Krina T. Zondervan

The complex interplay among factors that influence allelic association

Small effect sizes, common-disease/common-variant versus rare variant influences, biased single nucleotide polymorphism ascertainment and low linkage disequilibrium have recently been discussed as impediments to association studies. Such a focus on the individual factors that highlight their maximum potential effect (whether positive or deleterious) is often optimistic as, in practice, they do not operate in isolation. Instead, they work jointly to generate the disease gene architecture and to determine the ability of a study to discover it. Here, we consider how the effect size of the susceptibility locus, the frequency of the disease allele(s), the frequency of the marker allele(s) that are correlated with the disease allele(s) and the extent of linkage disequilibrium together influence genetic association studies.

Mapping cis- and trans-regulatory effects across multiple tissues in twins

Sequence-based variation in gene expression is a key driver of disease risk. Common variants regulating expression in cis have been mapped in many expression quantitative trait locus (eQTL) studies, typically in single tissues from unrelated individuals. Here, we present a comprehensive analysis of gene expression across multiple tissues conducted in a large set of mono- and dizygotic twins that allows systematic dissection of genetic (cis and trans) and non-genetic effects on gene expression. Using identity-by-descent estimates, we show that at least 40% of the total heritable cis effect on expression cannot be accounted for by common cis variants, a finding that reveals the contribution of low-frequency and rare regulatory variants with respect to both transcriptional regulation and complex trait susceptibility. We show that a substantial proportion of gene expression heritability is trans to the structural gene, and we identify several replicating trans variants that act predominantly in a tissue-restricted manner and may regulate the transcription of many genes.

Impact of endometriosis on quality of life and work productivity: a multicenter study across ten countries

OBJECTIVE To assess the impact of endometriosis on health-related quality of life (HRQoL) and work productivity. DESIGN Multicenter cross-sectional study with prospective recruitment. SETTING Sixteen clinical centers in ten countries. PATIENT(S) A total of 1,418 premenopausal women, aged 18-45 years, without a previous surgical diagnosis of endometriosis, having laparoscopy to investigate symptoms or to be sterilized. INTERVENTION(S) None. MAIN OUTCOME MEASURE(S) Diagnostic delay, HRQoL, and work productivity. RESULT(S) There was a delay of 6.7 years, principally in primary care, between onset of symptoms and a surgical diagnosis of endometriosis, which was longer in centers where women received predominantly state-funded health care (8.3 vs. 5.5 years). Delay was positively associated with the number of pelvic symptoms (chronic pelvic pain, dysmenorrhoea, dyspareunia, and heavy periods) and a higher body mass index. Physical HRQoL was significantly reduced in affected women compared with those with similar symptoms and no endometriosis. Each affected woman lost on average 10.8 hours (SD 12.2) of work weekly, mainly owing to reduced effectiveness while working. Loss of work productivity translated into significant costs per woman/week, from US

The architecture of gene regulatory variation across multiple human tissues: the MuTHER study.

4 in Nigeria to US

Linkage disequilibrium mapping via cladistic analysis of single-nucleotide polymorphism haplotypes.

456 in Italy. CONCLUSION(S) Endometriosis impairs HRQoL and work productivity across countries and ethnicities, yet women continue to experience diagnostic delays in primary care. A higher index of suspicion is needed to expedite specialist assessment of symptomatic women. Future research should seek to clarify pain mechanisms in relation to endometriosis severity.

Basic statistical analysis in genetic case-control studies

While there have been studies exploring regulatory variation in one or more tissues, the complexity of tissue-specificity in multiple primary tissues is not yet well understood. We explore in depth the role of cis-regulatory variation in three human tissues: lymphoblastoid cell lines (LCL), skin, and fat. The samples (156 LCL, 160 skin, 166 fat) were derived simultaneously from a subset of well-phenotyped healthy female twins of the MuTHER resource. We discover an abundance of cis-eQTLs in each tissue similar to previous estimates (858 or 4.7% of genes). In addition, we apply factor analysis (FA) to remove effects of latent variables, thus more than doubling the number of our discoveries (1,822 eQTL genes). The unique study design (Matched Co-Twin Analysis—MCTA) permits immediate replication of eQTLs using co-twins (93%–98%) and validation of the considerable gain in eQTL discovery after FA correction. We highlight the challenges of comparing eQTLs between tissues. After verifying previous significance threshold-based estimates of tissue-specificity, we show their limitations given their dependency on statistical power. We propose that continuous estimates of the proportion of tissue-shared signals and direct comparison of the magnitude of effect on the fold change in expression are essential properties that jointly provide a biologically realistic view of tissue-specificity. Under this framework we demonstrate that 30% of eQTLs are shared among the three tissues studied, while another 29% appear exclusively tissue-specific. However, even among the shared eQTLs, a substantial proportion (10%–20%) have significant differences in the magnitude of fold change between genotypic classes across tissues. Our results underline the need to account for the complexity of eQTL tissue-specificity in an effort to assess consequences of such variants for complex traits.

Priorities for Endometriosis Research: Recommendations From an International Consensus Workshop

We present a novel approach to disease-gene mapping via cladistic analysis of single-nucleotide polymorphism (SNP) haplotypes obtained from large-scale, population-based association studies, applicable to whole-genome screens, candidate-gene studies, or fine-scale mapping. Clades of haplotypes are tested for association with disease, exploiting the expected similarity of chromosomes with recent shared ancestry in the region flanking the disease gene. The method is developed in a logistic-regression framework and can easily incorporate covariates such as environmental risk factors or additional unlinked loci to allow for population structure. To evaluate the power of this approach to detect disease-marker association, we have developed a simulation algorithm to generate high-density SNP data with short-range linkage disequilibrium based on empirical patterns of haplotype diversity. The results of the simulation study highlight substantial gains in power over single-locus tests for a wide range of disease models, despite overcorrection for multiple testing.

Designing candidate gene and genome-wide case-control association studies

This protocol describes how to perform basic statistical analysis in a population-based genetic association case-control study. The steps described involve the (i) appropriate selection of measures of association and relevance of disease models; (ii) appropriate selection of tests of association; (iii) visualization and interpretation of results; (iv) consideration of appropriate methods to control for multiple testing; and (v) replication strategies. Assuming no previous experience with software such as PLINK, R or Haploview, we describe how to use these popular tools for handling single-nucleotide polymorphism data in order to carry out tests of association and visualize and interpret results. This protocol assumes that data quality assessment and control has been performed, as described in a previous protocol, so that samples and markers deemed to have the potential to introduce bias to the study have been identified and removed. Study design, marker selection and quality control of case-control studies have also been discussed in earlier protocols. The protocol should take ∼1 h to complete.

The genetic basis of endometriosis.

Endometriosis is an estrogen-dependent disorder where endometrial tissue forms lesions outside the uterus. Endometriosis affects an estimated 10% of women in the reproductive-age group, rising to 30% to 50% in patients with infertility and/or pain, with significant impact on their physical, mental, and social well-being. There is no known cure, and most current medical treatments are not suitable long term due to their side-effect profiles. Endometriosis has an estimated annual cost in the United States of

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

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