@article {9253, title = {A framework for detecting noncoding rare-variant associations of large-scale whole-genome sequencing studies.}, journal = {Nat Methods}, volume = {19}, year = {2022}, month = {2022 Dec}, pages = {1599-1611}, abstract = {

Large-scale whole-genome sequencing studies have enabled analysis of noncoding rare-variant (RV) associations with complex human diseases and traits. Variant-set analysis is a powerful approach to study RV association. However, existing methods have limited ability in analyzing the noncoding genome. We propose a computationally efficient and robust noncoding RV association detection framework, STAARpipeline, to automatically annotate a whole-genome sequencing study and perform flexible noncoding RV association analysis, including gene-centric analysis and fixed window-based and dynamic window-based non-gene-centric analysis by incorporating variant functional annotations. In gene-centric analysis, STAARpipeline uses STAAR to group noncoding variants based on functional categories of genes and incorporate multiple functional annotations. In non-gene-centric analysis, STAARpipeline uses SCANG-STAAR to incorporate dynamic window sizes and multiple functional annotations. We apply STAARpipeline to identify noncoding RV sets associated with four lipid traits in 21,015 discovery samples from the Trans-Omics for Precision Medicine (TOPMed) program and replicate several of them in an additional 9,123 TOPMed samples. We also analyze five non-lipid TOPMed traits.

}, keywords = {Genetic Variation, Genome, Genome-Wide Association Study, Humans, Phenotype, Whole Genome Sequencing}, issn = {1548-7105}, doi = {10.1038/s41592-022-01640-x}, author = {Li, Zilin and Li, Xihao and Zhou, Hufeng and Gaynor, Sheila M and Selvaraj, Margaret Sunitha and Arapoglou, Theodore and Quick, Corbin and Liu, Yaowu and Chen, Han and Sun, Ryan and Dey, Rounak and Arnett, Donna K and Auer, Paul L and Bielak, Lawrence F and Bis, Joshua C and Blackwell, Thomas W and Blangero, John and Boerwinkle, Eric and Bowden, Donald W and Brody, Jennifer A and Cade, Brian E and Conomos, Matthew P and Correa, Adolfo and Cupples, L Adrienne and Curran, Joanne E and de Vries, Paul S and Duggirala, Ravindranath and Franceschini, Nora and Freedman, Barry I and G{\"o}ring, Harald H H and Guo, Xiuqing and Kalyani, Rita R and Kooperberg, Charles and Kral, Brian G and Lange, Leslie A and Lin, Bridget M and Manichaikul, Ani and Manning, Alisa K and Martin, Lisa W and Mathias, Rasika A and Meigs, James B and Mitchell, Braxton D and Montasser, May E and Morrison, Alanna C and Naseri, Take and O{\textquoteright}Connell, Jeffrey R and Palmer, Nicholette D and Peyser, Patricia A and Psaty, Bruce M and Raffield, Laura M and Redline, Susan and Reiner, Alexander P and Reupena, Muagututi{\textquoteright}a Sefuiva and Rice, Kenneth M and Rich, Stephen S and Smith, Jennifer A and Taylor, Kent D and Taub, Margaret A and Vasan, Ramachandran S and Weeks, Daniel E and Wilson, James G and Yanek, Lisa R and Zhao, Wei and Rotter, Jerome I and Willer, Cristen J and Natarajan, Pradeep and Peloso, Gina M and Lin, Xihong} } @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 {9158, title = {Whole genome sequence association analysis of fasting glucose and fasting insulin levels in diverse cohorts from the NHLBI TOPMed program.}, journal = {Commun Biol}, volume = {5}, year = {2022}, month = {2022 07 28}, pages = {756}, abstract = {

The genetic determinants of fasting glucose (FG) and fasting insulin (FI) have been studied mostly through genome arrays, resulting in over 100 associated variants. We extended this work with high-coverage whole genome sequencing analyses from fifteen cohorts in NHLBI{\textquoteright}s Trans-Omics for Precision Medicine (TOPMed) program. Over 23,000 non-diabetic individuals from five race-ethnicities/populations (African, Asian, European, Hispanic and Samoan) were included. Eight variants were significantly associated with FG or FI across previously identified regions MTNR1B, G6PC2, GCK, GCKR and FOXA2. We additionally characterize suggestive associations with FG or FI near previously identified SLC30A8, TCF7L2, and ADCY5 regions as well as APOB, PTPRT, and ROBO1. Functional annotation resources including the Diabetes Epigenome Atlas were compiled for each signal (chromatin states, annotation principal components, and others) to elucidate variant-to-function hypotheses. We provide a catalog of nucleotide-resolution genomic variation spanning intergenic and intronic regions creating a foundation for future sequencing-based investigations of glycemic traits.

