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

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

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

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

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

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

}, keywords = {Adult, Aged, Aged, 80 and over, Aging, Cohort Studies, Female, Genetic Variation, Genome-Wide Association Study, Genomics, Heart Diseases, Humans, Male, Middle Aged, Polymorphism, Single Nucleotide, Research Design, Sequence Analysis, DNA}, issn = {1942-3268}, doi = {10.1161/CIRCGENETICS.113.000350}, author = {Lin, Honghuang and Wang, Min and Brody, Jennifer A and Bis, Joshua C and Dupuis, Jos{\'e}e and Lumley, Thomas and McKnight, Barbara and Rice, Kenneth M and Sitlani, Colleen M and Reid, Jeffrey G and Bressler, Jan and Liu, Xiaoming and Davis, Brian C and Johnson, Andrew D and O{\textquoteright}Donnell, Christopher J and Kovar, Christie L and Dinh, Huyen and Wu, Yuanqing and Newsham, Irene and Chen, Han and Broka, Andi and DeStefano, Anita L and Gupta, Mayetri and Lunetta, Kathryn L and Liu, Ching-Ti and White, Charles C and Xing, Chuanhua and Zhou, Yanhua and Benjamin, Emelia J and Schnabel, Renate B and Heckbert, Susan R and Psaty, Bruce M and Muzny, Donna M and Cupples, L Adrienne and Morrison, Alanna C and Boerwinkle, Eric} } @article {7167, title = {Genome-Wide Association Study of the Modified Stumvoll Insulin Sensitivity Index Identifies BCL2 and FAM19A2 as Novel Insulin Sensitivity Loci.}, journal = {Diabetes}, volume = {65}, year = {2016}, month = {2016 Oct}, pages = {3200-11}, abstract = {

Genome-wide association studies (GWAS) have found few common variants that influence fasting measures of insulin sensitivity. We hypothesized that a GWAS of an integrated assessment of fasting and dynamic measures of insulin sensitivity would detect novel common variants. We performed a GWAS of the modified Stumvoll Insulin Sensitivity Index (ISI) within the Meta-Analyses of Glucose and Insulin-Related Traits Consortium. Discovery for genetic association was performed in 16,753 individuals, and replication was attempted for the 23 most significant novel loci in 13,354 independent individuals. Association with ISI was tested in models adjusted for age, sex, and BMI and in a model analyzing the combined influence of the genotype effect adjusted for BMI and the interaction effect between the genotype and BMI on ISI (model 3). In model 3, three variants reached genome-wide significance: rs13422522 (NYAP2; P = 8.87 {\texttimes} 10(-11)), rs12454712 (BCL2; P = 2.7 {\texttimes} 10(-8)), and rs10506418 (FAM19A2; P = 1.9 {\texttimes} 10(-8)). The association at NYAP2 was eliminated by conditioning on the known IRS1 insulin sensitivity locus; the BCL2 and FAM19A2 associations were independent of known cardiometabolic loci. In conclusion, we identified two novel loci and replicated known variants associated with insulin sensitivity. Further studies are needed to clarify the causal variant and function at the BCL2 and FAM19A2 loci.

}, issn = {1939-327X}, doi = {10.2337/db16-0199}, author = {Walford, Geoffrey A and Gustafsson, Stefan and Rybin, Denis and Stan{\v c}{\'a}kov{\'a}, Alena and Chen, Han and Liu, Ching-Ti and Hong, Jaeyoung and Jensen, Richard A and Rice, Ken and Morris, Andrew P and M{\"a}gi, Reedik and T{\"o}njes, Anke and Prokopenko, Inga and Kleber, Marcus E and Delgado, Graciela and Silbernagel, G{\"u}nther and Jackson, Anne U and Appel, Emil V and Grarup, Niels and Lewis, Joshua P and Montasser, May E and Landenvall, Claes and Staiger, Harald and Luan, Jian{\textquoteright}an and Frayling, Timothy M and Weedon, Michael N and Xie, Weijia and Morcillo, Sonsoles and Mart{\'\i}nez-Larrad, Mar{\'\i}a Teresa and Biggs, Mary L and Chen, Yii-Der Ida and Corbaton-Anchuelo, Arturo and F{\ae}rch, Kristine and G{\'o}mez-Zumaquero, Juan Miguel and Goodarzi, Mark O and Kizer, Jorge R and Koistinen, Heikki A and Leong, Aaron and Lind, Lars and Lindgren, Cecilia and Machicao, Fausto and Manning, Alisa K and Mart{\'\i}n-N{\'u}{\~n}ez, Gracia Mar{\'\i}a and Rojo-Mart{\'\i}nez, Gemma and Rotter, Jerome I and Siscovick, David S and Zmuda, Joseph M and Zhang, Zhongyang and Serrano-R{\'\i}os, Manuel and Smith, Ulf and Soriguer, Federico and Hansen, Torben and J{\o}rgensen, Torben J and Linnenberg, Allan and Pedersen, Oluf and Walker, Mark and Langenberg, Claudia and Scott, Robert A and Wareham, Nicholas J and Fritsche, Andreas and H{\"a}ring, Hans-Ulrich and Stefan, Norbert and Groop, Leif and O{\textquoteright}Connell, Jeff R and Boehnke, Michael and Bergman, Richard N and Collins, Francis S and Mohlke, Karen L and Tuomilehto, Jaakko and M{\"a}rz, Winfried and Kovacs, Peter and Stumvoll, Michael and Psaty, Bruce M and Kuusisto, Johanna and Laakso, Markku and Meigs, James B and Dupuis, Jos{\'e}e and Ingelsson, Erik and Florez, Jose C} } @article {7264, title = {Meta-analysis identifies common and rare variants influencing blood pressure and overlapping with metabolic trait loci.}, journal = {Nat Genet}, volume = {48}, year = {2016}, month = {2016 Oct}, pages = {1162-70}, abstract = {

Meta-analyses of association results for blood pressure using exome-centric single-variant and gene-based tests identified 31 new loci in a discovery stage among 146,562 individuals, with follow-up and meta-analysis in 180,726 additional individuals (total n = 327,288). These blood pressure-associated loci are enriched for known variants for cardiometabolic traits. Associations were also observed for the aggregation of rare and low-frequency missense variants in three genes, NPR1, DBH, and PTPMT1. In addition, blood pressure associations at 39 previously reported loci were confirmed. The identified variants implicate biological pathways related to cardiometabolic traits, vascular function, and development. Several new variants are inferred to have roles in transcription or as hubs in protein-protein interaction networks. Genetic risk scores constructed from the identified variants were strongly associated with coronary disease and myocardial infarction. This large collection of blood pressure-associated loci suggests new therapeutic strategies for hypertension, emphasizing a link with cardiometabolic risk.

}, issn = {1546-1718}, doi = {10.1038/ng.3660}, author = {Liu, Chunyu and Kraja, Aldi T and Smith, Jennifer A and Brody, Jennifer A and Franceschini, Nora and Bis, Joshua C and Rice, Kenneth and Morrison, Alanna C and Lu, Yingchang and Weiss, Stefan and Guo, Xiuqing and Palmas, Walter and Martin, Lisa W and Chen, Yii-Der Ida and Surendran, Praveen and Drenos, Fotios and Cook, James P and Auer, Paul L and Chu, Audrey Y and Giri, Ayush and Zhao, Wei and Jakobsdottir, Johanna and Lin, Li-An and Stafford, Jeanette M and Amin, Najaf and Mei, Hao and Yao, Jie and Voorman, Arend and Larson, Martin G and Grove, Megan L and Smith, Albert V and Hwang, Shih-Jen and Chen, Han and Huan, Tianxiao and Kosova, Gulum and Stitziel, Nathan O and Kathiresan, Sekar and Samani, Nilesh and Schunkert, Heribert and Deloukas, Panos and Li, Man and Fuchsberger, Christian and Pattaro, Cristian and Gorski, Mathias and Kooperberg, Charles and Papanicolaou, George J and Rossouw, Jacques E and Faul, Jessica D and Kardia, Sharon L R and Bouchard, Claude and Raffel, Leslie J and Uitterlinden, Andr{\'e} G and Franco, Oscar H and Vasan, Ramachandran S and O{\textquoteright}Donnell, Christopher J and Taylor, Kent D and Liu, Kiang and Bottinger, Erwin P and Gottesman, Omri and Daw, E Warwick and Giulianini, Franco and Ganesh, Santhi and Salfati, Elias and Harris, Tamara B and Launer, Lenore J and D{\"o}rr, Marcus and Felix, Stephan B and Rettig, Rainer and V{\"o}lzke, Henry and Kim, Eric and Lee, Wen-Jane and Lee, I-Te and Sheu, Wayne H-H and Tsosie, Krystal S and Edwards, Digna R Velez and Liu, Yongmei and Correa, Adolfo and Weir, David R and V{\"o}lker, Uwe and Ridker, Paul M and Boerwinkle, Eric and Gudnason, Vilmundur and Reiner, Alexander P and van Duijn, Cornelia M and Borecki, Ingrid B and Edwards, Todd L and Chakravarti, Aravinda and Rotter, Jerome I and Psaty, Bruce M and Loos, Ruth J F and Fornage, Myriam and Ehret, Georg B and Newton-Cheh, Christopher and Levy, Daniel and Chasman, Daniel I} } @article {7675, title = {Multiethnic Meta-Analysis Identifies RAI1 as a Possible Obstructive Sleep Apnea-related Quantitative Trait Locus in Men.}, journal = {Am J Respir Cell Mol Biol}, volume = {58}, year = {2018}, month = {2018 Mar}, pages = {391-401}, abstract = {

