Yurii S. Aulchenko

GenABEL: an R library for genome-wide association analysis

UNLABELLED Here we describe an R library for genome-wide association (GWA) analysis. It implements effective storage and handling of GWA data, fast procedures for genetic data quality control, testing of association of single nucleotide polymorphisms with binary or quantitative traits, visualization of results and also provides easy interfaces to standard statistical and graphical procedures implemented in base R and special R libraries for genetic analysis. We evaluated GenABEL using one simulated and two real data sets. We conclude that GenABEL enables the analysis of GWA data on desktop computers. AVAILABILITY http://cran.r-project.org.

Genome-wide association study of blood pressure and hypertension

Blood pressure is a major cardiovascular disease risk factor. To date, few variants associated with interindividual blood pressure variation have been identified and replicated. Here we report results of a genome-wide association study of systolic (SBP) and diastolic (DBP) blood pressure and hypertension in the CHARGE Consortium (n = 29,136), identifying 13 SNPs for SBP, 20 for DBP and 10 for hypertension at P < 4 × 10−7. The top ten loci for SBP and DBP were incorporated into a risk score; mean BP and prevalence of hypertension increased in relation to the number of risk alleles carried. When ten CHARGE SNPs for each trait were included in a joint meta-analysis with the Global BPgen Consortium (n = 34,433), four CHARGE loci attained genome-wide significance (P < 5 × 10−8) for SBP (ATP2B1, CYP17A1, PLEKHA7, SH2B3), six for DBP (ATP2B1, CACNB2, CSK-ULK3, SH2B3, TBX3-TBX5, ULK4) and one for hypertension (ATP2B1). Identifying genes associated with blood pressure advances our understanding of blood pressure regulation and highlights potential drug targets for the prevention or treatment of hypertension.

Genome-wide Analysis of Genetic Loci Associated With Alzheimer Disease

CONTEXT Genome-wide association studies (GWAS) have recently identified CLU, PICALM, and CR1 as novel genes for late-onset Alzheimer disease (AD). OBJECTIVES To identify and strengthen additional loci associated with AD and confirm these in an independent sample and to examine the contribution of recently identified genes to AD risk prediction in a 3-stage analysis of new and previously published GWAS on more than 35,000 persons (8371 AD cases). DESIGN, SETTING, AND PARTICIPANTS In stage 1, we identified strong genetic associations (P < 10(-3)) in a sample of 3006 AD cases and 14,642 controls by combining new data from the population-based Cohorts for Heart and Aging Research in Genomic Epidemiology consortium (1367 AD cases [973 incident]) with previously reported results from the Translational Genomics Research Institute and the Mayo AD GWAS. We identified 2708 single-nucleotide polymorphisms (SNPs) with P < 10(-3). In stage 2, we pooled results for these SNPs with the European AD Initiative (2032 cases and 5328 controls) to identify 38 SNPs (10 loci) with P < 10(-5). In stage 3, we combined data for these 10 loci with data from the Genetic and Environmental Risk in AD consortium (3333 cases and 6995 controls) to identify 4 SNPs with P < 1.7x10(-8). These 4 SNPs were replicated in an independent Spanish sample (1140 AD cases and 1209 controls). Genome-wide association analyses were completed in 2007-2008 and the meta-analyses and replication in 2009. MAIN OUTCOME MEASURE Presence of Alzheimer disease. RESULTS Two loci were identified to have genome-wide significance for the first time: rs744373 near BIN1 (odds ratio [OR],1.13; 95% confidence interval [CI],1.06-1.21 per copy of the minor allele; P = 1.59x10(-11)) and rs597668 near EXOC3L2/BLOC1S3/MARK4 (OR, 1.18; 95% CI, 1.07-1.29; P = 6.45x10(-9)). Associations of these 2 loci plus the previously identified loci CLU and PICALM with AD were confirmed in the Spanish sample (P < .05). However, although CLU and PICALM were confirmed to be associated with AD in this independent sample, they did not improve the ability of a model that included age, sex, and APOE to predict incident AD (improvement in area under the receiver operating characteristic curve from 0.847 to 0.849 in the Rotterdam Study and 0.702 to 0.705 in the Cardiovascular Health Study). CONCLUSIONS Two genetic loci for AD were found for the first time to reach genome-wide statistical significance. These findings were replicated in an independent population. Two recently reported associations were also confirmed. These loci did not improve AD risk prediction. While not clinically useful, they may implicate biological pathways useful for future research.

