Crawford, Dana C.

Several loci have been discovered and replicated for urine albumin-creatinine ratio (ACR), a measure of kidney function, in European populations; however, other ethnic populations such as African-Americans have been less studied for this trait. We examined the association of genetic variants on the Metabochip with natural log transformed ACR (ln(ACR)) in 4,629 African-Americans from the Population Architecture using Genomics and Epidemiology (PAGE) Study which includes participants from the Atherosclerosis Risk in Communities (ARIC), Cardiovascular Health Study (CHS), Coronary Artery Risk Development in Young Adults (CARDIA), and Epidemiologic Architecture for Genes Linked to the Environment (EAGLE-BioVU) studies. We then replicated our findings in 1,533 African-American participants from the Multi-Ethnic Study of Atherosclerosis (MESA). Linear regression models were used to examine associations, assuming additive effects of the SNPs. Within each study, models were adjusted for age, field center (where applicable), and principal components of ancestry, and stratified by sex. Fixed effects inverse variance weighted meta-analysis was used to combine results from contributing studies. Array-wide statistical significance was concluded for SNP associations of p-value less than 2.8 x 10-7. Mean ln(ACR) ranged from 1.4 to 2.9 mg/g, average age ranged from 44 to 77 years, and percent female participants ranged from 59-65% in the contributing studies. Four SNPs were significantly associated array-wide with ACR in the discovery sample: rs17399841 in GRHL2 (β=-0.67, MAF=0.01, p=2.7x10-9), rs153914 near SPATA9/RHOBTB3 (β=-0.59, MAF=0.01, p=1.0x10-7), rs17439845 in LEF1 (β=-0.49, MAF=0.02, p=1.3x10-7), and rs11836243 near ZNF641/ANP32D (β=0.23, MAF=0.12, p=1.8x10-7). Of these four loci, only the association of rs17439845 with ACR replicated in MESA (β=-0.36, p=0.038), with a combined discovery-replication beta of -0.46 and p value of 1.8x10-8. Combined discovery-replication for rs17399841, rs153914, and rs11836243 no longer achieved array-wide significance (p-values 9.84x10-7, 1.98x10-5, and 1.32x10-4, respectively). LEF1 is a mediator in the Wnt signaling pathway, and has been previously implicated in systemic lupus erythematosus and osteoporosis, as well as previously associated with waist circumference, echocardiographic traits, bone mineral density, and blood count traits. Continuing studies in diverse populations are warranted to identify a set of loci important in contributing to the variation in ACR.

PR interval (PR) prolongation is a predictor of atrial fibrillation, a common cardiac arrhythmia, and is an established risk factor for pacemaker implantation, heart failure, stroke, and all-cause mortality. Previous genome-wide association studies in European (EU) and African (AA) descent populations have identified multiple loci associated with PR. However, it is unclear whether these loci are relevant in other racial groups, including Hispanic/Latinos (HL). Extending studies to include admixed populations such as HL also provides the potential to narrow regions associated with PR and identify population specific variants. Therefore, we evaluated 1,135 SNPs from four previously identified PR loci ( SCN5A, SCN10A, CAV1-CAV2, and TBX5-TBX3 ), fine-mapped on the Illumina Metabochip for association with PR. Associations were examined using linear regression assuming an additive genetic model and adjusting for global ancestry and clinical covariates (age, sex, RR interval, body mass index, height, systolic blood pressure) in 11,800 HL participants from participating studies of the Population Architecture using Genomics and Epidemiology (PAGE) consortia. Three of the four loci ( SCN5A, SCN10A, and CAV1-CAV2 ) generalized to HL populations ( P <7.1E-4 based on the number of SNPs correlated at r 2 >0.2 with EU-identified index SNPs). At each of these loci, we identified SNPs with stronger evidence of association with PR than the EU-identified index SNP when examined in the HL population ( SCN5A : rs7374540; SCN10A : rs6801957; CAV-CAV2 : rs717957). Conditional analyses also identified two additional independent SNPs at each of the three generalized loci ( SCN5A : rs3796387, rs9861242; SCN10A : rs10428132, rs7433723; CAV1-CAV2 : rs3801995, rs3807994), representing potential secondary signals. Furthermore, linkage disequilibrium (LD) patterns in Hispanics enabled the narrowing of the regions flanking the EU-identified index SNPs. For example, at the CAV1-CAV2 locus, HL LD patterns indicated that seven SNPs were in LD with rs3807989, the EU-identified index SNP, compared to 17 SNPs that were in LD with rs3807989 when applying EU LD patterns, reducing the size of the region by 71 kilobases. Finally, we examined evidence for population specific signals outside of the specific regions identified in EU populations which were associated with PR in Hispanics by focusing on SNPs uncorrelated with the EU index signals. At all four loci, multiple SNPs had evidence of association independent of the signals examined based on EU results. In conclusion, the same genetic loci that influence PR in European descent populations are also important in Hispanic populations, although we detected evidence for population specific SNPs. These results emphasize the importance of examining genetic associations in diverse populations in order to narrow the genomic interval for future functional interrogation.

