@article {9099, title = {Insights From a Large-Scale Whole-Genome Sequencing Study of Systolic Blood Pressure, Diastolic Blood Pressure, and Hypertension.}, journal = {Hypertension}, year = {2022}, month = {2022 Jun 02}, pages = {101161HYPERTENSIONAHA12219324}, abstract = {

BACKGROUND: The availability of whole-genome sequencing data in large studies has enabled the assessment of coding and noncoding variants across the allele frequency spectrum for their associations with blood pressure.

METHODS: We conducted a multiancestry whole-genome sequencing analysis of blood pressure among 51 456 Trans-Omics for Precision Medicine and Centers for Common Disease Genomics program participants (stage-1). Stage-2 analyses leveraged array data from UK Biobank (N=383 145), Million Veteran Program (N=318 891), and Reasons for Geographic and Racial Differences in Stroke (N=10 643) participants, along with whole-exome sequencing data from UK Biobank (N=199 631) participants.

RESULTS: Two blood pressure signals achieved genome-wide significance in meta-analyses of stage-1 and stage-2 single variant findings (<5{\texttimes}10). Among them, a rare intergenic variant at novel locus, , was associated with lower systolic blood pressure in stage-1 (beta [SE]=-32.6 [6.0]; =4.99{\texttimes}10) but not stage-2 analysis (=0.11). Furthermore, a novel common variant at the known locus was suggestively associated with diastolic blood pressure in stage-1 (beta [SE]=-0.36 [0.07]; =4.18{\texttimes}10) and attained genome-wide significance in stage-2 (beta [SE]=-0.29 [0.03]; =7.28{\texttimes}10). Nineteen additional signals suggestively associated with blood pressure in meta-analysis of single and aggregate rare variant findings (<1{\texttimes}10 and <1{\texttimes}10, respectively).

DISCUSSION: We report one promising but unconfirmed rare variant for blood pressure and, more importantly, contribute insights for future blood pressure sequencing studies. Our findings suggest promise of aggregate analyses to complement single variant analysis strategies and the need for larger, diverse samples, and family studies to enable robust rare variant identification.

}, issn = {1524-4563}, doi = {10.1161/HYPERTENSIONAHA.122.19324}, author = {Kelly, Tanika N and Sun, Xiao and He, Karen Y and Brown, Michael R and Taliun, Sarah A Gagliano and Hellwege, Jacklyn N and Irvin, Marguerite R and Mi, Xuenan and Brody, Jennifer A and Franceschini, Nora and Guo, Xiuqing and Hwang, Shih-Jen and de Vries, Paul S and Gao, Yan and Moscati, Arden and Nadkarni, Girish N and Yanek, Lisa R and Elfassy, Tali and Smith, Jennifer A and Chung, Ren-Hua and Beitelshees, Amber L and Patki, Amit and Aslibekyan, Stella and Blobner, Brandon M and Peralta, Juan M and Assimes, Themistocles L and Palmas, Walter R and Liu, Chunyu and Bress, Adam P and Huang, Zhijie and Becker, Lewis C and Hwa, Chii-Min and O{\textquoteright}Connell, Jeffrey R and Carlson, Jenna C and Warren, Helen R and Das, Sayantan and Giri, Ayush and Martin, Lisa W and Craig Johnson, W and Fox, Ervin R and Bottinger, Erwin P and Razavi, Alexander C and Vaidya, Dhananjay and Chuang, Lee-Ming and Chang, Yen-Pei C and Naseri, Take and Jain, Deepti and Kang, Hyun Min and Hung, Adriana M and Srinivasasainagendra, Vinodh and Snively, Beverly M and Gu, Dongfeng and Montasser, May E and Reupena, Muagututi{\textquoteright}a Sefuiva and Heavner, Benjamin D and LeFaive, Jonathon and Hixson, James E and Rice, Kenneth M and Wang, Fei Fei and Nielsen, Jonas B and Huang, Jianfeng and Khan, Alyna T and Zhou, Wei and Nierenberg, Jovia L and Laurie, Cathy C and Armstrong, Nicole D and Shi, Mengyao and Pan, Yang and Stilp, Adrienne M and Emery, Leslie and Wong, Quenna and Hawley, Nicola L and Minster, Ryan L and Curran, Joanne E and Munroe, Patricia B and Weeks, Daniel E and North, Kari E and Tracy, Russell P and Kenny, Eimear E and Shimbo, Daichi and Chakravarti, Aravinda and Rich, Stephen S and Reiner, Alex P and Blangero, John and Redline, Susan and Mitchell, Braxton D and Rao, Dabeeru C and Ida Chen, Yii-Der and Kardia, Sharon L R and Kaplan, Robert C and Mathias, Rasika A and He, Jiang and Psaty, Bruce M and Fornage, Myriam and Loos, Ruth J F and Correa, Adolfo and Boerwinkle, Eric and Rotter, Jerome I and Kooperberg, Charles and Edwards, Todd L and Abecasis, Goncalo R and Zhu, Xiaofeng and Levy, Daniel and Arnett, Donna K and Morrison, Alanna C} } @article {9418, title = {Rare variants in long non-coding RNAs are associated with blood lipid levels in the TOPMed Whole Genome Sequencing Study.}, journal = {medRxiv}, year = {2023}, month = {2023 Jun 29}, abstract = {

