Bredikhin, D., Zeller, G., & Stegle, O. (2022) · Nature Methods · doi:10.1038/s41592-021-01343-9 · Cited by 189 · see citing works

Relationship of DNA Methylation and Gene Expression in Idiopathic Pulmonary Fibrosis

Yang, I. V., Pedersen, B. S., Rabinovich, E., Hennessy, C. E., Davidson, E., Murphy, E., Guardela, B. J., Tedrow, J. R., Zhang, Y., Singh, M. K., et al. (2014) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.201408-1452oc · Cited by 186 · see citing works

Genetic Regulation of Adipose Gene Expression and Cardio-Metabolic Traits

Civelek, M., Wu, Y., Pan, C., Raulerson, C. K., Ko, A., He, A., Tilford, C. A., Saleem, N. K., Stančáková, A., Scott, L. J., et al. (2017) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2017.01.027 · Cited by 173 · see citing works

Transcript Expression Data from Human Islets Links Regulatory Signals from Genome-Wide Association Studies for Type 2 Diabetes and Glycemic Traits to Their Downstream Effectors

van de Bunt, M., Fox, J. E. M., Dai, X.-Q., Barrett, A., Grey, C. L., Li, L., Bennett, A. J., Johnson, P. R. V., Rajotte, R. V., Gaulton, K. J., et al. (2015) · PLoS Genetics · doi:10.1371/journal.pgen.1005694 · Cited by 174 · see citing works

Genome-wide association study identifies multiple susceptibility loci for multiple myeloma

Mitchell, J. S., Ni, L., Weinhold, N., Försti, A., Ali, M., van Duin, M., Þorleifsson, G., Johnson, D. C., Chen, B., Halvarsson, B.-M., et al. (2016) · Nature Communications · doi:10.1038/ncomms12050 · Cited by 178 · see citing works

Transcriptomic signatures across human tissues identify functional rare genetic variation

Ferraro, N. M., Strober, B. J., Einson, J. E., Abell, N. S., Aguet, F., Barbeira, A., Brandt, M. K., Bućan, M., Castel, S. E., Davis, J. R., et al. (2020) · Science · doi:10.1126/science.aaz5900 · Cited by 155 · see citing works

An atlas of alternative polyadenylation quantitative trait loci contributing to complex trait and disease heritability

Li, L., Huang, K.-L., Gao, Y., Cui, Y., Wang, G. T., Elrod, N. D., Li, Y., Chen, Y. E., Ji, P., Peng, F., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00864-5 · Cited by 172 · see citing works

Co-expression networks reveal the tissue-specific regulation of transcription and splicingPreprint

Saha, A., Kim, Y., Gewirtz, A. D., Jo, B., Gao, C., McDowell, I. C., Engelhardt, B. E., & Battle, A. J. (2017) · Genome Research · doi:10.1101/gr.216721.116 · Cited by 171 · see citing works

Mapping regulatory variants controlling gene expression in drought response and tolerance in maize

Liu, S., Li, C., Wang, H., Wang, S., Yang, S., Liu, X., Yan, J., Li, B., Beatty, M. K., Zastrow‐Hayes, G., et al. (2020) · Genome biology · doi:10.1186/s13059-020-02069-1 · Cited by 170 · see citing works

Detection of aberrant gene expression events in RNA sequencing data

Yépez, V. A., Mertes, C., Müller, M., Klaproth-Andrade, D., Wachutka, L., Frésard, L., Gušić, M., Scheller, I. F., Goldberg, P. F., Prokisch, H., et al. (2021) · Nature Protocols · doi:10.1038/s41596-020-00462-5 · Cited by 167 · see citing works

Genetic architecture of gene expression traits across diverse populations

Mogil, L. S., Andaleon, A., Badalamenti, A., Dickinson, S. P., Guo, X., Rotter, J. I., Johnson, W. C., Im, H. K., Liu, Y., & Wheeler, H. E. (2018) · PLoS Genetics · doi:10.1371/journal.pgen.1007586 · Cited by 156 · see citing works

A concerted neuron–astrocyte program declines in ageing and schizophrenia

Ling, E., Nemesh, J. C., Goldman, M., Kamitaki, N., Reed, N. M., Handsaker, R. E., Genovese, G., Vogelgsang, J. S., Gerges, S., Kashin, S., et al. (2024) · Nature · doi:10.1038/s41586-024-07109-5 · Cited by 161 · see citing works

Visualizing the structure of RNA-seq expression data using grade of membership models

Dey, K. K., Hsiao, C. J., & Stephens, M. (2017) · PLoS Genetics · doi:10.1371/journal.pgen.1006599 · Cited by 161 · see citing works

Mapping the genetic architecture of human traits to cell types in the kidney identifies mechanisms of disease and potential treatments

