Browsing by Author "Morrison, Alanna C."

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    A framework for detecting noncoding rare-variant associations of large-scale whole-genome sequencing studies
    (Springer Nature, 2022) Li, Zilin; Li, Xihao; Zhou, Hufeng; Gaynor, Sheila M.; Selvaraj, Margaret Sunitha; Arapoglou, Theodore; Quick, Corbin; Liu, Yaowu; Chen, Han; Sun, Ryan; Dey, Rounak; Arnett, Donna K.; Auer, Paul L.; Bielak, Lawrence F.; Bis, Joshua C.; Blackwell, Thomas W.; Blangero, John; Boerwinkle, Eric; Bowden, Donald W.; Brody, Jennifer A.; Cade, Brian E.; Conomos, Matthew P.; Correa, Adolfo; Cupples, L. Adrienne; Curran, Joanne E.; de Vries, Paul S.; Duggirala, Ravindranath; Franceschini, Nora; Freedman, Barry I.; Göring, Harald H. H.; Guo, Xiuqing; Kalyani, Rita R.; Kooperberg, Charles; Kral, Brian G.; Lange, Leslie A.; Lin, Bridget M.; Manichaikul, Ani; Manning, Alisa K.; Martin, Lisa W.; Mathias, Rasika A.; Meigs, James B.; Mitchell, Braxton D.; Montasser, May E.; Morrison, Alanna C.; Naseri, Take; O'Connell, Jeffrey R.; Palmer, Nicholette D.; Peyser, Patricia A.; Psaty, Bruce M.; Raffield, Laura M.; Redline, Susan; Reiner, Alexander P.; Reupena, Muagututi'a Sefuiva; Rice, Kenneth M.; Rich, Stephen S.; Smith, Jennifer A.; Taylor, Kent D.; Taub, Margaret A.; Vasan, Ramachandran S.; Weeks, Daniel E.; Wilson, James G.; Yanek, Lisa R.; Zhao, Wei; NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium; TOPMed Lipids Working Group; Rotter, Jerome I.; Willer, Cristen J.; Natarajan, Pradeep; Peloso, Gina M.; Lin, Xihong; Biostatistics and Health Data Science, School of Medicine
    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.
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    Author Correction: Whole-Genome Sequencing Analysis of Human Metabolome in Multi-Ethnic Populations
    (Springer Nature, 2023-10-19) Feofanova, Elena V.; Brown, Michael R.; Alkis, Taryn; Manuel, Astrid M.; Li, Xihao; Tahir, Usman A.; Li, Zilin; Mendez, Kevin M.; Kelly, Rachel S.; Qi, Qibin; Chen, Han; Larson, Martin G.; Lemaitre, Rozenn N.; Morrison, Alanna C.; Grieser, Charles; Wong, Kari E.; Gerszten, Robert E.; Zhao, Zhongming; Lasky-Su, Jessica; NHLBI Trans-Omics for Precision Medicine (TOPMed); Yu, Bing; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public Health
    Correction to: Nature Communications 10.1038/s41467-023-38800-2, published online 30 May2023 In this article, the author name Robert E. Gerszten was incorrectly written as Robert E. Gersztern. The original article has been corrected.
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    Impact of Rare and Common Genetic Variants on Diabetes Diagnosis by Hemoglobin A1c in Multi-Ancestry Cohorts: The Trans-Omics for Precision Medicine Program
    (Elsevier, 2019-09-26) Sarnowski, Chloé; Leong, Aaron; Raffield, Laura M.; Wu, Peitao; de Vries, Paul S.; DiCorpo, Daniel; Guo, Xiuqing; Xu, Huichun; Liu, Yongmei; Zheng, Xiuwen; Hu, Yao; Brody, Jennifer A.; Goodarzi, Mark O.; Hidalgo, Bertha A.; Highland, Heather M.; Jain, Deepti; Liu, Ching-Ti; Naik, Rakhi P.; O’Connell, Jeffrey R.; Perry, James A.; Porneala, Bianca C.; Selvin, Elizabeth; Wessel, Jennifer; Psaty, Bruce M.; Curran, Joanne E.; Peralta, Juan M.; Blangero, John; Kooperberg, Charles; Mathias, Rasika; Johnson, Andrew D.; Reiner, Alexander P.; Mitchell, Braxton D.; Cupples, L. Adrienne; Vasan, Ramachandran S.; Correa, Adolfo; Morrison, Alanna C.; Boerwinkle, Eric; Rotter, Jerome I.; Rich, Stephen S.; Manning, Alisa K.; Dupuis, Josée; Meigs, James B.; TOPMed Diabetes Working Group; TOPMed Hematology Working Group; TOPMed Hemostasis Working Group; National Heart, Lung, and Blood Institute TOPMed Consortium; Epidemiology, School of Public Health
    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.
