@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 {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} } @article {9537, title = {Type 2 Diabetes Modifies the Association of CAD Genomic Risk Variants With Subclinical Atherosclerosis.}, journal = {Circ Genom Precis Med}, year = {2023}, month = {2023 Nov 28}, pages = {e004176}, abstract = {

BACKGROUND: Individuals with type 2 diabetes (T2D) have an increased risk of coronary artery disease (CAD), but questions remain about the underlying pathology. Identifying which CAD loci are modified by T2D in the development of subclinical atherosclerosis (coronary artery calcification [CAC], carotid intima-media thickness, or carotid plaque) may improve our understanding of the mechanisms leading to the increased CAD in T2D.

METHODS: We compared the common and rare variant associations of known CAD loci from the literature on CAC, carotid intima-media thickness, and carotid plaque in up to 29 670 participants, including up to 24 157 normoglycemic controls and 5513 T2D cases leveraging whole-genome sequencing data from the Trans-Omics for Precision Medicine program. We included first-order T2D interaction terms in each model to determine whether CAD loci were modified by T2D. The genetic main and interaction effects were assessed using a joint test to determine whether a CAD variant, or gene-based rare variant set, was associated with the respective subclinical atherosclerosis measures and then further determined whether these loci had a significant interaction test.

RESULTS: Using a Bonferroni-corrected significance threshold of <1.6{\texttimes}10, we identified 3 genes (, , and ) associated with CAC and 2 genes ( and ) associated with carotid intima-media thickness and carotid plaque, respectively, through gene-based rare variant set analysis. Both and also had significantly different associations for CAC in T2D cases versus controls. No significant interaction tests were identified through the candidate single-variant analysis.

CONCLUSIONS: These results highlight T2D as an important modifier of rare variant associations in CAD loci with CAC.

}, issn = {2574-8300}, doi = {10.1161/CIRCGEN.123.004176}, author = {Hasbani, Natalie R and Westerman, Kenneth E and Heon Kwak, Soo and Chen, Han and Li, Xihao and DiCorpo, Daniel and Wessel, Jennifer and Bis, Joshua C and Sarnowski, Chloe and Wu, Peitao and Bielak, Lawrence F and Guo, Xiuqing and Heard-Costa, Nancy and Kinney, Gregory and Mahaney, Michael C and Montasser, May E and Palmer, Nicholette D and Raffield, Laura M and Terry, James G and Yanek, Lisa R and Bon, Jessica and Bowden, Donald W and Brody, Jennifer A and Duggirala, Ravindranath and Jacobs, David R and Kalyani, Rita R and Lange, Leslie A and Mitchell, Braxton D and Smith, Jennifer A and Taylor, Kent D and Carson, April and Curran, Joanne E and Fornage, Myriam and Freedman, Barry I and Gabriel, Stacey and Gibbs, Richard A and Gupta, Namrata and Kardia, Sharon L R and Kral, Brian G and Momin, Zeineen and Newman, Anne B and Post, Wendy S and Viaud-Martinez, Karine A and Young, Kendra A and Becker, Lewis C and Bertoni, Alain and Blangero, John and Carr, John J and Pratte, Katherine and Psaty, Bruce M and Rich, Stephen S and Wu, Joseph C and Malhotra, Rajeev and Peyser, Patricia A and Morrison, Alanna C and Vasan, Ramachandran S and Lin, Xihong and Rotter, Jerome I and Meigs, James B and Manning, Alisa K and de Vries, Paul S} } @article {9484, title = {WHOLE GENOME SEQUENCING ANALYSIS OF BODY MASS INDEX IDENTIFIES NOVEL AFRICAN ANCESTRY-SPECIFIC RISK ALLELE.}, journal = {medRxiv}, year = {2023}, month = {2023 Aug 22}, abstract = {

Obesity is a major public health crisis associated with high mortality rates. Previous genome-wide association studies (GWAS) investigating body mass index (BMI) have largely relied on imputed data from European individuals. This study leveraged whole-genome sequencing (WGS) data from 88,873 participants from the Trans-Omics for Precision Medicine (TOPMed) Program, of which 51\% were of non-European population groups. We discovered 18 BMI-associated signals ( < 5 {\texttimes} 10 ). Notably, we identified and replicated a novel low frequency single nucleotide polymorphism (SNP) in that was common in individuals of African descent. Using a diverse study population, we further identified two novel secondary signals in known BMI loci and pinpointed two likely causal variants in the and loci. Our work demonstrates the benefits of combining WGS and diverse cohorts in expanding current catalog of variants and genes confer risk for obesity, bringing us one step closer to personalized medicine.

