A panoramic view of cotton resistance to Verticillium dahliae: From genetic architectures to precision genomic selection

Abstract

Investigating the genetic regulatory mechanisms underlying complex traits forms the foundation for crop improvement. Verticillium wilt (VW), caused by Verticillium dahliae (V. dahliae), is one of the most devastating diseases affecting crop production worldwide. However, the genetic basis underlying crop resistance to V. dahliae remains largely obscure, hindering progress in the genomic selection for VW resistance breeding. Here, we unraveled the genetic architectures and regulatory landscape of VW resistance in cotton by combining genome-wide association studies (GWAS) and transcriptome-wide association studies (TWAS) using 1152 transcriptomes derived from 290 cotton accessions. We identified 10 reliable quantitative trait loci (QTLs) associated with VW resistance across multiple environments. These QTLs showed a pyramiding resistance effect and exhibited promising efficacy in the genomic prediction of cotton's VW resistance supported by an F_2:3 population. Moreover, trace analysis of these elite alleles revealed a notably increased utilization of Lsnp1, Lsnp4, Lsnp5, Lsnp8, and Lsnp9, which potentially contribute to the improvement of VW resistance in Chinese cotton breeding since the 1990s. We also identified remarkable gene modules and expression QTL (eQTL) hotspots related to the regulation of reactive oxygen species (ROS) homeostasis and immune response. Furthermore, 15 candidate causal genes were prioritized by TWAS. Knocking down eight genes with a negative effect significantly enhanced cotton resistance to V. dahliae. Among them, GhARM, encoding an armadillo (ARM)-repeat protein, was verified to modulate cotton resistance to V. dahliae by regulating ROS homeostasis. Overall, this study updates the understanding of the genetic basis and regulatory mechanisms of cotton's VW resistance, providing valuable strategies for VW management through genomic selection in cotton breeding.

Keywords

armadillo-repeat protein / cotton / genomic selection / genome-wide association studies / ROS homeostasis / transcriptome-wide association studies / Verticillium wilt

References

[1] Man, Mingwu, Yaqian Zhu, Lulu Liu, Lei Luo, Xinpei Han, Lu Qiu, Fuguang Li, Maozhi Ren, and Yadi Xing. 2022. “Defense Mechanisms of Cotton Fusarium and Verticillium Wilt and Comparison of Pathogenic Response in Cotton and Humans.” International Journal of Molecular Sciences 23: 12217. https://doi.org/10.3390/ijms232012217

[2] Fradin, Emilie F., and Bart P. H. J. Thomma. 2006. “Physiology and Molecular Aspects of Verticillium Wilt Diseases Caused by V. dahliae and V. albo-atrum.” Molecular Plant Pathology 7: 71-86. https://doi.org/10.1111/j.1364-3703.2006.00323.x

[3] Wang, Yiqin, Chengzhen Liang, Shenjie Wu, Xueyan Zhang, Jiuyou Tang, Guiliang Jian, Gaili Jiao, Fuguang Li, and Chengcai Chu. 2016. “Significant Improvement of Cotton Verticillium Wilt Resistance by Manipulating the Expression of Gastrodia Antifungal Proteins.” Molecular Plant 9: 1436-1439. https://doi.org/10.1016/j.molp.2016.06.013

[4] Zhang, Yun, Yuanxue Yang, Xiuyun Lu, Aiyu Wang, Chao Xue, Ming Zhao, and Jianhua Zhang. 2023. “The Effects and Interrelationships of Intercropping on Cotton Verticillium Wilt and Soil Microbial Communities.” BMC Microbiology 23: 41. https://doi.org/10.1186/s12866-023-02780-6

[5] Kawchuk, Lawrence M., John Hachey, Dermot R. Lynch, Frank Kulcsar, Gijs Van Rooijen, Doug R. Waterer, Albert Robertson, et al. 2001. “Tomato Ve Disease Resistance Genes Encode Cell Surface-Like Receptors.” Proceedings of the National Academy of Sciences 98: 6511-6515. https://doi.org/10.1073/pnas.091114198

