@article {6091, title = {Large-scale association analyses identify new loci influencing glycemic traits and provide insight into the underlying biological pathways.}, journal = {Nat Genet}, volume = {44}, year = {2012}, month = {2012 Sep}, pages = {991-1005}, abstract = {

Through genome-wide association meta-analyses of up to 133,010 individuals of European ancestry without diabetes, including individuals newly genotyped using the Metabochip, we have increased the number of confirmed loci influencing glycemic traits to 53, of which 33 also increase type 2 diabetes risk (q < 0.05). Loci influencing fasting insulin concentration showed association with lipid levels and fat distribution, suggesting impact on insulin resistance. Gene-based analyses identified further biologically plausible loci, suggesting that additional loci beyond those reaching genome-wide significance are likely to represent real associations. This conclusion is supported by an excess of directionally consistent and nominally significant signals between discovery and follow-up studies. Functional analysis of these newly discovered loci will further improve our understanding of glycemic control.

}, keywords = {Adult, Animals, Blood Glucose, Fasting, Female, Gene Frequency, Genome-Wide Association Study, Humans, Insulin, Male, Metabolic Networks and Pathways, Mice, Osmolar Concentration, Quantitative Trait Loci}, issn = {1546-1718}, doi = {10.1038/ng.2385}, author = {Scott, Robert A and Lagou, Vasiliki and Welch, Ryan P and Wheeler, Eleanor and Montasser, May E and Luan, Jian{\textquoteright}an and M{\"a}gi, Reedik and Strawbridge, Rona J and Rehnberg, Emil and Gustafsson, Stefan and Kanoni, Stavroula and Rasmussen-Torvik, Laura J and Yengo, Loic and Lecoeur, C{\'e}cile and Shungin, Dmitry and Sanna, Serena and Sidore, Carlo and Johnson, Paul C D and Jukema, J Wouter and Johnson, Toby and Mahajan, Anubha and Verweij, Niek and Thorleifsson, Gudmar and Hottenga, Jouke-Jan and Shah, Sonia and Smith, Albert V and Sennblad, Bengt and Gieger, Christian and Salo, Perttu and Perola, Markus and Timpson, Nicholas J and Evans, David M and Pourcain, Beate St and Wu, Ying and Andrews, Jeanette S and Hui, Jennie and Bielak, Lawrence F and Zhao, Wei and Horikoshi, Momoko and Navarro, Pau and Isaacs, Aaron and O{\textquoteright}Connell, Jeffrey R and Stirrups, Kathleen and Vitart, Veronique and Hayward, Caroline and Esko, T{\~o}nu and Mihailov, Evelin and Fraser, Ross M and Fall, Tove and Voight, Benjamin F and Raychaudhuri, Soumya and Chen, Han and Lindgren, Cecilia M and Morris, Andrew P and Rayner, Nigel W and Robertson, Neil and Rybin, Denis and Liu, Ching-Ti and Beckmann, Jacques S and Willems, Sara M and Chines, Peter S and Jackson, Anne U and Kang, Hyun Min and Stringham, Heather M and Song, Kijoung and Tanaka, Toshiko and Peden, John F and Goel, Anuj and Hicks, Andrew A and An, Ping and M{\"u}ller-Nurasyid, Martina and Franco-Cereceda, Anders and Folkersen, Lasse and Marullo, Letizia and Jansen, Hanneke and Oldehinkel, Albertine J and Bruinenberg, Marcel and Pankow, James S and North, Kari E and Forouhi, Nita G and Loos, Ruth J F and Edkins, Sarah and Varga, Tibor V and Hallmans, G{\"o}ran and Oksa, Heikki and Antonella, Mulas and Nagaraja, Ramaiah and Trompet, Stella and Ford, Ian and Bakker, Stephan J L and Kong, Augustine and Kumari, Meena and Gigante, Bruna and Herder, Christian and Munroe, Patricia B and Caulfield, Mark and Antti, Jula and Mangino, Massimo and Small, Kerrin and Miljkovic, Iva and Liu, Yongmei and Atalay, Mustafa and Kiess, Wieland and James, Alan L and Rivadeneira, Fernando and Uitterlinden, Andr{\'e} G and Palmer, Colin N A and Doney, Alex S F and Willemsen, Gonneke and Smit, Johannes H and Campbell, Susan and Polasek, Ozren and Bonnycastle, Lori L and Hercberg, Serge and Dimitriou, Maria and Bolton, Jennifer L and Fowkes, Gerard R and Kovacs, Peter and Lindstr{\"o}m, Jaana and Zemunik, Tatijana and Bandinelli, Stefania and Wild, Sarah H and Basart, Hanneke V and Rathmann, Wolfgang and Grallert, Harald and Maerz, Winfried and Kleber, Marcus E and Boehm, Bernhard O and Peters, Annette and Pramstaller, Peter P and Province, Michael A and Borecki, Ingrid B and Hastie, Nicholas D and Rudan, Igor and Campbell, Harry and Watkins, Hugh and Farrall, Martin and Stumvoll, Michael and Ferrucci, Luigi and Waterworth, Dawn M and Bergman, Richard N and Collins, Francis S and Tuomilehto, Jaakko and Watanabe, Richard M and de Geus, Eco J C and Penninx, Brenda W and Hofman, Albert and Oostra, Ben A and Psaty, Bruce M and Vollenweider, Peter and Wilson, James F and Wright, Alan F and Hovingh, G Kees and Metspalu, Andres and Uusitupa, Matti and Magnusson, Patrik K E and Kyvik, Kirsten O and Kaprio, Jaakko and Price, Jackie F and Dedoussis, George V and Deloukas, Panos and Meneton, Pierre and Lind, Lars and Boehnke, Michael and Shuldiner, Alan R and van Duijn, Cornelia M and Morris, Andrew D and Toenjes, Anke and Peyser, Patricia A and Beilby, John P and K{\"o}rner, Antje and Kuusisto, Johanna and Laakso, Markku and Bornstein, Stefan R and Schwarz, Peter E H and Lakka, Timo A and Rauramaa, Rainer and Adair, Linda S and Smith, George Davey and Spector, Tim D and Illig, Thomas and de Faire, Ulf and Hamsten, Anders and Gudnason, Vilmundur and Kivimaki, Mika and Hingorani, Aroon and Keinanen-Kiukaanniemi, Sirkka M and Saaristo, Timo E and Boomsma, Dorret I and Stefansson, Kari and van der Harst, Pim and Dupuis, Jos{\'e}e and Pedersen, Nancy L and Sattar, Naveed and Harris, Tamara B and Cucca, Francesco and Ripatti, Samuli and Salomaa, Veikko and Mohlke, Karen L and Balkau, Beverley and Froguel, Philippe and Pouta, Anneli and Jarvelin, Marjo-Riitta and Wareham, Nicholas J and Bouatia-Naji, Nabila and McCarthy, Mark I and Franks, Paul W and Meigs, James B and Teslovich, Tanya M and Florez, Jose C and Langenberg, Claudia and Ingelsson, Erik and Prokopenko, Inga and Barroso, In{\^e}s} } @article {6578, title = {Strategies to design and analyze targeted sequencing data: cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium Targeted Sequencing Study.}, journal = {Circ Cardiovasc Genet}, volume = {7}, year = {2014}, month = {2014 Jun}, pages = {335-43}, abstract = {

