@article {6591, title = {Pharmacogenetic meta-analysis of genome-wide association studies of LDL cholesterol response to statins.}, journal = {Nat Commun}, volume = {5}, year = {2014}, month = {2014 Oct 28}, pages = {5068}, abstract = {

Statins effectively lower LDL cholesterol levels in large studies and the observed interindividual response variability may be partially explained by genetic variation. Here we perform a pharmacogenetic meta-analysis of genome-wide association studies (GWAS) in studies addressing the LDL cholesterol response to statins, including up to 18,596 statin-treated subjects. We validate the most promising signals in a further 22,318 statin recipients and identify two loci, SORT1/CELSR2/PSRC1 and SLCO1B1, not previously identified in GWAS. Moreover, we confirm the previously described associations with APOE and LPA. Our findings advance the understanding of the pharmacogenetic architecture of statin response.

}, keywords = {Cholesterol, LDL, Genome-Wide Association Study, Humans, Hydroxymethylglutaryl-CoA Reductase Inhibitors, Pharmacogenetics, Polymorphism, Single Nucleotide}, issn = {2041-1723}, doi = {10.1038/ncomms6068}, author = {Postmus, Iris and Trompet, Stella and Deshmukh, Harshal A and Barnes, Michael R and Li, Xiaohui and Warren, Helen R and Chasman, Daniel I and Zhou, Kaixin and Arsenault, Benoit J and Donnelly, Louise A and Wiggins, Kerri L and Avery, Christy L and Griffin, Paula and Feng, QiPing and Taylor, Kent D and Li, Guo and Evans, Daniel S and Smith, Albert V and de Keyser, Catherine E and Johnson, Andrew D and de Craen, Anton J M and Stott, David J and Buckley, Brendan M and Ford, Ian and Westendorp, Rudi G J and Slagboom, P Eline and Sattar, Naveed and Munroe, Patricia B and Sever, Peter and Poulter, Neil and Stanton, Alice and Shields, Denis C and O{\textquoteright}Brien, Eoin and Shaw-Hawkins, Sue and Chen, Y-D Ida and Nickerson, Deborah A and Smith, Joshua D and Dub{\'e}, Marie Pierre and Boekholdt, S Matthijs and Hovingh, G Kees and Kastelein, John J P and McKeigue, Paul M and Betteridge, John and Neil, Andrew and Durrington, Paul N and Doney, Alex and Carr, Fiona and Morris, Andrew and McCarthy, Mark I and Groop, Leif and Ahlqvist, Emma and Bis, Joshua C and Rice, Kenneth and Smith, Nicholas L and Lumley, Thomas and Whitsel, Eric A and St{\"u}rmer, Til and Boerwinkle, Eric and Ngwa, Julius S and O{\textquoteright}Donnell, Christopher J and Vasan, Ramachandran S and Wei, Wei-Qi and Wilke, Russell A and Liu, Ching-Ti and Sun, Fangui and Guo, Xiuqing and Heckbert, Susan R and Post, Wendy and Sotoodehnia, Nona and Arnold, Alice M and Stafford, Jeanette M and Ding, Jingzhong and Herrington, David M and Kritchevsky, Stephen B and Eiriksdottir, Gudny and Launer, Leonore J and Harris, Tamara B and Chu, Audrey Y and Giulianini, Franco and MacFadyen, Jean G and Barratt, Bryan J and Nyberg, Fredrik and Stricker, Bruno H and Uitterlinden, Andr{\'e} G and Hofman, Albert and Rivadeneira, Fernando and Emilsson, Valur and Franco, Oscar H and Ridker, Paul M and Gudnason, Vilmundur and Liu, Yongmei and Denny, Joshua C and Ballantyne, Christie M and Rotter, Jerome I and Adrienne Cupples, L and Psaty, Bruce M and Palmer, Colin N A and Tardif, Jean-Claude and Colhoun, Helen M and Hitman, Graham and Krauss, Ronald M and Wouter Jukema, J and Caulfield, Mark J} } @article {6220, title = {Predicting stroke through genetic risk functions: the CHARGE Risk Score Project.}, journal = {Stroke}, volume = {45}, year = {2014}, month = {2014 Feb}, pages = {403-12}, abstract = {

BACKGROUND AND PURPOSE: Beyond the Framingham Stroke Risk Score, prediction of future stroke may improve with a genetic risk score (GRS) based on single-nucleotide polymorphisms associated with stroke and its risk factors.

