Maksim Struchalin

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.

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.

NRXN3 Is a Novel Locus for Waist Circumference: A Genome-Wide Association Study from the CHARGE Consortium

Central abdominal fat is a strong risk factor for diabetes and cardiovascular disease. To identify common variants influencing central abdominal fat, we conducted a two-stage genome-wide association analysis for waist circumference (WC). In total, three loci reached genome-wide significance. In stage 1, 31,373 individuals of Caucasian descent from eight cohort studies confirmed the role of FTO and MC4R and identified one novel locus associated with WC in the neurexin 3 gene [NRXN3 (rs10146997, p = 6.4×10−7)]. The association with NRXN3 was confirmed in stage 2 by combining stage 1 results with those from 38,641 participants in the GIANT consortium (p = 0.009 in GIANT only, p = 5.3×10−8 for combined analysis, n = 70,014). Mean WC increase per copy of the G allele was 0.0498 z-score units (0.65 cm). This SNP was also associated with body mass index (BMI) [p = 7.4×10−6, 0.024 z-score units (0.10 kg/m2) per copy of the G allele] and the risk of obesity (odds ratio 1.13, 95% CI 1.07–1.19; p = 3.2×10−5 per copy of the G allele). The NRXN3 gene has been previously implicated in addiction and reward behavior, lending further evidence that common forms of obesity may be a central nervous system-mediated disorder. Our findings establish that common variants in NRXN3 are associated with WC, BMI, and obesity.

Genetic variants associated with cardiac structure and function: A meta-analysis and replication of genome-wide association data

CONTEXT Echocardiographic measures of left ventricular (LV) structure and function are heritable phenotypes of cardiovascular disease. OBJECTIVE To identify common genetic variants associated with cardiac structure and function by conducting a meta-analysis of genome-wide association data in 5 population-based cohort studies (stage 1) with replication (stage 2) in 2 other community-based samples. DESIGN, SETTING, AND PARTICIPANTS Within each of 5 community-based cohorts comprising the EchoGen consortium (stage 1; n = 12 612 individuals of European ancestry; 55% women, aged 26-95 years; examinations between 1978-2008), we estimated the association between approximately 2.5 million single-nucleotide polymorphisms (SNPs; imputed to the HapMap CEU panel) and echocardiographic traits. In stage 2, SNPs significantly associated with traits in stage 1 were tested for association in 2 other cohorts (n = 4094 people of European ancestry). Using a prespecified P value threshold of 5 x 10(-7) to indicate genome-wide significance, we performed an inverse variance-weighted fixed-effects meta-analysis of genome-wide association data from each cohort. MAIN OUTCOME MEASURES Echocardiographic traits: LV mass, internal dimensions, wall thickness, systolic dysfunction, aortic root, and left atrial size. RESULTS In stage 1, 16 genetic loci were associated with 5 echocardiographic traits: 1 each with LV internal dimensions and systolic dysfunction, 3 each with LV mass and wall thickness, and 8 with aortic root size. In stage 2, 5 loci replicated (6q22 locus associated with LV diastolic dimensions, explaining <1% of trait variance; 5q23, 12p12, 12q14, and 17p13 associated with aortic root size, explaining 1%-3% of trait variance). CONCLUSIONS We identified 5 genetic loci harboring common variants that were associated with variation in LV diastolic dimensions and aortic root size, but such findings explained a very small proportion of variance. Further studies are required to replicate these findings, identify the causal variants at or near these loci, characterize their functional significance, and determine whether they are related to overt cardiovascular disease.

Common variants at 12q14 and 12q24 are associated with hippocampal volume

Aging is associated with reductions in hippocampal volume that are accelerated by Alzheimers disease and vascular risk factors. Our genome-wide association study (GWAS) of dementia-free persons (n = 9,232) identified 46 SNPs at four loci with P values of <4.0 × 10−7. In two additional samples (n = 2,318), associations were replicated at 12q14 within MSRB3-WIF1 (discovery and replication; rs17178006; P = 5.3 × 10−11) and at 12q24 near HRK-FBXW8 (rs7294919; P = 2.9 × 10−11). Remaining associations included one SNP at 2q24 within DPP4 (rs6741949; P = 2.9 × 10−7) and nine SNPs at 9p33 within ASTN2 (rs7852872; P = 1.0 × 10−7); along with the chromosome 12 associations, these loci were also associated with hippocampal volume (P < 0.05) in a third younger, more heterogeneous sample (n = 7,794). The SNP in ASTN2 also showed suggestive association with decline in cognition in a largely independent sample (n = 1,563). These associations implicate genes related to apoptosis (HRK), development (WIF1), oxidative stress (MSR3B), ubiquitination (FBXW8) and neuronal migration (ASTN2), as well as enzymes targeted by new diabetes medications (DPP4), indicating new genetic influences on hippocampal size and possibly the risk of cognitive decline and dementia.

