Whole-exome sequencing of 2,000 Danish individuals and the role of rare coding variants in type 2 diabetes.

It has been hypothesized that, in aggregate, rare variants in coding regions of genes explain a substantial fraction of the heritability of common diseases. We sequenced the exomes of 1,000 Danish cases with common forms of type 2 diabetes (including body mass index > 27.5 kg/m(2) and hypertension) and 1,000 healthy controls to an average depth of 56×. Our simulations suggest that our study had the statistical power to detect at least one causal gene (a gene containing causal mutations) if the heritability of these common diseases was explained by rare variants in the coding regions of a limited number of genes. We applied a series of gene-based tests to detect such susceptibility genes. However, no gene showed a significant association with disease risk after we corrected for the number of genes analyzed. Thus, we could reject a model for the genetic architecture of type 2 diabetes where rare nonsynonymous variants clustered in a modest number of genes (fewer than 20) are responsible for the majority of disease risk.

Direct androgen receptor regulation of sexually dimorphic gene expression in the mammalian kidney.

Mammalian organs exhibit distinct physiology, disease susceptibility and injury responses between the sexes. In the mouse kidney, sexually dimorphic gene activity maps predominantly to proximal tubule (PT) segments. Bulk RNA-seq data demonstrated sex differences were established from 4 and 8 weeks after birth under gonadal control. Hormone injection studies and genetic removal of androgen and estrogen receptors demonstrated androgen receptor (AR) mediated regulation of gene activity in PT cells as the regulatory mechanism. Interestingly, caloric restriction feminizes the male kidney. Single-nuclear multiomic analysis identified putative cis-regulatory regions and cooperating factors mediating PT responses to AR activity in the mouse kidney. In the human kidney, a limited set of genes showed conserved sex-linked regulation while analysis of the mouse liver underscored organ-specific differences in the regulation of sexually dimorphic gene expression. These findings raise interesting questions on the evolution, physiological significance, and disease and metabolic linkage, of sexually dimorphic gene activity.

Direct androgen receptor control of sexually dimorphic gene expression in the mammalian kidney.

Mammalian organs exhibit distinct physiology, disease susceptibility, and injury responses between the sexes. In the mouse kidney, sexually dimorphic gene activity maps predominantly to proximal tubule (PT) segments. Bulk RNA sequencing (RNA-seq) data demonstrated that sex differences were established from 4 and 8 weeks after birth under gonadal control. Hormone injection studies and genetic removal of androgen and estrogen receptors demonstrated androgen receptor (AR)-mediated regulation of gene activity in PT cells as the regulatory mechanism. Interestingly, caloric restriction feminizes the male kidney. Single-nuclear multiomic analysis identified putative cis-regulatory regions and cooperating factors mediating PT responses to AR activity in the mouse kidney. In the human kidney, a limited set of genes showed conserved sex-linked regulation, whereas analysis of the mouse liver underscored organ-specific differences in the regulation of sexually dimorphic gene expression. These findings raise interesting questions on the evolution, physiological significance, disease, and metabolic linkage of sexually dimorphic gene activity.

Human metabolic profiles are stably controlled by genetic and environmental variation.

¹H Nuclear Magnetic Resonance spectroscopy (¹H NMR) is increasingly used to measure metabolite concentrations in sets of biological samples for top-down systems biology and molecular epidemiology. For such purposes, knowledge of the sources of human variation in metabolite concentrations is valuable, but currently sparse. We conducted and analysed a study to create such a resource. In our unique design, identical and non-identical twin pairs donated plasma and urine samples longitudinally. We acquired ¹H NMR spectra on the samples, and statistically decomposed variation in metabolite concentration into familial (genetic and common-environmental), individual-environmental, and longitudinally unstable components. We estimate that stable variation, comprising familial and individual-environmental factors, accounts on average for 60% (plasma) and 47% (urine) of biological variation in ¹H NMR-detectable metabolite concentrations. Clinically predictive metabolic variation is likely nested within this stable component, so our results have implications for the effective design of biomarker-discovery studies. We provide a power-calculation method which reveals that sample sizes of a few thousand should offer sufficient statistical precision to detect ¹H NMR-based biomarkers quantifying predisposition to disease.