Obstructive sleep apnea (OSA) is a common heritable disorder displaying marked sexual dimorphism in disease prevalence and progression. Previous genetic association studies have identified a few genetic loci associated with OSA and related quantitative traits, but they have only focused on single ethnic groups, and a large proportion of the heritability remains unexplained. The apnea-hypopnea index (AHI) is a commonly used quantitative measure characterizing OSA severity. Because OSA differs by sex, and the pathophysiology of obstructive events differ in rapid eye movement (REM) and non-REM (NREM) sleep, we hypothesized that additional genetic association signals would be identified by analyzing the NREM/REM-specific AHI and by conducting sex-specific analyses in multiethnic samples. We performed genome-wide association tests for up to 19,733 participants of African, Asian, European, and Hispanic/Latino American ancestry in 7 studies. We identified rs12936587 on chromosome 17 as a possible quantitative trait locus for NREM AHI in men (N = 6,737; P = 1.7 {\texttimes} 10) but not in women (P = 0.77). The association with NREM AHI was replicated in a physiological research study (N = 67; P = 0.047). This locus overlapping the RAI1 gene and encompassing genes PEMT1, SREBF1, and RASD1 was previously reported to be associated with coronary artery disease, lipid metabolism, and implicated in Potocki-Lupski syndrome and Smith-Magenis syndrome, which are characterized by abnormal sleep phenotypes. We also identified gene-by-sex interactions in suggestive association regions, suggesting that genetic variants for AHI appear to vary by sex, consistent with the clinical observations of strong sexual dimorphism.

}, issn = {1535-4989}, doi = {10.1165/rcmb.2017-0237OC}, author = {Chen, Han and Cade, Brian E and Gleason, Kevin J and Bjonnes, Andrew C and Stilp, Adrienne M and Sofer, Tamar and Conomos, Matthew P and Ancoli-Israel, Sonia and Arens, Raanan and Azarbarzin, Ali and Bell, Graeme I and Below, Jennifer E and Chun, Sung and Evans, Daniel S and Ewert, Ralf and Frazier-Wood, Alexis C and Gharib, Sina A and Haba-Rubio, Jos{\'e} and Hagen, Erika W and Heinzer, Raphael and Hillman, David R and Johnson, W Craig and Kutalik, Zolt{\'a}n and Lane, Jacqueline M and Larkin, Emma K and Lee, Seung Ku and Liang, Jingjing and Loredo, Jose S and Mukherjee, Sutapa and Palmer, Lyle J and Papanicolaou, George J and Penzel, Thomas and Peppard, Paul E and Post, Wendy S and Ramos, Alberto R and Rice, Ken and Rotter, Jerome I and Sands, Scott A and Shah, Neomi A and Shin, Chol and Stone, Katie L and Stubbe, Beate and Sul, Jae Hoon and Tafti, Mehdi and Taylor, Kent D and Teumer, Alexander and Thornton, Timothy A and Tranah, Gregory J and Wang, Chaolong and Wang, Heming and Warby, Simon C and Wellman, D Andrew and Zee, Phyllis C and Hanis, Craig L and Laurie, Cathy C and Gottlieb, Daniel J and Patel, Sanjay R and Zhu, Xiaofeng and Sunyaev, Shamil R and Saxena, Richa and Lin, Xihong and Redline, Susan} } @article {8049, title = {Admixture mapping identifies novel loci for obstructive sleep apnea in Hispanic/Latino Americans.}, journal = {Hum Mol Genet}, volume = {28}, year = {2019}, month = {2019 02 15}, pages = {675-687}, abstract = {

Obstructive sleep apnea (OSA) is a common disorder associated with increased risk of cardiovascular disease and mortality. Its prevalence and severity vary across ancestral background. Although OSA traits are heritable, few genetic associations have been identified. To identify genetic regions associated with OSA and improve statistical power, we applied admixture mapping on three primary OSA traits [the apnea hypopnea index (AHI), overnight average oxyhemoglobin saturation (SaO2) and percentage time SaO2~<~90\%] and a secondary trait (respiratory event duration) in a Hispanic/Latino American population study of 11~575 individuals with significant variation in ancestral background. Linear mixed models were performed using previously inferred African, European and Amerindian local genetic ancestry markers. Global African ancestry was associated with a lower AHI, higher SaO2 and shorter event duration. Admixture mapping analysis of the primary OSA traits identified local African ancestry at the chromosomal region 2q37 as genome-wide significantly associated with AHI (P~<~5.7~{\texttimes}~10-5), and European and Amerindian ancestries at 18q21 suggestively associated with both AHI and percentage time SaO2~<~90\% (P~<~10-3). Follow-up joint ancestry-SNP association analyses identified novel variants in ferrochelatase (FECH), significantly associated with AHI and percentage time SaO2~<~90\% after adjusting for multiple tests (P~<~8~{\texttimes}~10-6). These signals contributed to the admixture mapping associations and were replicated in independent cohorts. In this first admixture mapping study of OSA, novel associations with variants in the iron/heme metabolism pathway suggest a role for iron in influencing respiratory traits underlying OSA.

}, issn = {1460-2083}, doi = {10.1093/hmg/ddy387}, author = {Wang, Heming and Cade, Brian E and Sofer, Tamar and Sands, Scott A and Chen, Han and Browning, Sharon R and Stilp, Adrienne M and Louie, Tin L and Thornton, Timothy A and Johnson, W Craig and Below, Jennifer E and Conomos, Matthew P and Evans, Daniel S and Gharib, Sina A and Guo, Xiuqing and Wood, Alexis C and Mei, Hao and Yaffe, Kristine and Loredo, Jose S and Ramos, Alberto R and Barrett-Connor, Elizabeth and Ancoli-Israel, Sonia and Zee, Phyllis C and Arens, Raanan and Shah, Neomi A and Taylor, Kent D and Tranah, Gregory J and Stone, Katie L and Hanis, Craig L and Wilson, James G and Gottlieb, Daniel J and Patel, Sanjay R and Rice, Ken and Post, Wendy S and Rotter, Jerome I and Sunyaev, Shamil R and Cai, Jianwen and Lin, Xihong and Purcell, Shaun M and Laurie, Cathy C and Saxena, Richa and Redline, Susan and Zhu, Xiaofeng} } @article {8044, title = {Associations of variants In the hexokinase 1 and interleukin 18 receptor regions with oxyhemoglobin saturation during sleep.}, journal = {PLoS Genet}, volume = {15}, year = {2019}, month = {2019 04}, pages = {e1007739}, abstract = {