Background Intracranial aneurysms ( IAs ), abdominal aortic aneurysms ( AAAs ), and thoracic aortic aneurysms ( TAAs ) all have a familial predisposition. Given that aneurysm types are known to co‐occur, we hypothesized that there may be shared genetic risk factors for IAs , AAAs, and TAAs . Methods and Results We performed a mega‐analysis of 1000 Genomes Project‐imputed genome‐wide association study ( GWAS ) data of 4 previously published aneurysm cohorts: 2 IA cohorts (in total 1516 cases, 4305 controls), 1 AAA cohort (818 cases, 3004 controls), and 1 TAA cohort (760 cases, 2212 controls), and observed associations of 4 known IA , AAA , and/or TAA risk loci (9p21, 18q11, 15q21, and 2q33) with consistent effect directions in all 4 cohorts. We calculated polygenic scores based on IA ‐, AAA ‐, and TAA ‐associated SNP s and tested these scores for association to case‐control status in the other aneurysm cohorts; this revealed no shared polygenic effects. Similarly, linkage disequilibrium–score regression analyses did not show significant correlations between any pair of aneurysm subtypes. Last, we evaluated the evidence for 14 previously published aneurysm risk single‐nucleotide polymorphisms through collaboration in extended aneurysm cohorts, with a total of 6548 cases and 16 843 controls ( IA ) and 4391 cases and 37 904 controls ( AAA ), and found nominally significant associations for IA risk locus 18q11 near RBBP 8 to AAA (odds ratio [ OR ]=1.11; P =4.1×10 −5 ) and for TAA risk locus 15q21 near FBN 1 to AAA ( OR =1.07; P =1.1×10 −3 ). Conclusions Although there was no evidence for polygenic overlap between IAs , AAAs , and TAAs , we found nominally significant effects of two established risk loci for IAs and TAAs in AAAs . These two loci will require further replication.

Genetic variation can affect drug response in multiple ways, although it remains unclear how rare genetic variants affect drug response. The electronic Medical Records and Genomics (eMERGE) Network, collaborating with the Pharmacogenomics Research Network, began eMERGE‐PGx, a targeted sequencing study to assess genetic variation in 82 pharmacogenes critical for implementation of “precision medicine.” The February 2015 eMERGE‐PGx data release includes sequence‐derived data from ∼5,000 clinical subjects. We present the variant frequency spectrum categorized by variant type, ancestry, and predicted function. We found 95.12% of genes have variants with a scaled Combined Annotation‐Dependent Depletion score above 20, and 96.19% of all samples had one or more Clinical Pharmacogenetics Implementation Consortium Level A actionable variants. These data highlight the distribution and scope of genetic variation in relevant pharmacogenes, identifying challenges associated with implementing clinical sequencing for drug treatment at a broader level, underscoring the importance for multifaceted research in the execution of precision medicine.

ABSTRACTBioinformatics approaches to examine gene‐gene models provide a means to discover interactions between multiple genes that underlie complex disease. Extensive computational demands and adjusting for multiple testing make uncovering genetic interactions a challenge. Here, we address these issues using our knowledge‐driven filtering method, Biofilter, to identify putative single nucleotide polymorphism (SNP) interaction models for cataract susceptibility, thereby reducing the number of models for analysis. Models were evaluated in 3,377 European Americans (1,185 controls, 2,192 cases) from the Marshfield Clinic, a study site of the Electronic Medical Records and Genomics (eMERGE) Network, using logistic regression. All statistically significant models from the Marshfield Clinic were then evaluated in an independent dataset of 4,311 individuals (742 controls, 3,569 cases), using independent samples from additional study sites in the eMERGE Network: Mayo Clinic, Group Health/University of Washington, Vanderbilt University Medical Center, and Geisinger Health System. Eighty‐three SNP‐SNP models replicated in the independent dataset at likelihood ratio test P < 0.05. Among the most significant replicating models was rs12597188 (intron of CDH1)–rs11564445 (intron of CTNNB1). These genes are known to be involved in processes that include: cell‐to‐cell adhesion signaling, cell‐cell junction organization, and cell‐cell communication. Further Biofilter analysis of all replicating models revealed a number of common functions among the genes harboring the 83 replicating SNP‐SNP models, which included signal transduction and PI3K‐Akt signaling pathway. These findings demonstrate the utility of Biofilter as a biology‐driven method, applicable for any genome‐wide association study dataset.

