@article {6563, title = {Effects of long-term averaging of quantitative blood pressure traits on the detection of genetic associations.}, journal = {Am J Hum Genet}, volume = {95}, year = {2014}, month = {2014 Jul 03}, pages = {49-65}, abstract = {

Blood pressure (BP) is a heritable, quantitative trait with intraindividual variability and susceptibility to measurement error. Genetic studies of BP generally use single-visit measurements and thus cannot remove variability occurring over months or years. We leveraged the idea that averaging BP measured across time would improve phenotypic accuracy and thereby increase statistical power to detect genetic associations. We studied systolic BP (SBP), diastolic BP (DBP), mean arterial pressure (MAP), and pulse pressure (PP) averaged over multiple years in 46,629 individuals of European ancestry. We identified 39 trait-variant associations across 19 independent loci (p < 5 {\texttimes} 10(-8)); five associations (in four loci) uniquely identified by our LTA analyses included those of SBP and MAP at 2p23 (rs1275988, near KCNK3), DBP at 2q11.2 (rs7599598, in FER1L5), and PP at 6p21 (rs10948071, near CRIP3) and 7p13 (rs2949837, near IGFBP3). Replication analyses conducted in cohorts with single-visit BP data showed positive replication of associations and a nominal association (p < 0.05). We estimated a 20\% gain in statistical power with long-term average (LTA) as compared to single-visit BP association studies. Using LTA analysis, we identified genetic loci influencing BP. LTA might be one way of increasing the power of genetic associations for continuous traits in extant samples for other phenotypes that are measured serially over time.

}, keywords = {Blood Pressure, Genome-Wide Association Study, Humans, Longitudinal Studies, Phenotype, Polymorphism, Single Nucleotide, Quantitative Trait Loci}, issn = {1537-6605}, doi = {10.1016/j.ajhg.2014.06.002}, author = {Ganesh, Santhi K and Chasman, Daniel I and Larson, Martin G and Guo, Xiuqing and Verwoert, Germain and Bis, Joshua C and Gu, Xiangjun and Smith, Albert V and Yang, Min-Lee and Zhang, Yan and Ehret, Georg and Rose, Lynda M and Hwang, Shih-Jen and Papanicolau, George J and Sijbrands, Eric J and Rice, Kenneth and Eiriksdottir, Gudny and Pihur, Vasyl and Ridker, Paul M and Vasan, Ramachandran S and Newton-Cheh, Christopher and Raffel, Leslie J and Amin, Najaf and Rotter, Jerome I and Liu, Kiang and Launer, Lenore J and Xu, Ming and Caulfield, Mark and Morrison, Alanna C and Johnson, Andrew D and Vaidya, Dhananjay and Dehghan, Abbas and Li, Guo and Bouchard, Claude and Harris, Tamara B and Zhang, He and Boerwinkle, Eric and Siscovick, David S and Gao, Wei and Uitterlinden, Andr{\'e} G and Rivadeneira, Fernando and Hofman, Albert and Willer, Cristen J and Franco, Oscar H and Huo, Yong and Witteman, Jacqueline C M and Munroe, Patricia B and Gudnason, Vilmundur and Palmas, Walter and van Duijn, Cornelia and Fornage, Myriam and Levy, Daniel and Psaty, Bruce M and Chakravarti, Aravinda} } @article {7145, title = {An Empirical Comparison of Joint and Stratified Frameworks for Studying G {\texttimes} E Interactions: Systolic Blood Pressure and Smoking in the CHARGE Gene-Lifestyle Interactions Working Group.}, journal = {Genet Epidemiol}, volume = {40}, year = {2016}, month = {2016 Jul}, pages = {404-15}, abstract = {