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    Investigating Gene-Diet Interactions Impacting the Association Between Macronutrient Intake and Glycemic Traits
    (American Diabetes Association, 2023) Westerman, Kenneth E.; Walker, Maura E.; Gaynor, Sheila M.; Wessel, Jennifer; DiCorpo, Daniel; Ma, Jiantao; Alonso, Alvaro; Aslibekyan, Stella; Baldridge, Abigail S.; Bertoni, Alain G.; Biggs, Mary L.; Brody, Jennifer A.; Chen, Yii-Der Ida; Dupuis, Joseé; Goodarzi, Mark O.; Guo, Xiuqing; Hasbani, Natalie R.; Heath, Adam; Hidalgo, Bertha; Irvin, Marguerite R.; Johnson, W. Craig; Kalyani, Rita R.; Lange, Leslie; Lemaitre, Rozenn N.; Liu, Ching-Ti; Liu, Simin; Moon, Jee-Young; Nassir, Rami; Pankow, James S.; Pettinger, Mary; Raffield, Laura M.; Rasmussen-Torvik, Laura J.; Selvin, Elizabeth; Senn, Mackenzie K.; Shadyab, Aladdin H.; Smith, Albert V.; Smith, Nicholas L.; Steffen, Lyn; Talegakwar, Sameera; Taylor, Kent D.; de Vries, Paul S.; Wilson, James G.; Wood, Alexis C.; Yanek, Lisa R.; Yao, Jie; Zheng, Yinan; Boerwinkle, Eric; Morrison, Alanna C.; Fornage, Miriam; Russell, Tracy P.; Psaty, Bruce M.; Levy, Daniel; Heard-Costa, Nancy L.; Ramachandran, Vasan S.; Mathias, Rasika A.; Arnett, Donna K.; Kaplan, Robert; North, Kari E.; Correa, Adolfo; Carson, April; Rotter, Jerome I.; Rich, Stephen S.; Manson, JoAnn E.; Reiner, Alexander P.; Kooperberg, Charles; Florez, Jose C.; Meigs, James B.; Merino, Jordi; Tobias, Deirdre K.; Chen, Han; Manning, Alisa K.; Epidemiology, Richard M. Fairbanks School of Public Health
    Few studies have demonstrated reproducible gene-diet interactions (GDIs) impacting metabolic disease risk factors, likely due in part to measurement error in dietary intake estimation and insufficient capture of rare genetic variation. We aimed to identify GDIs across the genetic frequency spectrum impacting the macronutrient-glycemia relationship in genetically and culturally diverse cohorts. We analyzed 33,187 participants free of diabetes from 10 National Heart, Lung, and Blood Institute Trans-Omics for Precision Medicine program cohorts with whole-genome sequencing, self-reported diet, and glycemic trait data. We fit cohort-specific, multivariable-adjusted linear mixed models for the effect of diet, modeled as an isocaloric substitution of carbohydrate for fat, and its interactions with common and rare variants genome-wide. In main effect meta-analyses, participants consuming more carbohydrate had modestly lower glycemic trait values (e.g., for glycated hemoglobin [HbA1c], -0.013% HbA1c/250 kcal substitution). In GDI meta-analyses, a common African ancestry-enriched variant (rs79762542) reached study-wide significance and replicated in the UK Biobank cohort, indicating a negative carbohydrate-HbA1c association among major allele homozygotes only. Simulations revealed that >150,000 samples may be necessary to identify similar macronutrient GDIs under realistic assumptions about effect size and measurement error. These results generate hypotheses for further exploration of modifiable metabolic disease risk in additional cohorts with African ancestry. Article highlights: We aimed to identify genetic modifiers of the dietary macronutrient-glycemia relationship using whole-genome sequence data from 10 Trans-Omics for Precision Medicine program cohorts. Substitution models indicated a modest reduction in glycemia associated with an increase in dietary carbohydrate at the expense of fat. Genome-wide interaction analysis identified one African ancestry-enriched variant near the FRAS1 gene that may interact with macronutrient intake to influence hemoglobin A1c. Simulation-based power calculations accounting for measurement error suggested that substantially larger sample sizes may be necessary to discover further gene-macronutrient interactions.