[6] Jing, Lijun, Caitao Chang, Zhenjiu Sun, Wutai Wang, Wenming Liu, and Rongqi Cai. 2000. “Preliminary Study on the Heredity of the Resistance to Verticillium Wilt in Eggplant.” Acta Horticulturae Sinica 16: 58-61. https://doi.org/10.3321/j.issn:1000-7091.2001.02.012

[7] Concibido, V. C., G. A. Secor, and S. H. Jansky. 1994. “Evaluation of Resistance to Verticillium Wilt in Diploid, Wild Potato Interspecific Hybrids.” Euphytica 76: 145-152. https://doi.org/10.1007/BF00024033

[8] Zhang, Jinfa, Jiwen Yu, Wenfeng Pei, Xingli Li, Joseph Said, Mingzhou Song, and Soum Sanogo. 2015. “Genetic Analysis of Verticillium Wilt Resistance in a Backcross Inbred Line Population and a Meta-Analysis of Quantitative Trait Loci for Disease Resistance in Cotton.” BMC Genomics 16: 577. https://doi.org/10.1186/s12864-015-1682-2

[9] Ma, Zhiying, Yan Zhang, Liqiang Wu, Guiyin Zhang, Zhengwen Sun, Zhikun Li, Yafei Jiang, et al. 2021. “High-Quality Genome Assembly and Resequencing of Modern Cotton Cultivars Provide Resources for Crop Improvement.” Nature Genetics 53: 1385-1391. https://doi.org/10.1038/s41588-021-00910-2

[10] Li, Tinggang, Xuefeng Ma, Nanyang Li, Lei Zhou, Zheng Liu, Huanyong Han, Yuejing Gui, Yuming Bao, Jieyin Chen, and Xiaofeng Dai. 2017. “Genome-Wide Association Study Discovered Candidate Genes of Verticillium Wilt Resistance in Upland Cotton (Gossypium hirsutum L.).” Plant Biotechnology Journal 15: 1520-1532. https://doi.org/10.1111/pbi.12734

[11] Zhang, Yan, Bin Chen, Zhengwen Sun, Zhengwen Liu, Yanru Cui, Huifeng Ke, Zhicheng Wang, et al. 2021. “A Large-Scale Genomic Association Analysis Identifies a Fragment in Dt11 Chromosome Conferring Cotton Verticillium Wilt Resistance.” Plant Biotechnology Journal 19: 2126-2138. https://doi.org/10.1111/pbi.13650

[12] Zhang, Yihao, Yaning Zhang, Xiaoyang Ge, Yuan Yuan, Yuying Jin, Ye Wang, Lihong Zhao, et al. 2023. “Genome-Wide Association Analysis Reveals a Novel Pathway Mediated by a Dual-TIR Domain Protein for Pathogen Resistance in Cotton.” Genome Biology 24: 111. https://doi.org/10.1186/s13059-023-02950-9

[13] GTEx Consortium. 2020. “The GTEx Consortium Atlas of Genetic Regulatory Effects Across Human Tissues.” Science 369: 1318-1330. https://doi.org/10.1126/science.aaz1776

[14] De Klein, Niek, Ellen A. Tsai, Martijn Vochteloo, Denis Baird, Yunfeng Huang, Chia-Yen Chen, Sipko van Dam, et al. 2023. “Brain Expression Quantitative Trait Locus and Network Analyses Reveal Downstream Effects and Putative Drivers for Brain-Related Diseases.” Nature Genetics 55: 377-388. https://doi.org/10.1038/s41588-023-01300-6

[15] Liu, Shengxue, Cuiping Li, Hongwei Wang, Shuhui Wang, Shiping Yang, Xiaohu Liu, Jianbing Yan, et al. 2020. “Mapping Regulatory Variants Controlling Gene Expression in Drought Response and Tolerance in Maize.” Genome Biology 21: 163. https://doi.org/10.1186/s13059-020-02069-1

[16] Wu, Xi, Hui Feng, Di Wu, Shijuan Yan, Pei Zhang, Wenbin Wang, Jun Zhang, et al. 2021. “Using High-Throughput Multiple Optical Phenotyping to Decipher the Genetic Architecture of Maize Drought Tolerance.” Genome Biology 22: 185. https://doi.org/10.1186/s13059-021-02377-0