Studying gene-environment (G {\texttimes} E) interactions is important, as they extend our knowledge of the genetic architecture of complex traits and may help to identify novel variants not detected via analysis of main effects alone. The main statistical framework for studying G {\texttimes} E interactions uses a single regression model that includes both the genetic main and G {\texttimes} E interaction effects (the "joint" framework). The alternative "stratified" framework combines results from genetic main-effect analyses carried out separately within the exposed and unexposed groups. Although there have been several investigations using theory and simulation, an empirical comparison of the two frameworks is lacking. Here, we compare the two frameworks using results from genome-wide association studies of systolic blood pressure for 3.2 million low frequency and 6.5 million common variants across 20 cohorts of European ancestry, comprising 79,731 individuals. Our cohorts have sample sizes ranging from 456 to 22,983 and include both family-based and population-based samples. In cohort-specific analyses, the two frameworks provided similar inference for population-based cohorts. The agreement was reduced for family-based cohorts. In meta-analyses, agreement between the two frameworks was less than that observed in cohort-specific analyses, despite the increased sample size. In meta-analyses, agreement depended on (1) the minor allele frequency, (2) inclusion of family-based cohorts in meta-analysis, and (3) filtering scheme. The stratified framework appears to approximate the joint framework well only for common variants in population-based cohorts. We conclude that the joint framework is the preferred approach and should be used to control false positives when dealing with low-frequency variants and/or family-based cohorts.

}, issn = {1098-2272}, doi = {10.1002/gepi.21978}, author = {Sung, Yun Ju and Winkler, Thomas W and Manning, Alisa K and Aschard, Hugues and Gudnason, Vilmundur and Harris, Tamara B and Smith, Albert V and Boerwinkle, Eric and Brown, Michael R and Morrison, Alanna C and Fornage, Myriam and Lin, Li-An and Richard, Melissa and Bartz, Traci M and Psaty, Bruce M and Hayward, Caroline and Polasek, Ozren and Marten, Jonathan and Rudan, Igor and Feitosa, Mary F and Kraja, Aldi T and Province, Michael A and Deng, Xuan and Fisher, Virginia A and Zhou, Yanhua and Bielak, Lawrence F and Smith, Jennifer and Huffman, Jennifer E and Padmanabhan, Sandosh and Smith, Blair H and Ding, Jingzhong and Liu, Yongmei and Lohman, Kurt and Bouchard, Claude and Rankinen, Tuomo and Rice, Treva K and Arnett, Donna and Schwander, Karen and Guo, Xiuqing and Palmas, Walter and Rotter, Jerome I and Alfred, Tamuno and Bottinger, Erwin P and Loos, Ruth J F and Amin, Najaf and Franco, Oscar H and van Duijn, Cornelia M and Vojinovic, Dina and Chasman, Daniel I and Ridker, Paul M and Rose, Lynda M and Kardia, Sharon and Zhu, Xiaofeng and Rice, Kenneth and Borecki, Ingrid B and Rao, Dabeeru C and Gauderman, W James and Cupples, L Adrienne} } @article {7809, title = {Exome-chip meta-analysis identifies novel loci associated with cardiac conduction, including ADAMTS6.}, journal = {Genome Biol}, volume = {19}, year = {2018}, month = {2018 07 17}, pages = {87}, abstract = {

BACKGROUND: Genome-wide association studies conducted on QRS duration, an electrocardiographic measurement associated with heart failure and sudden cardiac death, have led to novel biological insights into cardiac function. However, the variants identified fall predominantly in non-coding regions and their underlying mechanisms remain unclear.

RESULTS: Here, we identify putative functional coding variation associated with changes in the QRS interval duration by combining Illumina HumanExome BeadChip genotype data from 77,898 participants of European ancestry and 7695 of African descent in our discovery cohort, followed by replication in 111,874~individuals of European ancestry from the UK Biobank and deCODE cohorts. We identify ten novel loci, seven within coding regions, including ADAMTS6, significantly associated with QRS duration in gene-based analyses. ADAMTS6 encodes a secreted metalloprotease of currently unknown function. In vitro validation analysis shows that the QRS-associated variants lead to impaired ADAMTS6 secretion and loss-of function analysis in mice demonstrates a previously unappreciated role for ADAMTS6 in connexin 43 gap junction expression, which is essential for myocardial conduction.

