@article {7553, title = {Analysis commons, a team approach to discovery in a big-data environment for genetic epidemiology.}, journal = {Nat Genet}, volume = {49}, year = {2017}, month = {2017 Oct 27}, pages = {1560-1563}, issn = {1546-1718}, doi = {10.1038/ng.3968}, author = {Brody, Jennifer A and Morrison, Alanna C and Bis, Joshua C and O{\textquoteright}Connell, Jeffrey R and Brown, Michael R and Huffman, Jennifer E and Ames, Darren C and Carroll, Andrew and Conomos, Matthew P and Gabriel, Stacey and Gibbs, Richard A and Gogarten, Stephanie M and Gupta, Namrata and Jaquish, Cashell E and Johnson, Andrew D and Lewis, Joshua P and Liu, Xiaoming and Manning, Alisa K and Papanicolaou, George J and Pitsillides, Achilleas N and Rice, Kenneth M and Salerno, William and Sitlani, Colleen M and Smith, Nicholas L and Heckbert, Susan R and Laurie, Cathy C and Mitchell, Braxton D and Vasan, Ramachandran S and Rich, Stephen S and Rotter, Jerome I and Wilson, James G and Boerwinkle, Eric and Psaty, Bruce M and Cupples, L Adrienne} } @article {8666, title = {Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program.}, journal = {Nature}, volume = {590}, year = {2021}, month = {2021 02}, pages = {290-299}, abstract = {

The Trans-Omics for Precision Medicine (TOPMed) programme seeks to elucidate the genetic architecture and biology of heart, lung, blood and sleep disorders, with the ultimate goal of improving diagnosis, treatment and prevention of these diseases. The initial phases of the programme focused on whole-genome sequencing of individuals with rich phenotypic data and diverse backgrounds. Here we describe the TOPMed goals and design as well as the available resources and early insights obtained from the sequence data. The resources include a variant browser, a genotype imputation server, and genomic and phenotypic data that are available through dbGaP (Database of Genotypes and Phenotypes). In the first 53,831 TOPMed samples, we detected more than 400~million single-nucleotide and insertion or deletion variants after alignment with the reference genome. Additional previously undescribed variants were detected through assembly of unmapped reads and customized analysis in highly variable loci. Among the more than 400~million detected variants, 97\% have frequencies of less than 1\% and 46\% are singletons that are present in only one individual (53\% among unrelated individuals). These rare variants provide insights into mutational processes and recent human evolutionary history. The extensive catalogue of genetic variation in TOPMed studies provides unique opportunities for exploring the contributions of rare and noncoding sequence variants to phenotypic variation. Furthermore, combining TOPMed haplotypes with modern imputation methods improves the power and reach of genome-wide association studies to include variants down to a frequency of approximately 0.01\%.