A complete classification of epistatic two-locus models.

BACKGROUND: The study of epistasis is of great importance in statistical genetics in fields such as linkage and association analysis and QTL mapping. In an effort to classify the types of epistasis in the case of two biallelic loci Li and Reich listed and described all models in the simplest case of 0/1 penetrance values. However, they left open the problem of finding a classification of two-locus models with continuous penetrance values. RESULTS: We provide a complete classification of biallelic two-locus models. In addition to solving the classification problem for dichotomous trait disease models, our results apply to any instance where real numbers are assigned to genotypes, and provide a complete framework for studying epistasis in QTL data. Our approach is geometric and we show that there are 387 distinct types of two-locus models, which can be reduced to 69 when symmetry between loci and alleles is accounted for. The model types are defined by 86 circuits, which are linear combinations of genotype values, each of which measures a fundamental unit of interaction. CONCLUSION: The circuits provide information on epistasis beyond that contained in the additive x additive, additive x dominance, and dominance x dominance interaction terms. We discuss the connection between our classification and standard epistatic models and demonstrate its utility by analyzing a previously published dataset.

Association mapping from sequencing reads using k-mers.

Genome wide association studies (GWAS) rely on microarrays, or more recently mapping of sequencing reads, to genotype individuals. The reliance on prior sequencing of a reference genome limits the scope of association studies, and also precludes mapping associations outside of the reference. We present an alignment free method for association studies of categorical phenotypes based on counting [Formula: see text]-mers in whole-genome sequencing reads, testing for associations directly between [Formula: see text]-mers and the trait of interest, and local assembly of the statistically significant [Formula: see text]-mers to identify sequence differences. An analysis of the 1000 genomes data show that sequences identified by our method largely agree with results obtained using the standard approach. However, unlike standard GWAS, our method identifies associations with structural variations and sites not present in the reference genome. We also demonstrate that population stratification can be inferred from [Formula: see text]-mers. Finally, application to an E.coli dataset on ampicillin resistance validates the approach.

Placenta and appetite genes GDF15 and IGFBP7 are associated with hyperemesis gravidarum.

Hyperemesis gravidarum (HG), severe nausea and vomiting of pregnancy, occurs in 0.3-2% of pregnancies and is associated with maternal and fetal morbidity. The cause of HG remains unknown, but familial aggregation and results of twin studies suggest that understanding the genetic contribution is essential for comprehending the disease etiology. Here, we conduct a genome-wide association study (GWAS) for binary (HG) and ordinal (severity of nausea and vomiting) phenotypes of pregnancy complications. Two loci, chr19p13.11 and chr4q12, are genome-wide significant (p < 5 × 10-8) in both association scans and are replicated in an independent cohort. The genes implicated at these two loci are GDF15 and IGFBP7 respectively, both known to be involved in placentation, appetite, and cachexia. While proving the casual roles of GDF15 and IGFBP7 in nausea and vomiting of pregnancy requires further study, this GWAS provides insights into the genetic risk factors contributing to the disease.

A meta-analysis of genome-wide association studies identifies 17 new Parkinson's disease risk loci.

Common variant genome-wide association studies (GWASs) have, to date, identified >24 risk loci for Parkinson's disease (PD). To discover additional loci, we carried out a GWAS comparing 6,476 PD cases with 302,042 controls, followed by a meta-analysis with a recent study of over 13,000 PD cases and 95,000 controls at 9,830 overlapping variants. We then tested 35 loci (P < 1 × 10-6) in a replication cohort of 5,851 cases and 5,866 controls. We identified 17 novel risk loci (P < 5 × 10-8) in a joint analysis of 26,035 cases and 403,190 controls. We used a neurocentric strategy to assign candidate risk genes to the loci. We identified protein-altering or cis-expression quantitative trait locus (cis-eQTL) variants in linkage disequilibrium with the index variant in 29 of the 41 PD loci. These results indicate a key role for autophagy and lysosomal biology in PD risk, and suggest potential new drug targets for PD.