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

}, keywords = {Adolescent, Adult, Aged, Aged, 80 and over, Cell Adhesion Molecules, Neuronal, Computational Biology, Extracellular Matrix Proteins, Female, Gene Regulatory Networks, Genetic Variation, Genome-Wide Association Study, Hexokinase, Humans, Hypoxia, Interleukin-18 Receptor alpha Subunit, Male, Middle Aged, Nerve Tissue Proteins, NLR Family, Pyrin Domain-Containing 3 Protein, Oxygen, Oxyhemoglobins, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Serine Endopeptidases, Sleep, Sleep Apnea Syndromes, Young Adult}, issn = {1553-7404}, doi = {10.1371/journal.pgen.1007739}, author = {Cade, Brian E and Chen, Han and Stilp, Adrienne M and Louie, Tin and Ancoli-Israel, Sonia and Arens, Raanan and Barfield, Richard and Below, Jennifer E and Cai, Jianwen and Conomos, Matthew P and Evans, Daniel S and Frazier-Wood, Alexis C and Gharib, Sina A and Gleason, Kevin J and Gottlieb, Daniel J and Hillman, David R and Johnson, W Craig and Lederer, David J and Lee, Jiwon and Loredo, Jose S and Mei, Hao and Mukherjee, Sutapa and Patel, Sanjay R and Post, Wendy S and Purcell, Shaun M and Ramos, Alberto R and Reid, Kathryn J and Rice, Ken and Shah, Neomi A and Sofer, Tamar and Taylor, Kent D and Thornton, Timothy A and Wang, Heming and Yaffe, Kristine and Zee, Phyllis C and Hanis, Craig L and Palmer, Lyle J and Rotter, Jerome I and Stone, Katie L and Tranah, Gregory J and Wilson, James G and Sunyaev, Shamil R and Laurie, Cathy C and Zhu, Xiaofeng and Saxena, Richa and Lin, Xihong and Redline, Susan} } @article {8096, title = {Epigenome-wide association analysis of daytime sleepiness in the Multi-Ethnic Study of Atherosclerosis reveals African-American-specific associations.}, journal = {Sleep}, year = {2019}, month = {2019 May 29}, abstract = {

STUDY OBJECTIVES: Daytime sleepiness is a consequence of inadequate sleep, sleep-wake control disorder, or other medical conditions. Population variability in prevalence of daytime sleepiness is likely due to genetic and biological factors as well as social and environmental influences. DNA methylation (DNAm) potentially influences multiple health outcomes. Here, we explored the association between DNAm and daytime sleepiness quantified by the Epworth Sleepiness Scale (ESS).

METHODS: We performed multi-ethnic and ethnic-specific epigenome-wide association studies for DNAm and ESS in the Multi-Ethnic Study of Atherosclerosis (MESA; n = 619) and the Cardiovascular Health Study (n = 483), with cross-study replication and meta-analysis. Genetic variants near ESS-associated DNAm were analyzed for methylation quantitative trait loci and followed with replication of genotype-sleepiness associations in the UK Biobank.

RESULTS: In MESA only, we detected four DNAm-ESS associations: one across all race/ethnic groups; three in African-Americans (AA) only. Two of the MESA AA associations, in genes KCTD5 and RXRA, nominally replicated in CHS (p-value < 0.05). In the AA meta-analysis, we detected 14 DNAm-ESS associations (FDR q-value < 0.05, top association p-value = 4.26 {\texttimes} 10-8). Three DNAm sites mapped to genes (CPLX3, GFAP, and C7orf50) with biological relevance. We also found evidence for associations with DNAm sites in RAI1, a gene associated with sleep and circadian phenotypes. UK Biobank follow-up analyses detected SNPs in RAI1, RXRA, and CPLX3 with nominal sleepiness associations.

CONCLUSIONS: We identified methylation sites in multiple genes possibly implicated in daytime sleepiness. Most significant DNAm-ESS associations were specific to AA. Future work is needed to identify mechanisms driving ancestry-specific methylation effects.

}, issn = {1550-9109}, doi = {10.1093/sleep/zsz101}, author = {Barfield, Richard and Wang, Heming and Liu, Yongmei and Brody, Jennifer A and Swenson, Brenton and Li, Ruitong and Bartz, Traci M and Sotoodehnia, Nona and Chen, Yii-der I and Cade, Brian E and Chen, Han and Patel, Sanjay R and Zhu, Xiaofeng and Gharib, Sina A and Johnson, W Craig and Rotter, Jerome I and Saxena, Richa and Purcell, Shaun and Lin, Xihong and Redline, Susan and Sofer, Tamar} } @article {8202, title = {Multi-ancestry sleep-by-SNP interaction analysis in 126,926 individuals reveals lipid loci stratified by sleep duration.}, journal = {Nat Commun}, volume = {10}, year = {2019}, month = {2019 Nov 12}, pages = {5121}, abstract = {

Both short and long sleep are associated with an adverse lipid profile, likely through different biological pathways. To elucidate the biology of sleep-associated adverse lipid profile, we conduct multi-ancestry genome-wide sleep-SNP interaction analyses on three lipid traits (HDL-c, LDL-c and triglycerides). In the total study sample (discovery + replication) of 126,926 individuals from 5 different ancestry groups, when considering either long or short total sleep time interactions in joint analyses, we identify 49 previously unreported lipid loci, and 10 additional previously unreported lipid loci in a restricted sample of European-ancestry cohorts. In addition, we identify new gene-sleep interactions for known lipid loci such as LPL and PCSK9. The previously unreported lipid loci have a modest explained variance in lipid levels: most notable, gene-short-sleep interactions explain 4.25\% of the variance in triglyceride level. Collectively, these findings contribute to our understanding of the biological mechanisms involved in sleep-associated adverse lipid profiles.

}, issn = {2041-1723}, doi = {10.1038/s41467-019-12958-0}, author = {Noordam, Raymond and Bos, Maxime M and Wang, Heming and Winkler, Thomas W and Bentley, Amy R and Kilpel{\"a}inen, Tuomas O and de Vries, Paul S and Sung, Yun Ju and Schwander, Karen and Cade, Brian E and Manning, Alisa and Aschard, Hugues and Brown, Michael R and Chen, Han and Franceschini, Nora and Musani, Solomon K and Richard, Melissa and Vojinovic, Dina and Aslibekyan, Stella and Bartz, Traci M and de Las Fuentes, Lisa and Feitosa, Mary and Horimoto, Andrea R and Ilkov, Marjan and Kho, Minjung and Kraja, Aldi and Li, Changwei and Lim, Elise and Liu, Yongmei and Mook-Kanamori, Dennis O and Rankinen, Tuomo and Tajuddin, Salman M and van der Spek, Ashley and Wang, Zhe and Marten, Jonathan and Laville, Vincent and Alver, Maris and Evangelou, Evangelos and Graff, Maria E and He, Meian and Kuhnel, Brigitte and Lyytik{\"a}inen, Leo-Pekka and Marques-Vidal, Pedro and Nolte, Ilja M and Palmer, Nicholette D and Rauramaa, Rainer and Shu, Xiao-Ou and Snieder, Harold and Weiss, Stefan and Wen, Wanqing and Yanek, Lisa R and Adolfo, Correa and Ballantyne, Christie and Bielak, Larry and Biermasz, Nienke R and Boerwinkle, Eric and Dimou, Niki and Eiriksdottir, Gudny and Gao, Chuan and Gharib, Sina A and Gottlieb, Daniel J and Haba-Rubio, Jos{\'e} and Harris, Tamara B and Heikkinen, Sami and Heinzer, Raphael and Hixson, James E and Homuth, Georg and Ikram, M Arfan and Komulainen, Pirjo and Krieger, Jose E and Lee, Jiwon and Liu, Jingmin and Lohman, Kurt K and Luik, Annemarie I and M{\"a}gi, Reedik and Martin, Lisa W and Meitinger, Thomas and Metspalu, Andres and Milaneschi, Yuri and Nalls, Mike A and O{\textquoteright}Connell, Jeff and Peters, Annette and Peyser, Patricia and Raitakari, Olli T and Reiner, Alex P and Rensen, Patrick C N and Rice, Treva K and Rich, Stephen S and Roenneberg, Till and Rotter, Jerome I and Schreiner, Pamela J and Shikany, James and Sidney, Stephen S and Sims, Mario and Sitlani, Colleen M and Sofer, Tamar and Strauch, Konstantin and Swertz, Morris A and Taylor, Kent D and Uitterlinden, Andr{\'e} G and van Duijn, Cornelia M and V{\"o}lzke, Henry and Waldenberger, Melanie and Wallance, Robert B and van Dijk, Ko Willems and Yu, Caizheng and Zonderman, Alan B and Becker, Diane M and Elliott, Paul and Esko, T{\~o}nu and Gieger, Christian and Grabe, Hans J and Lakka, Timo A and Lehtim{\"a}ki, Terho and North, Kari E and Penninx, Brenda W J H and Vollenweider, Peter and Wagenknecht, Lynne E and Wu, Tangchun and Xiang, Yong-Bing and Zheng, Wei and Arnett, Donna K and Bouchard, Claude and Evans, Michele K and Gudnason, Vilmundur and Kardia, Sharon and Kelly, Tanika N and Kritchevsky, Stephen B and Loos, Ruth J F and Pereira, Alexandre C and Province, Mike and Psaty, Bruce M and Rotimi, Charles and Zhu, Xiaofeng and Amin, Najaf and Cupples, L Adrienne and Fornage, Myriam and Fox, Ervin F and Guo, Xiuqing and Gauderman, W James and Rice, Kenneth and Kooperberg, Charles and Munroe, Patricia B and Liu, Ching-Ti and Morrison, Alanna C and Rao, Dabeeru C and van Heemst, Diana and Redline, Susan} } @article {8199, title = {Sequencing Analysis at 8p23 Identifies Multiple Rare Variants in DLC1 Associated with Sleep-Related Oxyhemoglobin Saturation Level.}, journal = {Am J Hum Genet}, volume = {105}, year = {2019}, month = {2019 Nov 07}, pages = {1057-1068}, abstract = {