[17] Zhang, Fei, Jinfeng Wu, Nir Sade, Si Wu, Aiman Egbaria, Alisdair R. Fernie, Jianbing Yan, et al. 2021. “Genomic Basis Underlying the Metabolome-Mediated Drought Adaptation of Maize.” Genome Biology 22: 260. https://doi.org/10.1186/s13059-021-02481-1

[18] Liang, Zhikai, Zachary A. Myers, Dominic Petrella, Julia Engelhorn, Thomas Hartwig, and Nathan M. Springer. 2022. “Mapping Responsive Genomic Elements to Heat Stress in a Maize Diversity Panel.” Genome Biology 23: 234. https://doi.org/10.1186/s13059-022-02807-7

[19] Sun, Guangchao, Huihui Yu, Peng Wang, Martha Lopez-Guerrero, Ravi V. Mural, Olivier N. Mizero, Marcin Grzybowski, et al. 2023. “A Role for Heritable Transcriptomic Variation in Maize Adaptation to Temperate Environments.” Genome Biology 24: 55. https://doi.org/10.1186/s13059-023-02891-3

[20] Tang, Shan, Hu Zhao, Shaoping Lu, Liangqian Yu, Guofang Zhang, Yuting Zhang, Qing-Yong Yang, et al. 2021. “Genome- and Transcriptome-Wide Association Studies Provide Insights Into the Genetic Basis of Natural Variation of Seed Oil Content in Brassica napus.” Molecular Plant 14: 470-487. https://doi.org/10.1016/j.molp.2020.12.003

[21] Tan, Zengdong, Yan Peng, Yao Xiong, Feng Xiong, Yuting Zhang, Ning Guo, Zhuo Tu, et al. 2022. “Comprehensive Transcriptional Variability Analysis Reveals Gene Networks Regulating Seed Oil Content of Brassica napus.” Genome Biology 23: 233. https://doi.org/10.1186/s13059-022-02801-z

[22] Zhang, Yuting, Hui Zhang, Hu Zhao, Yefan Xia, Xiangbo Zheng, Ruyi Fan, Zengdong Tan, et al. 2022. “Multi-Omics Analysis Dissects the Genetic Architecture of Seed Coat Content in Brassica napus.” Genome Biology 23: 86. https://doi.org/10.1186/s13059-022-02647-5

[23] Li, Long, Zhitao Tian, Jie Chen, Zengdong Tan, Yuting Zhang, Hu Zhao, Xiaowei Wu, et al. 2023. “Characterization of Novel Loci Controlling Seed Oil Content in Brassica napus by Marker Metabolite-Based Multi-Omics Analysis.” Genome Biology 24: 141. https://doi.org/10.1186/s13059-023-02984-z

[24] Ming, Luchang, Debao Fu, Zhaona Wu, Hu Zhao, Xingbing Xu, Tingting Xu, Xiaohu Xiong, et al. 2023. “Transcriptome-Wide Association Analyses Reveal the Impact of Regulatory Variants on Rice Panicle Architecture and Causal Gene Regulatory Networks.” Nature Communications 14: 7501. https://doi.org/10.1038/s41467-023-43077-6

[25] Wei, Hua, Xianmeng Wang, Zhipeng Zhang, Longbo Yang, Qianqian Zhang, Yilin Li, Huiying He, et al. 2024. “Uncovering Key Salt-Tolerant Regulators Through a Combined eQTL and GWAS Analysis Using the Super Pan-Genome in Rice.” National Science Review 11: nwae043. https://doi.org/10.1093/nsr/nwae043

[26] Li, Zhonghua, Pengcheng Wang, Chunyuan You, Jiwen Yu, Xiangnan Zhang, Feilin Yan, Zhengxiu Ye, et al. 2020. “Combined GWAS and eQTL Analysis Uncovers a Genetic Regulatory Network Orchestrating the Initiation of Secondary Cell Wall Development in Cotton.” New Phytologist 226: 1738-1752. https://doi.org/10.1111/nph.16468

