Data for GAW20: Genome-Wide DNA Sequence Variation and Epigenome-Wide DNA Methylation Before and After Fenofibrate Treatment in a Family Study of Metabolic Phenotypes

GAW20 provided participants with an opportunity to comprehensively examine genetic and epigenetic variation among related individuals in the context of drug treatment response. GAW20 used data from 188 families (N = 1105) participating in the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) study (clinicaltrials.gov identifier NCT00083369), which included CD4+ T-cell DNA methylation at 463,995 cytosine-phosphate-guanine (CpG) sites measured before and after a 3-week treatment with fenofibrate, single-nucleotide variation at 906,600 loci, metabolic syndrome components ascertained before and after the drug intervention, and relevant covariates. All GOLDN participants were of European descent, with an average age of 48 years. In addition, approximately half were women and approximately 40% met the diagnostic criteria for metabolic syndrome. Unique advantages of the GAW20data set included longitudinal (3 weeks apart) measurements of DNA methylation, the opportunity to explore the contributions of both genotype and DNA methylation to the interindividual variability in drug treatment response, and the familial relationships between study participants. The principal disadvantage of GAW20/GOLDN data was the spurious correlation between batch effects and fenofibrate effects on methylation, which arose because the pre- and posttreatment methylation data were generated and normalized separately, and any attempts to remove time-dependent technical artifacts would also remove biologically meaningful changes brought on by fenofibrate. Despite this limitation, the GAW20 data set offered informative, multilayered omics data collected in a large population-based study of common disease traits, which resulted in creative approaches to integration and analysis of inherited human variation

Do rare variant genotypes predict common variant genotypes?

The synthetic association hypothesis proposes that common genetic variants detectable in genome-wide association studies may reflect the net phenotypic effect of multiple rare polymorphisms distributed broadly within the focal gene rather than, as often assumed, the effect of common functional variants in high linkage disequilibrium with the focal marker. In a recent study, Dickson and colleagues demonstrated synthetic association in simulations and in two well-characterized, highly polymorphic human disease genes. The converse of this hypothesis is that rare variant genotypes must be correlated with common variant genotypes often enough to make the phenomenon of synthetic association possible. Here we used the exome genotype data provided for Genetic Analysis Workshop 17 to ask how often, how well, and under what conditions rare variant genotypes predict the genotypes of common variants within the same gene. We found nominal evidence of correlation between rare and common variants in 21-30% of cases examined for unrelated individuals; this rate increased to 38-44% for related individuals, underscoring the segregation that underlies synthetic association

Mitochondrial genetic effects on latent class variables associated with susceptibility to alcoholism

We report the results of statistical genetic analyses of data from the Collaborative Study on the Genetics of Alcoholism prepared for the Genetic Analysis Workshop 14 to detect and characterize maternally inherited mitochondrial genetic effects on variation in latent class psychiatric/behavioral variables employed in the diagnosis of alcoholism. Using published extensions to variance decomposition methods for statistical genetic analysis of continuous and discrete traits we: 1) estimated the proportion of the variance in each trait due to the effects of mitochondrial DNA (mtDNA), 2) tested for pleiotropy, both mitochondrial genetic and residual additive genetic, between trait pairs, and 3) evaluated whether the simultaneous estimation of mitochondrial genetic effects on these traits improves our ability to detect and localize quantitative trait loci (QTL) in the nuclear genome. After correction for multiple testing, we find significant (p < 0.009) mitochondrial genetic contributions to the variance for two latent class variables. Although we do detect significant residual additive genetic correlations between the two traits, there is no evidence of a residual mitochondrial genetic correlation between them. Evidence for autosomal QTL for these traits is improved when linkage screens are conditioned on significant mitochondrial genetic effects. We conclude that mitochondrial genes may contribute to variation in some latent class psychiatric/behavioral variables associated with alcoholism

Defining alcohol-related phenotypes in humans. The Collaborative Study on the Genetics of Alcoholism.

Alcoholism is a disease that runs in families and results at least in part from genetic risk factors. The Collaborative Study on the Genetics of Alcoholism (COGA) is a Federally funded effort to identify and characterize those genetic factors. The study involves more than 1,000 alcoholic subjects and their families, with researchers conducting comprehensive psychological, physiological, electrophysiological, and genetic analyses of the participants. These analyses have identified several traits, or phenotypes, that appear to be genetically determined, such as the presence of alcohol dependence, the level of response to alcohol, the presence of coexisting depression, or the maximum number of drinks a person consumes per occasion. Genetic analyses have identified regions on several chromosomes that are associated with these phenotypes and need to be studied further

Shared genetic variance between obesity and white matter integrity in Mexican Americans.