Objective Genome-wide association studies have identified a large number of single nucleotide polymorphisms (SNPs) associated with a wide array of cancer sites. Several of these variants demonstrate associations with multiple cancers, suggesting pleiotropic effects and shared biological mechanisms across some cancers. We hypothesised that SNPs previously associated with other cancers may additionally be associated with colorectal cancer. In a large-scale study, we examined 171 SNPs previously associated with 18 different cancers for their associations with colorectal cancer. Design We examined 13 338 colorectal cancer cases and 40 967 controls from three consortia: Population Architecture using Genomics and Epidemiology (PAGE), Genetic Epidemiology of Colorectal Cancer (GECCO), and the Colon Cancer Family Registry (CCFR). Study-specific logistic regression results, adjusted for age, sex, principal components of genetic ancestry, and/or study specific factors (as relevant) were combined using fixed-effect meta-analyses to evaluate the association between each SNP and colorectal cancer risk. A Bonferroni-corrected p value of 2.92×10 −4 was used to determine statistical significance of the associations. Results Two correlated SNPs—rs10090154 and rs4242382—in Region 1 of chromosome 8q24, a prostate cancer susceptibility region, demonstrated statistically significant associations with colorectal cancer risk. The most significant association was observed with rs4242382 (meta-analysis OR=1.12; 95% CI 1.07 to 1.18; p=1.74×10 −5 ), which also demonstrated similar associations across racial/ethnic populations and anatomical sub-sites. Conclusions This is the first study to clearly demonstrate Region 1 of chromosome 8q24 as a susceptibility locus for colorectal cancer; thus, adding colorectal cancer to the list of cancer sites linked to this particular multicancer risk region at 8q24.

Abstract To identify novel genetic loci influencing interindividual variation in red blood cell (RBC) traits in African-Americans, we conducted a genome-wide association study (GWAS) in 2315 individuals, divided into discovery (n = 1904) and replication (n = 411) cohorts. The traits included hemoglobin concentration (HGB), hematocrit (HCT), RBC count, mean corpuscular volume (MCV), mean corpuscular hemoglobin (MCH), and mean corpuscular hemoglobin concentration (MCHC). Patients were participants in the electronic MEdical Records and GEnomics (eMERGE) network and underwent genotyping of ~1.2 million single-nucleotide polymorphisms on the Illumina Human1M-Duo array. Association analyses were performed adjusting for age, sex, site, and population stratification. Three loci previously associated with resistance to malaria—HBB (11p15.4), HBA1/HBA2 (16p13.3), and G6PD (Xq28)—were associated (P ≤ 1 × 10−6) with RBC traits in the discovery cohort. The loci replicated in the replication cohort (P ≤ 0.02), and were significant at a genome-wide significance level (P &lt; 5 × 10−8) in the combined cohort. The proportions of variance in RBC traits explained by significant variants at these loci were as follows: rs7120391 (near HBB) 1.3% of MCHC, rs9924561 (near HBA1/A2) 5.5% of MCV, 6.9% of MCH and 2.9% of MCHC, and rs1050828 (in G6PD) 2.4% of RBC count, 2.9% of MCV, and 1.4% of MCH, respectively. We were not able to replicate loci identified by a previous GWAS of RBC traits in a European ancestry cohort of similar sample size, suggesting that the genetic architecture of RBC traits differs by race. In conclusion, genetic variants that confer resistance to malaria are associated with RBC traits in African-Americans.

AbstractGenome‐wide association studies (GWAS) are being conducted at an unprecedented rate in population‐based cohorts and have increased our understanding of the pathophysiology of complex disease. Regardless of context, the practical utility of this information will ultimately depend upon the quality of the original data. Quality control (QC) procedures for GWAS are computationally intensive, operationally challenging, and constantly evolving. Here we enumerate some of the challenges in QC of GWAS data and describe the approaches that the electronic MEdical Records and Genomics (eMERGE) network is using for quality assurance in GWAS data, thereby minimizing potential bias and error in GWAS results. We discuss common issues associated with QC of GWAS data, including data file formats, software packages for data manipulation and analysis, sex chromosome anomalies, sample identity, sample relatedness, population substructure, batch effects, and marker quality. We propose best practices and discuss areas of ongoing and future research. Curr. Protoc. Hum. Genet. 68:1.19.1‐1.19.18 © 2011 by John Wiley & Sons, Inc.