Loci influencing lipid levels and coronary heart disease risk in 16 European population cohorts

Recent genome-wide association (GWA) studies of lipids have been conducted in samples ascertained for other phenotypes, particularly diabetes. Here we report the first GWA analysis of loci affecting total cholesterol (TC), low-density lipoprotein (LDL) cholesterol, high-density lipoprotein (HDL) cholesterol and triglycerides sampled randomly from 16 population-based cohorts and genotyped using mainly the Illumina HumanHap300-Duo platform. Our study included a total of 17,797–22,562 persons, aged 18–104 years and from geographic regions spanning from the Nordic countries to Southern Europe. We established 22 loci associated with serum lipid levels at a genome-wide significance level (P < 5 × 10−8), including 16 loci that were identified by previous GWA studies. The six newly identified loci in our cohort samples are ABCG5 (TC, P = 1.5 × 10−11; LDL, P = 2.6 × 10−10), TMEM57 (TC, P = 5.4 × 10−10), CTCF-PRMT8 region (HDL, P = 8.3 × 10−16), DNAH11 (LDL, P = 6.1 × 10−9), FADS3-FADS2 (TC, P = 1.5 × 10−10; LDL, P = 4.4 × 10−13) and MADD-FOLH1 region (HDL, P = 6 × 10−11). For three loci, effect sizes differed significantly by sex. Genetic risk scores based on lipid loci explain up to 4.8% of variation in lipids and were also associated with increased intima media thickness (P = 0.001) and coronary heart disease incidence (P = 0.04). The genetic risk score improves the screening of high-risk groups of dyslipidemia over classical risk factors.

Twenty bone-mineral-density loci identified by large-scale meta-analysis of genome-wide association studies

Bone mineral density (BMD) is a heritable complex trait used in the clinical diagnosis of osteoporosis and the assessment of fracture risk. We performed meta-analysis of five genome-wide association studies of femoral neck and lumbar spine BMD in 19,195 subjects of Northern European descent. We identified 20 BMD loci that reached genome-wide significance (GWS; P < 5 × 10−8), of which 13 map to regions not previously associated with this trait: 1p31.3 (GPR177), 2p21 (SPTBN1), 3p22 (CTNNB1), 4q21.1 (MEPE), 5q14 (MEF2C), 7p14 (STARD3NL), 7q21.3 (FLJ42280), 11p11.2 (LRP4, ARHGAP1, F2), 11p14.1 (DCDC5), 11p15 (SOX6), 16q24 (FOXL1), 17q21 (HDAC5) and 17q12 (CRHR1). The meta-analysis also confirmed at GWS level seven known BMD loci on 1p36 (ZBTB40), 6q25 (ESR1), 8q24 (TNFRSF11B), 11q13.4 (LRP5), 12q13 (SP7), 13q14 (TNFSF11) and 18q21 (TNFRSF11A). The many SNPs associated with BMD map to genes in signaling pathways with relevance to bone metabolism and highlight the complex genetic architecture that underlies osteoporosis and variation in BMD.

Meta-Analysis of 28,141 Individuals Identifies Common Variants within Five New Loci That Influence Uric Acid Concentrations

Elevated serum uric acid levels cause gout and are a risk factor for cardiovascular disease and diabetes. To investigate the polygenetic basis of serum uric acid levels, we conducted a meta-analysis of genome-wide association scans from 14 studies totalling 28,141 participants of European descent, resulting in identification of 954 SNPs distributed across nine loci that exceeded the threshold of genome-wide significance, five of which are novel. Overall, the common variants associated with serum uric acid levels fall in the following nine regions: SLC2A9 (p = 5.2×10−201), ABCG2 (p = 3.1×10−26), SLC17A1 (p = 3.0×10−14), SLC22A11 (p = 6.7×10−14), SLC22A12 (p = 2.0×10−9), SLC16A9 (p = 1.1×10−8), GCKR (p = 1.4×10−9), LRRC16A (p = 8.5×10−9), and near PDZK1 (p = 2.7×10−9). Identified variants were analyzed for gender differences. We found that the minor allele for rs734553 in SLC2A9 has greater influence in lowering uric acid levels in women and the minor allele of rs2231142 in ABCG2 elevates uric acid levels more strongly in men compared to women. To further characterize the identified variants, we analyzed their association with a panel of metabolites. rs12356193 within SLC16A9 was associated with DL-carnitine (p = 4.0×10−26) and propionyl-L-carnitine (p = 5.0×10−8) concentrations, which in turn were associated with serum UA levels (p = 1.4×10−57 and p = 8.1×10−54, respectively), forming a triangle between SNP, metabolites, and UA levels. Taken together, these associations highlight additional pathways that are important in the regulation of serum uric acid levels and point toward novel potential targets for pharmacological intervention to prevent or treat hyperuricemia. In addition, these findings strongly support the hypothesis that transport proteins are key in regulating serum uric acid levels.