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    Large-scale genomic analyses link reproductive ageing to hypothalamic signaling, breast cancer susceptibility and BRCA1-mediated DNA repair
    (SpringerNature, 2015-11) Day, Felix R.; Ruth, Katherine S.; Thompson, Deborah J.; Lunetta, Kathryn L.; Pervjakova, Natalia; Chasman, Daniel I.; Stolk, Lisette; Finucane, Hilary K.; Sulem, Patrick; Bulik-Sullivan, Brendan; Esko, Tõnu; Johnson, Andrew D.; Elks, Cathy E.; Franceschini, Nora; He, Chunyan; Altmaier, Elisabeth; Brody, Jennifer A.; Franke, Lude L.; Huffman, Jennifer E.; Keller, Margaux F.; McArdle, Patrick F.; Nutile, Teresa; Porcu, Eleonora; Robino, Antonietta; Rose, Lynda M.; Schick, Ursula M.; Smith, Jennifer A.; Teumer, Alexander; Traglia, Michela; Vuckovic, Dragana; Yao, Jie; Zhao, Wei; Albrecht, Eva; Amin, Najaf; Corre, Tanguy; Hottenga, Jouke-Jan; Mangino, Massimo; Smith, Albert V.; Tanaka, Toshiko; Abecasis, Goncalo; Andrulis, Irene L.; Anton-Culver, Hoda; Antoniou, Antonis C.; Arndt, Volker; Arnold, Alice M.; Barbieri, Caterina; Beckmann, Matthias W.; Beeghly-Fadiel, Alicia; Benitez, Javier; Bernstein, Leslie; Bielinski, Suzette J.; Blomqvist, Carl; Boerwinkle, Eric; Bogdanova, Natalia V.; Bojesen, Stig E.; Bolla, Manjeet K.; Borresen-Dale, Anne-Lise; Boutin, Thibaud S.; Brauch, Hiltrud; Brenner, Hermann; Brüning, Thomas; Burwinkel, Barbara; Campbell, Archie; Campbell, Harry; Chanock, Stephen J.; Chapman, J. Ross; Chen, Yii-Der Ida; Chenevix-Trench, Georgia; Couch, Fergus J.; Coviello, Andrea D.; Cox, Angela; Czene, Kamila; Darabi, Hatef; De Vivo, Immaculata; Demerath, Ellen W.; Dennis, Joe; Devilee, Peter; Dörk, Thilo; dos-Santos-Silva, Isabel; Dunning, Alison M.; Eicher, John D.; Fasching, Peter A.; Faul, Jessica D.; Figueroa, Jonine; Flesch-Janys, Dieter; Gandin, Ilaria; Garcia, Melissa E.; García-Closas, Montserrat; Giles, Graham G.; Girotto, Giorgia G.; Goldberg, Mark S.; González-Neira, Anna; Goodarzi, Mark O.; Grove, Megan L.; Gudbjartsson, Daniel F.; Guénel, Pascal; Guo, Xiuqing; Haiman, Christopher A.; Hall, Per; Hamann, Ute; Henderson, Brian E.; Hocking, Lynne J.; Hofman, Albert; Homuth, Georg; Hooning, Maartje J.; Hopper, John L.; Hu, Frank B.; Huang, Jinyan; Humphreys, Keith; Hunter, David J.; Jakubowska, Anna; Jones, Samuel E.; Kabisch, Maria; Karasia, David; Knight, Julia A.; Kolcic, Ivana; Kooperberg, Charles; Kosma, Veli-Matti; Kriebel, Jennifer; Kristensen, Vessela; Lambrechts, Diether; Langenberg, Claudia; Li, Jingmei; Li, Xin; Lindström, Sara; Liu, Yongmei; Luan, Jian’an; Lubinski, Jan; Mägi, Reedik; Mannermaa, Arto; Manz, Judith; Margolin, Sara; Marten, Jonathan; Martin, Nicholas G.; Masciullo, Corrado; Meindl, Alfons; Michailidou, Kyriaki; Mihailov, Evelin; Milani, Lili; Milne, Roger L.; Müller-Nurasyid, Martina; Nalls, Michael; Neale, Ben M.; Nevanlinna, Heli; Neven, Patrick; Newman, Anne B.; Nordestgaard, Børge G.; Olson, Janet E.; Padmanabhan, Sandosh; Peterlongo, Paolo; Peters, Ulrike; Petersmann, Astrid; Peto, Julian; Pharoah, Paul D.P.; Pirastu, Nicola N.; Pirie, Ailith; Pistis, Giorgio; Polasek, Ozren; Porteous, David; Psaty, Bruce M.; Pylkäs, Katri; Radice, Paolo; Raffel, Leslie J.; Rivadeneira, Fernando; Rudan, Igor; Rudolph, Anja; Anja, Daniela; Sala, Cinzia F.; Sanna, Serena; Sawyer, Elinor J.; Schlessinger, David; Schmidt, Marjanka K.; Schmidt, Frank; Schmutzler, Rita K.; Schoemaker, Minouk J.; Scott, Robert A.; Seynaeve, Caroline M.; Simard, Jacques; Sorice, Rossella; Southey, Melissa C.; Stöckl, Doris; Strauch, Konstantin; Swerdlow, Anthony; Taylor, Kent D.; Thorsteinsdottir, Unnur; Toland, Amanda