Sheng, X., Guan, Y., Ma, Z., Wu, J., Liu, H., Qiu, C., Vitale, S., Miao, Z., Seasock, M. J., Palmer, M., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00909-9 · Cited by 153 · see citing works

Quantifying the regulatory effect size of cis -acting genetic variation using allelic fold changePreprint

Mohammadi, P., Castel, S. E., Brown, A. A., & Lappalainen, T. (2017) · Genome Research · doi:10.1101/gr.216747.116 · Cited by 146 · see citing works

An atlas of genetic scores to predict multi-omic traits

Xu, Y., Ritchie, S. C., Liang, Y., Timmers, P. R. H. J., Pietzner, M., Lannelongue, L., Lambert, S. A., Tahir, U. A., May-Wilson, S., Foguet, C., et al. (2023) · Nature · doi:10.1038/s41586-023-05844-9 · Cited by 143 · see citing works

Expression quantitative trait loci in the developing human brain and their enrichment in neuropsychiatric disorders

O’Brien, H., Hannon, E. J., Hill, M., Toste, C., Robertson, M. J., Morgan, J., McLaughlin, G. M., Lewis, C. M., Schalkwyk, L. C., Hall, L. S., et al. (2018) · Genome biology · doi:10.1186/s13059-018-1567-1 · Cited by 146 · see citing works

Trans-ethnic kidney function association study reveals putative causal genes and effects on kidney-specific disease aetiologies

Morris, A. P., Le, T. H., Wu, H., Akbarov, A., van der Most, P. J., Hemani, G., Smith, G. D., Mahajan, A., Gaulton, K. J., Nadkarni, G. N., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-07867-7 · Cited by 146 · see citing works

Genome of Solanum pimpinellifolium provides insights into structural variants during tomato breeding

Wang, X., Gao, L., Jiao, C., Stravoravdis, S., Hosmani, P. S., Saha, S., Zhang, J., Mainiero, S., Strickler, S. R., Catalá, C., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-19682-0 · Cited by 146 · see citing works

Survey of the Heritability and Sparse Architecture of Gene Expression Traits across Human Tissues

Wheeler, H. E., Shah, K. P., Brenner, J., Garcia, T., Aquino-Michaels, K., Cox, N. J., Nicolae, D. L., & Im, H. K. (2016) · PLoS Genetics · doi:10.1371/journal.pgen.1006423 · Cited by 135 · see citing works

Genetic ancestry, admixture and health determinants in Latin America

Norris, E. T., Wang, L., Conley, A. B., Rishishwar, L., Mariño‐Ramírez, L., Valderrama-Aguirre, A., & Jordan, I. K. (2018) · BMC Genomics · doi:10.1186/s12864-018-5195-7 · Cited by 141 · see citing works

An Efficient Multiple-Testing Adjustment for eQTL Studies that Accounts for Linkage Disequilibrium between Variants

Davis, J. R., Frésard, L., Knowles, D. A., Pala, M., Bustamante, C. D., Battle, A. J., & Montgomery, S. B. (2015) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2015.11.021 · Cited by 138 · see citing works

CUBIC: an atlas of genetic architecture promises directed maize improvement

Liu, H., Wang, X., Xiao, Y., Luo, J., Qiao, F., Yang, W., Zhang, R., Meng, Y., Sun, J., Yan, S., et al. (2020) · Genome biology · doi:10.1186/s13059-020-1930-x · Cited by 138 · see citing works

Differentially expressed genes reflect disease-induced rather than disease-causing changes in the transcriptome

Porcu, E., Sadler, M. C., Lepik, K., Auwerx, C., Wood, A. R., Weihs, A., Sleiman, M. S. B., Ribeiro, D. M., Bandinelli, S., Tanaka, T., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-25805-y · Cited by 135 · see citing works

Systems genetics identifies a convergent gene network for cognition and neurodevelopmental disease

Johnson, M. R., Shkura, K., Langley, S. R., Delahaye‐Duriez, A., Srivastava, P. K., Hill, W. D., Rackham, O. J. L., Davies, G., Harris, S. E., Moreno‐Moral, A., et al. (2015) · Nature Neuroscience · doi:10.1038/nn.4205 · Cited by 132 · see citing works

Integrative transcriptomic analysis of the amyotrophic lateral sclerosis spinal cord implicates glial activation and suggests new risk genes

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

Testing and controlling for horizontal pleiotropy with probabilistic Mendelian randomization in transcriptome-wide association studies

Yuan, Z., Zhu, H., Zeng, P., Yang, S., Sun, S., Yang, C., Liu, J., & Zhou, X. (2020) · Nature Communications · doi:10.1038/s41467-020-17668-6 · Cited by 134 · see citing works

Non-parametric genetic prediction of complex traits with latent Dirichlet process regression models

Zeng, P., & Zhou, X. (2017) · Nature Communications · doi:10.1038/s41467-017-00470-2 · Cited by 132 · see citing works

