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Characterizing the complex relationship between genetic, epigenetic, and transcriptomic variation has the potential to increase understanding about the mechanisms underpinning health and disease phenotypes. We undertook a comprehensive analysis of common genetic variation on DNA methylation (DNAm) by using the Illumina EPIC array to profile samples from the UK Household Longitudinal study. We identified 12,689,548 significant DNA methylation quantitative trait loci (mQTL) associations (p < 6.52 × 10−14) occurring between 2,907,234 genetic variants and 93,268 DNAm sites, including a large number not identified by previous DNAm-profiling methods. We demonstrate the utility of these data for interpreting the functional consequences of common genetic variation associated with > 60 human traits by using summary-data-based Mendelian randomization (SMR) to identify 1,662 pleiotropic associations between 36 complex traits and 1,246 DNAm sites. We also use SMR to characterize the relationship between DNAm and gene expression and thereby identify 6,798 pleiotropic associations between 5,420 DNAm sites and the transcription of 1,702 genes. Our mQTL database and SMR results are available via a searchable online database as a resource to the research community.

Objective: The objective was to provide an overall assessment of genetic linkage data of BMI and BMI-defined obesity using a nonparametric genome scan meta-analysis. Research Methods and Procedures: We identified 37 published studies containing data on over 31,000 individuals from more than >10,000 families and obtained genome-wide logarithm of the odds (LOD) scores, non-parametric linkage (NPL) scores, or maximum likelihood scores (MLS). BMI was analyzed in a pooled set of all studies, as a subgroup of 10 studies that used BMI-defined obesity, and for subgroups ascertained through type 2 diabetes, hypertension, or subjects of European ancestry. Results: Bins at chromosome 13q13.2- q33.1, 12q23-q24.3 achieved suggestive evidence of linkage to BMI in the pooled analysis and samples ascertained for hypertension. Nominal evidence of linkage to these regions and suggestive evidence for 11q13.3-22.3 were also observed for BMI-defined obesity. The FTO obesity gene locus at 16q12.2 also showed nominal evidence for linkage. However, overall distribution of summed rank p values

Summary Common disorders with genetic susceptibilities involve the action of multiple genes interacting with each other and with environmental factors, making it difficult to localize the specific genetic loci responsible. An important route to the disentangling of this complex inheritance is through the study of normal physiological variation in quantitative risk factors that may underlie liability to disease. We present an analysis of HDL-cholesterol (HDL-C), which is inversely correlated with risk of heart disease. A variety of HDL subphenotypes were analyzed, including HDL particle–size classes and the concentrations and proportions of esterified and unesterified HDL-C. Results of a complete genomic screen in large, randomly ascertained pedigrees implicated two loci, one on chromosome 8 and the other on chromosome 15, that influence a component of HDL-C—namely, unesterified HDL 2a -C. Multivariate analyses of multiple HDL phenotypes and simultaneous multilocus analysis of the quantitative-trait loci identified permit further characterization of the genetic effects on HDL-C. These analyses suggest that the action of the chromosome 8 locus is specific to unesterified cholesterol levels, whereas the chromosome 15 locus appears to influence both HDL-C concentration and distribution of cholesterol among HDL particle sizes.

SummaryGenomewide searches for loci influencing complex human traits and diseases such as diabetes, hypertension, and obesity are often plagued by low power and interpretive difficulties. Attempts to remedy these difficulties have typically relied on, and have promoted the use of, novel subject-ascertainment schemes, larger sample sizes, a greater density of DNA markers, and more-sophisticated statistical modeling and analysis strategies. Many of these remedies can be costly to implement. We investigate the utility of a simple statistical model for the mapping of quantitative-trait loci that incorporates multiple phenotypic or diagnostic endpoints into a gene-mapping analysis. The approach considers finding a linear combination of multiple phenotypic values that maximizes the evidence for linkage to a locus. Our results suggest that substantial increases in the power to map loci can be obtained with the proposed technique, although the increase in power obtained is a function of the size and direction of the residual correlation among the phenotypes used in the analysis. Extensive simulation studies are described that justify these claims, for cases in which two phenotypic measures are analyzed. This approach can be easily extended to cover more-complex situations and may provide a basis for more insightful genetic-analysis paradigms.