[27] Wang, Maojun, Jianying Li, Zhengyang Qi, Yuexuan Long, Liuling Pei, Xianhui Huang, Corrinne E. Grover, et al. 2022. “Genomic Innovation and Regulatory Rewiring During Evolution of the Cotton Genus Gossypium.” Nature Genetics 54: 1959-1971. https://doi.org/10.1038/s41588-022-01237-2

[28] You, Jiaqi, Zhenping Liu, Zhengyang Qi, Yizan Ma, Mengling Sun, Ling Su, Hao Niu, et al. 2023. “Regulatory Controls of Duplicated Gene Expression During Fiber Development in Allotetraploid Cotton.” Nature Genetics 55: 1987-1997. https://doi.org/10.1038/s41588-023-01530-8

[29] Ma, Yizan, Ling Min, Junduo Wang, Yaoyao Li, Yuanlong Wu, Qin Hu, Yuanhao Ding, et al. 2021. “A Combination of Genome-Wide and Transcriptome-Wide Association Studies Reveals Genetic Elements Leading to Male Sterility During High Temperature Stress in Cotton.” New Phytologist 231: 165-181. https://doi.org/10.1111/nph.17325

[30] Zhao, Ting, Hongyu Wu, Xutong Wang, Yongyan Zhao, Luyao Wang, Jiaying Pan, Huan Mei, et al. 2023. “Integration of eQTL and Machine Learning to Dissect Causal Genes With Pleiotropic Effects in Genetic Regulation Networks of Seed Cotton Yield.” Cell Reports 42: 113111. https://doi.org/10.1016/j.celrep.2023.113111

[31] Liu, Shiming, Xiaojun Zhang, Shenghua Xiao, Jun Ma, Weijun Shi, Tao Qin, Hui Xi, et al 2021. “A Single-Nucleotide Mutation in a GLUTAMATE RECEPTOR-LIKE Gene Confers Resistance to Fusarium Wilt in Gossypium hirsutum.” Advanced Science 8: 2002723. https://doi.org/10.1002/advs.202002723

[32] Li, Yiqian, Zhanfeng Si, Guoping Wang, Zhuolin Shi, Jinwen Chen, Guoan Qi, Shangkun Jin, et al. 2023. “Genomic Insights Into the Genetic Basis of Cotton Breeding in China.” Molecular Plant 16: 662-677. https://doi.org/10.1016/j.molp.2023.01.012

[33] Ma, Zhiying, Shoupu He, Xingfen Wang, Junling Sun, Yan Zhang, Guiyin Zhang, Liqiang Wu, et al. 2018. “Resequencing a Core Collection of Upland Cotton Identifies Genomic Variation and Loci Influencing Fiber Quality and Yield.” Nature Genetics 50: 803-813. https://doi.org/10.1038/s41588-018-0119-7

[34] He, Shoupu, Gaofei Sun, Xiaoli Geng, Wenfang Gong, Panhong Dai, Yinhua Jia, Weijun Shi, et al. 2021. “The Genomic Basis of Geographic Differentiation and Fiber Improvement in Cultivated Cotton.” Nature Genetics 53: 916-924. https://doi.org/10.1038/s41588-021-00844-9

[35] Li, Jianying, Daojun Yuan, Pengcheng Wang, Qiongqiong Wang, Mengling Sun, Zhenping Liu, Huan Si, et al. 2021. “Cotton Pan-Genome Retrieves the Lost Sequences and Genes During Domestication and Selection.” Genome Biology 22: 119. https://doi.org/10.1186/s13059-021-02351-w

[36] Li, Libei, Chi Zhang, Jianqin Huang, Qibao Liu, Hengling Wei, Hantao Wang, Guoyuan Liu, Lijiao Gu, and Shuxun Yu. 2021. “Genomic Analyses Reveal the Genetic Basis of Early Maturity and Identification of Loci and Candidate Genes in Upland Cotton (Gossypium hirsutum L.” Plant Biotechnology Journal 19: 109-123. https://doi.org/10.1111/pbi.13446

