@article {6875, title = {Drug-Gene Interactions of Antihypertensive Medications and Risk of Incident Cardiovascular Disease: A Pharmacogenomics Study from the CHARGE Consortium.}, journal = {PLoS One}, volume = {10}, year = {2015}, month = {2015}, pages = {e0140496}, abstract = {

BACKGROUND: Hypertension is a major risk factor for a spectrum of cardiovascular diseases (CVD), including myocardial infarction, sudden death, and stroke. In the US, over 65 million people have high blood pressure and a large proportion of these individuals are prescribed antihypertensive medications. Although large long-term clinical trials conducted in the last several decades have identified a number of effective antihypertensive treatments that reduce the risk of future clinical complications, responses to therapy and protection from cardiovascular events vary among individuals.

METHODS: Using a genome-wide association study among 21,267 participants with pharmaceutically treated hypertension, we explored the hypothesis that genetic variants might influence or modify the effectiveness of common antihypertensive therapies on the risk of major cardiovascular outcomes. The classes of drug treatments included angiotensin-converting enzyme inhibitors, beta-blockers, calcium channel blockers, and diuretics. In the setting of the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium, each study performed array-based genome-wide genotyping, imputed to HapMap Phase II reference panels, and used additive genetic models in proportional hazards or logistic regression models to evaluate drug-gene interactions for each of four therapeutic drug classes. We used meta-analysis to combine study-specific interaction estimates for approximately 2 million single nucleotide polymorphisms (SNPs) in a discovery analysis among 15,375 European Ancestry participants (3,527 CVD cases) with targeted follow-up in a case-only study of 1,751 European Ancestry GenHAT participants as well as among 4,141 African-Americans (1,267 CVD cases).

RESULTS: Although drug-SNP interactions were biologically plausible, exposures and outcomes were well measured, and power was sufficient to detect modest interactions, we did not identify any statistically significant interactions from the four antihypertensive therapy meta-analyses (Pinteraction > 5.0{\texttimes}10-8). Similarly, findings were null for meta-analyses restricted to 66 SNPs with significant main effects on coronary artery disease or blood pressure from large published genome-wide association studies (Pinteraction >= 0.01). Our results suggest that there are no major pharmacogenetic influences of common SNPs on the relationship between blood pressure medications and the risk of incident CVD.

}, keywords = {African Americans, Aged, Antihypertensive Agents, Cardiovascular Diseases, European Continental Ancestry Group, Female, Genome-Wide Association Study, Humans, Hypertension, Incidence, Male, Middle Aged, Polymorphism, Single Nucleotide, Treatment Outcome}, issn = {1932-6203}, doi = {10.1371/journal.pone.0140496}, author = {Bis, Joshua C and Sitlani, Colleen and Irvin, Ryan and Avery, Christy L and Smith, Albert Vernon and Sun, Fangui and Evans, Daniel S and Musani, Solomon K and Li, Xiaohui and Trompet, Stella and Krijthe, Bouwe P and Harris, Tamara B and Quibrera, P Miguel and Brody, Jennifer A and Demissie, Serkalem and Davis, Barry R and Wiggins, Kerri L and Tranah, Gregory J and Lange, Leslie A and Sotoodehnia, Nona and Stott, David J and Franco, Oscar H and Launer, Lenore J and St{\"u}rmer, Til and Taylor, Kent D and Cupples, L Adrienne and Eckfeldt, John H and Smith, Nicholas L and Liu, Yongmei and Wilson, James G and Heckbert, Susan R and Buckley, Brendan M and Ikram, M Arfan and Boerwinkle, Eric and Chen, Yii-Der Ida and de Craen, Anton J M and Uitterlinden, Andr{\'e} G and Rotter, Jerome I and Ford, Ian and Hofman, Albert and Sattar, Naveed and Slagboom, P Eline and Westendorp, Rudi G J and Gudnason, Vilmundur and Vasan, Ramachandran S and Lumley, Thomas and Cummings, Steven R and Taylor, Herman A and Post, Wendy and Jukema, J Wouter and Stricker, Bruno H and Whitsel, Eric A and Psaty, Bruce M and Arnett, Donna} } @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 {9588, title = {Determinants of mosaic chromosomal alteration fitness.}, journal = {medRxiv}, year = {2023}, month = {2023 Oct 21}, abstract = {