Long non-coding RNAs (lncRNAs) are known to perform important regulatory functions. Large-scale whole genome sequencing (WGS) studies and new statistical methods for variant set tests now provide an opportunity to assess the associations between rare variants in lncRNA genes and complex traits across the genome. In this study, we used high-coverage WGS from 66,329 participants of diverse ancestries with blood lipid levels (LDL-C, HDL-C, TC, and TG) in the National Heart, Lung, and Blood Institute (NHLBI) Trans-Omics for Precision Medicine (TOPMed) program to investigate the role of lncRNAs in lipid variability. We aggregated rare variants for 165,375 lncRNA genes based on their genomic locations and conducted rare variant aggregate association tests using the STAAR (variant-Set Test for Association using Annotation infoRmation) framework. We performed STAAR conditional analysis adjusting for common variants in known lipid GWAS loci and rare coding variants in nearby protein coding genes. Our analyses revealed 83 rare lncRNA variant sets significantly associated with blood lipid levels, all of which were located in known lipid GWAS loci (in a {\textpm}500 kb window of a Global Lipids Genetics Consortium index variant). Notably, 61 out of 83 signals (73\%) were conditionally independent of common regulatory variations and rare protein coding variations at the same loci. We replicated 34 out of 61 (56\%) conditionally independent associations using the independent UK Biobank WGS data. Our results expand the genetic architecture of blood lipids to rare variants in lncRNA, implicating new therapeutic opportunities.