[37] Li, Ran, Xiyue Ma, Yejing Zhang, Yongjun Zhang, He Zhu, Shengnan Shao, Dandan Zhang, et al. 2023. “Genome-Wide Identification and Analysis of a Cotton Secretome Reveals Its Role in Resistance Against Verticillium dahliae.” BMC Biology 21: 166. https://doi.org/10.1186/s12915-023-01650-x

[38] Qiu, Ping, Jiayue Li, Lin Zhang, Kun Chen, Jianmin Shao, Baoxin Zheng, Hang Yuan, et al. 2023. “Polyethyleneimine-Coated MXene Quantum Dots Improve Cotton Tolerance to Verticillium dahliae by Maintaining ROS Homeostasis.” Nature Communications 14: 7392. https://doi.org/10.1038/s41467-023-43192-4

[39] Liu, Jie, Pan Gong, Ruobin Lu, Rosa Lozano-Durán, Xueping Zhou, and Fangfang Li. 2024. “Chloroplast Immunity: A Cornerstone of Plant Defense.” Molecular Plant 17: 686-688. https://doi.org/10.1016/j.molp.2024.03.012

[40] Yang, Zhiquan, Jing Wang, Yiming Huang, Shengbo Wang, Lulu Wei, Dongxu Liu, Yonglin Weng, et al. 2023. “CottonMD: A Multi-Omics Database for Cotton Biological Study.” Nucleic Acids Research 51: D1446-D1456. https://doi.org/10.1093/nar/gkac863

[41] Zhao, Yunlei, Hongmei Wang, Wei Chen, and Yunhai Li. 2014. “Genetic Structure, Linkage Disequilibrium and Association Mapping of Verticillium Wilt Resistance in Elite Cotton (Gossypium hirsutum L.) Germplasm Population.” PLoS One 9: e86308. https://doi.org/10.1371/journal.pone.0086308

[42] Jiang, Feng, Jun Zhao, Lei Zhou, Wangzhen Guo, and Tianzhen Zhang. 2009. “Molecular Mapping of Verticillium Wilt Resistance QTL Clustered on Chromosomes D7 and D9 in Upland Cotton.” Science in China Series C: Life Sciences 52: 872-884. https://doi.org/10.1007/s11427-009-0110-8

[43] Xi, Hui, Xuekun Zhang, Dingyi Yang, Jianyun Zhao, Jili Shen, Zhaoguang Yang, Chunyuan You, Xinhui Nie, and Longfu Zhu. 2021. “Culture Characteristics and Pathogenicity Differentiation of Verticillium dahliae of Cotton in Xinjiang.” Acta Phytopathologica Sinica 51: 592-606. https://doi.org/10.13926/j.cnki.apps.000705

[44] Baroudy, Farah, Alexander I. Putman, Wassim Habib, Krishna D. Puri, Krishna V. Subbarao, and Franco Nigro. 2019. “Genetic Diversity of Verticillium dahliae Populations From Olive and Potato in Lebanon.” Plant Disease 103: 656-667. https://doi.org/10.1094/PDIS-03-18-0420-RE

[45] Wang, Furong, Renzhong Liu, Liuming Wang, Chuanyun Zhang, Guodong Liu, Qinhong Liu, Xiaobo Ma, and Jun Zhang. 2008. “Molecular Markers of Verticillium Wilt Resistance in Upland Cotton (Gossypium hirsutum L.) Cultivar and Their Effects on Assisted Phenotypic Selection.” Cotton Science 19: 424-430. https://doi.org/10.11963/cs080407-s07

[46] Wang, Hongwei, Silong Sun, Wenyang Ge, Lanfei Zhao, Bingqian Hou, Kai Wang, Zhongfan Lyu, et al. 2020. “Horizontal Gene Transfer of Fhb7 From Fungus Underlies Fusarium Head Blight Resistance in Wheat.” Science 368: eaba5435. https://doi.org/10.1126/science.aba5435

[47] Li, Ran, Yongjun Zhang, Xiyue Ma, Songke Li, Steven J. Klosterman, Jieyin Chen, Krishna V. Subbarao, and Xiaofeng Dai. 2023. “Genome Resource for the Verticillium Wilt Resistant Gossypium hirsutum Cultivar Zhongzhimian No. 2.” Molecular Plant-Microbe Interactions® 36: 68-72. https://doi.org/10.1094/MPMI-10-22-0205-A