CONCLUSIONS: Our approach identifies novel coding and non-coding variants underlying ventricular depolarization and provides a possible mechanism for the ADAMTS6-associated conduction changes.

}, issn = {1474-760X}, doi = {10.1186/s13059-018-1457-6}, author = {Prins, Bram P and Mead, Timothy J and Brody, Jennifer A and Sveinbjornsson, Gardar and Ntalla, Ioanna and Bihlmeyer, Nathan A and van den Berg, Marten and Bork-Jensen, Jette and Cappellani, Stefania and Van Duijvenboden, Stefan and Klena, Nikolai T and Gabriel, George C and Liu, Xiaoqin and Gulec, Cagri and Grarup, Niels and Haessler, Jeffrey and Hall, Leanne M and Iorio, Annamaria and Isaacs, Aaron and Li-Gao, Ruifang and Lin, Honghuang and Liu, Ching-Ti and Lyytik{\"a}inen, Leo-Pekka and Marten, Jonathan and Mei, Hao and M{\"u}ller-Nurasyid, Martina and Orini, Michele and Padmanabhan, Sandosh and Radmanesh, Farid and Ramirez, Julia and Robino, Antonietta and Schwartz, Molly and van Setten, Jessica and Smith, Albert V and Verweij, Niek and Warren, Helen R and Weiss, Stefan and Alonso, Alvaro and Arnar, David O and Bots, Michiel L and de Boer, Rudolf A and Dominiczak, Anna F and Eijgelsheim, Mark and Ellinor, Patrick T and Guo, Xiuqing and Felix, Stephan B and Harris, Tamara B and Hayward, Caroline and Heckbert, Susan R and Huang, Paul L and Jukema, J W and K{\"a}h{\"o}nen, Mika and Kors, Jan A and Lambiase, Pier D and Launer, Lenore J and Li, Man and Linneberg, Allan and Nelson, Christopher P and Pedersen, Oluf and Perez, Marco and Peters, Annette and Polasek, Ozren and Psaty, Bruce M and Raitakari, Olli T and Rice, Kenneth M and Rotter, Jerome I and Sinner, Moritz F and Soliman, Elsayed Z and Spector, Tim D and Strauch, Konstantin and Thorsteinsdottir, Unnur and Tinker, Andrew and Trompet, Stella and Uitterlinden, Andre and Vaartjes, Ilonca and van der Meer, Peter and V{\"o}lker, Uwe and V{\"o}lzke, Henry and Waldenberger, Melanie and Wilson, James G and Xie, Zhijun and Asselbergs, Folkert W and D{\"o}rr, Marcus and van Duijn, Cornelia M and Gasparini, Paolo and Gudbjartsson, Daniel F and Gudnason, Vilmundur and Hansen, Torben and K{\"a}{\"a}b, Stefan and Kanters, J{\o}rgen K and Kooperberg, Charles and Lehtim{\"a}ki, Terho and Lin, Henry J and Lubitz, Steven A and Mook-Kanamori, Dennis O and Conti, Francesco J and Newton-Cheh, Christopher H and Rosand, Jonathan and Rudan, Igor and Samani, Nilesh J and Sinagra, Gianfranco and Smith, Blair H and Holm, Hilma and Stricker, Bruno H and Ulivi, Sheila and Sotoodehnia, Nona and Apte, Suneel S and van der Harst, Pim and Stefansson, Kari and Munroe, Patricia B and Arking, Dan E and Lo, Cecilia W and Jamshidi, Yalda} } @article {7784, title = {ExomeChip-Wide Analysis of 95 626 Individuals Identifies 10 Novel Loci Associated With QT and JT Intervals.}, journal = {Circ Genom Precis Med}, volume = {11}, year = {2018}, month = {2018 Jan}, pages = {e001758}, abstract = {

BACKGROUND: QT interval, measured through a standard ECG, captures the time it takes for the cardiac ventricles to depolarize and repolarize. JT interval is the component of the QT interval that reflects ventricular repolarization alone. Prolonged QT interval has been linked to higher risk of sudden cardiac arrest.