METHODS: The study includes 4 population-based cohorts with 2047 first incident strokes from 22,720 initially stroke-free European origin participants aged >=55 years, who were followed for up to 20 years. GRSs were constructed with 324 single-nucleotide polymorphisms implicated in stroke and 9 risk factors. The association of the GRS to first incident stroke was tested using Cox regression; the GRS predictive properties were assessed with area under the curve statistics comparing the GRS with age and sex, Framingham Stroke Risk Score models, and reclassification statistics. These analyses were performed per cohort and in a meta-analysis of pooled data. Replication was sought in a case-control study of ischemic stroke.

RESULTS: In the meta-analysis, adding the GRS to the Framingham Stroke Risk Score, age and sex model resulted in a significant improvement in discrimination (all stroke: Δjoint area under the curve=0.016, P=2.3{\texttimes}10(-6); ischemic stroke: Δjoint area under the curve=0.021, P=3.7{\texttimes}10(-7)), although the overall area under the curve remained low. In all the studies, there was a highly significantly improved net reclassification index (P<10(-4)).

CONCLUSIONS: The single-nucleotide polymorphisms associated with stroke and its risk factors result only in a small improvement in prediction of future stroke compared with the classical epidemiological risk factors for stroke.

}, keywords = {Age Factors, Aged, Aged, 80 and over, Area Under Curve, Case-Control Studies, Cohort Studies, European Continental Ancestry Group, Female, Genetic Predisposition to Disease, Genome-Wide Association Study, Genotype, Humans, Male, Middle Aged, Polymorphism, Single Nucleotide, Regression Analysis, Risk Factors, ROC Curve, Sex Factors, Stroke}, issn = {1524-4628}, doi = {10.1161/STROKEAHA.113.003044}, author = {Ibrahim-Verbaas, Carla A and Fornage, Myriam and Bis, Joshua C and Choi, Seung Hoan and Psaty, Bruce M and Meigs, James B and Rao, Madhu and Nalls, Mike and Fontes, Jo{\~a}o D and O{\textquoteright}Donnell, Christopher J and Kathiresan, Sekar and Ehret, Georg B and Fox, Caroline S and Malik, Rainer and Dichgans, Martin and Schmidt, Helena and Lahti, Jari and Heckbert, Susan R and Lumley, Thomas and Rice, Kenneth and Rotter, Jerome I and Taylor, Kent D and Folsom, Aaron R and Boerwinkle, Eric and Rosamond, Wayne D and Shahar, Eyal and Gottesman, Rebecca F and Koudstaal, Peter J and Amin, Najaf and Wieberdink, Renske G and Dehghan, Abbas and Hofman, Albert and Uitterlinden, Andr{\'e} G and DeStefano, Anita L and Debette, Stephanie and Xue, Luting and Beiser, Alexa and Wolf, Philip A and DeCarli, Charles and Ikram, M Arfan and Seshadri, Sudha and Mosley, Thomas H and Longstreth, W T and van Duijn, Cornelia M and Launer, Lenore J} } @article {8102, title = {Pharmacogenomics of statin-related myopathy: Meta-analysis of rare variants from whole-exome sequencing.}, journal = {PLoS One}, volume = {14}, year = {2019}, month = {2019}, pages = {e0218115}, abstract = {

AIMS: Statin-related myopathy (SRM), which includes rhabdomyolysis, is an uncommon but important adverse drug reaction because the number of people prescribed statins world-wide is large. Previous association studies of common genetic variants have had limited success in identifying a genetic basis for this adverse drug reaction. We conducted a multi-site whole-exome sequencing study to investigate whether rare coding variants confer an increased risk of SRM.