[48] Song, Bo, Weidong Ning, Di Wei, Mengyun Jiang, Kun Zhu, Xingwei Wang, David Edwards, Damaris A. Odeny, and Shifeng Cheng. 2023. “Plant Genome Resequencing and Population Genomics: Current Status and Future Prospects.” Molecular Plant 16: 1252-1268. https://doi.org/10.1016/j.molp.2023.07.009

[49] Adam, Yagoub, Chaimae Samtal, Jean-tristan Brandenburg, Oluwadamilare Falola, and Ezekiel Adebiyi. 2021. “Performing Post-Genome-Wide Association Study Analysis: Overview, Challenges and Recommendations.” F1000Research 10: 1002. https://doi.org/10.12688/f1000research.53962.1

[50] Li, Shengnan, Dexing Lin, Yunwei Zhang, Min Deng, Yongxing Chen, Bin Lv, Boshu Li, et al. 2022. “Genome-Edited Powdery Mildew Resistance in Wheat Without Growth Penalties.” Nature 602: 455-460. https://doi.org/10.1038/s41586-022-04395-9

[51] Oliva, Ricardo, Chonghui Ji, Genelou Atienza-Grande, José C. Huguet-Tapia, Alvaro Perez-Quintero, Ting Li, Joon-Seob Eom, et al. 2019. “Broad-Spectrum Resistance to Bacterial Blight in Rice Using Genome Editing.” Nature Biotechnology 37: 1344-1350. https://doi.org/10.1038/s41587-019-0267-z

[52] Wang, Ning, Chunlei Tang, Xin Fan, Mengying He, Pengfei Gan, Shan Zhang, Zeyu Hu, et al. 2022. “Inactivation of a Wheat Protein Kinase Gene Confers Broad-Spectrum Resistance to Rust Fungi.” Cell 185: 2961-2974.e19. https://doi.org/10.1016/j.cell.2022.06.027

[53] Koseoglou, Eleni, Jan M. van derWolf, Richard G. F. Visser, and Yuling Bai. 2022. “Susceptibility Reversed: Modified Plant Susceptibility Genes for Resistance to Bacteria.” Trends in Plant Science 27: 69-79. https://doi.org/10.1016/j.tplants.2021.07.018

[54] Qi, Yetong, Jiahui Wu, Zhu Yang, Hongjun Li, Lang Liu, Haixia Wang, Xinyuan Sun, et al. 2024. “Chloroplast Elongation Factors Break the Growth-Immunity Trade-Off by Simultaneously Promoting Yield and Defence.” Nature Plants 10: 1576-1591. https://doi.org/10.1038/s41477-024-01793-x

[55] De Torres Zabala, Marta, George Littlejohn, Siddharth Jayaraman, David Studholme, Trevor Bailey, Tracy Lawson, Michael Tillich, et al. 2015. “Chloroplasts Play a Central Role in Plant Defence and Are Targeted by Pathogen Effectors.” Nature Plants 1: 15074. https://doi.org/10.1038/nplants.2015.74

[56] Wang, Maojun, Lili Tu, Min Lin, Zhongxu Lin, Pengcheng Wang, Qingyong Yang, Zhengxiu Ye, et al. 2017. “Asymmetric Subgenome Selection and Cis-Regulatory Divergence During Cotton Domestication.” Nature Genetics 49: 579-587. https://doi.org/10.1038/ng.3807

[57] Chen, Shifu, Yanqing Zhou, Yaru Chen, and Jia Gu. 2018. “Fastp: An Ultra-Fast All-In-One FASTQ Preprocessor.” Bioinformatics 34: i884-i890. https://doi.org/10.1093/bioinformatics/bty560

[58] Kendig, Katherine I., Saurabh Baheti, Matthew A. Bockol, Travis M. Drucker, Steven N. Hart, Jacob R. Heldenbrand, Mikel Hernaez, et al. 2019. “Sentieon DNASeq Variant Calling Workflow Demonstrates Strong Computational Performance and Accuracy.” Frontiers in Genetics 10: 1664-8021. https://doi.org/10.3389/fgene.2019.00736