BACKGROUND: Genome-wide association studies have identified thousands of genetic variants that influence a variety of diseases and health-related quantitative traits. However, the causal variants underlying the majority of genetic associations remain unknown. Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium Targeted Sequencing Study aims to follow up genome-wide association study signals and identify novel associations of the allelic spectrum of identified variants with cardiovascular-related traits.

METHODS AND RESULTS: The study included 4231 participants from 3 CHARGE cohorts: the Atherosclerosis Risk in Communities Study, the Cardiovascular Health Study, and the Framingham Heart Study. We used a case-cohort design in which we selected both a random sample of participants and participants with extreme phenotypes for each of 14 traits. We sequenced and analyzed 77 genomic loci, which had previously been associated with >=1 of 14 phenotypes. A total of 52 736 variants were characterized by sequencing and passed our stringent quality control criteria. For common variants (minor allele frequency >=1\%), we performed unweighted regression analyses to obtain P values for associations and weighted regression analyses to obtain effect estimates that accounted for the sampling design. For rare variants, we applied 2 approaches: collapsed aggregate statistics and joint analysis of variants using the sequence kernel association test.

CONCLUSIONS: We sequenced 77 genomic loci in participants from 3 cohorts. We established a set of filters to identify high-quality variants and implemented statistical and bioinformatics strategies to analyze the sequence data and identify potentially functional variants within genome-wide association study loci.