E.; Tomlinson, Ian; Truong, Thérèse; Tryggvadottir, Laufey; Turner, Stephen T.; Vozzi, Diego; Wang, Qin; Wellons, Melissa; Willemsen, Gonneke; Wilson, James F.; Winqvist, Robert; Wolffenbuttel, Bruce B.H.R.; Wright, Alan F.; Yannoukakos, Drakoulis; Zemunik, Tatijana; Zheng, Wei; Zygmunt, Marek; Bergmann, Sven; Boomsma, Dorret I.; Buring, Julie E.; Ferrucci, Luigi; Montgomery, Grant W.; Gudnason, Vilmundur; Spector, Tim D.; van Duijn, Cornelia M; Alizadeh, Behrooz Z.; Ciullo, Marina; Crisponi, Laura; Easton, Douglas F.; Gasparini, Paolo P.; Gieger, Christian; Harris, Tamara B.; Hayward, Caroline; Kardia, Sharon L.R.; Kraft, Peter; McKnight, Barbara; Metspalu, Andres; Morrison, Alanna C.; Reiner, Alex P.; Ridker, Paul M.; Rotter, Jerome I.; Toniolo, Daniela; Uitterlinden, André G.; Ulivi, Sheila; Völzke, Henry; Wareham, Nicholas J.; Weir, David R.; Yerges-Armstrong, Laura M.; Price, Alkes L.; Stefansson, Kari; Visser, Jenny A.; Ong, Ken K.; Chang-Claude, Jenny; Murabito, Joanne M.; Perry, John R.B.; Murray, Anna; Department of Epidemiology, Richard M. Fairbanks School of Public Health
    Menopause timing has a substantial impact on infertility and risk of disease, including breast cancer, but the underlying mechanisms are poorly understood. We report a dual strategy in ∼70,000 women to identify common and low-frequency protein-coding variation associated with age at natural menopause (ANM). We identified 44 regions with common variants, including two regions harboring additional rare missense alleles of large effect. We found enrichment of signals in or near genes involved in delayed puberty, highlighting the first molecular links between the onset and end of reproductive lifespan. Pathway analyses identified major association with DNA damage response (DDR) genes, including the first common coding variant in BRCA1 associated with any complex trait. Mendelian randomization analyses supported a causal effect of later ANM on breast cancer risk (∼6% increase in risk per year; P = 3 × 10(-14)), likely mediated by prolonged sex hormone exposure rather than DDR mechanisms.
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    Low-frequency and rare exome chip variants associate with fasting glucose and type 2 diabetes susceptibility
    (Nature Publishing Group, 2015-01-29) Wessel, Jennifer; Chu, Audrey Y.; Willems, Sara M.; Wang, Shuai; Yaghootkar, Hanieh; Brody, Jennifer A.; Dauriz, Marco; Hivert, Marie-France; Raghavan, Sridharan; Lipovich, Leonard; Hidalgo, Bertha; Fox, Keolu; Huffman, Jennifer E.; An, Ping; Lu, Yingchang; Rasmussen-Torvik, Laura J.; Grarup, Niels; Ehm, Margaret G.; Li, Li; Baldridge, Abigail S.; Stančáková, Alena; Abrol, Ravinder; Besse, Céline; Boland, Anne; Bork-Jensen, Jette; Fornage, Myriam; Freitag, Daniel F.; Garcia, Melissa E.; Guo, Xiuqing; Hara, Kazuo; Isaacs, Aaron; Jakobsdottir, Johanna; Lange, Leslie A.; Layton, Jill C.; Li, Man; Hua Zhao, Jing; Meidtner, Karina; Morrison, Alanna C.; Nalls, Mike A.; Peters, Marjolein J.; Sabater-Lleal, Maria; Schurmann, Claudia; Silveira, Angela; Smith, Albert V.; Southam, Lorraine; Stoiber, Marcus H.; Strawbridge, Rona J.; Taylor, Kent D.; Varga, Tibor V.; Allin, Kristine H.; Amin, Najaf; Aponte, Jennifer L.; Aung, Tin; Barbieri, Caterina; Bihlmeyer, Nathan A.; Boehnke, Michael; Bombieri, Cristina; Bowden, Donald W.; Burns, Sean M.; Chen, Yuning; Chen, Yii-DerI; Cheng, Ching-Yu; Correa, Adolfo; Czajkowski, Jacek; Dehghan, Abbas; Ehret, Georg B.; Eiriksdottir, Gudny; Escher, Stefan A.; Farmaki, Aliki-Eleni; Frånberg, Mattias; Gambaro, Giovanni; Giulianini, Franco; Goddard, William