Estimating heritability and genetic correlations from large health datasets in the absence of genetic data

Typically, estimating genetic parameters, such as disease heritability and between-disease genetic correlations, demands large datasets containing all relevant phenotypic measures and detailed knowledge of family relationships or, alternatively, genotypic and phenotypic data for numerous unrelated individuals. Here, we suggest an alternative, efficient estimation approach through the construction of two disease metrics from large health datasets: temporal disease prevalence curves and low-dimensional disease embeddings. We present eleven thousand heritability estimates corresponding to five study types: twins, traditional family studies, health records-based family studies, single nucleotide polymorphisms, and polygenic risk scores. We also compute over six hundred thousand estimates of genetic, environmental and phenotypic correlations. Furthermore, we find that: (1) disease curve shapes cluster into five general patterns; (2) early-onset diseases tend to have lower prevalence than late-onset diseases (Spearmans rho = 0.32, p amp;lt; 10(-16)); and (3) the disease onset age and heritability are negatively correlated (rho = -0.46, p amp;lt; 10(-16)).Funding Agencies|DARPA Big Mechanism program under ARO [W911NF1410333]; National Institutes of HealthUnited States Department of Health &amp; Human ServicesNational Institutes of Health (NIH) - USA [R01HL122712, 1P50MH094267, U01HL108634-01]; King Abdullah University of Science and Technology (KAUST)King Abdullah University of Science &amp; Technology [FCC/1/1976-18-01, FCC/1/1976-23-01, FCC/1/1976-25-01, FCC/1/1976-26-01, FCS/1/4102-02-01]</p

Estimating heritability and genetic correlations from large health datasets in the absence of genetic data.

Typically, estimating genetic parameters, such as disease heritability and between-disease genetic correlations, demands large datasets containing all relevant phenotypic measures and detailed knowledge of family relationships or, alternatively, genotypic and phenotypic data for numerous unrelated individuals. Here, we suggest an alternative, efficient estimation approach through the construction of two disease metrics from large health datasets: temporal disease prevalence curves and low-dimensional disease embeddings. We present eleven thousand heritability estimates corresponding to five study types: twins, traditional family studies, health records-based family studies, single nucleotide polymorphisms, and polygenic risk scores. We also compute over six hundred thousand estimates of genetic, environmental and phenotypic correlations. Furthermore, we find that: (1) disease curve shapes cluster into five general patterns; (2) early-onset diseases tend to have lower prevalence than late-onset diseases (Spearman\u27s ρ = 0.32, p \u3c 1

A phenome-wide comparative analysis of genetic discordance between obesity and type 2 diabetes

Obesity and type 2 diabetes are causally related, yet there is considerable heterogeneity in the consequences of both conditions and the mechanisms of action are poorly defined. Here we show a genetic-driven approach defining two obesity profiles that convey highly concordant and discordant diabetogenic effects. We annotate and then compare association signals for these profiles across clinical and molecular phenotypic layers. Key differences are identified in a wide range of traits, including cardiovascular mortality, fat distribution, liver metabolism, blood pressure, specific lipid fractions and blood levels of proteins involved in extracellular matrix remodelling. We find marginal differences in abundance of Bacteroidetes and Firmicutes bacteria in the gut. Instrumental analyses reveal prominent causal roles for waist-to-hip ratio, blood pressure and cholesterol content of high-density lipoprotein particles in the development of diabetes in obesity. We prioritize 17 genes from the discordant signature that convey protection against type 2 diabetes in obesity, which may represent logical targets for precision medicine approaches.</p

Rare Copy Number Variants in \u3cem\u3eNRXN1\u3c/em\u3e and \u3cem\u3eCNTN6\u3c/em\u3e Increase Risk for Tourette Syndrome

Tourette syndrome (TS) is a model neuropsychiatric disorder thought to arise from abnormal development and/or maintenance of cortico-striato-thalamo-cortical circuits. TS is highly heritable, but its underlying genetic causes are still elusive, and no genome-wide significant loci have been discovered to date. We analyzed a European ancestry sample of 2,434 TS cases and 4,093 ancestry-matched controls for rare (\u3c 1% frequency) copy-number variants (CNVs) using SNP microarray data. We observed an enrichment of global CNV burden that was prominent for large (\u3e 1 Mb), singleton events (OR = 2.28, 95% CI [1.39–3.79], p = 1.2 × 10−3) and known, pathogenic CNVs (OR = 3.03 [1.85–5.07], p = 1.5 × 10−5). We also identified two individual, genome-wide significant loci, each conferring a substantial increase in TS risk (NRXN1 deletions, OR = 20.3, 95% CI [2.6–156.2]; CNTN6 duplications, OR = 10.1, 95% CI [2.3–45.4]). Approximately 1% of TS cases carry one of these CNVs, indicating that rare structural variation contributes significantly to the genetic architecture of TS

Parent-of-origin-specific allelic associations among 106 genomic loci for age at menarche.