METHODS AND RESULTS: SRM 3-5 cases (N = 505) and statin treatment-tolerant controls (N = 2047) were recruited from multiple sites in North America and Europe. SRM 3-5 was defined as symptoms consistent with muscle injury and an elevated creatine phosphokinase level >4 times upper limit of normal without another likely cause of muscle injury. Whole-exome sequencing and variant calling was coordinated from two analysis centres, and results of single-variant and gene-based burden tests were meta-analysed. No genome-wide significant associations were identified. Given the large number of cases, we had 80\% power to identify a variant with minor allele frequency of 0.01 that increases the risk of SRM 6-fold at genome-wide significance.

CONCLUSIONS: In this large whole-exome sequencing study of severe statin-related muscle injury conducted to date, we did not find evidence that rare coding variants are responsible for this adverse drug reaction. Larger sample sizes would be required to identify rare variants with small effects, but it is unclear whether such findings would be clinically actionable.

}, issn = {1932-6203}, doi = {10.1371/journal.pone.0218115}, author = {Floyd, James S and Bloch, Katarzyna M and Brody, Jennifer A and Maroteau, Cyrielle and Siddiqui, Moneeza K and Gregory, Richard and Carr, Daniel F and Molokhia, Mariam and Liu, Xiaoming and Bis, Joshua C and Ahmed, Ammar and Liu, Xuan and Hallberg, P{\"a}r and Yue, Qun-Ying and Magnusson, Patrik K E and Brisson, Diane and Wiggins, Kerri L and Morrison, Alanna C and Khoury, Etienne and McKeigue, Paul and Stricker, Bruno H and Lapeyre-Mestre, Maryse and Heckbert, Susan R and Gallagher, Arlene M and Chinoy, Hector and Gibbs, Richard A and Bondon-Guitton, Emmanuelle and Tracy, Russell and Boerwinkle, Eric and Gaudet, Daniel and Conforti, Anita and van Staa, Tjeerd and Sitlani, Colleen M and Rice, Kenneth M and Maitland-van der Zee, Anke-Hilse and Wadelius, Mia and Morris, Andrew P and Pirmohamed, Munir and Palmer, Colin A N and Psaty, Bruce M and Alfirevic, Ana} } @article {9086, title = {Proteomics and Population Biology in the Cardiovascular Health Study (CHS): design of a study with mentored access and active data sharing.}, journal = {Eur J Epidemiol}, year = {2022}, month = {2022 Jul 05}, abstract = {

BACKGROUND: In the last decade, genomic studies have identified and replicated thousands of genetic associations with measures of health and disease and contributed to the understanding of the etiology of a variety of health conditions. Proteins are key biomarkers in clinical medicine and often drug-therapy targets. Like genomics, proteomics can advance our understanding of biology.

METHODS AND RESULTS: In the setting of the Cardiovascular Health Study (CHS), a cohort study of older adults, an aptamer-based method that has high sensitivity for low-abundance proteins was used to assay 4979 proteins in frozen, stored plasma from 3188 participants (61\% women, mean age 74~years). CHS provides active support, including central analysis, for seven phenotype-specific working groups (WGs). Each CHS WG is led by one or two senior investigators and includes 10 to 20 early or mid-career scientists. In this setting of mentored access, the proteomic data and analytic methods are widely shared with the WGs and investigators so that they may evaluate associations between baseline levels of circulating proteins and the incidence of a variety of health outcomes in prospective cohort analyses. We describe the design of CHS, the CHS Proteomics Study, characteristics of participants, quality control measures, and structural characteristics of the data provided to CHS WGs. We additionally highlight plans for validation and replication of novel proteomic associations.

CONCLUSION: The CHS Proteomics Study offers an opportunity for collaborative data sharing to improve our understanding of the etiology of a variety of health conditions in older adults.

