@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 {8975, title = {Rare coding variants in 35 genes associate with circulating lipid levels-A multi-ancestry analysis of 170,000 exomes.}, journal = {Am J Hum Genet}, volume = {109}, year = {2022}, month = {2022 01 06}, pages = {81-96}, abstract = {

Large-scale gene sequencing studies for complex traits have the potential to identify causal genes with therapeutic implications. We performed gene-based association testing of blood lipid levels with rare (minor allele frequency < 1\%) predicted damaging coding variation by using sequence data from >170,000 individuals from multiple ancestries: 97,493 European, 30,025 South Asian, 16,507 African, 16,440 Hispanic/Latino, 10,420 East Asian, and 1,182 Samoan. We identified 35 genes associated with circulating lipid levels; some of these genes have not been previously associated with lipid levels when using rare coding variation from population-based samples. We prioritize 32 genes in array-based genome-wide association study (GWAS) loci based on aggregations of rare coding variants; three (EVI5, SH2B3, and PLIN1) had no prior association of rare coding variants with lipid levels. Most of our associated genes showed evidence of association among multiple ancestries. Finally, we observed an enrichment of gene-based associations for low-density lipoprotein cholesterol drug target genes and for genes closest to GWAS index single-nucleotide polymorphisms (SNPs). Our results demonstrate that gene-based associations can be beneficial for drug target development and provide evidence that the gene closest to the array-based GWAS index SNP is often the functional gene for blood lipid levels.

}, keywords = {Alleles, Blood Glucose, Case-Control Studies, Computational Biology, Databases, Genetic, Diabetes Mellitus, Type 2, Exome, Genetic Predisposition to Disease, Genetic Variation, Genetics, Population, Genome-Wide Association Study, Humans, Lipid Metabolism, Lipids, Liver, Molecular Sequence Annotation, Multifactorial Inheritance, Open Reading Frames, Phenotype, Polymorphism, Single Nucleotide}, issn = {1537-6605}, doi = {10.1016/j.ajhg.2021.11.021}, author = {Hindy, George and Dornbos, Peter and Chaffin, Mark D and Liu, Dajiang J and Wang, Minxian and Selvaraj, Margaret Sunitha and Zhang, David and Park, Joseph and Aguilar-Salinas, Carlos A and Antonacci-Fulton, Lucinda and Ardissino, Diego and Arnett, Donna K and Aslibekyan, Stella and Atzmon, Gil and Ballantyne, Christie M and Barajas-Olmos, Francisco and Barzilai, Nir and Becker, Lewis C and Bielak, Lawrence F and Bis, Joshua C and Blangero, John and Boerwinkle, Eric and Bonnycastle, Lori L and Bottinger, Erwin and Bowden, Donald W and Bown, Matthew J and Brody, Jennifer A and Broome, Jai G and Burtt, Noel P and Cade, Brian E and Centeno-Cruz, Federico and Chan, Edmund and Chang, Yi-Cheng and Chen, Yii-der I and Cheng, Ching-Yu and Choi, Won Jung and Chowdhury, Rajiv and Contreras-Cubas, Cecilia and C{\'o}rdova, Emilio J and Correa, Adolfo and Cupples, L Adrienne and Curran, Joanne E and Danesh, John and de Vries, Paul S and DeFronzo, Ralph A and Doddapaneni, Harsha and Duggirala, Ravindranath and Dutcher, Susan K and Ellinor, Patrick T and Emery, Leslie S and Florez, Jose C and Fornage, Myriam and Freedman, Barry I and Fuster, Valentin and Garay-Sevilla, Ma Eugenia and Garc{\'\i}a-Ortiz, Humberto and Germer, Soren and Gibbs, Richard A and Gieger, Christian and Glaser, Benjamin and Gonzalez, Clicerio and Gonzalez-Villalpando, Maria Elena and Graff, Mariaelisa and Graham, Sarah E and Grarup, Niels and Groop, Leif C and Guo, Xiuqing and Gupta, Namrata and Han, Sohee and Hanis, Craig L and Hansen, Torben and He, Jiang and Heard-Costa, Nancy L and Hung, Yi-Jen and Hwang, Mi Yeong and Irvin, Marguerite R and Islas-Andrade, Sergio and Jarvik, Gail P and Kang, Hyun Min and Kardia, Sharon L R and Kelly, Tanika and Kenny, Eimear E and Khan, Alyna T and Kim, Bong-Jo and Kim, Ryan W and Kim, Young Jin and Koistinen, Heikki A and Kooperberg, Charles and Kuusisto, Johanna and Kwak, Soo Heon and Laakso, Markku and Lange, Leslie A and Lee, Jiwon and Lee, Juyoung and Lee, Seonwook and Lehman, Donna M and Lemaitre, Rozenn N and Linneberg, Allan and Liu, Jianjun and Loos, Ruth J F and Lubitz, Steven A and Lyssenko, Valeriya and Ma, Ronald C W and Martin, Lisa Warsinger and Mart{\'\i}nez-Hern{\'a}ndez, Ang{\'e}lica and Mathias, Rasika A and McGarvey, Stephen T and McPherson, Ruth and Meigs, James B and Meitinger, Thomas and Melander, Olle and Mendoza-Caamal, Elvia and Metcalf, Ginger A and Mi, Xuenan and Mohlke, Karen L and Montasser, May E and Moon, Jee-Young and Moreno-Macias, Hortensia and Morrison, Alanna C and Muzny, Donna M and Nelson, Sarah C and Nilsson, Peter M and O{\textquoteright}Connell, Jeffrey R and Orho-Melander, Marju and Orozco, Lorena and Palmer, Colin N A and Palmer, Nicholette D and Park, Cheol Joo and Park, Kyong Soo and Pedersen, Oluf and Peralta, Juan M and Peyser, Patricia A and Post, Wendy S and Preuss, Michael and Psaty, Bruce M and Qi, Qibin and Rao, D C and Redline, Susan and Reiner, Alexander P and Revilla-Monsalve, Cristina and Rich, Stephen S and Samani, Nilesh and Schunkert, Heribert and Schurmann, Claudia and Seo, Daekwan and Seo, Jeong-Sun and Sim, Xueling and Sladek, Rob and Small, Kerrin S and So, Wing Yee and Stilp, Adrienne M and Tai, E Shyong and Tam, Claudia H T and Taylor, Kent D and Teo, Yik Ying and Thameem, Farook and Tomlinson, Brian and Tsai, Michael Y and Tuomi, Tiinamaija and Tuomilehto, Jaakko and Tusi{\'e}-Luna, Teresa and Udler, Miriam S and van Dam, Rob M and Vasan, Ramachandran S and Viaud Martinez, Karine A and Wang, Fei Fei and Wang, Xuzhi and Watkins, Hugh and Weeks, Daniel E and Wilson, James G and Witte, Daniel R and Wong, Tien-Yin and Yanek, Lisa R and Kathiresan, Sekar and Rader, Daniel J and Rotter, Jerome I and Boehnke, Michael and McCarthy, Mark I and Willer, Cristen J and Natarajan, Pradeep and Flannick, Jason A and Khera, Amit V and Peloso, Gina M} } @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} } @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} }