Age at menarche is a marker of timing of puberty in females. It varies widely between individuals, is a heritable trait and is associated with risks for obesity, type 2 diabetes, cardiovascular disease, breast cancer and all-cause mortality. Studies of rare human disorders of puberty and animal models point to a complex hypothalamic-pituitary-hormonal regulation, but the mechanisms that determine pubertal timing and underlie its links to disease risk remain unclear. Here, using genome-wide and custom-genotyping arrays in up to 182,416 women of European descent from 57 studies, we found robust evidence (P < 5 × 10(-8)) for 123 signals at 106 genomic loci associated with age at menarche. Many loci were associated with other pubertal traits in both sexes, and there was substantial overlap with genes implicated in body mass index and various diseases, including rare disorders of puberty. Menarche signals were enriched in imprinted regions, with three loci (DLK1-WDR25, MKRN3-MAGEL2 and KCNK9) demonstrating parent-of-origin-specific associations concordant with known parental expression patterns. Pathway analyses implicated nuclear hormone receptors, particularly retinoic acid and γ-aminobutyric acid-B2 receptor signalling, among novel mechanisms that regulate pubertal timing in humans. Our findings suggest a genetic architecture involving at least hundreds of common variants in the coordinated timing of the pubertal transition

Factors that Impact Susceptibility to Fiber-Induced Health Effects

Asbestos and related fibers are associated with a number of adverse health effects, including malignant mesothelioma (MM), an aggressive cancer that generally develops in the surface serosal cells of the pleural, pericardial, and peritoneal cavities. Although approximately 80% of individuals with MM are exposed to asbestos, fewer than 5% of asbestos workers develop MM. In addition to asbestos, other mineralogical, environmental, genetic, and possibly viral factors might contribute to MM susceptibility. Given this complex etiology of MM, understanding susceptibility to MM needs to be a priority for investigators in order to reduce exposure of those most at risk to known environmental carcinogens. In this review, the current body of literature related to fiber-associated disease susceptibility including age, sex, nutrition, genetics, asbestos, and other mineral exposure is addressed with a focus on MM, and critical areas for further study are recommended

Loss of Heterozygosity at the CYP2D6 Locus in Breast Cancer: Implications for Germline Pharmacogenetic Studies

Controversy exists regarding the impact of CYP2D6 genotype on tamoxifen responsiveness. We examined loss of heterozygosity (LOH) at the CYP2D6 locus and determined its impact on genotyping error when tumor tissue is used as a DNA source

Effect of D222G Mutation in the Hemagglutinin Protein on Receptor Binding, Pathogenesis and Transmissibility of the 2009 Pandemic H1N1 Influenza Virus

Influenza viruses isolated during the 2009 H1N1 pandemic generally lack known molecular determinants of virulence associated with previous pandemic and highly pathogenic avian influenza viruses. The frequency of the amino acid substitution D222G in the hemagglutinin (HA) of 2009 H1N1 viruses isolated from severe but not mild human cases represents the first molecular marker associated with enhanced disease. To assess the relative contribution of this substitution in virus pathogenesis, transmission, and tropism, we introduced D222G by reverse genetics in the wild-type HA of the 2009 H1N1 virus, A/California/04/09 (CA/04). A dose-dependent glycan array analysis with the D222G virus showed a modest reduction in the binding avidity to human-like (α2-6 sialylated glycan) receptors and an increase in the binding to avian-like (α2-3 sialylated glycan) receptors in comparison with wild-type virus. In the ferret pathogenesis model, the D222G mutant virus was found to be similar to wild-type CA/04 virus with respect to lethargy, weight loss and replication efficiency in the upper and lower respiratory tract. Moreover, based on viral detection, the respiratory droplet transmission properties of these two viruses were found to be similar. The D222G virus failed to productively infect mice inoculated by the ocular route, but exhibited greater viral replication and weight loss than wild-type CA/04 virus in mice inoculated by the intranasal route. In a more relevant human cell model, D222G virus replicated with delayed kinetics compared with wild-type virus but to higher titer in human bronchial epithelial cells. These findings suggest that although the D222G mutation does not influence virus transmission, it may be considered a molecular marker for enhanced replication in certain cell types.Centers for Disease Control and Prevention (U.S.)United States. National Institutes of Health (merit award R37 GM057073-13)Singapore-MIT Alliance for Research and Technolog