Average arterial oxyhemoglobin saturation during sleep (AvSpOS) is a clinically relevant measure of physiological stress associated with sleep-disordered breathing, and this measure predicts incident cardiovascular disease and mortality. Using high-depth whole-genome sequencing data from the National Heart, Lung, and Blood Institute (NHLBI) Trans-Omics for Precision Medicine (TOPMed) project and focusing on genes with linkage evidence on chromosome 8p23, we observed that six coding and 51 noncoding variants in a gene that encodes the GTPase-activating protein (DLC1) are significantly associated with AvSpOS and replicated in independent subjects. The combined DLC1 association evidence of discovery and replication cohorts reaches genome-wide significance in European Americans (p = 7.9~{\texttimes} 10). A risk score for these variants, built on an independent dataset, explains 0.97\% of the AvSpOS variation and contributes to the linkage evidence. The 51 noncoding variants are enriched in regulatory features in a human lung fibroblast cell line and contribute to DLC1 expression variation. Mendelian randomization analysis using these variants indicates a significant causal effect of DLC1 expression in fibroblasts on AvSpOS. Multiple sources of information, including genetic variants, gene expression, and methylation, consistently suggest that DLC1 is a gene associated with AvSpOS.

}, issn = {1537-6605}, doi = {10.1016/j.ajhg.2019.10.002}, author = {Liang, Jingjing and Cade, Brian E and He, Karen Y and Wang, Heming and Lee, Jiwon and Sofer, Tamar and Williams, Stephanie and Li, Ruitong and Chen, Han and Gottlieb, Daniel J and Evans, Daniel S and Guo, Xiuqing and Gharib, Sina A and Hale, Lauren and Hillman, David R and Lutsey, Pamela L and Mukherjee, Sutapa and Ochs-Balcom, Heather M and Palmer, Lyle J and Rhodes, Jessica and Purcell, Shaun and Patel, Sanjay R and Saxena, Richa and Stone, Katie L and Tang, Weihong and Tranah, Gregory J and Boerwinkle, Eric and Lin, Xihong and Liu, Yongmei and Psaty, Bruce M and Vasan, Ramachandran S and Cho, Michael H and Manichaikul, Ani and Silverman, Edwin K and Barr, R Graham and Rich, Stephen S and Rotter, Jerome I and Wilson, James G and Redline, Susan and Zhu, Xiaofeng} } @article {8407, title = {Role of Rare and Low-Frequency Variants in Gene-Alcohol Interactions on Plasma Lipid Levels.}, journal = {Circ Genom Precis Med}, volume = {13}, year = {2020}, month = {2020 Aug}, pages = {e002772}, abstract = {

BACKGROUND: Alcohol intake influences plasma lipid levels, and such effects may be moderated by genetic variants. We aimed to characterize the role of aggregated rare and low-frequency protein-coding variants in gene by alcohol consumption interactions associated with fasting plasma lipid levels.

METHODS: In the Cohorts for Heart and Aging Research in Genomic Epidemiology consortium, fasting plasma triglycerides and high- and low-density lipoprotein cholesterol were measured in 34 153 individuals with European ancestry from 5 discovery studies and 32 277 individuals from 6 replication studies. Rare and low-frequency functional protein-coding variants (minor allele frequency, <=5\%) measured by an exome array were aggregated by genes and evaluated by a gene-environment interaction test and a joint test of genetic main and gene-environment interaction effects. Two dichotomous self-reported alcohol consumption variables, current drinker, defined as any recurrent drinking behavior, and regular drinker, defined as the subset of current drinkers who consume at least 2 drinks per week, were considered.

RESULTS: We discovered and replicated 21 gene-lipid associations at 13 known lipid loci through the joint test. Eight loci (, , , , , , , and ) remained significant after conditioning on the common index single-nucleotide polymorphism identified by previous genome-wide association studies, suggesting an independent role for rare and low-frequency variants at these loci. One significant gene-alcohol interaction on triglycerides in a novel locus was significantly discovered (=6.65{\texttimes}10 for the interaction test) and replicated at nominal significance level (=0.013) in .

CONCLUSIONS: In conclusion, this study applied new gene-based statistical approaches and suggested that rare and low-frequency genetic variants interacted with alcohol consumption on lipid levels.

}, issn = {2574-8300}, doi = {10.1161/CIRCGEN.119.002772}, author = {Wang, Zhe and Chen, Han and Bartz, Traci M and Bielak, Lawrence F and Chasman, Daniel I and Feitosa, Mary F and Franceschini, Nora and Guo, Xiuqing and Lim, Elise and Noordam, Raymond and Richard, Melissa A and Wang, Heming and Cade, Brian and Cupples, L Adrienne and de Vries, Paul S and Giulanini, Franco and Lee, Jiwon and Lemaitre, Rozenn N and Martin, Lisa W and Reiner, Alex P and Rich, Stephen S and Schreiner, Pamela J and Sidney, Stephen and Sitlani, Colleen M and Smith, Jennifer A and Willems van Dijk, Ko and Yao, Jie and Zhao, Wei and Fornage, Myriam and Kardia, Sharon L R and Kooperberg, Charles and Liu, Ching-Ti and Mook-Kanamori, Dennis O and Province, Michael A and Psaty, Bruce M and Redline, Susan and Ridker, Paul M and Rotter, Jerome I and Boerwinkle, Eric and Morrison, Alanna C} } @article {8838, title = {BinomiRare: A robust test for association of a rare genetic variant with a binary outcome for mixed models and any case-control proportion.}, journal = {HGG Adv}, volume = {2}, year = {2021}, month = {2021 Jul 08}, abstract = {

Whole-genome sequencing (WGS) and whole-exome sequencing studies have become increasingly available and are being used to identify rare genetic variants associated with health and disease outcomes. Investigators routinely use mixed models to account for genetic relatedness or other clustering variables (e.g., family or household) when testing genetic associations. However, no existing tests of the association of a rare variant with a binary outcome in the presence of correlated data control the type 1 error where there are (1) few individuals harboring the rare allele, (2) a small proportion of cases relative to controls, and (3) covariates to adjust for. Here, we address all three issues in developing a framework for testing rare variant association with a binary trait in individuals harboring at least one risk allele. In this framework, we estimate outcome probabilities under the null hypothesis and then use them, within the individuals with at least one risk allele, to test variant associations. We extend the BinomiRare test, which was previously proposed for independent observations, and develop the Conway-Maxwell-Poisson (CMP) test and study their properties in simulations. We show that the BinomiRare test always controls the type 1 error, while the CMP test sometimes does not. We then use the BinomiRare test to test the association of rare genetic variants in target genes with small-vessel disease (SVD) stroke, short sleep, and venous thromboembolism (VTE), in whole-genome sequence data from the Trans-Omics for Precision Medicine (TOPMed) program.