A longitudinal genome-wide association study of anti-tumor necrosis factor response among Japanese patients with rheumatoid arthritis.

BACKGROUND: Studies of Caucasian patients with rheumatoid arthritis (RA) to identify genetic biomarkers of anti-tumor necrosis factor (TNF) response have used response at a single time point as the phenotype with which single nucleotide polymorphism (SNP) associations have been tested. The findings have been inconsistent across studies. Among Japanese patients, only a few SNPs have been investigated. We report here the first genome-wide association study (GWAS) to identify genetic biomarkers of anti-TNF response among Japanese RA patients, using response at 2 time-points for a more reliable clinical phenotype over time. METHODS: Disease Activity Scores based on 28 joint counts (DAS28) were assessed at baseline (before initial therapy), and after 3 and 6 months in 487 Japanese RA patients starting anti-TNF therapy for the first time or switching to a new anti-TNF agent. A genome-wide panel of SNPs was genotyped and additional SNPs were imputed. Using change in DAS28 scores from baseline at both 3 (ΔDAS-3) and 6 months (ΔDAS-6) as the response phenotype, a longitudinal genome-wide association analysis was conducted using generalized estimating equations (GEE) models, adjusting for baseline DAS28, treatment duration, type of anti-TNF agent and concomitant methotrexate. Cross-sectional analyses were performed using multivariate linear regression models, with response from a single time point (ΔDAS-3 or ΔDAS-6) as phenotype; all other variables were the same as in the GEE models. RESULTS: In the GEE models, borderline significant association was observed at 3 chromosomal regions (6q15: rs284515, p = 6.6x10(-7); 6q27: rs75908454, p = 6.3x10(-7) and 10q25.3: rs1679568, p = 8.1x10(-7)), extending to numerous SNPs in linkage disequilibrium (LD) across each region. Potential candidate genes in these regions include MAP3K7, BACH2 (6q15), GFRA1 (10q25.3), and WDR27 (6q27). The association at GFRA1 replicates a previous finding from a Caucasian dataset. In the cross-sectional analyses, ΔDAS-6 was significantly associated with the 6q15 locus (rs284511, p = 2.5x10(-8)). No other significant or borderline significant associations were identified. CONCLUSION: Three genomic regions demonstrated significant or borderline significant associations with anti-TNF response in our dataset of Japanese RA patients, including a locus previously associated among Caucasians. Using repeated measures of response as phenotype enhanced the power to detect these associations.

The power of two-locus affected sib-pair linkage analysis to detect interacting disease loci.

It has been shown that two-locus linkage analysis can, for some two-locus disease models, be used to detect effects at disease loci that do not reach significance in a genome scan. However, few examples exist where two-locus linkage has been successfully used to map genes. We study the possible gain in power of affected sib-pair nonparametric two-locus linkage analysis for two-locus models which fulfil the two-locus triangle constraints. Using a new parameterization of the two-locus joint identity-by-descent sharing probabilities we can, for fixed marginal sharing at both of two unlinked disease loci, derive a two-locus distribution such that the power of a two-locus analysis is maximized. In a simulation study we look at two test statistics, the two-locus maximum likelihood score and the correlation between nonparametric linkage scores, and study power as a function of marginal sharing. We show that in a best-case scenario two-locus linkage can have considerable power to detect pairs of interacting loci if there is a moderate increase in allele sharing at one of the two loci, even if there is a very small increase in allele sharing at the other locus. But we also show that the power to detect interacting loci in a two-locus analysis decreases as the marginal sharing at the two loci decreases and for any distribution with a small increase in allele sharing at both loci the power of a two-locus analysis is always low.