A.; Goel, Anuj; Gottesman, Omri; Grove, Megan L.; Gustafsson, Stefan; Hai, Yang; Hallmans, Göran; Heo, Jiyoung; Hoffmann, Per; Ikram, Mohammad K.; Jensen, Richard A.; Jørgensen, Marit E.; Jørgensen, Torben; Karaleftheri, Maria; Khor, Chiea C.; Kirkpatrick, Andrea; Kraja, Aldi T.; Kuusisto, Johanna; Lange, Ethan M.; Lee, I. T.; Lee, Wen-Jane; Leong, Aaron; Liao, Jiemin; Liu, Chunyu; Liu, Yongmei; Lindgren, Cecilia M.; Linneberg, Allan; Malerba, Giovanni; Mamakou, Vasiliki; Marouli, Eirini; Maruthur, Nisa M.; Matchan, Angela; McKean-Cowdin, Roberta; McLeod, Olga; Metcalf, Ginger A.; Mohlke, Karen L.; Muzny, Donna M.; Ntalla, Ioanna; Palmer, Nicholette D.; Pasko, Dorota; Peter, Andreas; Rayner, Nigel W.; Renström, Frida; Rice, Ken; Sala, Cinzia F.; Sennblad, Bengt; Serafetinidis, Ioannis; Smith, Jennifer A.; Soranzo, Nicole; Speliotes, Elizabeth K.; Stahl, Eli A.; Stirrups, Kathleen; Tentolouris, Nikos; Thanopoulou, Anastasia; Torres, Mina; Traglia, Michela; Tsafantakis, Emmanouil; Javad, Sundas; Yanek, Lisa R.; Zengini, Eleni; Becker, Diane M.; Bis, Joshua C.; Brown, James B.; Adrienne Cupples, L.; Hansen, Torben; Ingelsson, Erik; Karter, Andrew J.; Lorenzo, Carlos; Mathias, Rasika A.; Norris, Jill M.; Peloso, Gina M.; Sheu, Wayne H.-H.; Toniolo, Daniela; Vaidya, Dhananjay; Varma, Rohit; Wagenknecht, Lynne E.; Boeing, Heiner; Bottinger, Erwin P.; Dedoussis, George; Deloukas, Panos; Ferrannini, Ele; Franco, Oscar H.; Franks, Paul W.; Gibbs, Richard A.; Gudnason, Vilmundur; Hamsten, Anders; Harris, Tamara B.; Hattersley, Andrew T.; Hayward, Caroline; Hofman, Albert; Jansson, Jan-Håkan; Langenberg, Claudia; Launer, Lenore J.; Levy, Daniel; Oostra, Ben A.; O'Donnell, Christopher J.; O'Rahilly, Stephen; Padmanabhan, Sandosh; Pankow, James S.; Polasek, Ozren; Province, Michael A.; Rich, Stephen S.; Ridker, Paul M.; Rudan, Igor; Schulze, Matthias B.; Smith, Blair H.; Uitterlinden, André G.; Walker, Mark; Watkins, Hugh; Wong, Tien Y.; Zeggini, Eleftheria; Laakso, Markku; Borecki, Ingrid B.; Chasman, Daniel I.; Pedersen, Oluf; Psaty, Bruce M.; Shyong Tai, E.; van Duijn, Cornelia M.; Wareham, Nicholas J.; Waterworth, Dawn M.; Boerwinkle, Eric; Linda Kao, W. H.; Florez, Jose C.; Loos, Ruth J. F.; Wilson, James G.; Frayling, Timothy M.; Siscovick, David S.; Dupuis, Josée; Rotter, Jerome I.; Meigs, James B.; Scott, Robert A.; Goodarzi, Mark O.; Department of Epidemiology, Richard M. Fairbanks School of Public Health
    Fasting ​glucose and ​insulin are intermediate traits for type 2 diabetes. Here we explore the role of coding variation on these traits by analysis of variants on the HumanExome BeadChip in 60,564 non-diabetic individuals and in 16,491 T2D cases and 81,877 controls. We identify a novel association of a low-frequency nonsynonymous SNV in ​GLP1R (A316T; rs10305492; MAF=1.4%) with lower FG (β=−0.09±0.01 mmol l−1, P=3.4 × 10−12), T2D risk (OR[95%CI]=0.86[0.76–0.96], P=0.010), early ​insulin secretion (β=−0.07±0.035 pmolinsulin mmolglucose−1, P=0.048), but higher 2-h ​glucose (β=0.16±0.05 mmol l−1, P=4.3 × 10−4). We identify a gene-based association with FG at ​G6PC2 (pSKAT=6.8 × 10−6) driven by four rare protein-coding SNVs (H177Y, Y207S, R283X and S324P). We identify rs651007 (MAF=20%) in the first intron of ​ABO at the putative promoter of an antisense lncRNA, associating with higher FG (β=0.02±0.004 mmol l−1, P=1.3 × 10−8). Our approach identifies novel coding variant associations and extends the allelic spectrum of variation underlying diabetes-related quantitative traits and T2D susceptibility.