METHODS AND RESULTS: We performed an ExomeChip-wide analysis for both QT and JT intervals, including 209 449 variants, both common and rare, in 17 341 genes from the Illumina Infinium HumanExome BeadChip. We identified 10 loci that modulate QT and JT interval duration that have not been previously reported in the literature using single-variant statistical models in a meta-analysis of 95 626 individuals from 23 cohorts (comprised 83 884 European ancestry individuals, 9610 blacks, 1382 Hispanics, and 750 Asians). This brings the total number of ventricular repolarization associated loci to 45. In addition, our approach of using coding variants has highlighted the role of 17 specific genes for involvement in ventricular repolarization, 7 of which are in novel loci.

CONCLUSIONS: Our analyses show a role for myocyte internal structure and interconnections in modulating QT interval duration, adding to previous known roles of potassium, sodium, and calcium ion regulation, as well as autonomic control. We anticipate that these discoveries will open new paths to the goal of making novel remedies for the prevention of lethal ventricular arrhythmias and sudden cardiac arrest.

}, issn = {2574-8300}, doi = {10.1161/CIRCGEN.117.001758}, author = {Bihlmeyer, Nathan A and Brody, Jennifer A and Smith, Albert Vernon and Warren, Helen R and Lin, Honghuang and Isaacs, Aaron and Liu, Ching-Ti and Marten, Jonathan and Radmanesh, Farid and Hall, Leanne M and Grarup, Niels and Mei, Hao and M{\"u}ller-Nurasyid, Martina and Huffman, Jennifer E and Verweij, Niek and Guo, Xiuqing and Yao, Jie and Li-Gao, Ruifang and van den Berg, Marten and Weiss, Stefan and Prins, Bram P and van Setten, Jessica and Haessler, Jeffrey and Lyytik{\"a}inen, Leo-Pekka and Li, Man and Alonso, Alvaro and Soliman, Elsayed Z and Bis, Joshua C and Austin, Tom and Chen, Yii-Der Ida and Psaty, Bruce M and Harrris, Tamara B and Launer, Lenore J and Padmanabhan, Sandosh and Dominiczak, Anna and Huang, Paul L and Xie, Zhijun and Ellinor, Patrick T and Kors, Jan A and Campbell, Archie and Murray, Alison D and Nelson, Christopher P and Tobin, Martin D and Bork-Jensen, Jette and Hansen, Torben and Pedersen, Oluf and Linneberg, Allan and Sinner, Moritz F and Peters, Annette and Waldenberger, Melanie and Meitinger, Thomas and Perz, Siegfried and Kolcic, Ivana and Rudan, Igor and de Boer, Rudolf A and van der Meer, Peter and Lin, Henry J and Taylor, Kent D and de Mutsert, Ren{\'e}e and Trompet, Stella and Jukema, J Wouter and Maan, Arie C and Stricker, Bruno H C and Rivadeneira, Fernando and Uitterlinden, Andre and V{\"o}lker, Uwe and Homuth, Georg and V{\"o}lzke, Henry and Felix, Stephan B and Mangino, Massimo and Spector, Timothy D and Bots, Michiel L and Perez, Marco and Raitakari, Olli T and K{\"a}h{\"o}nen, Mika and Mononen, Nina and Gudnason, Vilmundur and Munroe, Patricia B and Lubitz, Steven A and van Duijn, Cornelia M and Newton-Cheh, Christopher H and Hayward, Caroline and Rosand, Jonathan and Samani, Nilesh J and Kanters, J{\o}rgen K and Wilson, James G and K{\"a}{\"a}b, Stefan and Polasek, Ozren and van der Harst, Pim and Heckbert, Susan R and Rotter, Jerome I and Mook-Kanamori, Dennis O and Eijgelsheim, Mark and D{\"o}rr, Marcus and Jamshidi, Yalda and Asselbergs, Folkert W and Kooperberg, Charles and Lehtim{\"a}ki, Terho and Arking, Dan E and Sotoodehnia, Nona} } @article {9379, title = {Evaluating the use of blood pressure polygenic risk scores across race/ethnic background groups.}, journal = {Nat Commun}, volume = {14}, year = {2023}, month = {2023 Jun 02}, pages = {3202}, abstract = {