Metabolic reprogramming through fatty acid transport protein 1 (FATP1) regulates macrophage inflammatory potential and adipose inflammation

Johnson, A. R., Qin, Y., Cozzo, A. J., Freemerman, A. J., Huang, M. J., Zhao, L., Sampey, B. P., Milner, J. J., Beck, M. A., Damania, B., et al. (2016) · Molecular Metabolism · doi:10.1016/j.molmet.2016.04.005 · Cited by 130 · see citing works

The genetic regulatory signature of type 2 diabetes in human skeletal muscle

Scott, L. J., Erdos, M. R., Huyghe, J. R., Welch, R., Beck, A. T., Wolford, B. N., Chines, P. S., Didion, J. P., Narisu, N., Stringham, H. M., et al. (2016) · Nature Communications · doi:10.1038/ncomms11764 · Cited by 131 · see citing works

Systematic analysis of gene expression patterns associated with postmortem interval in human tissues

Zhu, Y., Wang, L., Yin, Y., & Yang, E. (2017) · Scientific Reports · doi:10.1038/s41598-017-05882-0 · Cited by 129 · see citing works

A global transcriptional network connecting noncoding mutations to changes in tumor gene expression

Zhang, W., Bojorquez-Gomez, A., Velez, D. O., Xu, G., Sanchez, K. S., Shen, J. P., Chen, K., Licon, K., Melton, C. A., Olson, K. M., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0091-2 · Cited by 122 · see citing works

Germline genetic polymorphisms influence tumor gene expression and immune cell infiltration

Lim, Y. W., Chen‐Harris, H., Mayba, O., Lianoglou, S., Wüster, A., Bhangale, T., Khan, Z., Mariathasan, S., Daemen, A., Reeder, J., et al. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1804506115 · Cited by 119 · see citing works

Interferon inducible X-linked gene CXorf21 may contribute to sexual dimorphism in Systemic Lupus Erythematosus

Odhams, C. A., Roberts, A. L., Vester, S. K., Duarte, C., Beales, C. T., Clarke, A. J., Lindinger, S., Daffern, S. J., Zito, A., Chen, L., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-10106-2 · Cited by 120 · see citing works

Multi-perspective quality control of Illumina RNA sequencing data analysis

Sheng, Q., Vickers, K. C., Zhao, S., Wang, J., Samuels, D. C., Koues, O. I., Shyr, Y., & Guo, Y. P. (2016) · Briefings in Functional Genomics · doi:10.1093/bfgp/elw035 · Cited by 116 · see citing works

Gene-by-environment interactions in urban populations modulate risk phenotypes

Favé, M.-J., Lamaze, F. C., Soave, D. M., Hodgkinson, A., Gauvin, H., Bruat, V., Grenier, J., Gbeha, E., Skead, K., Smargiassi, A., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-03202-2 · Cited by 119 · see citing works

Nested epistasis enhancer networks for robust genome regulation

Lin, X., Liu, Y., Liu, S., Zhu, X., Wu, L., Zhu, Y., Zhao, D., Xu, X., Chemparathy, A. G., Wang, H., et al. (2022) · Science · doi:10.1126/science.abk3512 · Cited by 111 · see citing works

Natural variation in gene expression and viral susceptibility revealed by neural progenitor cell villages

Wells, M. F., Nemesh, J., Ghosh, S., Mitchell, J. M., Salick, M. R., Mello, C. J., Meyer, D., Pietilainen, O., Piccioni, F., Guss, E. J., et al. (2023) · Cell Stem Cell · doi:10.1016/j.stem.2023.01.010 · Cited by 116 · see citing works

Pitfalls and opportunities for applying PEER factors in single-cell eQTL analysesPreprint

Xue, A., Yazar, S., Neavin, D. R., & Powell, J. E. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.08.02.502566 · Cited by 1 · see citing works

Pathway-Based Factor Analysis of Gene Expression Data Produces Highly Heritable Phenotypes That Associate with Age

Brown, A. A., Ding, Z., Viñuela, A., Glass, D., Parts, L., Spector, T. D., Winn, J. M., & Durbin, R. M. (2015) · G3 Genes Genomes Genetics · doi:10.1534/g3.114.011411 · Cited by 12 · see citing works

R (2011) Joint genetic analysis of gene expression data with inferred cellular phenotypes. PLoS Genetics 7(1

Parts, L., Stegle, O., Winn, J., & Durbin, R. M. (2013)

Joint Modelling of Confounding Factors and Prominent Genetic Regulators Provides Increased Accuracy in Genetical Genomics

Fusi, N., Stegle, O., & Lawrence, N. D. (2011)

Accurate modeling of confounding variation in eQTL studies leads to a great increase in power to detect trans-regulatory effectsPreprint

Fusi, N., Stegle, O., & Lawrence, N. D. (2011) · Nature Precedings · doi:10.1371/journal.pcbi.1002330 · Cited by 103 · see citing works