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    Powerful, scalable and resource-efficient meta-analysis of rare variant associations in large whole genome sequencing studies
    (Springer Nature, 2023) Li, Xihao; Quick, Corbin; Zhou, Hufeng; Gaynor, Sheila M.; Liu, Yaowu; Chen, Han; Selvaraj, Margaret Sunitha; Sun, Ryan; Dey, Rounak; Arnett, Donna K.; Bielak, Lawrence F.; Bis, Joshua C.; Blangero, John; Boerwinkle, Eric; Bowden, Donald W.; Brody, Jennifer A.; Cade, Brian E.; Correa, Adolfo; Cupples, L. Adrienne; Curran, Joanne E.; de Vries, Paul S.; Duggirala, Ravindranath; Freedman, Barry I.; Göring, Harald H. H.; Guo, Xiuqing; Haessler, Jeffrey; Kalyani, Rita R.; Kooperberg, Charles; Kral, Brian G.; Lange, Leslie A.; Manichaikul, Ani; Martin, Lisa W.; McGarvey, Stephen T.; Mitchell, Braxton D.; Montasser, May E.; Morrison, Alanna C.; Naseri, Take; O'Connell, Jeffrey R.; Palmer, Nicholette D.; Peyser, Patricia A.; Psaty, Bruce M.; Raffield, Laura M.; Redline, Susan; Reiner, Alexander P.; Reupena, Muagututi'a Sefuiva; Rice, Kenneth M.; Rich, Stephen S.; Sitlani, Colleen M.; Smith, Jennifer A.; Taylor, Kent D.; Vasan, Ramachandran S.; Willer, Cristen J.; Wilson, James G.; Yanek, Lisa R.; Zhao, Wei; NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium; TOPMed Lipids Working Group; Rotter, Jerome I.; Natarajan, Pradeep; Peloso, Gina M.; Li, Zilin; Lin, Xihong; Biostatistics and Health Data Science, School of Medicine
    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.
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    Rare coding variants and X-linked loci associated with age at menarche
    (Nature Publishing Group, 2015-08-04) Lunetta, Kathryn L.; Day, Felix R.; Sulem, Patrick; Ruth, Katherine S.; Tung, Joyce Y.; Hinds, David A.; Esko, Tõnu; Elks, Cathy E.; Altmaier, Elisabeth; He, Chunyan; Huffman, Jennifer E.; Mihailov, Evelin; Porcu, Eleonora; Robino, Antonietta; Rose, Lynda M.; Schick, Ursula M.; Stolk, Lisette; Teumer, Alexander; Thompson, Deborah J.; Traglia, Michela; Wang, Carol A.; Yerges-Armstrong, Laura M.; Antoniou, Antonis C.; Barbieri, Caterina; Coviello, Andrea D.; Cucca, Francesco; Demerath, Ellen W.; Dunning, Alison M.; Gandin, Ilaria; Grove, Megan L.; Gudbjartsson, Daniel F.; Hocking, Lynne J.; Hofman, Albert; Huang, Jinyan; Jackson, Rebecca D.; Karasik, David; Kriebel, Jennifer; Lange, Ethan M.; Lange, Leslie A.; Langenberg, Claudia; Li, Xin; Luan, Jian'an; Mägi, Reedik; Morrison, Alanna C.; Padmanabhan, Sandosh; Pirie, Ailith; Polasek, Ozren; Porteous, David; Reiner, Alex P.; Rivadeneira, Fernando; Rudan, Igor; Sala, Cinzia F.; Schlessinger, David; Scott, Robert A.; Stöckl, Doris; Visser, Jenny A.; Völker, Uwe; Vozzi, Diego; Wilson, James G.; Zygmunt, Marek; Boerwinkle, Eric; Buring, Julie E.; Crisponi, Laura; Easton, Douglas F.; Hayward, Caroline; Hu, Frank B.; Liu, Simin; Metspalu, Andres; Pennell, Craig E.; Ridker, Paul M.; Strauch, Konstantin; Streeten, Elizabeth A.; Toniolo, Daniela; Uitterlinden, André G.; Ulivi, Sheila; Völzke, Henry; Wareham, Nicholas J.; Wellons, Melissa; Franceschini, Nora; Chasman, Daniel I.; Thorsteinsdottir, Unnur; Murray, Anna; Stefansson, Kari; Murabito, Joanne M.; Ong, Ken K.; Perry, John R. B.; Department of Epidemiology, Richard M. Fairbanks School of Public Health
