Braxton D. Mitchell

Genetic Epidemiology of Insulin Resistance and Visceral Adiposity ☆: The IRAS Family Study Design and Methods

PURPOSEnInsulin resistance and visceral adiposity are associated with increased risk of type 2 diabetes. In this report, we describe the methods of the IRAS Family Study, which was designed to identify the genetic and environmental risk factors for insulin resistance and visceral adiposity.nnnMETHODSnFamilies from two ethnic groups (African American and Hispanic) have been recruited from three clinical sites. Blood samples for DNA as well as other standard measures were collected. A CT scan (visceral adiposity) and a frequently sampled glucose tolerance test (insulin resistance) were performed. Preliminary estimates of heritability for indirect measures related to insulin resistance and visceral adiposity were obtained using a variance components approach in the first 93 families (approximately 1000 individuals).nnnRESULTSnEstimates of heritability ranged from low (0.08) for fasting insulin and HOMA, to moderate (0.28) for fasting glucose, to high (0.54) for BMI. After adjustment for age, gender and ethnicity, all heritability estimates were significantly greater than zero (p < 0.05).nnnCONCLUSIONSnThese results are consistent with the expectation that intermediate measures of insulin resistance and visceral adiposity are heritable, and that the IRAS Family Study has statistical power to detect these intermediate phenotypes of type 2 diabetes and atherosclerosis.

Measuring alcohol consumption for genomic meta-analyses of alcohol intake: opportunities and challenges

Whereas moderate drinking may have health benefits, excessive alcohol consumption causes many important acute and chronic diseases and is the third leading contributor to preventable death in the United States. Twin studies suggest that alcohol-consumption patterns are heritable (50%); however, multiple genetic variants of modest effect size are likely to contribute to this heritable variation. Genome-wide association studies provide a tool for discovering genetic loci that contribute to variations in alcohol consumption. Opportunities exist to identify susceptibility loci with modest effect by meta-analyzing together multiple studies. However, existing studies assessed many different aspects of alcohol use, such as typical compared with heavy drinking, and these different assessments can be difficult to reconcile. In addition, many studies lack the ability to distinguish between lifetime and recent abstention or to assess the pattern of drinking during the week, and a variety of such concerns surround the appropriateness of developing a common summary measure of alcohol intake. Combining such measures of alcohol intake can cause heterogeneity and exposure misclassification, cause a reduction in power, and affect the magnitude of genetic association signals. In this review, we discuss the challenges associated with harmonizing alcohol-consumption data from studies with widely different assessment instruments, with a particular focus on large-scale genetic studies.

Polymorphisms in migraine-associated gene, atp1a2, and ischemic stroke risk in a biracial population: the genetics of early onset stroke study.

In a recent meta-analysis migraine was associated with a two-fold increase in stroke risk. While the mechanism driving this association is unknown, one intriguing hypothesis is that migraineurs are genetically predisposed to developing ischemic stroke. Mutations in the ATP1A2 gene are implicated in familial hemiplegic migraine type II and increase the severity of ischemic brain injury in animal models. To further explore these observations, we assessed the association between ATP1A2 polymorphisms, migraine, and the risk of ischemic stroke in participants of the Genetics of Early-Onset Stroke Study, a population-based case–control study of ischemic stroke among men and women aged 15–49. Using responses to a headache symptoms questionnaire, subjects were classified as having no migraine, or migraine with or without visual aura. Evaluating a total of 134 ATP1A2 polymorphisms genotyped using a combination of Illumina platforms (Cardiovascular Gene-centric 50 K SNP Array and HumanOmni1-Quad_v1-0_B Bead Chip), only one polymorphism (rs2070704) demonstrated a nominally significant association with stroke in an age-, gender-, ethnicity-adjusted model (ORu2009=u20090.83, 95% CIu2009=u20090.71-0.98, pu2009=u20090.025) and in a vascular risk factor model adjusting for age, gender, ethnicity, hypertension, diabetes, smoking, and myocardial infarction (ORu2009=u20090.74, 95% CIu2009=u20090.63-0.89, pu2009=u20090.001). Ethnicity-stratified analyses demonstrated a significant association for rs2070704 among African-Americans (ORu2009=u20090.68, 95% CIu2009=u20090.53-0.90, pu2009=u20090.005) but not Caucasians (ORu2009=u20090.82, 95% CIu2009=u20090.64-1.04, pu2009=u20090.107). These associations were unchanged when migraine subtypes were included as co-variates. We did not observe an association between ATP1A2 polymorphisms and migraine. While our results do not demonstrate a strong relationship between ATP1A2 polymorphisms and migraine associated stroke risk, the results are hypothesis generating and indicate that an association between ATP1A2 polymorphisms and stroke risk may exist. Additional studies are required.