}, keywords = {Adult, Aged, Aged, 80 and over, Aging, Cohort Studies, Female, Genetic Variation, Genome-Wide Association Study, Genomics, Heart Diseases, Humans, Male, Middle Aged, Polymorphism, Single Nucleotide, Research Design, Sequence Analysis, DNA}, issn = {1942-3268}, doi = {10.1161/CIRCGENETICS.113.000350}, author = {Lin, Honghuang and Wang, Min and Brody, Jennifer A and Bis, Joshua C and Dupuis, Jos{\'e}e and Lumley, Thomas and McKnight, Barbara and Rice, Kenneth M and Sitlani, Colleen M and Reid, Jeffrey G and Bressler, Jan and Liu, Xiaoming and Davis, Brian C and Johnson, Andrew D and O{\textquoteright}Donnell, Christopher J and Kovar, Christie L and Dinh, Huyen and Wu, Yuanqing and Newsham, Irene and Chen, Han and Broka, Andi and DeStefano, Anita L and Gupta, Mayetri and Lunetta, Kathryn L and Liu, Ching-Ti and White, Charles C and Xing, Chuanhua and Zhou, Yanhua and Benjamin, Emelia J and Schnabel, Renate B and Heckbert, Susan R and Psaty, Bruce M and Muzny, Donna M and Cupples, L Adrienne and Morrison, Alanna C and Boerwinkle, Eric} } @article {8044, title = {Associations of variants In the hexokinase 1 and interleukin 18 receptor regions with oxyhemoglobin saturation during sleep.}, journal = {PLoS Genet}, volume = {15}, year = {2019}, month = {2019 04}, pages = {e1007739}, abstract = {

Sleep disordered breathing (SDB)-related overnight hypoxemia is associated with cardiometabolic disease and other comorbidities. Understanding the genetic bases for variations in nocturnal hypoxemia may help understand mechanisms influencing oxygenation and SDB-related mortality. We conducted genome-wide association tests across 10 cohorts and 4 populations to identify genetic variants associated with three correlated measures of overnight oxyhemoglobin saturation: average and minimum oxyhemoglobin saturation during sleep and the percent of sleep with oxyhemoglobin saturation under 90\%. The discovery sample consisted of 8,326 individuals. Variants with p < 1 {\texttimes} 10(-6) were analyzed in a replication group of 14,410 individuals. We identified 3 significantly associated regions, including 2 regions in multi-ethnic analyses (2q12, 10q22). SNPs in the 2q12 region associated with minimum SpO2 (rs78136548 p = 2.70 {\texttimes} 10(-10)). SNPs at 10q22 were associated with all three traits including average SpO2 (rs72805692 p = 4.58 {\texttimes} 10(-8)). SNPs in both regions were associated in over 20,000 individuals and are supported by prior associations or functional evidence. Four additional significant regions were detected in secondary sex-stratified and combined discovery and replication analyses, including a region overlapping Reelin, a known marker of respiratory complex neurons.These are the first genome-wide significant findings reported for oxyhemoglobin saturation during sleep, a phenotype of high clinical interest. Our replicated associations with HK1 and IL18R1 suggest that variants in inflammatory pathways, such as the biologically-plausible NLRP3 inflammasome, may contribute to nocturnal hypoxemia.

}, keywords = {Adolescent, Adult, Aged, Aged, 80 and over, Cell Adhesion Molecules, Neuronal, Computational Biology, Extracellular Matrix Proteins, Female, Gene Regulatory Networks, Genetic Variation, Genome-Wide Association Study, Hexokinase, Humans, Hypoxia, Interleukin-18 Receptor alpha Subunit, Male, Middle Aged, Nerve Tissue Proteins, NLR Family, Pyrin Domain-Containing 3 Protein, Oxygen, Oxyhemoglobins, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Serine Endopeptidases, Sleep, Sleep Apnea Syndromes, Young Adult}, issn = {1553-7404}, doi = {10.1371/journal.pgen.1007739}, author = {Cade, Brian E and Chen, Han and Stilp, Adrienne M and Louie, Tin and Ancoli-Israel, Sonia and Arens, Raanan and Barfield, Richard and Below, Jennifer E and Cai, Jianwen and Conomos, Matthew P and Evans, Daniel S and Frazier-Wood, Alexis C and Gharib, Sina A and Gleason, Kevin J and Gottlieb, Daniel J and Hillman, David R and Johnson, W Craig and Lederer, David J and Lee, Jiwon and Loredo, Jose S and Mei, Hao and Mukherjee, Sutapa and Patel, Sanjay R and Post, Wendy S and Purcell, Shaun M and Ramos, Alberto R and Reid, Kathryn J and Rice, Ken and Shah, Neomi A and Sofer, Tamar and Taylor, Kent D and Thornton, Timothy A and Wang, Heming and Yaffe, Kristine and Zee, Phyllis C and Hanis, Craig L and Palmer, Lyle J and Rotter, Jerome I and Stone, Katie L and Tranah, Gregory J and Wilson, James G and Sunyaev, Shamil R and Laurie, Cathy C and Zhu, Xiaofeng and Saxena, Richa and Lin, Xihong and Redline, Susan} } @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 {9161, title = {Non-linear machine learning models incorporating SNPs and PRS improve polygenic prediction in diverse human populations.}, journal = {Commun Biol}, volume = {5}, year = {2022}, month = {2022 08 22}, pages = {856}, abstract = {