We assess performance and limitations of polygenic risk scores (PRSs) for multiple blood pressure (BP) phenotypes in diverse population groups. We compare "clumping-and-thresholding" (PRSice2) and LD-based (LDPred2) methods to construct PRSs from each of multiple GWAS, as well as multi-PRS approaches that sum PRSs with and without weights, including PRS-CSx. We use datasets from the MGB Biobank, TOPMed study, UK biobank, and from All of Us to train, assess, and validate PRSs in groups defined by self-reported race/ethnic background (Asian, Black, Hispanic/Latino, and White). For both SBP and DBP, the PRS-CSx based PRS, constructed as a weighted sum of PRSs developed from multiple independent GWAS, perform best across all race/ethnic backgrounds. Stratified analysis in All of Us shows that PRSs are better predictive of BP in females compared to males, individuals without obesity, and middle-aged (40-60 years) compared to older and younger individuals.

}, keywords = {Blood Pressure, Ethnicity, Female, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Male, Multifactorial Inheritance, Population Health, Risk Factors}, issn = {2041-1723}, doi = {10.1038/s41467-023-38990-9}, author = {Kurniansyah, Nuzulul and Goodman, Matthew O and Khan, Alyna T and Wang, Jiongming and Feofanova, Elena and Bis, Joshua C and Wiggins, Kerri L and Huffman, Jennifer E and Kelly, Tanika and Elfassy, Tali and Guo, Xiuqing and Palmas, Walter and Lin, Henry J and Hwang, Shih-Jen and Gao, Yan and Young, Kendra and Kinney, Gregory L and Smith, Jennifer A and Yu, Bing and Liu, Simin and Wassertheil-Smoller, Sylvia and Manson, JoAnn E and Zhu, Xiaofeng and Chen, Yii-Der Ida and Lee, I-Te and Gu, C Charles and Lloyd-Jones, Donald M and Z{\"o}llner, Sebastian and Fornage, Myriam and Kooperberg, Charles and Correa, Adolfo and Psaty, Bruce M and Arnett, Donna K and Isasi, Carmen R and Rich, Stephen S and Kaplan, Robert C and Redline, Susan and Mitchell, Braxton D and Franceschini, Nora and Levy, Daniel and Rotter, Jerome I and Morrison, Alanna C and Sofer, Tamar} } @article {9703, title = {Epigenetic and proteomic signatures associate with clonal hematopoiesis expansion rate.}, journal = {Nat Aging}, year = {2024}, month = {2024 Jun 04}, abstract = {

Clonal hematopoiesis of indeterminate potential (CHIP), whereby somatic mutations in hematopoietic stem cells confer a selective advantage and drive clonal expansion, not only correlates with age but also confers increased risk of morbidity and mortality. Here, we leverage genetically predicted traits to identify factors that determine CHIP clonal expansion rate. We used the passenger-approximated clonal expansion rate method to quantify the clonal expansion rate for 4,370 individuals in the National Heart, Lung, and Blood Institute (NHLBI) Trans-Omics for Precision Medicine (TOPMed) cohort and calculated polygenic risk scores for DNA methylation aging, inflammation-related measures and circulating protein levels. Clonal expansion rate was significantly associated with both genetically predicted and measured epigenetic clocks. No associations were identified with inflammation-related lab values or diseases and CHIP expansion rate overall. A proteome-wide search identified predicted circulating levels of myeloid zinc finger 1 and anti-M{\"u}llerian hormone as associated with an increased CHIP clonal expansion rate and tissue inhibitor of metalloproteinase 1 and glycine N-methyltransferase as associated with decreased CHIP clonal expansion rate. Together, our findings identify epigenetic and proteomic patterns associated with the rate of hematopoietic clonal expansion.