}, doi = {10.1101/2023.06.28.23291966}, author = {Wang, Yuxuan and Selvaraj, Margaret Sunitha and Li, Xihao and Li, Zilin and Holdcraft, Jacob A and Arnett, Donna K and Bis, Joshua C and Blangero, John and Boerwinkle, Eric and Bowden, Donald W and Cade, Brian E and Carlson, Jenna C and Carson, April P and Chen, Yii-Der Ida and Curran, Joanne E and de Vries, Paul S and Dutcher, Susan K and Ellinor, Patrick T and Floyd, James S and Fornage, Myriam and Freedman, Barry I and Gabriel, Stacey and Germer, Soren and Gibbs, Richard A and Guo, Xiuqing and He, Jiang and Heard-Costa, Nancy and Hildalgo, Bertha and Hou, Lifang and Irvin, Marguerite R and Joehanes, Roby and Kaplan, Robert C and Kardia, Sharon Lr and Kelly, Tanika N and Kim, Ryan and Kooperberg, Charles and Kral, Brian G and Levy, Daniel and Li, Changwei and Liu, Chunyu and Lloyd-Jone, Don and Loos, Ruth Jf and Mahaney, Michael C and Martin, Lisa W and Mathias, Rasika A and Minster, Ryan L and Mitchell, Braxton D and Montasser, May E and Morrison, Alanna C and Murabito, Joanne M and Naseri, Take and O{\textquoteright}Connell, Jeffrey R and Palmer, Nicholette D and Preuss, Michael H and Psaty, Bruce M and Raffield, Laura M and Rao, Dabeeru C and Redline, Susan and Reiner, Alexander P and Rich, Stephen S and Ruepena, Muagututi{\textquoteright}a Sefuiva and Sheu, Wayne H-H and Smith, Jennifer A and Smith, Albert and Tiwari, Hemant K and Tsai, Michael Y and Viaud-Martinez, Karine A and Wang, Zhe and Yanek, Lisa R and Zhao, Wei and Rotter, Jerome I and Lin, Xihong and Natarajan, Pradeep and Peloso, Gina M} } @article {9543, title = {A statistical framework for powerful multi-trait rare variant analysis in large-scale whole-genome sequencing studies.}, journal = {bioRxiv}, year = {2023}, month = {2023 Nov 02}, abstract = {

Large-scale whole-genome sequencing (WGS) studies have improved our understanding of the contributions of coding and noncoding rare variants to complex human traits. Leveraging association effect sizes across multiple traits in WGS rare variant association analysis can improve statistical power over single-trait analysis, and also detect pleiotropic genes and regions. Existing multi-trait methods have limited ability to perform rare variant analysis of large-scale WGS data. We propose MultiSTAAR, a statistical framework and computationally-scalable analytical pipeline for functionally-informed multi-trait rare variant analysis in large-scale WGS studies. MultiSTAAR accounts for relatedness, population structure and correlation among phenotypes by jointly analyzing multiple traits, and further empowers rare variant association analysis by incorporating multiple functional annotations. We applied MultiSTAAR to jointly analyze three lipid traits (low-density lipoprotein cholesterol, high-density lipoprotein cholesterol and triglycerides) in 61,861 multi-ethnic samples from the Trans-Omics for Precision Medicine (TOPMed) Program. We discovered new associations with lipid traits missed by single-trait analysis, including rare variants within an enhancer of and an intergenic region on chromosome 1.

}, doi = {10.1101/2023.10.30.564764}, author = {Li, Xihao and Chen, Han and Selvaraj, Margaret Sunitha and Van Buren, Eric and Zhou, Hufeng and Wang, Yuxuan and Sun, Ryan and McCaw, Zachary R and Yu, Zhi and Arnett, Donna K and Bis, Joshua C and Blangero, John and Boerwinkle, Eric and Bowden, Donald W and Brody, Jennifer A and Cade, Brian E and Carson, April P and Carlson, Jenna C and Chami, Nathalie and Chen, Yii-Der Ida and Curran, Joanne E and de Vries, Paul S and Fornage, Myriam and Franceschini, Nora and Freedman, Barry I and Gu, Charles and Heard-Costa, Nancy L and He, Jiang and Hou, Lifang and Hung, Yi-Jen and Irvin, Marguerite R and Kaplan, Robert C and Kardia, Sharon L R and Kelly, Tanika and Konigsberg, Iain and Kooperberg, Charles and Kral, Brian G and Li, Changwei and Loos, Ruth J F and Mahaney, Michael C and Martin, Lisa W and Mathias, Rasika A and Minster, Ryan L and Mitchell, Braxton D and Montasser, May E and Morrison, Alanna C and Palmer, Nicholette D and Peyser, Patricia A and Psaty, Bruce M and Raffield, Laura M and Redline, Susan and Reiner, Alexander P and Rich, Stephen S and Sitlani, Colleen M and Smith, Jennifer A and Taylor, Kent D and Tiwari, Hemant and Vasan, Ramachandran S and Wang, Zhe and Yanek, Lisa R and Yu, Bing and Rice, Kenneth M and Rotter, Jerome I and Peloso, Gina M and Natarajan, Pradeep and Li, Zilin and Liu, Zhonghua and Lin, Xihong} } @article {9899, title = {A statistical framework for multi-trait rare variant analysis in large-scale whole-genome sequencing studies.}, journal = {Nat Comput Sci}, volume = {5}, year = {2025}, month = {2025 Feb}, pages = {125-143}, abstract = {