}, issn = {2666-2477}, doi = {10.1016/j.xhgg.2021.100040}, author = {Sofer, Tamar and Lee, Jiwon and Kurniansyah, Nuzulul and Jain, Deepti and Laurie, Cecelia A and Gogarten, Stephanie M and Conomos, Matthew P and Heavner, Ben and Hu, Yao and Kooperberg, Charles and Haessler, Jeffrey and Vasan, Ramachandran S and Cupples, L Adrienne and Coombes, Brandon J and Seyerle, Amanda and Gharib, Sina A and Chen, Han and O{\textquoteright}Connell, Jeffrey R and Zhang, Man and Gottlieb, Daniel J and Psaty, Bruce M and Longstreth, W T and Rotter, Jerome I and Taylor, Kent D and Rich, Stephen S and Guo, Xiuqing and Boerwinkle, Eric and Morrison, Alanna C and Pankow, James S and Johnson, Andrew D and Pankratz, Nathan and Reiner, Alex P and Redline, Susan and Smith, Nicholas L and Rice, Kenneth M and Schifano, Elizabeth D} } @article {8836, title = {Identification of novel and rare variants associated with handgrip strength using whole genome sequence data from the NHLBI Trans-Omics in Precision Medicine (TOPMed) Program.}, journal = {PLoS One}, volume = {16}, year = {2021}, month = {2021}, pages = {e0253611}, abstract = {

Handgrip strength is a widely used measure of muscle strength and a predictor of a range of morbidities including cardiovascular diseases and all-cause mortality. Previous genome-wide association studies of handgrip strength have focused on common variants primarily in persons of European descent. We aimed to identify rare and ancestry-specific genetic variants associated with handgrip strength by conducting whole-genome sequence association analyses using 13,552 participants from six studies representing diverse population groups from the Trans-Omics in Precision Medicine (TOPMed) Program. By leveraging multiple handgrip strength measures performed in study participants over time, we increased our effective sample size by 7-12\%. Single-variant analyses identified ten handgrip strength loci among African-Americans: four rare variants, five low-frequency variants, and one common variant. One significant and four suggestive genes were identified associated with handgrip strength when aggregating rare and functional variants; all associations were ancestry-specific. We additionally leveraged the different ancestries available in the UK Biobank to further explore the ancestry-specific association signals from the single-variant association analyses. In conclusion, our study identified 11 new loci associated with handgrip strength with rare and/or ancestry-specific genetic variations, highlighting the added value of whole-genome sequencing in diverse samples. Several of the associations identified using single-variant or aggregate analyses lie in genes with a function relevant to the brain or muscle or were reported to be associated with muscle or age-related traits. Further studies in samples with sequence data and diverse ancestries are needed to confirm these findings.

}, issn = {1932-6203}, doi = {10.1371/journal.pone.0253611}, author = {Sarnowski, Chloe and Chen, Han and Biggs, Mary L and Wassertheil-Smoller, Sylvia and Bressler, Jan and Irvin, Marguerite R and Ryan, Kathleen A and Karasik, David and Arnett, Donna K and Cupples, L Adrienne and Fardo, David W and Gogarten, Stephanie M and Heavner, Benjamin D and Jain, Deepti and Kang, Hyun Min and Kooperberg, Charles and Mainous, Arch G and Mitchell, Braxton D and Morrison, Alanna C and O{\textquoteright}Connell, Jeffrey R and Psaty, Bruce M and Rice, Kenneth and Smith, Albert V and Vasan, Ramachandran S and Windham, B Gwen and Kiel, Douglas P and Murabito, Joanne M and Lunetta, Kathryn L} } @article {8714, title = {Multi-ancestry genome-wide gene-sleep interactions identify novel loci for blood pressure.}, journal = {Mol Psychiatry}, year = {2021}, month = {2021 Apr 15}, abstract = {

Long and short sleep duration are associated with elevated blood pressure (BP), possibly through effects on molecular pathways that influence neuroendocrine and vascular systems. To gain new insights into the genetic basis of sleep-related BP variation, we performed genome-wide gene by short or long sleep duration interaction analyses on four BP traits (systolic BP, diastolic BP, mean arterial pressure, and pulse pressure) across five ancestry groups in two stages using 2 degree of freedom (df) joint test followed by 1df test of interaction effects. Primary multi-ancestry analysis in 62,969 individuals in stage 1 identified three novel gene by sleep interactions that were replicated in an additional 59,296 individuals in stage 2 (stage 1 + 2 P < 5 {\texttimes} 10), including rs7955964 (FIGNL2/ANKRD33) that increases BP among long sleepers, and rs73493041 (SNORA26/C9orf170) and rs10406644 (KCTD15/LSM14A) that increase BP among short sleepers (P < 5 {\texttimes} 10). Secondary ancestry-specific analysis identified another novel gene by long sleep interaction at rs111887471 (TRPC3/KIAA1109) in individuals of African ancestry (P = 2 {\texttimes} 10). Combined stage 1 and 2 analyses additionally identified significant gene by long sleep interactions at 10 loci including MKLN1 and RGL3/ELAVL3 previously associated with BP, and significant gene by short sleep interactions at 10 loci including C2orf43 previously associated with BP (P < 10). 2df test also identified novel loci for BP after modeling sleep that has known functions in sleep-wake regulation, nervous and cardiometabolic systems. This study indicates that sleep and primary mechanisms regulating BP may interact to elevate BP level, suggesting novel insights into sleep-related BP regulation.

}, issn = {1476-5578}, doi = {10.1038/s41380-021-01087-0}, author = {Wang, Heming and Noordam, Raymond and Cade, Brian E and Schwander, Karen and Winkler, Thomas W and Lee, Jiwon and Sung, Yun Ju and Bentley, Amy R and Manning, Alisa K and Aschard, Hugues and Kilpel{\"a}inen, Tuomas O and Ilkov, Marjan and Brown, Michael R and Horimoto, Andrea R and Richard, Melissa and Bartz, Traci M and Vojinovic, Dina and Lim, Elise and Nierenberg, Jovia L and Liu, Yongmei and Chitrala, Kumaraswamynaidu and Rankinen, Tuomo and Musani, Solomon K and Franceschini, Nora and Rauramaa, Rainer and Alver, Maris and Zee, Phyllis C and Harris, Sarah E and van der Most, Peter J and Nolte, Ilja M and Munroe, Patricia B and Palmer, Nicholette D and Kuhnel, Brigitte and Weiss, Stefan and Wen, Wanqing and Hall, Kelly A and Lyytik{\"a}inen, Leo-Pekka and O{\textquoteright}Connell, Jeff and Eiriksdottir, Gudny and Launer, Lenore J and de Vries, Paul S and Arking, Dan E and Chen, Han and Boerwinkle, Eric and Krieger, Jose E and Schreiner, Pamela J and Sidney, Stephen and Shikany, James M and Rice, Kenneth and Chen, Yii-Der Ida and Gharib, Sina A and Bis, Joshua C and Luik, Annemarie I and Ikram, M Arfan and Uitterlinden, Andr{\'e} G and Amin, Najaf and Xu, Hanfei and Levy, Daniel and He, Jiang and Lohman, Kurt K and Zonderman, Alan B and Rice, Treva K and Sims, Mario and Wilson, Gregory and Sofer, Tamar and Rich, Stephen S and Palmas, Walter and Yao, Jie and Guo, Xiuqing and Rotter, Jerome I and Biermasz, Nienke R and Mook-Kanamori, Dennis O and Martin, Lisa W and Barac, Ana and Wallace, Robert B and Gottlieb, Daniel J and Komulainen, Pirjo and Heikkinen, Sami and M{\"a}gi, Reedik and Milani, Lili and Metspalu, Andres and Starr, John M and Milaneschi, Yuri and Waken, R J and Gao, Chuan and Waldenberger, Melanie and Peters, Annette and Strauch, Konstantin and Meitinger, Thomas and Roenneberg, Till and V{\"o}lker, Uwe and D{\"o}rr, Marcus and Shu, Xiao-Ou and Mukherjee, Sutapa and Hillman, David R and K{\"a}h{\"o}nen, Mika and Wagenknecht, Lynne E and Gieger, Christian and Grabe, Hans J and Zheng, Wei and Palmer, Lyle J and Lehtim{\"a}ki, Terho and Gudnason, Vilmundur and Morrison, Alanna C and Pereira, Alexandre C and Fornage, Myriam and Psaty, Bruce M and van Duijn, Cornelia M and Liu, Ching-Ti and Kelly, Tanika N and Evans, Michele K and Bouchard, Claude and Fox, Ervin R and Kooperberg, Charles and Zhu, Xiaofeng and Lakka, Timo A and Esko, T{\~o}nu and North, Kari E and Deary, Ian J and Snieder, Harold and Penninx, Brenda W J H and Gauderman, W James and Rao, Dabeeru C and Redline, Susan and van Heemst, Diana} } @article {8920, title = {Whole-genome association analyses of sleep-disordered breathing phenotypes in the NHLBI TOPMed program.}, journal = {Genome Med}, volume = {13}, year = {2021}, month = {2021 08 26}, pages = {136}, abstract = {

BACKGROUND: Sleep-disordered breathing is a common disorder associated with significant morbidity. The genetic architecture of sleep-disordered breathing remains poorly understood. Through the NHLBI Trans-Omics for Precision Medicine (TOPMed) program, we performed the first whole-genome sequence analysis of sleep-disordered breathing.

