Exploring the unknown: assumptions about allelic architecture and strategies for susceptibility variant discovery

Identification of common-variant associations for many common disorders has been highly effective, but the loci detected so far typically explain only a small proportion of the genetic predisposition to disease. Extending explained genetic variance is one of the major near-term goals of human genetic research. Next-generation sequencing technologies offer great promise, but optimal strategies for their deployment remain uncertain, not least because we lack a clear view of the characteristics of the variants being sought. Here, I discuss what can and cannot be inferred about complex trait disease architecture from the information currently available and review the implications for future research strategies

New methods for finding disease-susceptibility genes: impact and potential

Improved techniques for defining disease-gene location and evaluating the biological candidacy of regional transcripts will hasten disease-gene discovery

Will the real disease gene please stand up?

A common dilemma arising in linkage studies of complex genetic diseases is the selection of positive signals, their follow-up with association studies and discrimination between true and false positive results. Several strategies for overcoming these issues have been devised. Using the Genetic Analysis Workshop 14 simulated dataset, we aimed to apply different analytical approaches and evaluate their performance in discerning real associations. We considered a) haplotype analyses, b) different methods adjusting for multiple testing, c) replication in a second dataset, and d) exhaustive genotyping of all markers in a sufficiently powered, large sample group. We found that haplotype-based analyses did not substantially improve over single-point analysis, although this may reflect the low levels of linkage disequilibrium simulated in the datasets provided. Multiple testing correction methods were in general found to be over-conservative. Replication of nominally positive results in a second dataset appears to be less stringent, resulting in the follow-up of false positives. Performing a comprehensive assay of all markers in a large, well-powered dataset appears to be the most effective strategy for complex disease gene identification

Metabolic syndrome in polycystic ovary syndrome

Zespół metaboliczny (MS, metabolic syndrome) i zespół policystycznych jajników (PCOS, polycystic ovary syndrome) są często rozpoznawanymi zaburzeniami u kobiet. Częstość występowania zespołu metabolicznego u kobiet z PCOS zależy od zastosowanych kryteriów diagnostycznych. W prezentowanej pracy autorzy rozważają tezę, że przyczyną obu zaburzeń może być insulinooporność, będąca następstwem otyłości brzusznej. Ponadto dokonują przeglądu literatury dotyczącej występowania MS u kobiet z PCOS oraz omawiają wpływ wyboru określonych kryteriów diagnostycznych MS i PCOS na oszacowanie czêstoœci ich występowania.Both metabolic syndrome (MS) and polycystic ovary syndrome (PCOS) are common among women. The exact prevalence of MS in women with PCOS is dependent upon the diagnostic criteria used for each. However, the frequent co-occurrence of both MS and PCOS in women is suggestive of a common aetiology. In this short review article we argue that insulin resistance, as a consequence of abdominal obesity, may represent such a common aetiology. We also review the literature on the prevalence of MS in women with PCOS and consider the impact that the particular criteria used to diagnose both MS and PCOS may have had on these estimates of prevalence

Assessing allele-specific expression across multiple tissues from RNA-seq read data

Motivation: RNA sequencing enables allele-specific expression (ASE) studies that complement standard genotype expression studies for common variants and, importantly, also allow measuring the regulatory impact of rare variants. The Genotype-Tissue Expression (GTEx) project is collecting RNA-seq data on multiple tissues of a same set of individuals and novel methods are required for the analysis of these data. Results: We present a statistical method to compare different patterns of ASE across tissues and to classify genetic variants according to their impact on the tissue-wide expression profile. We focus on strong ASE effects that we are expecting to see for protein-truncating variants, but our method can also be adjusted for other types of ASE effects. We illustrate the method with a real data example on a tissue-wide expression profile of a variant causal for lipoid proteinosis, and with a simulation study to assess our method more generally. Availability and implementation: http://www.well.ox.ac.uk/~rivas/mamba/. R-sources and data examples http://www.iki.fi/mpirinen/ Contact: [email protected] or [email protected] Supplementary information: Supplementary data are available at Bioinformatics onlin

Assessment of High-Sensitivity C-Reactive Protein Levels as Diagnostic Discriminator of Maturity-Onset Diabetes of the Young Due to HNF1A Mutations