Large-scale whole-genome sequencing (WGS) studies have improved our understanding of the contributions of coding and noncoding rare variants to complex human traits. Leveraging association effect sizes across multiple traits in WGS rare variant association analysis can improve statistical power over single-trait analysis, and also detect pleiotropic genes and regions. Existing multi-trait methods have limited ability to perform rare variant analysis of large-scale WGS data. We propose MultiSTAAR, a statistical framework and computationally scalable analytical pipeline for functionally informed multi-trait rare variant analysis in large-scale WGS studies. MultiSTAAR accounts for relatedness, population structure and correlation among phenotypes by jointly analyzing multiple traits, and further empowers rare variant association analysis by incorporating multiple functional annotations. We applied MultiSTAAR to jointly analyze three lipid traits in 61,838 multi-ethnic samples from the Trans-Omics for Precision Medicine (TOPMed) Program. We discovered and replicated new associations with lipid traits missed by single-trait analysis.

}, keywords = {Genetic Variation, Genome, Human, Genome-Wide Association Study, Humans, Lipids, Phenotype, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Whole Genome Sequencing}, issn = {2662-8457}, doi = {10.1038/s43588-024-00764-8}, author = {Li, Xihao and Chen, Han and Selvaraj, Margaret Sunitha and Van Buren, Eric and Zhou, Hufeng and Wang, Yuxuan and Sun, Ryan and McCaw, Zachary R and Yu, Zhi and Jiang, Min-Zhi and DiCorpo, Daniel and Gaynor, Sheila M and Dey, Rounak and Arnett, Donna K and Benjamin, Emelia J and Bis, Joshua C and Blangero, John and Boerwinkle, Eric and Bowden, Donald W and Brody, Jennifer A and Cade, Brian E and Carson, April P and Carlson, Jenna C and Chami, Nathalie and Chen, Yii-Der Ida and Curran, Joanne E and de Vries, Paul S and Fornage, Myriam and Franceschini, Nora and Freedman, Barry I and Gu, Charles and Heard-Costa, Nancy L and He, Jiang and Hou, Lifang and Hung, Yi-Jen and Irvin, Marguerite R and Kaplan, Robert C and Kardia, Sharon L R and Kelly, Tanika N and Konigsberg, Iain and Kooperberg, Charles and Kral, Brian G and Li, Changwei and Li, Yun and Lin, Honghuang and Liu, Ching-Ti and Loos, Ruth J F and Mahaney, Michael C and Martin, Lisa W and Mathias, Rasika A and Mitchell, Braxton D and Montasser, May E and Morrison, Alanna C and Naseri, Take and North, Kari E and Palmer, Nicholette D and Peyser, Patricia A and Psaty, Bruce M and Redline, Susan and Reiner, Alexander P and Rich, Stephen S and Sitlani, Colleen M and Smith, Jennifer A and Taylor, Kent D and Tiwari, Hemant K and Vasan, Ramachandran S and Viali, Satupa{\textquoteright}itea and Wang, Zhe and Wessel, Jennifer and Yanek, Lisa R and Yu, Bing and Dupuis, Jos{\'e}e and Meigs, James B and Auer, Paul L and Raffield, Laura M and Manning, Alisa K and Rice, Kenneth M and Rotter, Jerome I and Peloso, Gina M and Natarajan, Pradeep and Li, Zilin and Liu, Zhonghua and Lin, Xihong} }