}, issn = {1476-4687}, doi = {10.1038/s41586-021-03205-y}, author = {Taliun, Daniel and Harris, Daniel N and Kessler, Michael D and Carlson, Jedidiah and Szpiech, Zachary A and Torres, Raul and Taliun, Sarah A Gagliano and Corvelo, Andr{\'e} and Gogarten, Stephanie M and Kang, Hyun Min and Pitsillides, Achilleas N and LeFaive, Jonathon and Lee, Seung-Been and Tian, Xiaowen and Browning, Brian L and Das, Sayantan and Emde, Anne-Katrin and Clarke, Wayne E and Loesch, Douglas P and Shetty, Amol C and Blackwell, Thomas W and Smith, Albert V and Wong, Quenna and Liu, Xiaoming and Conomos, Matthew P and Bobo, Dean M and Aguet, Francois and Albert, Christine and Alonso, Alvaro and Ardlie, Kristin G and Arking, Dan E and Aslibekyan, Stella and Auer, Paul L and Barnard, John and Barr, R Graham and Barwick, Lucas and Becker, Lewis C and Beer, Rebecca L and Benjamin, Emelia J and Bielak, Lawrence F and Blangero, John and Boehnke, Michael and Bowden, Donald W and Brody, Jennifer A and Burchard, Esteban G and Cade, Brian E and Casella, James F and Chalazan, Brandon and Chasman, Daniel I and Chen, Yii-Der Ida and Cho, Michael H and Choi, Seung Hoan and Chung, Mina K and Clish, Clary B and Correa, Adolfo and Curran, Joanne E and Custer, Brian and Darbar, Dawood and Daya, Michelle and de Andrade, Mariza and DeMeo, Dawn L and Dutcher, Susan K and Ellinor, Patrick T and Emery, Leslie S and Eng, Celeste and Fatkin, Diane and Fingerlin, Tasha and Forer, Lukas and Fornage, Myriam and Franceschini, Nora and Fuchsberger, Christian and Fullerton, Stephanie M and Germer, Soren and Gladwin, Mark T and Gottlieb, Daniel J and Guo, Xiuqing and Hall, Michael E and He, Jiang and Heard-Costa, Nancy L and Heckbert, Susan R and Irvin, Marguerite R and Johnsen, Jill M and Johnson, Andrew D and Kaplan, Robert and Kardia, Sharon L R and Kelly, Tanika and Kelly, Shannon and Kenny, Eimear E and Kiel, Douglas P and Klemmer, Robert and Konkle, Barbara A and Kooperberg, Charles and K{\"o}ttgen, Anna and Lange, Leslie A and Lasky-Su, Jessica and Levy, Daniel and Lin, Xihong and Lin, Keng-Han and Liu, Chunyu and Loos, Ruth J F and Garman, Lori and Gerszten, Robert and Lubitz, Steven A and Lunetta, Kathryn L and Mak, Angel C Y and Manichaikul, Ani and Manning, Alisa K and Mathias, Rasika A and McManus, David D and McGarvey, Stephen T and Meigs, James B and Meyers, Deborah A and Mikulla, Julie L and Minear, Mollie A and Mitchell, Braxton D and Mohanty, Sanghamitra and Montasser, May E and Montgomery, Courtney and Morrison, Alanna C and Murabito, Joanne M and Natale, Andrea and Natarajan, Pradeep and Nelson, Sarah C and North, Kari E and O{\textquoteright}Connell, Jeffrey R and Palmer, Nicholette D and Pankratz, Nathan and Peloso, Gina M and Peyser, Patricia A and Pleiness, Jacob and Post, Wendy S and Psaty, Bruce M and Rao, D C and Redline, Susan and Reiner, Alexander P and Roden, Dan and Rotter, Jerome I and Ruczinski, Ingo and Sarnowski, Chloe and Schoenherr, Sebastian and Schwartz, David A and Seo, Jeong-Sun and Seshadri, Sudha and Sheehan, Vivien A and Sheu, Wayne H and Shoemaker, M Benjamin and Smith, Nicholas L and Smith, Jennifer A and Sotoodehnia, Nona and Stilp, Adrienne M and Tang, Weihong and Taylor, Kent D and Telen, Marilyn and Thornton, Timothy A and Tracy, Russell P and Van Den Berg, David J and Vasan, Ramachandran S and Viaud-Martinez, Karine A and Vrieze, Scott and Weeks, Daniel E and Weir, Bruce S and Weiss, Scott T and Weng, Lu-Chen and Willer, Cristen J and Zhang, Yingze and Zhao, Xutong and Arnett, Donna K and Ashley-Koch, Allison E and Barnes, Kathleen C and Boerwinkle, Eric and Gabriel, Stacey and Gibbs, Richard and Rice, Kenneth M and Rich, Stephen S and Silverman, Edwin K and Qasba, Pankaj and Gan, Weiniu and Papanicolaou, George J and Nickerson, Deborah A and Browning, Sharon R and Zody, Michael C and Z{\"o}llner, Sebastian and Wilson, James G and Cupples, L Adrienne and Laurie, Cathy C and Jaquish, Cashell E and Hernandez, Ryan D and O{\textquoteright}Connor, Timothy D and Abecasis, Goncalo R} } @article {8830, title = {Sugar-Sweetened Beverage Consumption May Modify Associations Between Genetic Variants in the CHREBP (Carbohydrate Responsive Element Binding Protein) Locus and HDL-C (High-Density Lipoprotein Cholesterol) and Triglyceride Concentrations.}, journal = {Circ Genom Precis Med}, volume = {14}, year = {2021}, month = {2021 Aug}, pages = {e003288}, abstract = {