    More than 100 loci have been identified for age at menarche by genome-wide association studies; however, collectively these explain only ~3% of the trait variance. Here we test two overlooked sources of variation in 192,974 European ancestry women: low-frequency protein-coding variants and X-chromosome variants. Five missense/nonsense variants (in ALMS1/LAMB2/TNRC6A/TACR3/PRKAG1) are associated with age at menarche (minor allele frequencies 0.08–4.6%; effect sizes 0.08–1.25 years per allele; P<5 × 10−8). In addition, we identify common X-chromosome loci at IGSF1 (rs762080, P=9.4 × 10−13) and FAAH2 (rs5914101, P=4.9 × 10−10). Highlighted genes implicate cellular energy homeostasis, post-transcriptional gene silencing and fatty-acid amide signalling. A frequently reported mutation in TACR3 for idiopathic hypogonatrophic hypogonadism (p.W275X) is associated with 1.25-year-later menarche (P=2.8 × 10−11), illustrating the utility of population studies to estimate the penetrance of reportedly pathogenic mutations. Collectively, these novel variants explain ~0.5% variance, indicating that these overlooked sources of variation do not substantially explain the ‘missing heritability’ of this complex trait.
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    Rare variants in long non-coding RNAs are associated with blood lipid levels in the TOPMed Whole Genome Sequencing Study
    (medRxiv, 2023-06-29) Wang, Yuxuan; Selvaraj, Margaret Sunitha; Li, Xihao; Li, Zilin; Holdcraft, Jacob A.; Arnett, Donna K.; Bis, Joshua C.; Blangero, John; Boerwinkle, Eric; Bowden, Donald W.; Cade, Brian E.; Carlson, Jenna C.; Carson, April P.; Chen, Yii-Der Ida; Curran, Joanne E.; de Vries, Paul S.; Dutcher, Susan K.; Ellinor, Patrick T.; Floyd, James S.; Fornage, Myriam; Freedman, Barry I.; Gabriel, Stacey; Germer, Soren; Gibbs, Richard A.; Guo, Xiuqing; He, Jiang; Heard-Costa, Nancy; Hildalgo, Bertha; Hou, Lifang; Irvin, Marguerite R.; Joehanes, Roby; Kaplan, Robert C.; Kardia, Sharon Lr.; Kelly, Tanika N.; Kim, Ryan; Kooperberg, Charles; Kral, Brian G.; Levy, Daniel; Li, Changwei; Liu, Chunyu; Lloyd-Jone, Don; Loos, Ruth Jf.; Mahaney, Michael C.; Martin, Lisa W.; Mathias, Rasika A.; Minster, Ryan L.; Mitchell, Braxton D.; Montasser, May E.; Morrison, Alanna C.; Murabito, Joanne M.; Naseri, Take; O'Connell, Jeffrey R.; Palmer, Nicholette D.; Preuss, Michael H.; Psaty, Bruce M.; Raffield, Laura M.; Rao, Dabeeru C.; Redline, Susan; Reiner, Alexander P.; Rich, Stephen S.; Ruepena, Muagututi'a Sefuiva; Sheu, Wayne H-H; Smith, Jennifer A.; Smith, Albert; Tiwari, Hemant K.; Tsai, Michael Y.; Viaud-Martinez, Karine A.; Wang, Zhe; Yanek, Lisa R.; Zhao, Wei; NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium; Rotter, Jerome I.; Lin, Xihong; Natarajan, Pradeep; Peloso, Gina M.; Biostatistics and Health Data Science, School of Medicine
    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 ±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.