METHODS: The study sample was comprised of 7988 individuals of diverse ancestry. Common-variant and pathway analyses included an additional 13,257 individuals. We examined five complementary traits describing different aspects of sleep-disordered breathing: the apnea-hypopnea index, average oxyhemoglobin desaturation per event, average and minimum oxyhemoglobin saturation across the sleep episode, and the percentage of sleep with oxyhemoglobin saturation < 90\%. We adjusted for age, sex, BMI, study, and family structure using MMSKAT and EMMAX mixed linear model approaches. Additional bioinformatics analyses were performed with MetaXcan, GIGSEA, and ReMap.

RESULTS: We identified a multi-ethnic set-based rare-variant association (p = 3.48 {\texttimes} 10) on chromosome X with ARMCX3. Additional rare-variant associations include ARMCX3-AS1, MRPS33, and C16orf90. Novel common-variant loci were identified in the NRG1 and SLC45A2 regions, and previously associated loci in the IL18RAP and ATP2B4 regions were associated with novel phenotypes. Transcription factor binding site enrichment identified associations with genes implicated with respiratory and craniofacial traits. Additional analyses identified significantly associated pathways.

CONCLUSIONS: We have identified the first gene-based rare-variant associations with objectively measured sleep-disordered breathing traits. Our results increase the understanding of the genetic architecture of sleep-disordered breathing and highlight associations in genes that modulate lung development, inflammation, respiratory rhythmogenesis, and HIF1A-mediated hypoxic response.

}, issn = {1756-994X}, doi = {10.1186/s13073-021-00917-8}, author = {Cade, Brian E and Lee, Jiwon and Sofer, Tamar and Wang, Heming and Zhang, Man and Chen, Han and Gharib, Sina A and Gottlieb, Daniel J and Guo, Xiuqing and Lane, Jacqueline M and Liang, Jingjing and Lin, Xihong and Mei, Hao and Patel, Sanjay R and Purcell, Shaun M and Saxena, Richa and Shah, Neomi A and Evans, Daniel S and Hanis, Craig L and Hillman, David R and Mukherjee, Sutapa and Palmer, Lyle J and Stone, Katie L and Tranah, Gregory J and Abecasis, Goncalo R and Boerwinkle, Eric A and Correa, Adolfo and Cupples, L Adrienne and Kaplan, Robert C and Nickerson, Deborah A and North, Kari E and Psaty, Bruce M and Rotter, Jerome I and Rich, Stephen S and Tracy, Russell P and Vasan, Ramachandran S and Wilson, James G and Zhu, Xiaofeng and Redline, Susan} } @article {9246, title = {Correlations between complex human phenotypes vary by genetic background, gender, and environment.}, journal = {Cell Rep Med}, volume = {3}, year = {2022}, month = {2022 Dec 20}, pages = {100844}, abstract = {

We develop a closed-form Haseman-Elston estimator for genetic and environmental correlation coefficients between complex phenotypes, which we term HEc, that is as precise as GCTA yet \~{}20{\texttimes} faster. We estimate genetic and environmental correlations between over 7,000 phenotype pairs in subgroups from the Trans-Omics in Precision Medicine (TOPMed) program. We demonstrate substantial differences in both heritabilities and genetic correlations for multiple phenotypes and phenotype pairs between individuals of self-reported Black, Hispanic/Latino, and White backgrounds. We similarly observe differences in many of the genetic and environmental correlations between genders. To estimate the contribution of genetics to the observed phenotypic correlation, we introduce "fractional genetic correlation" as the fraction of phenotypic correlation explained by genetics. Finally, we quantify the enrichment of correlations between phenotypic domains, each of which is comprised of multiple phenotypes. Altogether, we demonstrate that the observed correlations between complex human phenotypes depend on the genetic background of the individuals, their gender, and their environment.

}, keywords = {Female, Genetic Background, Humans, Male, Phenotype}, issn = {2666-3791}, doi = {10.1016/j.xcrm.2022.100844}, author = {Elgart, Michael and Goodman, Matthew O and Isasi, Carmen and Chen, Han and Morrison, Alanna C and de Vries, Paul S and Xu, Huichun and Manichaikul, Ani W and Guo, Xiuqing and Franceschini, Nora and Psaty, Bruce M and Rich, Stephen S and Rotter, Jerome I and Lloyd-Jones, Donald M and Fornage, Myriam and Correa, Adolfo and Heard-Costa, Nancy L and Vasan, Ramachandran S and Hernandez, Ryan and Kaplan, Robert C and Redline, Susan and Sofer, Tamar} } @article {9253, title = {A framework for detecting noncoding rare-variant associations of large-scale whole-genome sequencing studies.}, journal = {Nat Methods}, volume = {19}, year = {2022}, month = {2022 Dec}, pages = {1599-1611}, abstract = {

Large-scale whole-genome sequencing studies have enabled analysis of noncoding rare-variant (RV) associations with complex human diseases and traits. Variant-set analysis is a powerful approach to study RV association. However, existing methods have limited ability in analyzing the noncoding genome. We propose a computationally efficient and robust noncoding RV association detection framework, STAARpipeline, to automatically annotate a whole-genome sequencing study and perform flexible noncoding RV association analysis, including gene-centric analysis and fixed window-based and dynamic window-based non-gene-centric analysis by incorporating variant functional annotations. In gene-centric analysis, STAARpipeline uses STAAR to group noncoding variants based on functional categories of genes and incorporate multiple functional annotations. In non-gene-centric analysis, STAARpipeline uses SCANG-STAAR to incorporate dynamic window sizes and multiple functional annotations. We apply STAARpipeline to identify noncoding RV sets associated with four lipid traits in 21,015 discovery samples from the Trans-Omics for Precision Medicine (TOPMed) program and replicate several of them in an additional 9,123 TOPMed samples. We also analyze five non-lipid TOPMed traits.

}, keywords = {Genetic Variation, Genome, Genome-Wide Association Study, Humans, Phenotype, Whole Genome Sequencing}, issn = {1548-7105}, doi = {10.1038/s41592-022-01640-x}, author = {Li, Zilin and Li, Xihao and Zhou, Hufeng and Gaynor, Sheila M and Selvaraj, Margaret Sunitha and Arapoglou, Theodore and Quick, Corbin and Liu, Yaowu and Chen, Han and Sun, Ryan and Dey, Rounak and Arnett, Donna K and Auer, Paul L and Bielak, Lawrence F and Bis, Joshua C and Blackwell, Thomas W and Blangero, John and Boerwinkle, Eric and Bowden, Donald W and Brody, Jennifer A and Cade, Brian E and Conomos, Matthew P and Correa, Adolfo and Cupples, L Adrienne and Curran, Joanne E and de Vries, Paul S and Duggirala, Ravindranath and Franceschini, Nora and Freedman, Barry I and G{\"o}ring, Harald H H and Guo, Xiuqing and Kalyani, Rita R and Kooperberg, Charles and Kral, Brian G and Lange, Leslie A and Lin, Bridget M and Manichaikul, Ani and Manning, Alisa K and Martin, Lisa W and Mathias, Rasika A and Meigs, James B and Mitchell, Braxton D and Montasser, May E and Morrison, Alanna C and Naseri, Take and O{\textquoteright}Connell, Jeffrey R and Palmer, Nicholette D and Peyser, Patricia A and Psaty, Bruce M and Raffield, Laura M and Redline, Susan and Reiner, Alexander P and Reupena, Muagututi{\textquoteright}a Sefuiva and Rice, Kenneth M and Rich, Stephen S and Smith, Jennifer A and Taylor, Kent D and Taub, Margaret A and Vasan, Ramachandran S and Weeks, Daniel E and Wilson, James G and Yanek, Lisa R and Zhao, Wei and Rotter, Jerome I and Willer, Cristen J and Natarajan, Pradeep and Peloso, Gina M and Lin, Xihong} } @article {9161, title = {Non-linear machine learning models incorporating SNPs and PRS improve polygenic prediction in diverse human populations.}, journal = {Commun Biol}, volume = {5}, year = {2022}, month = {2022 08 22}, pages = {856}, abstract = {