Genomewide Association Studies of Stroke

BACKGROUND The genes underlying the risk of stroke in the general population remain undetermined. METHODS We carried out an analysis of genomewide association data generated from four large cohorts composing the Cohorts for Heart and Aging Research in Genomic Epidemiology consortium, including 19,602 white persons (mean [+/-SD] age, 63+/-8 years) in whom 1544 incident strokes (1164 ischemic strokes) developed over an average follow-up of 11 years. We tested the markers most strongly associated with stroke in a replication cohort of 2430 black persons with 215 incident strokes (191 ischemic strokes), another cohort of 574 black persons with 85 incident strokes (68 ischemic strokes), and 652 Dutch persons with ischemic stroke and 3613 unaffected persons. RESULTS Two intergenic single-nucleotide polymorphisms on chromosome 12p13 and within 11 kb of the gene NINJ2 were associated with stroke (P<5x10(-8)). NINJ2 encodes an adhesion molecule expressed in glia and shows increased expression after nerve injury. Direct genotyping showed that rs12425791 was associated with an increased risk of total (i.e., all types) and ischemic stroke, with hazard ratios of 1.30 (95% confidence interval [CI], 1.19 to 1.42) and 1.33 (95% CI, 1.21 to 1.47), respectively, yielding population attributable risks of 11% and 12% in the discovery cohorts. Corresponding hazard ratios were 1.35 (95% CI, 1.01 to 1.79; P=0.04) and 1.42 (95% CI, 1.06 to 1.91; P=0.02) in the large cohort of black persons and 1.17 (95% CI, 1.01 to 1.37; P=0.03) and 1.19 (95% CI, 1.01 to 1.41; P=0.04) in the Dutch sample; the results of an underpowered analysis of the smaller black cohort were nonsignificant. CONCLUSIONS A genetic locus on chromosome 12p13 is associated with an increased risk of stroke.

Multiple loci associated with indices of renal function and chronic kidney disease

Chronic kidney disease (CKD) has a heritable component and is an important global public health problem because of its high prevalence and morbidity. We conducted genome-wide association studies (GWAS) to identify susceptibility loci for glomerular filtration rate, estimated by serum creatinine (eGFRcrea) and cystatin C (eGFRcys), and CKD (eGFRcrea < 60 ml/min/1.73 m2) in European-ancestry participants of four population-based cohorts (ARIC, CHS, FHS, RS; n = 19,877; 2,388 CKD cases), and tested for replication in 21,466 participants (1,932 CKD cases). We identified significant SNP associations (P < 5 × 10−8) with CKD at the UMOD locus, with eGFRcrea at UMOD, SHROOM3 and GATM-SPATA5L1, and with eGFRcys at CST and STC1. UMOD encodes the most common protein in human urine, Tamm-Horsfall protein, and rare mutations in UMOD cause mendelian forms of kidney disease. Our findings provide new insights into CKD pathogenesis and underscore the importance of common genetic variants influencing renal function and disease.

ProbABEL package for genome-wide association analysis of imputed data

BackgroundOver the last few years, genome-wide association (GWA) studies became a tool of choice for the identification of loci associated with complex traits. Currently, imputed single nucleotide polymorphisms (SNP) data are frequently used in GWA analyzes. Correct analysis of imputed data calls for the implementation of specific methods which take genotype imputation uncertainty into account.ResultsWe developed the ProbABEL software package for the analysis of genome-wide imputed SNP data and quantitative, binary, and time-till-event outcomes under linear, logistic, and Cox proportional hazards models, respectively. For quantitative traits, the package also implements a fast two-step mixed model-based score test for association in samples with differential relationships, facilitating analysis in family-based studies, studies performed in human genetically isolated populations and outbred animal populations.ConclusionsProbABEL package provides fast efficient way to analyze imputed data in genome-wide context and will facilitate future identification of complex trait loci.

Genomewide Rapid Association Using Mixed Model and Regression: A Fast and Simple Method For Genomewide Pedigree-Based Quantitative Trait Loci Association Analysis

For pedigree-based quantitative trait loci (QTL) association analysis, a range of methods utilizing within-family variation such as transmission-disequilibrium test (TDT)-based methods have been developed. In scenarios where stratification is not a concern, methods exploiting between-family variation in addition to within-family variation, such as the measured genotype (MG) approach, have greater power. Application of MG methods can be computationally demanding (especially for large pedigrees), making genomewide scans practically infeasible. Here we suggest a novel approach for genomewide pedigree-based quantitative trait loci (QTL) association analysis: genomewide rapid association using mixed model and regression (GRAMMAR). The method first obtains residuals adjusted for family effects and subsequently analyzes the association between these residuals and genetic polymorphisms using rapid least-squares methods. At the final step, the selected polymorphisms may be followed up with the full measured genotype (MG) analysis. In a simulation study, we compared type 1 error, power, and operational characteristics of the proposed method with those of MG and TDT-based approaches. For moderately heritable (30%) traits in human pedigrees the power of the GRAMMAR and the MG approaches is similar and is much higher than that of TDT-based approaches. When using tabulated thresholds, the proposed method is less powerful than MG for very high heritabilities and pedigrees including large sibships like those observed in livestock pedigrees. However, there is little or no difference in empirical power of MG and the proposed method. In any scenario, GRAMMAR is much faster than MG and enables rapid analysis of hundreds of thousands of markers.