@article {8205, title = {Impact of Rare and Common Genetic Variants on Diabetes Diagnosis by Hemoglobin A1c in Multi-Ancestry Cohorts: The Trans-Omics for Precision Medicine Program.}, journal = {Am J Hum Genet}, volume = {105}, year = {2019}, month = {2019 Oct 03}, pages = {706-718}, abstract = {

Hemoglobin A1c (HbA1c) is widely used to diagnose diabetes and assess glycemic control in individuals with diabetes. However, nonglycemic determinants, including genetic variation, may influence how accurately HbA1c reflects underlying glycemia. Analyzing the NHLBI Trans-Omics for Precision Medicine (TOPMed) sequence data in 10,338 individuals from five studies and four ancestries (6,158 Europeans, 3,123 African-Americans, 650 Hispanics, and 407 East Asians), we confirmed five regions associated with HbA1c (GCK in Europeans and African-Americans, HK1 in Europeans and Hispanics, FN3K and/or FN3KRP in Europeans, and G6PD in African-Americans and Hispanics) and we identified an African-ancestry-specific low-frequency variant (rs1039215 in HBG2 and HBE1, minor allele frequency (MAF) = 0.03). The most associated G6PD variant (rs1050828-T, p.Val98Met, MAF = 12\% in African-Americans, MAF = 2\% in Hispanics) lowered HbA1c (-0.88\% in hemizygous males, -0.34\% in heterozygous females) and explained 23\% of HbA1c variance in African-Americans and 4\% in Hispanics. Additionally, we identified a rare distinct G6PD coding variant (rs76723693, p.Leu353Pro, MAF = 0.5\%; -0.98\% in hemizygous males, -0.46\% in heterozygous females) and detected significant association with HbA1c when aggregating rare missense variants in G6PD. We observed similar magnitude and direction of effects for rs1039215 (HBG2) and rs76723693 (G6PD) in the two largest TOPMed African American cohorts, and we replicated the rs76723693 association in the UK Biobank African-ancestry participants. These variants in G6PD and HBG2 were monomorphic in the European and Asian samples. African or Hispanic ancestry individuals carrying G6PD variants may be underdiagnosed for diabetes when screened with HbA1c. Thus, assessment of these variants should be considered for incorporation into precision medicine approaches for diabetes diagnosis.

}, issn = {1537-6605}, doi = {10.1016/j.ajhg.2019.08.010}, author = {Sarnowski, Chloe and Leong, Aaron and Raffield, Laura M and Wu, Peitao and de Vries, Paul S and DiCorpo, Daniel and Guo, Xiuqing and Xu, Huichun and Liu, Yongmei and Zheng, Xiuwen and Hu, Yao and Brody, Jennifer A and Goodarzi, Mark O and Hidalgo, Bertha A and Highland, Heather M and Jain, Deepti and Liu, Ching-Ti and Naik, Rakhi P and O{\textquoteright}Connell, Jeffrey R and Perry, James A and Porneala, Bianca C and Selvin, Elizabeth and Wessel, Jennifer and Psaty, Bruce M and Curran, Joanne E and Peralta, Juan M and Blangero, John and Kooperberg, Charles and Mathias, Rasika and Johnson, Andrew D and Reiner, Alexander P and Mitchell, Braxton D and Cupples, L Adrienne and Vasan, Ramachandran S and Correa, Adolfo and Morrison, Alanna C and Boerwinkle, Eric and Rotter, Jerome I and Rich, Stephen S and Manning, Alisa K and Dupuis, Jos{\'e}e and Meigs, James B} } @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 {9506, title = {Clonal Hematopoiesis of Indeterminate Potential (CHIP) and Incident Type 2 Diabetes Risk.}, journal = {Diabetes Care}, year = {2023}, month = {2023 Sep 27}, abstract = {

OBJECTIVE: Clonal hematopoiesis of indeterminate potential (CHIP) is an aging-related accumulation of somatic mutations in hematopoietic stem cells, leading to clonal expansion. CHIP presence has been implicated in atherosclerotic coronary heart disease (CHD) and all-cause mortality, but its association with incident type 2 diabetes (T2D) is unknown. We hypothesized that CHIP is associated with elevated risk of T2D.

RESEARCH DESIGN AND METHODS: CHIP was derived from whole-genome sequencing of blood DNA in the National Heart, Lung, and Blood Institute Trans-Omics for Precision Medicine (TOPMed) prospective cohorts. We performed analysis for 17,637 participants from six cohorts, without prior T2D, cardiovascular disease, or cancer. We evaluated baseline CHIP versus no CHIP prevalence with incident T2D, including associations with DNMT3A, TET2, ASXL1, JAK2, and TP53 variants. We estimated multivariable-adjusted hazard ratios (HRs) and 95\% confidence intervals (CIs) with adjustment for age, sex, BMI, smoking, alcohol, education, self-reported race/ethnicity, and combined cohorts{\textquoteright} estimates via fixed-effects meta-analysis.

RESULTS: Mean (SD) age was 63.4 (11.5) years, 76\% were female, and CHIP prevalence was 6.0\% (n = 1,055) at baseline. T2D was diagnosed in n = 2,467 over mean follow-up of 9.8 years. Participants with CHIP had 23\% (CI = 1.04, 1.45) higher risk of T2D than those with no CHIP. Specifically, higher risk was for TET2 (HR 1.48; CI = 1.05, 2.08) and ASXL1 (HR 1.76; CI = 1.03, 2.99) mutations; DNMT3A was nonsignificant (HR 1.15; CI = 0.93, 1.43). Statistical power was limited for JAK2 and TP53 analyses.

CONCLUSIONS: CHIP was associated with higher incidence of T2D. CHIP mutations located on genes implicated in CHD and mortality were also related to T2D, suggesting shared aging-related pathology.

}, issn = {1935-5548}, doi = {10.2337/dc23-0805}, author = {Tobias, Deirdre K and Manning, Alisa K and Wessel, Jennifer and Raghavan, Sridharan and Westerman, Kenneth E and Bick, Alexander G and DiCorpo, Daniel and Whitsel, Eric A and Collins, Jason and Correa, Adolfo and Cupples, L Adrienne and Dupuis, Jos{\'e}e and Goodarzi, Mark O and Guo, Xiuqing and Howard, Barbara and Lange, Leslie A and Liu, Simin and Raffield, Laura M and Reiner, Alex P and Rich, Stephen S and Taylor, Kent D and Tinker, Lesley and Wilson, James G and Wu, Peitao and Carson, April P and Vasan, Ramachandran S and Fornage, Myriam and Psaty, Bruce M and Kooperberg, Charles and Rotter, Jerome I and Meigs, James and Manson, JoAnn E} } @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 {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} }