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    Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program
    (Springer Nature, 2021) Taliun, Daniel; Harris, Daniel N.; Kessler, Michael D.; Carlson, Jedidiah; Szpiech, Zachary A.; Torres, Raul; Gagliano Taliun, Sarah A.; Corvelo, André; Gogarten, Stephanie M.; Kang, Hyun Min; Pitsillides, Achilleas N.; LeFaive, Jonathon; Lee, Seung-Been; Tian, Xiaowen; Browning, Brian L.; Das, Sayantan; Emde, Anne-Katrin; Clarke, Wayne E.; Loesch, Douglas P.; Shetty, Amol C.; Blackwell, Thomas W.; Smith, Albert V.; Wong, Quenna; Liu, Xiaoming; Conomos, Matthew P.; Bobo, Dean M.; Aguet, François; Albert, Christine; Alonso, Alvaro; Ardlie, Kristin G.; Arking, Dan E.; Aslibekyan, Stella; Auer, Paul L.; Barnard, John; Barr, R. Graham; Barwick, Lucas; Becker, Lewis C.; Beer, Rebecca L.; Benjamin, Emelia J.; Bielak, Lawrence F.; Blangero, John; Boehnke, Michael; Bowden, Donald W.; Brody, Jennifer A.; Burchard, Esteban G.; Cade, Brian E.; Casella, James F.; Chalazan, Brandon; Chasman, Daniel I.; Chen, Yii-Der Ida; Cho, Michael H.; Choi, Seung Hoan; Chung, Mina K.; Clish, Clary B.; Correa, Adolfo; Curran, Joanne E.; Custer, Brian; Darbar, Dawood; Daya, Michelle; de Andrade, Mariza; DeMeo, Dawn L.; Dutcher, Susan K.; Ellinor, Patrick T.; Emery, Leslie S.; Eng, Celeste; Fatkin, Diane; Fingerlin, Tasha; Forer, Lukas; Fornage, Myriam; Franceschini, Nora; Fuchsberger, Christian; Fullerton, Stephanie M.; Germer, Soren; Gladwin, Mark T.; Gottlieb, Daniel J.; Guo, Xiuqing; Hall, Michael E.; He, Jiang; Heard-Costa, Nancy L.; Heckbert, Susan R.; Irvin, Marguerite R.; Johnsen, Jill M.; Johnson, Andrew D.; Kaplan, Robert; Kardia, Sharon L. R.; Kelly, Tanika; Kelly, Shannon; Kenny, Eimear E.; Kiel, Douglas P.; Klemmer, Robert; Konkle, Barbara A.; Kooperberg, Charles; Köttgen, Anna; Lange, Leslie A.; Lasky-Su, Jessica; Levy, Daniel; Lin, Xihong; Lin, Keng-Han; Liu, Chunyu; Loos, Ruth J. F.; Garman, Lori; Gerszten, Robert; Lubitz, Steven A.; Lunetta, Kathryn L.; Mak, Angel C. Y.; Manichaikul, Ani; Manning, Alisa K.; Mathias, Rasika A.; McManus, David D.; McGarvey, Stephen T.; Meigs, James B.; Meyers, Deborah A.; Mikulla, Julie L.; Minear, Mollie A.; Mitchell, Braxton D.; Mohanty, Sanghamitra; Montasser, May E.; Montgomery, Courtney; Morrison, Alanna C.; Murabito, Joanne M.; Natale, Andrea; Natarajan, Pradeep; Nelson, Sarah C.; North, Kari E.; O'Connell, Jeffrey R.; Palmer, Nicholette D.; Pankratz, Nathan; Peloso, Gina M.; Peyser, Patricia A.; Pleiness, Jacob; Post, Wendy S.; Psaty, Bruce M.; Rao, D. C.; Redline, Susan; Reiner, Alexander P.; Roden, Dan; Rotter, Jerome I.; Ruczinski, Ingo; Sarnowski, Chloé; Schoenherr, Sebastian; Schwartz, David A.; Seo, Jeong-Sun; Seshadri, Sudha; Sheehan, Vivien A.; Sheu, Wayne H.; Shoemaker, M. Benjamin; Smith, Nicholas L.; Smith, Jennifer A.; Sotoodehnia, Nona; Stilp, Adrienne M.; Tang, Weihong; Taylor, Kent D.; Telen, Marilyn; Thornton, Timothy A.; Tracy, Russell P.; Van Den Berg, David J.; Vasan, Ramachandran S.; Viaud-Martinez, Karine A.; Vrieze, Scott; Weeks, Daniel E.; Weir, Bruce S.; Weiss, Scott T.; Weng, Lu-Chen; Willer, Cristen J.; Zhang, Yingze; Zhao, Xutong; Arnett, Donna K.; Ashley-Koch, Allison E.; Barnes, Kathleen C.; Boerwinkle, Eric; Gabriel, Stacey; Gibbs, Richard; Rice, Kenneth M.; Rich, Stephen S.; Silverman, Edwin K.; Qasba, Pankaj; Gan, Weiniu; NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium; Papanicolaou, George J.; Nickerson, Deborah A.; Browning, Sharon R.; Zody, Michael C.; Zöllner, Sebastian; Wilson, James G.; Cupples, L. Adrienne; Laurie, Cathy C.; Jaquish, Cashell E.; Hernandez, Ryan D.; O'Connor, Timothy D.; Abecasis, Gonçalo R.; Epidemiology, Richard M. Fairbanks School of Public Health
    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)1. 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%.