Polygenic risk scores (PRS) are commonly used to quantify the inherited susceptibility for a trait, yet they fail to account for non-linear and interaction effects between single nucleotide polymorphisms (SNPs). We address this via a machine learning approach, validated in nine complex phenotypes in a multi-ancestry population. We use an ensemble method of SNP selection followed by gradient boosted trees (XGBoost) to allow for non-linearities and interaction effects. We compare our results to the standard, linear PRS model developed using PRSice, LDpred2, and lassosum2. Combining a PRS as a feature in an XGBoost model results in a relative increase in the percentage variance explained compared to the standard linear PRS model by 22\% for height, 27\% for HDL cholesterol, 43\% for body mass index, 50\% for sleep duration, 58\% for systolic blood pressure, 64\% for total cholesterol, 66\% for triglycerides, 77\% for LDL cholesterol, and 100\% for diastolic blood pressure. Multi-ancestry trained models perform similarly to specific racial/ethnic group trained models and are consistently superior to the standard linear PRS models. This work demonstrates an effective method to account for non-linearities and interaction effects in genetics-based prediction models.

}, keywords = {Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Machine Learning, Multifactorial Inheritance, Polymorphism, Single Nucleotide}, issn = {2399-3642}, doi = {10.1038/s42003-022-03812-z}, author = {Elgart, Michael and Lyons, Genevieve and Romero-Brufau, Santiago and Kurniansyah, Nuzulul and Brody, Jennifer A and Guo, Xiuqing and Lin, Henry J and Raffield, Laura and Gao, Yan and Chen, Han and de Vries, Paul and Lloyd-Jones, Donald M and Lange, Leslie A and Peloso, Gina M and Fornage, Myriam and Rotter, Jerome I and Rich, Stephen S and Morrison, Alanna C and Psaty, Bruce M and Levy, Daniel and Redline, Susan and Sofer, Tamar} } @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 {9376, title = {Whole-Genome Sequencing Analysis of Human Metabolome in Multi-Ethnic Populations.}, journal = {Nat Commun}, volume = {14}, year = {2023}, month = {2023 May 30}, pages = {3111}, abstract = {

Circulating metabolite levels may reflect the state of the human organism in health and disease, however, the genetic architecture of metabolites is not fully understood. We have performed a whole-genome sequencing association analysis of both common and rare variants in up to 11,840 multi-ethnic participants from five studies with up to 1666 circulating metabolites. We have discovered 1985 novel variant-metabolite associations, and validated 761 locus-metabolite associations reported previously. Seventy-nine novel variant-metabolite associations have been replicated, including three genetic loci located on the X chromosome that have demonstrated its involvement in metabolic regulation. Gene-based analysis have provided further support for seven metabolite-replicated loci pairs and their biologically plausible genes. Among those novel replicated variant-metabolite pairs, follow-up analyses have revealed that 26 metabolites have colocalized with 21 tissues, seven metabolite-disease outcome associations have been putatively causal, and 7 metabolites might be regulated by plasma protein levels. Our results have depicted the genetic contribution to circulating metabolite levels, providing additional insights into understanding human disease.

}, keywords = {Ethnicity, Genome-Wide Association Study, Humans, Metabolome, Polymorphism, Single Nucleotide, Quantitative Trait Loci}, issn = {2041-1723}, doi = {10.1038/s41467-023-38800-2}, author = {Feofanova, Elena V and Brown, Michael R and Alkis, Taryn and Manuel, Astrid M and Li, Xihao and Tahir, Usman A and Li, Zilin and Mendez, Kevin M and Kelly, Rachel S and Qi, Qibin and Chen, Han and Larson, Martin G and Lemaitre, Rozenn N and Morrison, Alanna C and Grieser, Charles and Wong, Kari E and Gersztern, Robert E and Zhao, Zhongming and Lasky-Su, Jessica and Yu, Bing} } @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} }