}, keywords = {Diabetes Mellitus, Type 2, Fasting, Glucose, Humans, Insulin, National Heart, Lung, and Blood Institute (U.S.), Nerve Tissue Proteins, Polymorphism, Single Nucleotide, Precision Medicine, Receptors, Immunologic, United States}, issn = {2399-3642}, doi = {10.1038/s42003-022-03702-4}, author = {DiCorpo, Daniel and Gaynor, Sheila M and Russell, Emily M and Westerman, Kenneth E and Raffield, Laura M and Majarian, Timothy D and Wu, Peitao and Sarnowski, Chloe and Highland, Heather M and Jackson, Anne and Hasbani, Natalie R and de Vries, Paul S and Brody, Jennifer A and Hidalgo, Bertha and Guo, Xiuqing and Perry, James A and O{\textquoteright}Connell, Jeffrey R and Lent, Samantha and Montasser, May E and Cade, Brian E and Jain, Deepti and Wang, Heming and D{\textquoteright}Oliveira Albanus, Ricardo and Varshney, Arushi and Yanek, Lisa R and Lange, Leslie and Palmer, Nicholette D and Almeida, Marcio and Peralta, Juan M and Aslibekyan, Stella and Baldridge, Abigail S and Bertoni, Alain G and Bielak, Lawrence F and Chen, Chung-Shiuan and Chen, Yii-Der Ida and Choi, Won Jung and Goodarzi, Mark O and Floyd, James S and Irvin, Marguerite R and Kalyani, Rita R and Kelly, Tanika N and Lee, Seonwook and Liu, Ching-Ti and Loesch, Douglas and Manson, JoAnn E and Minster, Ryan L and Naseri, Take and Pankow, James S and Rasmussen-Torvik, Laura J and Reiner, Alexander P and Reupena, Muagututi{\textquoteright}a Sefuiva and Selvin, Elizabeth and Smith, Jennifer A and Weeks, Daniel E and Xu, Huichun and Yao, Jie and Zhao, Wei and Parker, Stephen and Alonso, Alvaro and Arnett, Donna K and Blangero, John and Boerwinkle, Eric and Correa, Adolfo and Cupples, L Adrienne and Curran, Joanne E and Duggirala, Ravindranath and He, Jiang and Heckbert, Susan R and Kardia, Sharon L R and Kim, Ryan W and Kooperberg, Charles and Liu, Simin and Mathias, Rasika A and McGarvey, Stephen T and Mitchell, Braxton D and Morrison, Alanna C and Peyser, Patricia A and Psaty, Bruce M and Redline, Susan and Shuldiner, Alan R and Taylor, Kent D and Vasan, Ramachandran S and Viaud-Martinez, Karine A and Florez, Jose C and Wilson, James G and Sladek, Robert and Rich, Stephen S and Rotter, Jerome I and Lin, Xihong and Dupuis, Jos{\'e}e and Meigs, James B and Wessel, Jennifer and Manning, Alisa K} } @article {9412, title = {Multi-ancestry transcriptome-wide association analyses yield insights into tobacco use biology and drug repurposing.}, journal = {Nat Genet}, volume = {55}, year = {2023}, month = {2023 Feb}, pages = {291-300}, abstract = {

Most transcriptome-wide association studies (TWASs) so far focus on European ancestry and lack diversity. To overcome this limitation, we aggregated genome-wide association study (GWAS) summary statistics, whole-genome sequences and expression quantitative trait locus (eQTL) data from diverse ancestries. We developed a new approach, TESLA (multi-ancestry integrative study using an optimal linear combination of association statistics), to integrate an eQTL dataset with a multi-ancestry GWAS. By exploiting shared phenotypic effects between ancestries and accommodating potential effect heterogeneities, TESLA improves power over other TWAS methods. When applied to tobacco use phenotypes, TESLA identified 273 new genes, up to 55\% more compared with alternative TWAS methods. These hits and subsequent fine mapping using TESLA point to target genes with biological relevance. In silico drug-repurposing analyses highlight several drugs with known efficacy, including dextromethorphan and galantamine, and new drugs such as muscle relaxants that may be repurposed for treating nicotine addiction.