}, issn = {1573-7284}, doi = {10.1007/s10654-022-00888-z}, author = {Austin, Thomas R and McHugh, Caitlin P and Brody, Jennifer A and Bis, Joshua C and Sitlani, Colleen M and Bartz, Traci M and Biggs, Mary L and Bansal, Nisha and B{\r u}zkov{\'a}, Petra and Carr, Steven A and deFilippi, Christopher R and Elkind, Mitchell S V and Fink, Howard A and Floyd, James S and Fohner, Alison E and Gerszten, Robert E and Heckbert, Susan R and Katz, Daniel H and Kizer, Jorge R and Lemaitre, Rozenn N and Longstreth, W T and McKnight, Barbara and Mei, Hao and Mukamal, Kenneth J and Newman, Anne B and Ngo, Debby and Odden, Michelle C and Vasan, Ramachandran S and Shojaie, Ali and Simon, Noah and Smith, George Davey and Davies, Neil M and Siscovick, David S and Sotoodehnia, Nona and Tracy, Russell P and Wiggins, Kerri L and Zheng, Jie and Psaty, Bruce M} } @article {9331, title = {Plasma Proteomic Associations With Incident Ischemic Stroke in Older Adults: The Cardiovascular Health Study.}, journal = {Neurology}, year = {2023}, month = {2023 Apr 04}, abstract = {

BACKGROUND: Plasma proteomics may elucidate novel insights into the pathophysiology of ischemic stroke (IS), identify biomarkers of IS risk, and guide development of nascent prevention strategies. We evaluated the relationship between the plasma proteome and IS risk in the population-based Cardiovascular Health Study (CHS).

METHODS: Eligible CHS participants were free of prevalent stroke and underwent quantification of 1298 plasma proteins using the aptamer-based SOMAScan assay platform from the 1992-1993 study visit. Multivariable Cox proportional hazards regression was used to evaluate associations between a 1-standard deviation increase in the log-2 transformed estimated plasma protein concentrations and incident IS, adjusting for demographics, IS risk factors, and estimated glomerular filtration rate. For proteins independently associated with incident IS, a secondary stratified analysis evaluated associations in subgroups defined by sex and race. Exploratory analyses evaluated plasma proteomic associations with cardioembolic and non-cardioembolic IS as well as proteins associated with IS risk in participants with left atrial dysfunction but without atrial fibrillation.

RESULTS: Of 2983 eligible participants, the mean age was 74.3 ({\textpm} 4.8) years, 61.2\% were women, and 15.4\% were Black. Over a median follow-up of 12.6 years, 450 participants experienced an incident IS. N-terminal pro-brain natriuretic peptide (NTproBNP, adjusted HR 1.37, 95\% CI 1.23-1.53, P=2.08x10) and macrophage metalloelastase (MMP12, adjusted HR 1.30, 95\% CI 1.16-1.45, P=4.55x10) were independently associated with IS risk. These two associations were similar in men and women and in Black and non-Black participants. In exploratory analyses, NTproBNP was independently associated with incident cardioembolic IS, E-selectin with incident non-cardioembolic IS, and secreted frizzled-related protein 1 with IS risk in participants with left atrial dysfunction.

CONCLUSIONS: In a cohort of older adults, NTproBNP and MMP12 were independently associated with IS risk. We identified plasma proteomic determinants of incident cardioembolic and non-cardioembolic IS and found a novel protein associated with IS risk in those with left atrial dysfunction.

}, issn = {1526-632X}, doi = {10.1212/WNL.0000000000207242}, author = {Kalani, Rizwan and Bartz, Traci M and Psaty, Bruce M and Elkind, Mitchell S V and Floyd, James S and Gerszten, Robert E and Shojaie, Ali and Heckbert, Susan R and Bis, Joshua C and Austin, Thomas R and Tirschwell, David L and Delaney, Joseph A C and Longstreth, W T} } @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} }