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

}, keywords = {Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Machine Learning, Multifactorial Inheritance, Polymorphism, Single Nucleotide}, issn = {2399-3642}, doi = {10.1038/s42003-022-03812-z}, author = {Elgart, Michael and Lyons, Genevieve and Romero-Brufau, Santiago and Kurniansyah, Nuzulul and Brody, Jennifer A and Guo, Xiuqing and Lin, Henry J and Raffield, Laura and Gao, Yan and Chen, Han and de Vries, Paul and Lloyd-Jones, Donald M and Lange, Leslie A and Peloso, Gina M and Fornage, Myriam and Rotter, Jerome I and Rich, Stephen S and Morrison, Alanna C and Psaty, Bruce M and Levy, Daniel and Redline, Susan and Sofer, Tamar} } @article {9101, title = {Targeted Genome Sequencing Identifies Multiple Rare Variants in Caveolin-1 Associated with Obstructive Sleep Apnea.}, journal = {Am J Respir Crit Care Med}, year = {2022}, month = {2022 Jul 13}, abstract = {

INTRODUCTION: Obstructive sleep apnea (OSA) is a common disorder associated with increased risk for cardiovascular disease, diabetes, and premature mortality. There is strong clinical and epi-demiologic evidence supporting the importance of genetic factors influencing OSA, but limited data implicating specific genes.

METHODS: Leveraging high depth genomic sequencing data from the National Heart, Lung, and Blood Institute (NHLBI) Trans-Omics for Precision Medicine (TOPMed) program and imputed genotype data from multiple population-based studies, we performed linkage analysis in the Cleve-land Family Study (CFS) followed by multi-stage gene-based association analyses in independent cohorts to search for rare variants contributing to OSA severity as assessed by the apnea-hypopnea index (AHI) in a total of 7,708 individuals of European ancestry.

RESULTS: Linkage analysis in CFS identified a suggestive linkage peak on chromosome 7q31 (LOD=2.31). Gene-based analysis identified 21 non-coding rare variants in Caveolin-1 (CAV1) associated with lower AHI after accounting for multiple comparisons (p=7.4{\texttimes}10-8). These non-coding variants together significantly contributed to the linkage evidence (p<10-3). Follow-up anal-ysis revealed significant associations between these variants and increased CAV1 expression, and increased CAV1 expression in peripheral monocytes was associated with lower AHI (p=0.024) and higher minimum overnight oxygen saturation (p=0.007).

CONCLUSION: Rare variants in CAV1, a membrane scaffolding protein essential in multiple cellular and metabolic functions, are associated with higher CAV1 gene expression and lower OSA severity, suggesting a novel target for modulating OSA severity.

}, issn = {1535-4970}, doi = {10.1164/rccm.202203-0618OC}, author = {Liang, Jingjing and Wang, Heming and Cade, Brian E and Kurniansyah, Nuzulul and He, Karen Y and Lee, Jiwon and Sands, Scott A and Brody, Jennifer and Chen, Han and Gottlieb, Daniel J and Evans, Daniel S and Guo, Xiuqing and Gharib, Sina A and Hale, Lauren and Hillman, David R and Lutsey, Pamela L and Mukherjee, Sutapa and Ochs-Balcom, Heather M and Palmer, Lyle J and Purcell, Shaun and Saxena, Richa and Patel, Sanjay R and Stone, Katie L and Tranah, Gregory J and Boerwinkle, Eric and Lin, Xihong and Liu, Yongmei and Psaty, Bruce M and Vasan, Ramachandran S and Manichaikul, Ani and Rich, Stephen S and Rotter, Jerome I and Sofer, Tamar and Redline, Susan and Zhu, Xiaofeng} } @article {9583, title = {Genome-Wide Interaction Analysis with DASH Diet Score Identified Novel Loci for Systolic Blood Pressure.}, journal = {medRxiv}, year = {2023}, month = {2023 Nov 11}, abstract = {

OBJECTIVE: We examined interactions between genotype and a Dietary Approaches to Stop Hypertension (DASH) diet score in relation to systolic blood pressure (SBP).

METHODS: We analyzed up to 9,420,585 biallelic imputed single nucleotide polymorphisms (SNPs) in up to 127,282 individuals of six population groups (91\% of European population) from the Cohorts for Heart and Aging Research in Genomic Epidemiology consortium (CHARGE; n=35,660) and UK Biobank (n=91,622) and performed European population-specific and cross-population meta-analyses.

RESULTS: We identified three loci in European-specific analyses and an additional four loci in cross-population analyses at P for interaction < 5e-8. We observed a consistent interaction between rs117878928 at 15q25.1 (minor allele frequency = 0.03) and the DASH diet score (P for interaction = 4e-8; P for heterogeneity = 0.35) in European population, where the interaction effect size was 0.42{\textpm}0.09 mm Hg (P for interaction = 9.4e-7) and 0.20{\textpm}0.06 mm Hg (P for interaction = 0.001) in CHARGE and the UK Biobank, respectively. The 1 Mb region surrounding rs117878928 was enriched with -expression quantitative trait loci (eQTL) variants (P = 4e-273) and -DNA methylation quantitative trait loci (mQTL) variants (P = 1e-300). While the closest gene for rs117878928 is , the highest narrow sense heritability accounted by SNPs potentially interacting with the DASH diet score in this locus was for gene at 15q25.1.

CONCLUSION: We demonstrated gene-DASH diet score interaction effects on SBP in several loci. Studies with larger diverse populations are needed to validate our findings.

}, doi = {10.1101/2023.11.10.23298402}, author = {Guirette, Melanie and Lan, Jessie and McKeown, Nicola and Brown, Michael R and Chen, Han and de Vries, Paul S and Kim, Hyunju and Rebholz, Casey M and Morrison, Alanna C and Bartz, Traci M and Fretts, Amanda M and Guo, Xiuqing and Lemaitre, Rozenn N and Liu, Ching-Ti and Noordam, Raymond and de Mutsert, Ren{\'e}e and Rosendaal, Frits R and Wang, Carol A and Beilin, Lawrence and Mori, Trevor A and Oddy, Wendy H and Pennell, Craig E and Chai, Jin Fang and Whitton, Clare and van Dam, Rob M and Liu, Jianjun and Tai, E Shyong and Sim, Xueling and Neuhouser, Marian L and Kooperberg, Charles and Tinker, Lesley and Franceschini, Nora and Huan, Tianxiao and Winkler, Thomas W and Bentley, Amy R and Gauderman, W James and Heerkens, Luc and Tanaka, Toshiko and van Rooij, Jeroen and Munroe, Patricia B and Warren, Helen R and Voortman, Trudy and Chen, Honglei and Rao, D C and Levy, Daniel and Ma, Jiantao} } @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 {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 {9581, title = {Whole genome sequence analysis of apparent treatment resistant hypertension status in participants from the Trans-Omics for Precision Medicine program.}, journal = {Front Genet}, volume = {14}, year = {2023}, month = {2023}, pages = {1278215}, abstract = {

Apparent treatment-resistant hypertension (aTRH) is characterized by the use of four or more antihypertensive (AHT) classes to achieve blood pressure (BP) control. In the current study, we conducted single-variant and gene-based analyses of aTRH among individuals from 12 Trans-Omics for Precision Medicine cohorts with whole-genome sequencing data. Cases were defined as individuals treated for hypertension (HTN) taking three different AHT classes, with average systolic BP >= 140 or diastolic BP >= 90~mmHg, or four or more medications regardless of BP ( = 1,705). A normotensive control group was defined as individuals with BP < 140/90~mmHg ( = 22,079), not on AHT medication. A second control group comprised individuals who were treatment responsive on one AHT medication with BP < 140/ 90~mmHg ( = 5,424). Logistic regression with kinship adjustment using the Scalable and Accurate Implementation of Generalized mixed models (SAIGE) was performed, adjusting for age, sex, and genetic ancestry. We assessed variants using SKAT-O in rare-variant analyses. Single-variant and gene-based tests were conducted in a pooled multi-ethnicity stratum, as well as self-reported ethnic/racial strata (European and African American). One variant in the known HTN locus, , was a top finding in the multi-ethnic analysis ( = 8.23E-07) for the normotensive control group [rs12476527, odds ratio (95\% confidence interval) = 0.80 (0.74-0.88)]. This variant was replicated in the Vanderbilt University Medical Center{\textquoteright}s DNA repository data. Aggregate gene-based signals included the genes and . Additional work validating these loci in larger, more diverse populations, is warranted to determine whether these regions influence the pathobiology of aTRH.