A genome-wide association study identifies a susceptibility locus for refractive errors and myopia at 15q14

Refractive errors are the most common ocular disorders worldwide and may lead to blindness. Although this trait is highly heritable, identification of susceptibility genes has been challenging. We conducted a genome-wide association study for refractive error in 5,328 individuals from a Dutch population-based study with replication in four independent cohorts (combined 10,280 individuals in the replication stage). We identified a significant association at chromosome 15q14 (rs634990, P = 2.21 × 10−14). The odds ratio of myopia compared to hyperopia for the minor allele (minor allele frequency = 0.47) was 1.41 (95% CI 1.16–1.70) for individuals heterozygous for the allele and 1.83 (95% CI 1.42–2.36) for individuals homozygous for the allele. The associated locus is near two genes that are expressed in the retina, GJD2 and ACTC1, and appears to harbor regulatory elements which may influence transcription of these genes. Our data suggest that common variants at 15q14 influence susceptibility for refractive errors in the general population.

Common Genetic Determinants of Intraocular Pressure and Primary Open-Angle Glaucoma

Intraocular pressure (IOP) is a highly heritable risk factor for primary open-angle glaucoma and is the only target for current glaucoma therapy. The genetic factors which determine IOP are largely unknown. We performed a genome-wide association study for IOP in 11,972 participants from 4 independent population-based studies in The Netherlands. We replicated our findings in 7,482 participants from 4 additional cohorts from the UK, Australia, Canada, and the Wellcome Trust Case-Control Consortium 2/Blue Mountains Eye Study. IOP was significantly associated with rs11656696, located in GAS7 at 17p13.1 (p = 1.4×10−8), and with rs7555523, located in TMCO1 at 1q24.1 (p = 1.6×10−8). In a meta-analysis of 4 case-control studies (total N = 1,432 glaucoma cases), both variants also showed evidence for association with glaucoma (p = 2.4×10−2 for rs11656696 and p = 9.1×10−4 for rs7555523). GAS7 and TMCO1 are highly expressed in the ciliary body and trabecular meshwork as well as in the lamina cribrosa, optic nerve, and retina. Both genes functionally interact with known glaucoma disease genes. These data suggest that we have identified two clinically relevant genes involved in IOP regulation.

Genome-Wide Association Study Identifies Novel Loci Associated with Circulating Phospho- and Sphingolipid Concentrations

Phospho- and sphingolipids are crucial cellular and intracellular compounds. These lipids are required for active transport, a number of enzymatic processes, membrane formation, and cell signalling. Disruption of their metabolism leads to several diseases, with diverse neurological, psychiatric, and metabolic consequences. A large number of phospholipid and sphingolipid species can be detected and measured in human plasma. We conducted a meta-analysis of five European family-based genome-wide association studies (N = 4034) on plasma levels of 24 sphingomyelins (SPM), 9 ceramides (CER), 57 phosphatidylcholines (PC), 20 lysophosphatidylcholines (LPC), 27 phosphatidylethanolamines (PE), and 16 PE-based plasmalogens (PLPE), as well as their proportions in each major class. This effort yielded 25 genome-wide significant loci for phospholipids (smallest P-value = 9.88×10−204) and 10 loci for sphingolipids (smallest P-value = 3.10×10−57). After a correction for multiple comparisons (P-value<2.2×10−9), we observed four novel loci significantly associated with phospholipids (PAQR9, AGPAT1, PKD2L1, PDXDC1) and two with sphingolipids (PLD2 and APOE) explaining up to 3.1% of the variance. Further analysis of the top findings with respect to within class molar proportions uncovered three additional loci for phospholipids (PNLIPRP2, PCDH20, and ABDH3) suggesting their involvement in either fatty acid elongation/saturation processes or fatty acid specific turnover mechanisms. Among those, 14 loci (KCNH7, AGPAT1, PNLIPRP2, SYT9, FADS1-2-3, DLG2, APOA1, ELOVL2, CDK17, LIPC, PDXDC1, PLD2, LASS4, and APOE) mapped into the glycerophospholipid and 12 loci (ILKAP, ITGA9, AGPAT1, FADS1-2-3, APOA1, PCDH20, LIPC, PDXDC1, SGPP1, APOE, LASS4, and PLD2) to the sphingolipid pathways. In large meta-analyses, associations between FADS1-2-3 and carotid intima media thickness, AGPAT1 and type 2 diabetes, and APOA1 and coronary artery disease were observed. In conclusion, our study identified nine novel phospho- and sphingolipid loci, substantially increasing our knowledge of the genetic basis for these traits.

