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.
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DOBA, IJS, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
Epigenetic modifications such as DNA methylation play a key role in gene regulation and disease susceptibility. However, little is known about the genome-wide frequency, localization, and function of ...methylation variation and how it is regulated by genetic and environmental factors. We utilized the Multiple Tissue Human Expression Resource (MuTHER) and generated Illumina 450K adipose methylome data from 648 twins. We found that individual CpGs had low variance and that variability was suppressed in promoters. We noted that DNA methylation variation was highly heritable (h2median = 0.34) and that shared environmental effects correlated with metabolic phenotype-associated CpGs. Analysis of methylation quantitative-trait loci (metQTL) revealed that 28% of CpGs were associated with nearby SNPs, and when overlapping them with adipose expression quantitative-trait loci (eQTL) from the same individuals, we found that 6% of the loci played a role in regulating both gene expression and DNA methylation. These associations were bidirectional, but there were pronounced negative associations for promoter CpGs. Integration of metQTL with adipose reference epigenomes and disease associations revealed significant enrichment of metQTL overlapping metabolic-trait or disease loci in enhancers (the strongest effects were for high-density lipoprotein cholesterol and body mass index BMI). We followed up with the BMI SNP rs713586, a cg01884057 metQTL that overlaps an enhancer upstream of ADCY3, and used bisulphite sequencing to refine this region. Our results showed widespread population invariability yet sequence dependence on adipose DNA methylation but that incorporating maps of regulatory elements aid in linking CpG variation to gene regulation and disease risk in a tissue-dependent manner.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Genetics and epigenetics of obesity Herrera, Blanca M; Keildson, Sarah; Lindgren, Cecilia M
Maturitas,
05/2011, Volume:
69, Issue:
1
Journal Article
Peer reviewed
Open access
Abstract Obesity results from interactions between environmental and genetic factors. Despite a relatively high heritability of common, non-syndromic obesity (40–70%), the search for genetic variants ...contributing to susceptibility has been a challenging task. Genome wide association (GWA) studies have dramatically changed the pace of detection of common genetic susceptibility variants. To date, more than 40 genetic variants have been associated with obesity and fat distribution. However, since these variants do not fully explain the heritability of obesity, other forms of variation, such as epigenetics marks, must be considered. Epigenetic marks, or “imprinting”, affect gene expression without actually changing the DNA sequence. Failures in imprinting are known to cause extreme forms of obesity (e.g. Prader–Willi syndrome), but have also been convincingly associated with susceptibility to obesity. Furthermore, environmental exposures during critical developmental periods can affect the profile of epigenetic marks and result in obesity. We review the most recent evidence for genetic and epigenetic mechanisms involved in the susceptibility and development of obesity. Only a comprehensive understanding of the underlying genetic and epigenetic mechanisms, and the metabolic processes they govern, will allow us to manage, and eventually prevent, obesity.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
Sex hormone-binding globulin (SHBG) is a glycoprotein responsible for the transport and biologic availability of sex steroid hormones, primarily testosterone and estradiol. SHBG has been associated ...with chronic diseases including type 2 diabetes (T2D) and with hormone-sensitive cancers such as breast and prostate cancer. We performed a genome-wide association study (GWAS) meta-analysis of 21,791 individuals from 10 epidemiologic studies and validated these findings in 7,046 individuals in an additional six studies. We identified twelve genomic regions (SNPs) associated with circulating SHBG concentrations. Loci near the identified SNPs included SHBG (rs12150660, 17p13.1, p = 1.8 × 10(-106)), PRMT6 (rs17496332, 1p13.3, p = 1.4 × 10(-11)), GCKR (rs780093, 2p23.3, p = 2.2 × 10(-16)), ZBTB10 (rs440837, 8q21.13, p = 3.4 × 10(-09)), JMJD1C (rs7910927, 10q21.3, p = 6.1 × 10(-35)), SLCO1B1 (rs4149056, 12p12.1, p = 1.9 × 10(-08)), NR2F2 (rs8023580, 15q26.2, p = 8.3 × 10(-12)), ZNF652 (rs2411984, 17q21.32, p = 3.5 × 10(-14)), TDGF3 (rs1573036, Xq22.3, p = 4.1 × 10(-14)), LHCGR (rs10454142, 2p16.3, p = 1.3 × 10(-07)), BAIAP2L1 (rs3779195, 7q21.3, p = 2.7 × 10(-08)), and UGT2B15 (rs293428, 4q13.2, p = 5.5 × 10(-06)). These genes encompass multiple biologic pathways, including hepatic function, lipid metabolism, carbohydrate metabolism and T2D, androgen and estrogen receptor function, epigenetic effects, and the biology of sex steroid hormone-responsive cancers including breast and prostate cancer. We found evidence of sex-differentiated genetic influences on SHBG. In a sex-specific GWAS, the loci 4q13.2-UGT2B15 was significant in men only (men p = 2.5 × 10(-08), women p = 0.66, heterogeneity p = 0.003). Additionally, three loci showed strong sex-differentiated effects: 17p13.1-SHBG and Xq22.3-TDGF3 were stronger in men, whereas 8q21.12-ZBTB10 was stronger in women. Conditional analyses identified additional signals at the SHBG gene that together almost double the proportion of variance explained at the locus. Using an independent study of 1,129 individuals, all SNPs identified in the overall or sex-differentiated or conditional analyses explained ~15.6% and ~8.4% of the genetic variation of SHBG concentrations in men and women, respectively. The evidence for sex-differentiated effects and allelic heterogeneity highlight the importance of considering these features when estimating complex trait variance.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Abstract only
Fatty acid desaturases (FADS) catalyze formation of mono‐ and polyunsaturated fatty acids (FA) and have been related to insulin sensitivity (IS). FADS activities differ between tissues ...and are influenced by genetic factors that may affect the link to IS.