Clonal hematopoiesis (CH) is characterized by the acquisition of a somatic mutation in a hematopoietic stem cell that results in a clonal expansion. These driver mutations can be single nucleotide variants in cancer driver genes or larger structural rearrangements called mosaic chromosomal alterations (mCAs). The factors that influence the variations in mCA fitness and ultimately result in different clonal expansion rates are not well-understood. We used the Passenger-Approximated Clonal Expansion Rate (PACER) method to estimate clonal expansion rate for 6,381 individuals in the NHLBI TOPMed cohort with gain, loss, and copy-neutral loss of heterozygosity mCAs. Our estimates of mCA fitness were correlated (R = 0.49) with an alternative approach that estimated fitness of mCAs in the UK Biobank using a theoretical probability distribution. Individuals with lymphoid-associated mCAs had a significantly higher white blood cell count and faster clonal expansion rate. In a cross-sectional analysis, genome-wide association study of estimates of mCA expansion rate identified , , and locus variants as modulators of mCA clonal expansion rate.

}, doi = {10.1101/2023.10.20.23297280}, author = {Pershad, Yash and Mack, Taralynn and Poisner, Hannah and Jakubek, Yasminka A and Stilp, Adrienne M and Mitchell, Braxton D and Lewis, Joshua P and Boerwinkle, Eric and Loos, Ruth J and Chami, Nathalie and Wang, Zhe and Barnes, Kathleen and Pankratz, Nathan and Fornage, Myriam and Redline, Susan and Psaty, Bruce M and Bis, Joshua C and Shojaie, Ali and Silverman, Edwin K and Cho, Michael H and Yun, Jeong and DeMeo, Dawn and Levy, Daniel and Johnson, Andrew and Mathias, Rasika and Taub, Margaret and Arnett, Donna and North, Kari and Raffield, Laura M and Carson, April and Doyle, Margaret F and Rich, Stephen S and Rotter, Jerome I and Guo, Xiuqing and Cox, Nancy and Roden, Dan M and Franceschini, Nora and Desai, Pinkal and Reiner, Alex and Auer, Paul L and Scheet, Paul and Jaiswal, Siddhartha and Weinstock, Joshua S and Bick, Alexander G} } @article {9538, title = {Mosaic chromosomal alterations in blood across ancestries using whole-genome sequencing.}, journal = {Nat Genet}, volume = {55}, year = {2023}, month = {2023 Nov}, pages = {1912-1919}, abstract = {

Megabase-scale mosaic chromosomal alterations (mCAs) in blood are prognostic markers for a host of human diseases. Here, to gain a better understanding of mCA rates in genetically diverse populations, we analyzed whole-genome sequencing data from 67,390 individuals from the National Heart, Lung, and Blood Institute Trans-Omics for Precision Medicine program. We observed higher sensitivity with whole-genome sequencing data, compared with array-based data, in uncovering mCAs at low mutant cell fractions and found that individuals of European ancestry have the highest rates of autosomal mCAs and the lowest rates of chromosome X mCAs, compared with individuals of African or Hispanic ancestry. Although further studies in diverse populations will be needed to replicate our findings, we report three loci associated with loss of chromosome X, associations between autosomal mCAs and rare variants in DCPS, ADM17, PPP1R16B and TET2 and ancestry-specific variants in ATM and MPL with mCAs in cis.

}, keywords = {Black People, Genome, Human, Genome-Wide Association Study, Hispanic or Latino, Humans, Mosaicism, Precision Medicine}, issn = {1546-1718}, doi = {10.1038/s41588-023-01553-1}, author = {Jakubek, Yasminka A and Zhou, Ying and Stilp, Adrienne and Bacon, Jason and Wong, Justin W and Ozcan, Zuhal and Arnett, Donna and Barnes, Kathleen and Bis, Joshua C and Boerwinkle, Eric and Brody, Jennifer A and Carson, April P and Chasman, Daniel I and Chen, Jiawen and Cho, Michael and Conomos, Matthew P and Cox, Nancy and Doyle, Margaret F and Fornage, Myriam and Guo, Xiuqing and Kardia, Sharon L R and Lewis, Joshua P and Loos, Ruth J F and Ma, Xiaolong and Machiela, Mitchell J and Mack, Taralynn M and Mathias, Rasika A and Mitchell, Braxton D and Mychaleckyj, Josyf C and North, Kari and Pankratz, Nathan and Peyser, Patricia A and Preuss, Michael H and Psaty, Bruce and Raffield, Laura M and Vasan, Ramachandran S and Redline, Susan and Rich, Stephen S and Rotter, Jerome I and Silverman, Edwin K and Smith, Jennifer A and Smith, Aaron P and Taub, Margaret and Taylor, Kent D and Yun, Jeong and Li, Yun and Desai, Pinkal and Bick, Alexander G and Reiner, Alexander P and Scheet, Paul and Auer, Paul L} }