peer reviewedObesity is a chronic metabolic disorder that may also lead to reduced white matter integrity, potentially due to shared genetic risk factors. Genetic correlation analyses were conducted in a large cohort of Mexican American families in San Antonio (N = 761, 58% females, ages 18-81 years; 41.3 +/- 14.5) from the Genetics of Brain Structure and Function Study. Shared genetic variance was calculated between measures of adiposity [(body mass index (BMI; kg/m(2)) and waist circumference (WC; in)] and whole-brain and regional measurements of cerebral white matter integrity (fractional anisotropy). Whole-brain average and regional fractional anisotropy values for 10 major white matter tracts were calculated from high angular resolution diffusion tensor imaging data (DTI; 1.7 x 1.7 x 3 mm; 55 directions). Additive genetic factors explained intersubject variance in BMI (heritability, h (2) = 0.58), WC (h (2) = 0.57), and FA (h (2) = 0.49). FA shared significant portions of genetic variance with BMI in the genu (rhoG = -0.25), body (rhoG = -0.30), and splenium (rhoG = -0.26) of the corpus callosum, internal capsule (rhoG = -0.29), and thalamic radiation (rhoG = -0.31) (all p's = 0.043). The strongest evidence of shared variance was between BMI/WC and FA in the superior fronto-occipital fasciculus (rhoG = -0.39, p = 0.020; rhoG = -0.39, p = 0.030), which highlights region-specific variation in neural correlates of obesity. This may suggest that increase in obesity and reduced white matter integrity share common genetic risk factors

High dimensional endophenotype ranking in the search for major depression risk genes

Background: Despite overwhelming evidence that major depression is highly heritable, recent studies have localized only a single depression-related locus reaching genome-wide significance and have yet to identify a causal gene. Focusing on family-based studies of quantitative intermediate phenotypes or endophenotypes, in tandem with studies of unrelated individuals using categorical diagnoses, should improve the likelihood of identifying major depression genes. However, there is currently no empirically derived statistically rigorous method for selecting optimal endophentypes for mental illnesses. Here, we describe the endophenotype ranking value, a new objective index of the genetic utility of endophenotypes for any heritable illness. Methods: Applying endophenotype ranking value analysis to a high-dimensional set of over 11,000 traits drawn from behavioral/neurocognitive, neuroanatomic, and transcriptomic phenotypic domains, we identified a set of objective endophenotypes for recurrent major depression in a sample of Mexican American individuals (n = 1122) from large randomly selected extended pedigrees. Results: Top-ranked endophenotypes included the Beck Depression Inventory, bilateral ventral diencephalon volume, and expression levels of the RNF123 transcript. To illustrate the utility of endophentypes in this context, each of these traits were utlized along with disease status in bivariate linkage analysis. A genome-wide significant quantitative trait locus was localized on chromsome 4p15 (logarithm of odds = 3.5) exhibiting pleiotropic effects on both the endophenotype (lymphocyte-derived expression levels of the RNF123 gene) and disease risk. Conclusions: The wider use of quantitative endophenotypes, combined with unbiased methods for selecting among these measures, should spur new insights into the biological mechanisms that influence mental illnesses like major depression

Diabetes-specific genetic effects on obesity traits in American Indian populations: the Strong Heart Family Study

<p>Abstract</p> <p>Background</p> <p>Body fat mass distribution and deposition are determined by multiple environmental and genetic factors. Obesity is associated with insulin resistance, hyperinsulinemia, and type 2 diabetes. We previously identified evidence for genotype-by-diabetes interaction on obesity traits in Strong Heart Family Study (SHFS) participants. To localize these genetic effects, we conducted genome-wide linkage scans of obesity traits in individuals with and without type 2 diabetes, and in the combined sample while modeling interaction with diabetes using maximum likelihood methods (SOLAR 2.1.4).</p> <p>Methods</p> <p>SHFS recruited American Indians from Arizona, North and South Dakota, and Oklahoma. Anthropometric measures and diabetes status were obtained during a clinic visit. Marker allele frequencies were derived using maximum likelihood methods estimated from all individuals and multipoint identity by descent sharing was estimated using Loki. We used variance component linkage analysis to localize quantitative trait loci (QTLs) influencing obesity traits. We tested for evidence of additive and QTL-specific genotype-by-diabetes interactions using the regions identified in the diabetes-stratified analyses.</p> <p>Results</p> <p>Among 245 diabetic and 704 non-diabetic American Indian individuals, we detected significant additive gene-by-diabetes interaction for weight and BMI (<it>P </it>< 0.02). In analysis accounting for QTL-specific interaction (<it>P </it>< 0.001), we detected a QTL for weight on chromosome 1 at 242 cM (LOD = 3.7). This chromosome region harbors the adiponectin receptor 1 gene, which has been previously associated with obesity.</p> <p>Conclusion</p> <p>These results suggest distinct genetic effects on body mass in individuals with diabetes compared to those without diabetes, and a possible role for one or more genes on chromosome 1 in the pathogenesis of obesity.</p

Exome sequences of multiplex, multigenerational families reveal schizophrenia risk loci with potential implications for neurocognitive performance