OBJECTIVE: Despite the clinical importance of an accurate diagnosis in individuals with monogenic forms of diabetes, restricted access to genetic testing leaves many patients with undiagnosed diabetes. Recently, common variation near the HNF1 homeobox A (HNF1A) gene was shown to influence C-reactive protein levels in healthy adults. We hypothesized that serum levels of high-sensitivity C-reactive protein (hs-CRP) could represent a clinically useful biomarker for the identification of HNF1A mutations causing maturity-onset diabetes of the young (MODY). RESEARCH DESIGN AND METHODS: Serum hs-CRP was measured in subjects with HNF1A-MODY (n = 31), autoimmune diabetes (n = 316), type 2 diabetes (n = 240), and glucokinase (GCK) MODY (n = 24) and in nondiabetic individuals (n = 198). The discriminative accuracy of hs-CRP was evaluated through receiver operating characteristic (ROC) curve analysis, and performance was compared with standard diagnostic criteria. Our primary analyses excluded approximately 11% of subjects in whom the single available hs-CRP measurement was >10 mg/l. RESULTS: Geometric mean (SD range) hs-CRP levels were significantly lower (

SAIL—a software system for sample and phenotype availability across biobanks and cohorts

Summary: The Sample avAILability system—SAIL—is a web based application for searching, browsing and annotating biological sample collections or biobank entries. By providing individual-level information on the availability of specific data types (phenotypes, genetic or genomic data) and samples within a collection, rather than the actual measurement data, resource integration can be facilitated. A flexible data structure enables the collection owners to provide descriptive information on their samples using existing or custom vocabularies. Users can query for the available samples by various parameters combining them via logical expressions. The system can be scaled to hold data from millions of samples with thousands of variables

Childhood cognitive ability moderates the later-life manifestation of type 2 diabetes genetic risk

Objective: The study investigated whether childhood cognitive ability moderates Type 2 diabetes polygenic risk manifestation in older age. Method: In 940 relatively healthy people (mean age 69.55 ± 0.85), we tested whether self-reported diabetes and hemoglobin HbA1c (HbA1c) levels were predicted by diabetes polygenic risk, cognitive ability measured about 60 years earlier, and their interaction. Polygenic risk scores aggregated the small effects of up to nearly 121,000 single-nucleotide polymorphisms (SNPs). Participants’ cognitive ability was measured at age 11. Results: Both polygenic risk and low childhood cognitive ability significantly predicted diabetes diagnosis. Polygenic risk interacted with cognitive ability (p = .02), predicting HbA1c levels more strongly in people with below-median cognitive ability (effect r = .21) than in people with above-median cognitive ability (effect r = .10). The interaction term was not significant for self-reported diabetes (p = .34), although the genetic risk-diabetes association showed a tendency of being stronger among those with below-median cognitive ability. Conclusions: Higher premorbid cognitive ability may provide some environmental protection against the manifestation of Type 2 diabetes genetic risk. This information may improve early identification of diabetes risk and inform intervention development

Low Frequency Variants in the Exons Only Encoding Isoform A of HNF1A Do Not Contribute to Susceptibility to Type 2 Diabetes

Background: There is considerable interest in the hypothesis that low frequency, intermediate penetrance variants contribute to the proportion of Type 2 Diabetes (T2D) susceptibility not attributable to the common variants uncovered through genome-wide association approaches. Genes previously implicated in monogenic and multifactorial forms of diabetes are obvious candidates in this respect. In this study, we focussed on exons 8-10 of the HNF1A gene since rare, penetrant mutations in these exons (which are only transcribed in selected HNF1A isoforms) are associated with a later age of diagnosis of Maturity onset diabetes of the young (MODY) than mutations in exons 1-7. The age of diagnosis in the subgroup of HNF1A-MODY individuals with exon 8-10 mutations overlaps with that of early multifactorial T2D, and we set out to test the hypothesis that these exons might also harbour low-frequency coding variants of intermediate penetrance that contribute to risk of multifactorial T2D. Methodology and principal findings: We performed targeted capillary resequencing of HNF1A exons 8-10 in 591 European T2D subjects enriched for genetic aetiology on the basis of an early age of diagnosis (≤ 45 years) and/or family history of T2D (≥ 1 affected sibling). PCR products were sequenced and compared to the published HNF1A sequence. We identified several variants (rs735396 [IVS9-24T>C], rs1169304 [IVS8+29T>C], c.1768+44C>T [IVS9+44C>T] and rd61953349 [c.1545G>A, p.T515T] but no novel non-synonymous coding variants were detected. Conclusions and significance: We conclude that low frequency, nonsynonymous coding variants in the terminal exons of HNF1A are unlikely to contribute to T2D-susceptibility in European samples. Nevertheless, the rationale for seeking low-frequency causal variants in genes known to contain rare, penetrant mutations remains strong and should motivate efforts to screen other genes in a similar fashion