Do Candidate Genes Affect the Brain's White Matter Microstructure? Large-Scale Evaluation of 6,165 Diffusion MRI Scans

Susceptibility genes for psychiatric and neurological disorders - including APOE, BDNF, CLU,CNTNAP2, COMT, DISC1, DTNBP1, ErbB4, HFE, NRG1, NTKR3, and ZNF804A - have been reported to affect white matter (WM) microstructure in the healthy human brain, as assessed through diffusion tensor imaging (DTI). However, effects of single nucleotide polymorphisms (SNPs) in these genes explain only a small fraction of the overall variance and are challenging to detect reliably in single cohort studies. To date, few studies have evaluated the reproducibility of these results. As part of the ENIGMA-DTI consortium, we pooled regional fractional anisotropy (FA) measures for 6,165 subjects (CEU ancestry N=4,458) from 11 cohorts worldwide to evaluate effects of 15 candidate SNPs by examining their associations with WM microstructure. Additive association tests were conducted for each SNP. We used several meta-analytic and mega-analytic designs, and we evaluated regions of interest at multiple granularity levels. The ENIGMA-DTI protocol was able to detect single-cohort findings as originally reported. Even so, in this very large sample, no significant associations remained after multiple-testing correction for the 15 SNPs investigated. Suggestive associations (1.3×10-4 < p < 0.05, uncorrected) were found for BDNF, COMT, and ZNF804A in specific tracts. Meta-and mega-analyses revealed similar findings. Regardless of the approach, the previously reported candidate SNPs did not show significant associations with WM microstructure in this largest genetic study of DTI to date; the negative findings are likely not due to insufficient power. Genome-wide studies, involving large-scale meta-analyses, may help to discover SNPs robustly influencing WM microstructure.

The Subtype Specificity of Genetic Loci Associated with Stroke in 16,664 cases and 32,792 controls

Background Genome-wide association studies have identified multiple loci associated with stroke. However, the specific stroke subtypes affected, and whether loci influence both ischaemic and haemorrhagic stroke, remains unknown. For loci associated with stroke, we aimed to infer the combination of stroke subtypes likely to be affected, and in doing so assess the extent to which such loci have homogeneous effects across stroke subtypes. Methods We performed Bayesian multinomial regression in 16,664 stroke cases and 32,792 controls of European ancestry to determine the most likely combination of stroke subtypes affected for loci with published genome-wide stroke associations, using model selection. Cases were subtyped under two commonly used stroke classification systems, Trial of Org 10172 Acute Stroke Treatment (TOAST) and Causative Classification of Stroke (CCS). All individuals had genotypes imputed to the Haplotype Reference Consortium 1.1 Panel. Results Sixteen loci were considered for analysis. Seven loci influenced both haemorrhagic and ischaemic stroke, three of which influenced ischaemic and haemorrhagic subtypes under both TOAST and CCS. Under CCS, 4 loci influenced both small vessel stroke and intracerebral haemorrhage. An EDNRA locus demonstrated opposing effects on ischaemic and haemorrhagic stroke. No loci were predicted to influence all stroke subtypes in the same direction and only one locus (12q24) was predicted to influence all ischaemic stroke subtypes. Conclusions Heterogeneity in the influence of stroke-associated loci on stroke subtypes is pervasive, reflecting differing causal pathways. However, overlap exists between haemorrhagic and ischaemic stroke, which may reflect shared pathobiology predisposing to small vessel arteriopathy. Stroke is a complex, heterogeneous disorder requiring tailored analytic strategies to decipher genetic mechanisms.

Shared Genetic Contributions to Atrial Fibrillation and Ischemic Stroke Risk

Atrial fibrillation is a prevalent arrhythmia associated with a five-fold increased risk of ischemic stroke, and specifically the cardioembolic stroke subtype. Genome-wide association studies of these traits have yielded overlapping risk loci, but genome-wide investigation of genetic susceptibility shared between stroke and atrial fibrillation is lacking. Comparing the genetic architectures of the two diseases could inform whether cardioembolic strokes are driven by inherited atrial fibrillation susceptibility, and may help elucidate ischemic stroke mechanisms. Here, we analyze genome-wide genotyping data and estimate SNP-based heritability in atrial fibrillation and cardioembolic stroke to be nearly identical (20.0% and 19.5%, respectively). Further, we find that the traits are genetically correlated (r=0.77 for SNPs with p -4 in a previous atrial fibrillation meta-analysis). Clinical studies are warranted to assess whether genetic susceptibility to atrial fibrillation can be leveraged to improve the diagnosis and care of ischemic stroke patients.Objective We sought to assess whether genetic risk factors for atrial fibrillation can explain cardioembolic stroke risk. Methods We evaluated genetic correlations between a prior genetic study of AF and AF in the presence of cardioembolic stroke using genome-wide genotypes from the Stroke Genetics Network (N = 3,190 AF cases, 3,000 cardioembolic stroke cases, and 28,026 referents). We tested whether a previously-validated AF polygenic risk score (PRS) associated with cardioembolic and other stroke subtypes after accounting for AF clinical risk factors. Results We observed strong correlation between previously reported genetic risk for AF, AF in the presence of stroke, and cardioembolic stroke (Pearson’s r=0.77 and 0.76, respectively, across SNPs with p < 4.4 × 10−4 in the prior AF meta-analysis). An AF PRS, adjusted for clinical AF risk factors, was associated with cardioembolic stroke (odds ratio (OR) per standard deviation (sd) = 1.40, p = 1.45×10−48), explaining ∼20% of the heritable component of cardioembolic stroke risk. The AF PRS was also associated with stroke of undetermined cause (OR per sd = 1.07, p = 0.004), but no other primary stroke subtypes (all p > 0.1). Conclusions Genetic risk for AF is associated with cardioembolic stroke, independent of clinical risk factors. Studies are warranted to determine whether AF genetic risk can serve as a biomarker for strokes caused by AF.