BACKGROUND: ChREBP (carbohydrate responsive element binding protein) is a transcription factor that responds to sugar consumption. Sugar-sweetened beverage (SSB) consumption and genetic variants in the locus have separately been linked to HDL-C (high-density lipoprotein cholesterol) and triglyceride concentrations. We hypothesized that SSB consumption would modify the association between genetic variants in the locus and dyslipidemia.

METHODS: Data from 11 cohorts from the Cohorts for Heart and Aging Research in Genomic Epidemiology consortium (N=63 599) and the UK Biobank (N=59 220) were used to quantify associations of SSB consumption, genetic variants, and their interaction on HDL-C and triglyceride concentrations using linear regression models. A total of 1606 single nucleotide polymorphisms within or near were considered. SSB consumption was estimated from validated questionnaires, and participants were grouped by their estimated intake.

RESULTS: In a meta-analysis, rs71556729 was significantly associated with higher HDL-C concentrations only among the highest SSB consumers (β, 2.12 [95\% CI, 1.16-3.07] mg/dL per allele; <0.0001), but not significantly among the lowest SSB consumers (=0.81; <0.0001). Similar results were observed for 2 additional variants (rs35709627 and rs71556736). For triglyceride, rs55673514 was positively associated with triglyceride concentrations only among the highest SSB consumers (β, 0.06 [95\% CI, 0.02-0.09] ln-mg/dL per allele, =0.001) but not the lowest SSB consumers (=0.84; =0.0005).

CONCLUSIONS: Our results identified genetic variants in the locus that may protect against SSB-associated reductions in HDL-C and other variants that may exacerbate SSB-associated increases in triglyceride concentrations. Registration: URL: https://www.clinicaltrials.gov; Unique identifier: NCT00005133, NCT00005121, NCT00005487, and NCT00000479.

}, issn = {2574-8300}, doi = {10.1161/CIRCGEN.120.003288}, author = {Haslam, Danielle E and Peloso, Gina M and Guirette, Melanie and Imamura, Fumiaki and Bartz, Traci M and Pitsillides, Achilleas N and Wang, Carol A and Li-Gao, Ruifang and Westra, Jason M and Pitk{\"a}nen, Niina and Young, Kristin L and Graff, Mariaelisa and Wood, Alexis C and Braun, Kim V E and Luan, Jian{\textquoteright}an and K{\"a}h{\"o}nen, Mika and Kiefte-de Jong, Jessica C and Ghanbari, Mohsen and Tintle, Nathan and Lemaitre, Rozenn N and Mook-Kanamori, Dennis O and North, Kari and Helminen, Mika and Mossavar-Rahmani, Yasmin and Snetselaar, Linda and Martin, Lisa W and Viikari, Jorma S and Oddy, Wendy H and Pennell, Craig E and Rosendall, Frits R and Ikram, M Arfan and Uitterlinden, Andr{\'e} G and Psaty, Bruce M and Mozaffarian, Dariush and Rotter, Jerome I and Taylor, Kent D and Lehtim{\"a}ki, Terho and Raitakari, Olli T and Livingston, Kara A and Voortman, Trudy and Forouhi, Nita G and Wareham, Nick J and de Mutsert, Ren{\'e}e and Rich, Steven S and Manson, JoAnn E and Mora, Samia and Ridker, Paul M and Merino, Jordi and Meigs, James B and Dashti, Hassan S and Chasman, Daniel I and Lichtenstein, Alice H and Smith, Caren E and Dupuis, Jos{\'e}e and Herman, Mark A and McKeown, Nicola M} } @article {9450, title = {Time-to-Event Genome-Wide Association Study for Incident Cardiovascular Disease in People with Type 2 Diabetes Mellitus.}, journal = {medRxiv}, year = {2023}, month = {2023 Jul 28}, abstract = {