}, issn = {2662-8465}, doi = {10.1038/s43587-024-00647-7}, author = {Mack, Taralynn M and Raddatz, Michael A and Pershad, Yash and Nachun, Daniel C and Taylor, Kent D and Guo, Xiuqing and Shuldiner, Alan R and O{\textquoteright}Connell, Jeffrey R and Kenny, Eimear E and Loos, Ruth J F and Redline, Susan and Cade, Brian E and Psaty, Bruce M and Bis, Joshua C and Brody, Jennifer A and Silverman, Edwin K and Yun, Jeong H and Cho, Michael H and DeMeo, Dawn L and Levy, Daniel and Johnson, Andrew D and Mathias, Rasika A and Yanek, Lisa R and Heckbert, Susan R and Smith, Nicholas L and Wiggins, Kerri L and Raffield, Laura M and Carson, April P and Rotter, Jerome I and Rich, Stephen S and Manichaikul, Ani W and Gu, C Charles and Chen, Yii-Der Ida and Lee, Wen-Jane and Shoemaker, M Benjamin and Roden, Dan M and Kooperberg, Charles and Auer, Paul L and Desai, Pinkal and Blackwell, Thomas W and Smith, Albert V and Reiner, Alexander P and Jaiswal, Siddhartha and Weinstock, Joshua S and Bick, Alexander G} } @article {9851, title = {The expected polygenic risk score (ePRS) framework: an equitable metric for quantifying polygenetic risk via modeling of ancestral makeup.}, journal = {medRxiv}, year = {2024}, month = {2024 Dec 20}, abstract = {

Polygenic risk scores (PRSs) depend on genetic ancestry due to differences in allele frequencies between ancestral populations. This leads to implementation challenges in diverse populations. We propose a framework to calibrate PRS based on ancestral makeup. We define a metric called "expected PRS" (ePRS), the expected value of a PRS based on one{\textquoteright}s global or local admixture patterns. We further define the "residual PRS" (rPRS), measuring the deviation of the PRS from the ePRS. Simulation studies confirm that it suffices to adjust for ePRS to obtain nearly unbiased estimates of the PRS-outcome association without further adjusting for PCs. Using the TOPMed dataset, the estimated effect size of the rPRS adjusting for the ePRS is similar to the estimated effect of the PRS adjusting for genetic PCs. Similarly, we applied the ePRS framework to six cardiovascular-related traits in the All of Us dataset, and the results are consistent with those from the TOPMed analysis. The ePRS framework can protect from population stratification in association analysis and provide an equitable strategy to quantify genetic risk across diverse populations.

}, doi = {10.1101/2024.03.05.24303738}, author = {Huang, Yu-Jyun and Kurniansyah, Nuzulul and Goodman, Matthew O and Spitzer, Brian W and Wang, Jiongming and Stilp, Adrienne and Laurie, Cecelia and de Vries, Paul S and Chen, Han and Min, Yuan-I and Sims, Mario and Peloso, Gina M and Guo, Xiuqing and Bis, Joshua C and Brody, Jennifer A and Raffield, Laura M and Smith, Jennifer A and Zhao, Wei and Rotter, Jerome I and Rich, Stephen S and Redline, Susan and Fornage, Myriam and Kaplan, Robert and Franceschini, Nora and Levy, Daniel and Morrison, Alanna C and Boerwinkle, Eric and Smith, Nicholas L and Kooperberg, Charles and Psaty, Bruce M and Z{\"o}llner, Sebastian and Sofer, Tamar} }