Predicting human height by Victorian and genomic methods

In the Victorian era, Sir Francis Galton showed that ‘when dealing with the transmission of stature from parents to children, the average height of the two parents, … is all we need care to know about them’ (1886). One hundred and twenty-two years after Galtons work was published, 54 loci showing strong statistical evidence for association to human height were described, providing us with potential genomic means of human height prediction. In a population-based study of 5748 people, we find that a 54-loci genomic profile explained 4–6% of the sex- and age-adjusted height variance, and had limited ability to discriminate tall/short people, as characterized by the area under the receiver-operating characteristic curve (AUC). In a family-based study of 550 people, with both parents having height measurements, we find that the Galtonian mid-parental prediction method explained 40% of the sex- and age-adjusted height variance, and showed high discriminative accuracy. We have also explored how much variance a genomic profile should explain to reach certain AUC values. For highly heritable traits such as height, we conclude that in applications in which parental phenotypic information is available (eg, medicine), the Victorian Galtons method will long stay unsurpassed, in terms of both discriminative accuracy and costs. For less heritable traits, and in situations in which parental information is not available (eg, forensics), genomic methods may provide an alternative, given that the variants determining an essential proportion of the traits variation can be identified.

Genome-Wide Association Studies of MRI-Defined Brain Infarcts Meta-Analysis From the CHARGE Consortium

Background and Purpose— Previous studies examining genetic associations with MRI-defined brain infarct have yielded inconsistent findings. We investigated genetic variation underlying covert MRI infarct in persons without histories of transient ischemic attack or stroke. We performed meta-analysis of genome-wide association studies of white participants in 6 studies comprising the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium. Methods— Using 2.2 million genotyped and imputed single nucleotide polymorphisms, each study performed cross-sectional genome-wide association analysis of MRI infarct using age- and sex-adjusted logistic regression models. Study-specific findings were combined in an inverse-variance-weighted meta-analysis, including 9401 participants with mean age 69.7 (19.4% of whom had ≥1 MRI infarct). Results— The most significant association was found with rs2208454 (minor allele frequency, 20%), located in intron 3 of MACRO domain containing 2 gene and in the downstream region of fibronectin leucine-rich transmembrane protein 3 gene. Each copy of the minor allele was associated with lower risk of MRI infarcts (odds ratio, 0.76; 95% confidence interval, 0.68–0.84; P=4.64×10−7). Highly suggestive associations (P<1.0×10−5) were also found for 22 other single nucleotide polymorphisms in linkage disequilibrium (r2>0.64) with rs2208454. The association with rs2208454 did not replicate in independent samples of 1822 white and 644 black participants, although 4 single nucleotide polymorphisms within 200 kb from rs2208454 were associated with MRI infarcts in the black population sample. Conclusions— This first community-based, genome-wide association study on covert MRI infarcts uncovered novel associations. Although replication of the association with top single nucleotide polymorphisms failed, possibly because of insufficient power, results in the black population sample are encouraging, and further efforts at replication are needed.Background and Purpose— Previous studies examining genetic associations with MRI-defined brain infarct have yielded inconsistent findings. We investigated genetic variation underlying covert MRI infarct in persons without histories of transient ischemic attack or stroke. We performed meta-analysis of genome-wide association studies of white participants in 6 studies comprising the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium. Methods— Using 2.2 million genotyped and imputed single nucleotide polymorphisms, each study performed cross-sectional genome-wide association analysis of MRI infarct using age- and sex-adjusted logistic regression models. Study-specific findings were combined in an inverse-variance-weighted meta-analysis, including 9401 participants with mean age 69.7 (19.4% of whom had ≥1 MRI infarct). Results— The most significant association was found with rs2208454 (minor allele frequency, 20%), located in intron 3 of MACRO domain containing 2 gene and in the downstream region of fibronectin leucine-rich transmembrane protein 3 gene. Each copy of the minor allele was associated with lower risk of MRI infarcts (odds ratio, 0.76; 95% confidence interval, 0.68–0.84; P =4.64×10−7). Highly suggestive associations ( P 0.64) with rs2208454. The association with rs2208454 did not replicate in independent samples of 1822 white and 644 black participants, although 4 single nucleotide polymorphisms within 200 kb from rs2208454 were associated with MRI infarcts in the black population sample. Conclusions— This first community-based, genome-wide association study on covert MRI infarcts uncovered novel associations. Although replication of the association with top single nucleotide polymorphisms failed, possibly because of insufficient power, results in the black population sample are encouraging, and further efforts at replication are needed. # Supplemental Appendix {#article-title-2}