BACKGROUND: Type 2 diabetes mellitus (T2D) confers a two- to three-fold increased risk of cardiovascular disease (CVD). However, the mechanisms underlying increased CVD risk among people with T2D are only partially understood. We hypothesized that a genetic association study among people with T2D at risk for developing incident cardiovascular complications could provide insights into molecular genetic aspects underlying CVD.

METHODS: From 16 studies of the Cohorts for Heart \& Aging Research in Genomic Epidemiology (CHARGE) Consortium, we conducted a multi-ancestry time-to-event genome-wide association study (GWAS) for incident CVD among people with T2D using Cox proportional hazards models. Incident CVD was defined based on a composite of coronary artery disease (CAD), stroke, and cardiovascular death that occurred at least one year after the diagnosis of T2D. Cohort-level estimated effect sizes were combined using inverse variance weighted fixed effects meta-analysis. We also tested 204 known CAD variants for association with incident CVD among patients with T2D.

RESULTS: A total of 49,230 participants with T2D were included in the analyses (31,118 European ancestries and 18,112 non-European ancestries) which consisted of 8,956 incident CVD cases over a range of mean follow-up duration between 3.2 and 33.7 years (event rate 18.2\%). We identified three novel, distinct genetic loci for incident CVD among individuals with T2D that reached the threshold for genome-wide significance ( <5.0{\texttimes}10 ): rs147138607 (intergenic variant between and ) with a hazard ratio (HR) 1.23, 95\% confidence interval (CI) 1.15 - 1.32, =3.6{\texttimes}10 , rs11444867 (intergenic variant near ) with HR 1.89, 95\% CI 1.52 - 2.35, =9.9{\texttimes}10 , and rs335407 (intergenic variant between and ) HR 1.25, 95\% CI 1.16 - 1.35, =1.5{\texttimes}10 . Among 204 known CAD loci, 32 were associated with incident CVD in people with T2D with <0.05, and 5 were significant after Bonferroni correction ( <0.00024, 0.05/204). A polygenic score of these 204 variants was significantly associated with incident CVD with HR 1.14 (95\% CI 1.12 - 1.16) per 1 standard deviation increase ( =1.0{\texttimes}10 ).

CONCLUSIONS: The data point to novel and known genomic regions associated with incident CVD among individuals with T2D.

CLINICAL PERSPECTIVE: We conducted a large-scale multi-ancestry time-to-event GWAS to identify genetic variants associated with CVD among people with T2D. Three variants were significantly associated with incident CVD in people with T2D: rs147138607 (intergenic variant between and ), rs11444867 (intergenic variant near ), and rs335407 (intergenic variant between and ). A polygenic score composed of known CAD variants identified in the general population was significantly associated with the risk of CVD in people with T2D. There are genetic risk factors specific to T2D that could at least partially explain the excess risk of CVD in people with T2D.In addition, we show that people with T2D have enrichment of known CAD association signals which could also explain the excess risk of CVD.