[59] Hu, Yan, Jiedan Chen, Lei Fang, Zhiyuan Zhang, Wei Ma, Yongchao Niu, Longzhen Ju, et al. 2019. “Gossypium barbadense and Gossypium hirsutum Genomes Provide Insights into the Origin and Evolution of Allotetraploid Cotton.” Nature Genetics 51: 739-748. https://doi.org/10.1038/s41588-019-0371-5

[60] Li, Heng, and Richard Durbin. 2009. “Fast and Accurate Short Read Alignment With Burrows-Wheeler Transform.” Bioinformatics 25: 1754-1760. https://doi.org/10.1093/bioinformatics/btp324

[61] Li, Heng, Bob Handsaker, Alec Wysoker, Tim Fennell, Jue Ruan, Nils Homer, Gabor Marth, Goncalo Abecasis, and Richard Durbin, Subgroup Genome Project Data Processing. 2009. “The Sequence Alignment/Map Format and SAMtools.” Bioinformatics 25: 2078-2079. https://doi.org/10.1093/bioinformatics/btp352

[62] Danecek, Petr, Adam Auton, Goncalo Abecasis, Cornelis A. Albers, Eric Banks, Mark A. DePristo, Robert E. Handsaker, et al. 2011. “The Variant Call Format and VCFtools.” Bioinformatics 27: 2156-2158. https://doi.org/10.1093/bioinformatics/btr330

[63] Zhou, Xiang, and Matthew Stephens. 2012. “Genome-Wide Efficient Mixed-Model Analysis for Association Studies.” Nature Genetics 44: 821-824. https://doi.org/10.1038/ng.2310

[64] Evanno, G., S. Regnaut, and J. GOUDET. 2005. “Detecting the Number of Clusters of Individuals Using the Software STRUCTURE: A Simulation Study.” Molecular Ecology 14: 2611-2620. https://doi.org/10.1111/j.1365-294X.2005.02553.x

[65] Friedman, Jerome, Trevor Hastie, and Robert Tibshirani. 2010. “Regularization Paths for Generalized Linear Models via Coordinate Descent.” Journal of Statistical Software 33: 1-22. https://doi.org/10.18637/jss.v033.i01

[66] Stegle, Oliver, Leopold Parts, Matias Piipari, John Winn, and Richard Durbin. 2012. “Using Probabilistic Estimation of Expression Residuals (PEER) to Obtain Increased Power and Interpretability of Gene Expression Analyses.” Nature Protocols 7: 500-507. https://doi.org/10.1038/nprot.2011.457

[67] Taylor-Weiner, Amaro, François Aguet, Nicholas J. Haradhvala, Sager Gosai, Shankara Anand, Jaegil Kim, Kristin Ardlie, Eliezer M. Van Allen, and Gad Getz. 2019. “Scaling Computational Genomics to Millions of Individuals with GPUs.” Genome Biology 20: 228. https://doi.org/10.1186/s13059-019-1836-7

[68] Li, Miaoxin, Juilian M. Y. Yeung, Stacey S. Cherny, and Pak C. Sham. 2012. “Evaluating the Effective Numbers of Independent Tests and Significant p-value Thresholds in Commercial Genotyping Arrays and Public Imputation Reference Datasets.” Human Genetics 131: 747-756. https://doi.org/10.1007/s00439-011-1118-2

[69] Gusev, Alexander, Arthur Ko, Huwenbo Shi, Gaurav Bhatia, Wonil Chung, Brenda W. J. H. Penninx, Rick Jansen, et al. 2016. “Integrative Approaches for Large-Scale Transcriptome-Wide Association Studies.” Nature Genetics 48: 245-252. https://doi.org/10.1038/ng.3506

[70] Zhu, Zhihong, Futao Zhang, Han Hu, Andrew Bakshi, Matthew R. Robinson, Joseph E. Powell, Grant W. Montgomery, et al. 2016. “Integration of Summary Data From GWAS and eQTL Studies Predicts Complex Trait Gene Targets.” Nature Genetics 48: 481-487. https://doi.org/10.1038/ng.3538

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