Schizophrenia is a serious mental illness, involving disruptions in thought and behavior, with a worldwide prevalence of about one percent. Although highly heritable, much of the genetic liability of schizophrenia is yet to be explained. We searched for susceptibility loci in multiplex, multigenerational families affected by schizophrenia, targeting protein-altering variation with in silico predicted functional effects. Exome sequencing was performed on 136 samples from eight European-American families, including 23 individuals diagnosed with schizophrenia or schizoaffective disorder. In total, 11,878 non-synonymous variants from 6,396 genes were tested for their association with schizophrenia spectrum disorders. Pathway enrichment analyses were conducted on gene-based test results, protein-protein interaction (PPI) networks, and epistatic effects. Using a significance threshold of FDR\u3c0.1, association was detected for rs10941112 (P=2.1×10−5; q-value=0.073) in AMACR, a gene involved in fatty acid metabolism and previously implicated in schizophrenia, with significant cis effects on gene expression (P=5.5×10−4), including brain tissue data from the Genotype-Tissue Expression project (minimum P=6.0×10−5). A second SNP, rs10378 located in TMEM176A, also shows risk effects in the exome data (P=2.8×10−5; q-value=0.073). Protein-protein interactions among our top gene-based association results (P\u3c0.05; n=359 genes) reveal significant enrichment of genes involved in NCAM-mediated neurite outgrowth (P=3.0×10−5), while exome-wide SNP-SNP interaction effects for rs10941112 and rs10378 indicate a potential role for kinase-mediated signaling involved in memory and learning. In conclusion, these association results implicate AMACR and TMEM176A in schizophrenia risk, whose effects may be modulated by genes involved in synaptic plasticity and neurocognitive performance

An ADH1B variant and peer drinking in progression to adolescent drinking milestones: Evidence of a gene-by-environment interaction

BACKGROUND: Adolescent drinking is an important public health concern, one that is influenced by both genetic and environmental factors. The functional variant rs1229984 in alcohol dehydrogenase 1B (ADH1B) has been associated at a genome-wide level with alcohol use disorders in diverse adult populations. However, few data are available regarding whether this variant influences early drinking behaviors and whether social context moderates this effect. This study examines the interplay between rs1229984 and peer drinking in the development of adolescent drinking milestones. METHODS: One thousand five hundred and fifty European and African American individuals who had a full drink of alcohol before age 18 were selected from a longitudinal study of youth as part of the Collaborative Study on the Genetics of Alcoholism (COGA). Cox proportional hazards regression, with G × E product terms in the final models, was used to study 2 primary outcomes during adolescence: age of first intoxication and age of first DSM-5 alcohol use disorder symptom. RESULTS: The minor A allele of rs1229984 was associated with a protective effect for first intoxication (HR = 0.56, 95% CI 0.41 to 0.76) and first DSM-5 symptom (HR = 0.45, 95% CI 0.26 to 0.77) in the final models. Reporting that most or all best friends drink was associated with a hazardous effect for first intoxication (HR = 1.81, 95% CI 1.62 to 2.01) and first DSM-5 symptom (HR = 2.17, 95% 1.88 to 2.50) in the final models. Furthermore, there was a significant G × E interaction for first intoxication (p = 0.002) and first DSM-5 symptom (p = 0.01). Among individuals reporting none or few best friends drinking, the ADH1B variant had a protective effect for adolescent drinking milestones, but for those reporting most or all best friends drinking, this effect was greatly reduced. CONCLUSIONS: Our results suggest that the risk factor of best friends drinking attenuates the protective effect of a well-established ADH1B variant for 2 adolescent drinking behaviors. These findings illustrate the interplay between genetic and environmental factors in the development of drinking milestones during adolescence

Epigenetic Age Acceleration Assessed with Human White-Matter Images

The accurate estimation of age using methylation data has proved a useful and heritable biomarker, with acceleration in epigenetic age predicting a number of age-related phenotypes. Measures of white matter integrity in the brain are also heritable and highly sensitive to both normal and pathological aging processes across adulthood. We consider the phenotypic and genetic interrelationships between epigenetic age acceleration and white matter integrity in humans. Our goal was to investigate processes that underlie interindividual variability in age-related changes in the brain. Using blood taken from a Mexican-American extended pedigree sample (n = 628; age = 23.28-93.11 years), epigenetic age was estimated using the method developed by Horvath (2013). For n = 376 individuals, diffusion tensor imaging scans were also available. The interrelationship between epigenetic age acceleration and global white matter integrity was investigated with variance decomposition methods. To test for neuroanatomical specificity, 16 specific tracts were additionally considered. We observed negative phenotypic correlations between epigenetic age acceleration and global white matter tract integrity (ρpheno = -0.119, p = 0.028), with evidence of shared genetic (ρgene = -0.463, p = 0.013) but not environmental influences. Negative phenotypic and genetic correlations with age acceleration were also seen for a number of specific white matter tracts, along with additional negative phenotypic correlations between granulocyte abundance and white matter integrity. These findings (i.e., increased acceleration in epigenetic age in peripheral blood correlates with reduced white matter integrity in the brain and shares common genetic influences) provide a window into the neurobiology of aging processes within the brain and a potential biomarker of normal and pathological brain aging