}, doi = {10.1101/2023.07.25.23293180}, author = {Kwak, Soo Heon and Hernandez-Cancela, Ryan B and DiCorpo, Daniel A and Condon, David E and Merino, Jordi and Wu, Peitao and Brody, Jennifer A and Yao, Jie and Guo, Xiuqing and Ahmadizar, Fariba and Meyer, Mariah and Sincan, Murat and Mercader, Josep M and Lee, Sujin and Haessler, Jeffrey and Vy, Ha My T and Lin, Zhaotong and Armstrong, Nicole D and Gu, Shaopeng and Tsao, Noah L and Lange, Leslie A and Wang, Ningyuan and Wiggins, Kerri L and Trompet, Stella and Liu, Simin and Loos, Ruth J F and Judy, Renae and Schroeder, Philip H and Hasbani, Natalie R and Bos, Maxime M and Morrison, Alanna C and Jackson, Rebecca D and Reiner, Alexander P and Manson, JoAnn E and Chaudhary, Ninad S and Carmichael, Lynn K and Chen, Yii-Der Ida and Taylor, Kent D and Ghanbari, Mohsen and van Meurs, Joyce and Pitsillides, Achilleas N and Psaty, Bruce M and Noordam, Raymond and Do, Ron and Park, Kyong Soo and Jukema, J Wouter and Kavousi, Maryam and Correa, Adolfo and Rich, Stephen S and Damrauer, Scott M and Hajek, Catherine and Cho, Nam H and Irvin, Marguerite R and Pankow, James S and Nadkarni, Girish N and Sladek, Robert and Goodarzi, Mark O and Florez, Jose C and Chasman, Daniel I and Heckbert, Susan R and Kooperberg, Charles and Dupuis, Jos{\'e}e and Malhotra, Rajeev and de Vries, Paul S and Liu, Ching-Ti and Rotter, Jerome I and Meigs, James B} } @article {9580, title = {Association analysis of mitochondrial DNA heteroplasmic variants: methods and application.}, journal = {medRxiv}, year = {2024}, month = {2024 Jan 13}, abstract = {

We rigorously assessed a comprehensive association testing framework for heteroplasmy, employing both simulated and real-world data. This framework employed a variant allele fraction (VAF) threshold and harnessed multiple gene-based tests for robust identification and association testing of heteroplasmy. Our simulation studies demonstrated that gene-based tests maintained an appropriate type I error rate at α=0.001. Notably, when 5\% or more heteroplasmic variants within a target region were linked to an outcome, burden-extension tests (including the adaptive burden test, variable threshold burden test, and z-score weighting burden test) outperformed the sequence kernel association test (SKAT) and the original burden test. Applying this framework, we conducted association analyses on whole-blood derived heteroplasmy in 17,507 individuals of African and European ancestries (31\% of African Ancestry, mean age of 62, with 58\% women) with whole genome sequencing data. We performed both cohort- and ancestry-specific association analyses, followed by meta-analysis on bothpooled samples and within each ancestry group. Our results suggest that mtDNA-Enco ded genes/regions are likely to exhibit varying rates in somatic aging, with the notably strong associations observed between heteroplasmy in the and genes ( <0.001) and advance aging by the Original Burden test. In contrast, SKAT identified significant associations ( <0.001) between diabetes and the aggregated effects of heteroplasmy in several protein-coding genes. Further research is warranted to validate these findings. In summary, our proposed statistical framework represents a valuable tool for facilitating association testing of heteroplasmy with disease traits in large human populations.

}, doi = {10.1101/2024.01.12.24301233}, author = {Sun, Xianbang and Bulekova, Katia and Yang, Jian and Lai, Meng and Pitsillides, Achilleas N and Liu, Xue and Zhang, Yuankai and Guo, Xiuqing and Yong, Qian and Raffield, Laura M and Rotter, Jerome I and Rich, Stephen S and Abecasis, Goncalo and Carson, April P and Vasan, Ramachandran S and Bis, Joshua C and Psaty, Bruce M and Boerwinkle, Eric and Fitzpatrick, Annette L and Satizabal, Claudia L and Arking, Dan E and Ding, Jun and Levy, Daniel and Liu, Chunyu} }