}, issn = {1664-8021}, doi = {10.3389/fgene.2023.1278215}, author = {Armstrong, Nicole D and Srinivasasainagendra, Vinodh and Ammous, Farah and Assimes, Themistocles L and Beitelshees, Amber L and Brody, Jennifer and Cade, Brian E and Ida Chen, Yii-Der and Chen, Han and de Vries, Paul S and Floyd, James S and Franceschini, Nora and Guo, Xiuqing and Hellwege, Jacklyn N and House, John S and Hwu, Chii-Min and Kardia, Sharon L R and Lange, Ethan M and Lange, Leslie A and McDonough, Caitrin W and Montasser, May E and O{\textquoteright}Connell, Jeffrey R and Shuey, Megan M and Sun, Xiao and Tanner, Rikki M and Wang, Zhe and Zhao, Wei and Carson, April P and Edwards, Todd L and Kelly, Tanika N and Kenny, Eimear E and Kooperberg, Charles and Loos, Ruth J F and Morrison, Alanna C and Motsinger-Reif, Alison and Psaty, Bruce M and Rao, Dabeeru C and Redline, Susan and Rich, Stephen S and Rotter, Jerome I and Smith, Jennifer A and Smith, Albert V and Irvin, Marguerite R and Arnett, Donna K} } @article {9376, title = {Whole-Genome Sequencing Analysis of Human Metabolome in Multi-Ethnic Populations.}, journal = {Nat Commun}, volume = {14}, year = {2023}, month = {2023 May 30}, pages = {3111}, abstract = {

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

}, keywords = {Ethnicity, Genome-Wide Association Study, Humans, Metabolome, Polymorphism, Single Nucleotide, Quantitative Trait Loci}, issn = {2041-1723}, doi = {10.1038/s41467-023-38800-2}, author = {Feofanova, Elena V and Brown, Michael R and Alkis, Taryn and Manuel, Astrid M and Li, Xihao and Tahir, Usman A and Li, Zilin and Mendez, Kevin M and Kelly, Rachel S and Qi, Qibin and Chen, Han and Larson, Martin G and Lemaitre, Rozenn N and Morrison, Alanna C and Grieser, Charles and Wong, Kari E and Gersztern, Robert E and Zhao, Zhongming and Lasky-Su, Jessica and Yu, Bing} } @article {9642, title = {A Large-Scale Genome-Wide Study of Gene-Sleep Duration Interactions for Blood Pressure in 811,405 Individuals from Diverse Populations.}, journal = {medRxiv}, year = {2024}, month = {2024 Mar 08}, abstract = {

Although both short and long sleep duration are associated with elevated hypertension risk, our understanding of their interplay with biological pathways governing blood pressure remains limited. To address this, we carried out genome-wide cross-population gene-by-short-sleep and long-sleep duration interaction analyses for three blood pressure traits (systolic, diastolic, and pulse pressure) in 811,405 individuals from diverse population groups. We discover 22 novel gene-sleep duration interaction loci for blood pressure, mapped to genes involved in neurological, thyroidal, bone metabolism, and hematopoietic pathways. Non-overlap between short sleep (12) and long sleep (10) interactions underscores the plausibility of distinct influences of both sleep duration extremes in cardiovascular health. With several of our loci reflecting specificity towards population background or sex, our discovery sheds light on the importance of embracing granularity when addressing heterogeneity entangled in gene-environment interactions, and in therapeutic design approaches for blood pressure management.

}, doi = {10.1101/2024.03.07.24303870}, author = {Nagarajan, Pavithra and Winkler, Thomas W and Bentley, Amy R and Miller, Clint L and Kraja, Aldi T and Schwander, Karen and Lee, Songmi and Wang, Wenyi and Brown, Michael R and Morrison, John L and Giri, Ayush and O{\textquoteright}Connell, Jeffrey R and Bartz, Traci M and de Las Fuentes, Lisa and Gudmundsdottir, Valborg and Guo, Xiuqing and Harris, Sarah E and Huang, Zhijie and Kals, Mart and Kho, Minjung and Lefevre, Christophe and Luan, Jian{\textquoteright}an and Lyytik{\"a}inen, Leo-Pekka and Mangino, Massimo and Milaneschi, Yuri and Palmer, Nicholette D and Rao, Varun and Rauramaa, Rainer and Shen, Botong and Stadler, Stefan and Sun, Quan and Tang, Jingxian and Th{\'e}riault, S{\'e}bastien and van der Graaf, Adriaan and van der Most, Peter J and Wang, Yujie and Weiss, Stefan and Westerman, Kenneth E and Yang, Qian and Yasuharu, Tabara and Zhao, Wei and Zhu, Wanying and Altschul, Drew and Ansari, Md Abu Yusuf and Anugu, Pramod and Argoty-Pantoja, Anna D and Arzt, Michael and Aschard, Hugues and Attia, John R and Bazzanno, Lydia and Breyer, Max A and Brody, Jennifer A and Cade, Brian E and Chen, Hung-Hsin and Ida Chen, Yii-Der and Chen, Zekai and de Vries, Paul S and Dimitrov, Latchezar M and Do, Anh and Du, Jiawen and Dupont, Charles T and Edwards, Todd L and Evans, Michele K and Faquih, Tariq and Felix, Stephan B and Fisher-Hoch, Susan P and Floyd, James S and Graff, Mariaelisa and Gu, Charles and Gu, Dongfeng and Hairston, Kristen G and Hanley, Anthony J and Heid, Iris M and Heikkinen, Sami and Highland, Heather M and Hood, Michelle M and K{\"a}h{\"o}nen, Mika and Karvonen-Gutierrez, Carrie A and Kawaguchi, Takahisa and Kazuya, Setoh and Kelly, Tanika N and Komulainen, Pirjo and Levy, Daniel and Lin, Henry J and Liu, Peter Y and Marques-Vidal, Pedro and McCormick, Joseph B and Mei, Hao and Meigs, James B and Menni, Cristina and Nam, Kisung and Nolte, Ilja M and Pacheco, Natasha L and Petty, Lauren E and Polikowsky, Hannah G and Province, Michael A and Psaty, Bruce M and Raffield, Laura M and Raitakari, Olli T and Rich, Stephen S and Riha, Renata L and Risch, Lorenz and Risch, Martin and Ruiz-Narvaez, Edward A and Scott, Rodney J and Sitlani, Colleen M and Smith, Jennifer A and Sofer, Tamar and Teder-Laving, Maris and V{\"o}lker, Uwe and Vollenweider, Peter and Wang, Guanchao and van Dijk, Ko Willems and Wilson, Otis D and Xia, Rui and Yao, Jie and Young, Kristin L and Zhang, Ruiyuan and Zhu, Xiaofeng and Below, Jennifer E and B{\"o}ger, Carsten A and Conen, David and Cox, Simon R and D{\"o}rr, Marcus and Feitosa, Mary F and Fox, Ervin R and Franceschini, Nora and Gharib, Sina A and Gudnason, Vilmundur and Harlow, Sioban D and He, Jiang and Holliday, Elizabeth G and Kutalik, Zolt{\'a}n and Lakka, Timo A and Lawlor, Deborah A and Lee, Seunggeun and Lehtim{\"a}ki, Terho and Li, Changwei and Liu, Ching-Ti and M{\"a}gi, Reedik and Matsuda, Fumihiko and Morrison, Alanna C and Penninx, Brenda Wjh and Peyser, Patricia A and Rotter, Jerome I and Snieder, Harold and Spector, Tim D and Wagenknecht, Lynne E and Wareham, Nicholas J and Zonderman, Alan B and North, Kari E and Fornage, Myriam and Hung, Adriana M and Manning, Alisa K and Gauderman, James and Chen, Han and Munroe, Patricia B and Rao, Dabeeru C and van Heemst, Diana and Redline, Susan and Noordam, Raymond and Wang, Heming} }