Asthma is a common chronic respiratory disease characterized by airway hyperresponsiveness (AHR). The genetics of asthma have been widely studied in mouse and human, and homologous genomic regions have been associated with mouse AHR and human asthma-related phenotypes. Our goal was to identify asthma-related genes by integrating AHR associations in mouse with human genome-wide association study (GWAS) data. We used Efficient Mixed Model Association (EMMA) analysis to conduct a GWAS of baseline AHR measures from males and females of 31 mouse strains. Genes near or containing SNPs with EMMA p-values

Raised blood pressure (BP) is a major risk factor for cardiovascular disease. Previous studies have identified 47 distinct genetic variants robustly associated with BP, but collectively these explain only a few percent of the heritability for BP phenotypes. To find additional BP loci, we used a bespoke gene-centric array to genotype an independent discovery sample of 25,118 individuals that combined hypertensive case-control and general population samples. We followed up four SNPs associated with BP at our p < 8.56 x 10(-7) study-specific significance threshold and six suggestively associated SNPs in a further 59,349 individuals. We identified and replicated a SNP at LSP1/TNNT3, a SNP at MTHFR-NPPB independent (r(2) = 0.33) of previous reports, and replicated SNPs at AGT and ATP2B1 reported previously. An analysis of combined discovery, and follow-up data identified SNPs significantly associated with BP at p < 8.56 x 10(-7) at four further loci (NPR3, FIFE, NOS3, and SOX6). The high number of discoveries made with modest genotyping effort can be attributed to using a large-scale yet targeted genotyping array and to the development of a weighting scheme that maximized power when meta-analyzing results from samples ascertained with extreme phenotypes, in combination with results from nonascertained or population samples. Chromatin immunoprecipitation and transcript expression data highlight potential gene regulatory mechanisms at the MTHFR and NOS3 loci. These results provide candidates for further study to help dissect mechanisms affecting BP and highlight the utility of studying SNPs and samples that are independent of those studied previously even when the sample size is smaller than that in previous studies. ispartof: American Journal of Human Genetics vol:89 issue:6 pages:688-700 ispartof: location:United States status: published

Background A strategy to improve genetic studies of schizophrenia involves the use of endophenotypes. Information on overlapping genetic contributions among endophenotypes may provide additional power, reveal biological pathways, and have practical implications for genetic research. Several cognitive endophenotypes, including intelligence, are likely to be modulated by overlapping genetic influences. Methods We quantified potential genetic and environmental correlations among endophenotypes for schizophrenia, including sensorimotor gating, openness, verbal fluency, early visual perception, spatial working memory, and intelligence, using variance component models in 35 patients and 145 relatives from 25 multigenerational Dutch families multiply affected with schizophrenia. Results Significant correlations were found between spatial working memory and intelligence (.45), verbal fluency and intelligence (.36), verbal fluency and spatial working memory (.20), and early visual perception and spatial working memory (.19). A strong genetic correlation (.75) accounted for 76% of the variance shared between spatial working memory and intelligence. Significant environmental correlations were found between verbal fluency and openness (.50) and between verbal fluency and spatial working memory (.58). Sensorimotor gating and openness showed few genetic or environmental correlations with other endophenotypes. Conclusions Our results suggest that intelligence strongly overlaps genetically with a known cognitive endophenotype for schizophrenia. Intelligence may thus be a promising endophenotype for genetic research in schizophrenia, even though the underlying genetic mechanism may still be complex. In contrast, sensorimotor gating and openness appear to represent separate genetic entities with simpler inheritance patterns and may therefore augment the detection of separate genetic pathways contributing to schizophrenia.

SummaryA cholesterol-lowering gene has been postulated from familial hypercholesterolemia (FH) families having heterozygous persons with normal LDL levels and homozygous individuals with LDL levels similar to those in persons with heterozygous FH. We studied such a family with FH that also had members without FH and with lower-than-normal LDL levels. We performed linkage analyses and identified a locus at 13q, defined by markers D13S156 and D13S158. FASTLINK and GENEHUNTER yielded LOD scores >5 and >4, respectively, whereas an affected-sib-pair analysis gave a peak multipoint LOD score of 4.8, corresponding to a P value of 1.26×10−6. A multipoint quantitative-trait-locus (QTL) linkage analysis with maximum-likelihood binomial QTL verified this locus as a QTL for LDL levels. To test the relevance of this QTL in an independent normal population, we studied MZ and DZ twin subjects. An MZ-DZ comparison confirmed genetic variance with regard to lipid concentrations. We then performed an identity-by-descent linkage analysis on the DZ twins, with markers at the 13q locus. We found strong evidence for linkage at this locus with LDL (P