}, doi = {10.1101/2023.08.21.23293271}, author = {Zhang, Xinruo and Brody, Jennifer A and Graff, Mariaelisa and Highland, Heather M and Chami, Nathalie and Xu, Hanfei and Wang, Zhe and Ferrier, Kendra and Chittoor, Geetha and Josyula, Navya S and Li, Xihao and Li, Zilin and Allison, Matthew A and Becker, Diane M and Bielak, Lawrence F and Bis, Joshua C and Boorgula, Meher Preethi and Bowden, Donald W and Broome, Jai G and Buth, Erin J and Carlson, Christopher S and Chang, Kyong-Mi and Chavan, Sameer and Chiu, Yen-Feng and Chuang, Lee-Ming and Conomos, Matthew P and DeMeo, Dawn L and Du, Margaret and Duggirala, Ravindranath and Eng, Celeste and Fohner, Alison E and Freedman, Barry I and Garrett, Melanie E and Guo, Xiuqing and Haiman, Chris and Heavner, Benjamin D and Hidalgo, Bertha and Hixson, James E and Ho, Yuk-Lam and Hobbs, Brian D and Hu, Donglei and Hui, Qin and Hwu, Chii-Min and Jackson, Rebecca D and Jain, Deepti and Kalyani, Rita R and Kardia, Sharon L R and Kelly, Tanika N and Lange, Ethan M and LeNoir, Michael and Li, Changwei and Marchand, Loic Le and McDonald, Merry-Lynn N and McHugh, Caitlin P and Morrison, Alanna C and Naseri, Take and O{\textquoteright}Connell, Jeffrey and O{\textquoteright}Donnell, Christopher J and Palmer, Nicholette D and Pankow, James S and Perry, James A and Peters, Ulrike and Preuss, Michael H and Rao, D C and Regan, Elizabeth A and Reupena, Sefuiva M and Roden, Dan M and Rodriguez-Santana, Jose and Sitlani, Colleen M and Smith, Jennifer A and Tiwari, Hemant K and Vasan, Ramachandran S and Wang, Zeyuan and Weeks, Daniel E and Wessel, Jennifer and Wiggins, Kerri L and Wilkens, Lynne R and Wilson, Peter W F and Yanek, Lisa R and Yoneda, Zachary T and Zhao, Wei and Z{\"o}llner, Sebastian and Arnett, Donna K and Ashley-Koch, Allison E and Barnes, Kathleen C and Blangero, John and Boerwinkle, Eric and Burchard, Esteban G and Carson, April P and Chasman, Daniel I and Chen, Yii-Der Ida and Curran, Joanne E and Fornage, Myriam and Gordeuk, Victor R and He, Jiang and Heckbert, Susan R and Hou, Lifang and Irvin, Marguerite R and Kooperberg, Charles and Minster, Ryan L and Mitchell, Braxton D and Nouraie, Mehdi and Psaty, Bruce M and Raffield, Laura M and Reiner, Alexander P and Rich, Stephen S and Rotter, Jerome I and Shoemaker, M Benjamin and Smith, Nicholas L and Taylor, Kent D and Telen, Marilyn J and Weiss, Scott T and Zhang, Yingze and Costa, Nancy Heard- and Sun, Yan V and Lin, Xihong and Cupples, L Adrienne and Lange, Leslie A and Liu, Ching-Ti and Loos, Ruth J F and North, Kari E and Justice, Anne E} } @article {9500, title = {Whole Genome Sequencing Based Analysis of Inflammation Biomarkers in the Trans-Omics for Precision Medicine (TOPMed) Consortium.}, journal = {bioRxiv}, year = {2023}, month = {2023 Sep 12}, abstract = {

Inflammation biomarkers can provide valuable insight into the role of inflammatory processes in many diseases and conditions. Sequencing based analyses of such biomarkers can also serve as an exemplar of the genetic architecture of quantitative traits. To evaluate the biological insight, which can be provided by a multi-ancestry, whole-genome based association study, we performed a comprehensive analysis of 21 inflammation biomarkers from up to 38,465 individuals with whole-genome sequencing from the Trans-Omics for Precision Medicine (TOPMed) program. We identified 22 distinct single-variant associations across 6 traits - E-selectin, intercellular adhesion molecule 1, interleukin-6, lipoprotein-associated phospholipase A2 activity and mass, and P-selectin - that remained significant after conditioning on previously identified associations for these inflammatory biomarkers. We further expanded upon known biomarker associations by pairing the single-variant analysis with a rare variant set-based analysis that further identified 19 significant rare variant set-based associations with 5 traits. These signals were distinct from both significant single variant association signals within TOPMed and genetic signals observed in prior studies, demonstrating the complementary value of performing both single and rare variant analyses when analyzing quantitative traits. We also confirm several previously reported signals from semi-quantitative proteomics platforms. Many of these signals demonstrate the extensive allelic heterogeneity and ancestry-differentiated variant-trait associations common for inflammation biomarkers, a characteristic we hypothesize will be increasingly observed with well-powered, large-scale analyses of complex traits.

}, doi = {10.1101/2023.09.10.555215}, author = {Jiang, Min-Zhi and Gaynor, Sheila M and Li, Xihao and Van Buren, Eric and Stilp, Adrienne and Buth, Erin and Wang, Fei Fei and Manansala, Regina and Gogarten, Stephanie M and Li, Zilin and Polfus, Linda M and Salimi, Shabnam and Bis, Joshua C and Pankratz, Nathan and Yanek, Lisa R and Durda, Peter and Tracy, Russell P and Rich, Stephen S and Rotter, Jerome I and Mitchell, Braxton D and Lewis, Joshua P and Psaty, Bruce M and Pratte, Katherine A and Silverman, Edwin K and Kaplan, Robert C and Avery, Christy and North, Kari and Mathias, Rasika A and Faraday, Nauder and Lin, Honghuang and Wang, Biqi and Carson, April P and Norwood, Arnita F and Gibbs, Richard A and Kooperberg, Charles and Lundin, Jessica and Peters, Ulrike and Dupuis, Jos{\'e}e and Hou, Lifang and Fornage, Myriam and Benjamin, Emelia J and Reiner, Alexander P and Bowler, Russell P and Lin, Xihong and Auer, Paul L and Raffield, Laura M} } @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} }