}, keywords = {Biology, Drug Repositioning, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Polymorphism, Single Nucleotide, Tobacco Use, Transcriptome}, issn = {1546-1718}, doi = {10.1038/s41588-022-01282-x}, author = {Chen, Fang and Wang, Xingyan and Jang, Seon-Kyeong and Quach, Bryan C and Weissenkampen, J Dylan and Khunsriraksakul, Chachrit and Yang, Lina and Sauteraud, Renan and Albert, Christine M and Allred, Nicholette D D and Arnett, Donna K and Ashley-Koch, Allison E and Barnes, Kathleen C and Barr, R Graham and Becker, Diane M and Bielak, Lawrence F and Bis, Joshua C and Blangero, John and Boorgula, Meher Preethi and Chasman, Daniel I and Chavan, Sameer and Chen, Yii-der I and Chuang, Lee-Ming and Correa, Adolfo and Curran, Joanne E and David, Sean P and Fuentes, Lisa de Las and Deka, Ranjan and Duggirala, Ravindranath and Faul, Jessica D and Garrett, Melanie E and Gharib, Sina A and Guo, Xiuqing and Hall, Michael E and Hawley, Nicola L and He, Jiang and Hobbs, Brian D and Hokanson, John E and Hsiung, Chao A and Hwang, Shih-Jen and Hyde, Thomas M and Irvin, Marguerite R and Jaffe, Andrew E and Johnson, Eric O and Kaplan, Robert and Kardia, Sharon L R and Kaufman, Joel D and Kelly, Tanika N and Kleinman, Joel E and Kooperberg, Charles and Lee, I-Te and Levy, Daniel and Lutz, Sharon M and Manichaikul, Ani W and Martin, Lisa W and Marx, Olivia and McGarvey, Stephen T and Minster, Ryan L and Moll, Matthew and Moussa, Karine A and Naseri, Take and North, Kari E and Oelsner, Elizabeth C and Peralta, Juan M and Peyser, Patricia A and Psaty, Bruce M and Rafaels, Nicholas and Raffield, Laura M and Reupena, Muagututi{\textquoteright}a Sefuiva and Rich, Stephen S and Rotter, Jerome I and Schwartz, David A and Shadyab, Aladdin H and Sheu, Wayne H-H and Sims, Mario and Smith, Jennifer A and Sun, Xiao and Taylor, Kent D and Telen, Marilyn J and Watson, Harold and Weeks, Daniel E and Weir, David R and Yanek, Lisa R and Young, Kendra A and Young, Kristin L and Zhao, Wei and Hancock, Dana B and Jiang, Bibo and Vrieze, Scott and Liu, Dajiang J} } @article {9239, title = {Powerful, scalable and resource-efficient meta-analysis of rare variant associations in large whole genome sequencing studies.}, journal = {Nat Genet}, volume = {55}, year = {2023}, month = {2023 Jan}, pages = {154-164}, abstract = {

Meta-analysis of whole genome sequencing/whole exome sequencing (WGS/WES) studies provides an attractive solution to the problem of collecting large sample sizes for discovering rare variants associated with complex phenotypes. Existing rare variant meta-analysis approaches are not scalable to biobank-scale WGS data. Here we present MetaSTAAR, a powerful and resource-efficient rare variant meta-analysis framework for large-scale WGS/WES studies. MetaSTAAR accounts for relatedness and population structure, can analyze both quantitative and dichotomous traits and boosts the power of rare variant tests by incorporating multiple variant functional annotations. Through meta-analysis of four lipid traits in 30,138 ancestrally diverse samples from 14 studies of the Trans Omics for Precision Medicine (TOPMed) Program, we show that MetaSTAAR performs rare variant meta-analysis at scale and produces results comparable to using pooled data. Additionally, we identified several conditionally significant rare variant associations with lipid traits. We further demonstrate that MetaSTAAR is scalable to biobank-scale cohorts through meta-analysis of TOPMed WGS data and UK Biobank WES data of ~200,000 samples.

}, keywords = {Exome Sequencing, Genome-Wide Association Study, Lipids, Phenotype, Whole Genome Sequencing}, issn = {1546-1718}, doi = {10.1038/s41588-022-01225-6}, author = {Li, Xihao and Quick, Corbin and Zhou, Hufeng and Gaynor, Sheila M and Liu, Yaowu and Chen, Han and Selvaraj, Margaret Sunitha and Sun, Ryan and Dey, Rounak and Arnett, Donna K and Bielak, Lawrence F and Bis, Joshua C and Blangero, John and Boerwinkle, Eric and Bowden, Donald W and Brody, Jennifer A and Cade, Brian E and Correa, Adolfo and Cupples, L Adrienne and Curran, Joanne E and de Vries, Paul S and Duggirala, Ravindranath and Freedman, Barry I and G{\"o}ring, Harald H H and Guo, Xiuqing and Haessler, Jeffrey and Kalyani, Rita R and Kooperberg, Charles and Kral, Brian G and Lange, Leslie A and Manichaikul, Ani and Martin, Lisa W and McGarvey, Stephen T and Mitchell, Braxton D and Montasser, May E and Morrison, Alanna C and Naseri, Take and O{\textquoteright}Connell, Jeffrey R and Palmer, Nicholette D and Peyser, Patricia A and Psaty, Bruce M and Raffield, Laura M and Redline, Susan and Reiner, Alexander P and Reupena, Muagututi{\textquoteright}a Sefuiva and Rice, Kenneth M and Rich, Stephen S and Sitlani, Colleen M and Smith, Jennifer A and Taylor, Kent D and Vasan, Ramachandran S and Willer, Cristen J and Wilson, James G and Yanek, Lisa R and Zhao, Wei and Rotter, Jerome I and Natarajan, Pradeep and Peloso, Gina M and Li, Zilin and Lin, Xihong} }