The study aimed at identifying genetic variants influencing estimated FADS activity in serum cholesterol esters (CE) and adipose tissue (AT); and to evaluate if identified loci affect IS.
GWAS of Δ9‐ (D9D), Δ6‐ (D6D), and Δ5‐desaturase (D5D) activities (estimated by product‐to‐precursor FA ratios analyzed by gas chromatography) in CE (n=1,453, 70% men) and AT (n=783, all men) were performed in two Swedish population‐based cohorts. Associated loci (P <5.0E‐8) were evaluated for associations with IS measured by hyperinsulinemic euglycemic clamp (n=554).
Variants in one locus (
FADS1
) were strongly associated with D5D in CE (P=1.9E‐70) and AT (P=1.1E‐27). Variants in three loci were associated at a genome wide‐significant level with CE D6D (
FADS2
, P=3.0E‐67;
PDXDCI
, P= 4.8E‐8; and near
MC4R
, P=3.7E‐8) and one with AT D9D (
PKDL1
, P=2.2E‐19). After Bonferroni correction, one locus was significantly associated with IS (
PDXDCI,
P=0.030).
FADS genes determined estimated FADS activity, although FA ratios in AT and CE represent FADS activities in separate tissues and are thus influenced by different genetic factors with potential effects on IS.
The Wellcome Trust funded the study.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Using an integrative approach in which genetic variation, gene expression, and clinical phenotypes are assessed in relevant tissues may help functionally characterize the contribution of genetics to ...disease susceptibility. We sought to identify genetic variation influencing skeletal muscle gene expression (expression quantitative trait loci eQTLs) as well as expression associated with measures of insulin sensitivity. We investigated associations of 3,799,401 genetic variants in expression of >7,000 genes from three cohorts (n = 104). We identified 287 genes with cis-acting eQTLs (false discovery rate FDR <5%; P < 1.96 × 10(-5)) and 49 expression-insulin sensitivity phenotype associations (i.e., fasting insulin, homeostasis model assessment-insulin resistance, and BMI) (FDR <5%; P = 1.34 × 10(-4)). One of these associations, fasting insulin/phosphofructokinase (PFKM), overlaps with an eQTL. Furthermore, the expression of PFKM, a rate-limiting enzyme in glycolysis, was nominally associated with glucose uptake in skeletal muscle (P = 0.026; n = 42) and overexpressed (Bonferroni-corrected P = 0.03) in skeletal muscle of patients with T2D (n = 102) compared with normoglycemic controls (n = 87). The PFKM eQTL (rs4547172; P = 7.69 × 10(-6)) was nominally associated with glucose uptake, glucose oxidation rate, intramuscular triglyceride content, and metabolic flexibility (P = 0.016-0.048; n = 178). We explored eQTL results using published data from genome-wide association studies (DIAGRAM and MAGIC), and a proxy for the PFKM eQTL (rs11168327; r(2) = 0.75) was nominally associated with T2D (DIAGRAM P = 2.7 × 10(-3)). Taken together, our analysis highlights PFKM as a potential regulator of skeletal muscle insulin sensitivity.
Small RNAs are functional molecules that modulate mRNA transcripts and have been implicated in the aetiology of several common diseases. However, little is known about the extent of their variability ...within the human population. Here, we characterise the extent, causes, and effects of naturally occurring variation in expression and sequence of small RNAs from adipose tissue in relation to genotype, gene expression, and metabolic traits in the MuTHER reference cohort. We profiled the expression of 15 to 30 base pair RNA molecules in subcutaneous adipose tissue from 131 individuals using high-throughput sequencing, and quantified levels of 591 microRNAs and small nucleolar RNAs. We identified three genetic variants and three RNA editing events. Highly expressed small RNAs are more conserved within mammals than average, as are those with highly variable expression. We identified 14 genetic loci significantly associated with nearby small RNA expression levels, seven of which also regulate an mRNA transcript level in the same region. In addition, these loci are enriched for variants significant in genome-wide association studies for body mass index. Contrary to expectation, we found no evidence for negative correlation between expression level of a microRNA and its target mRNAs. Trunk fat mass, body mass index, and fasting insulin were associated with more than twenty small RNA expression levels each, while fasting glucose had no significant associations. This study highlights the similar genetic complexity and shared genetic control of small RNA and mRNA transcripts, and gives a quantitative picture of small RNA expression variation in the human population.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Obesity results from an excess accumulation of body fat and is a major risk factor for Type 2 diabetes (T2D). Common, overall obesity is a complex trait influenced by both genetic and environmental ...factors. Recent genome-wide association studies have identified robust associations at 32 loci with overall obesity as well as 14 with body fat distribution. While overlapping evidence from both genetic and epidemiological studies suggests a link between obesity and T2D, specific fat distribution patterns, independent of overall adiposity, may play an equally important role in T2D risk. Understanding the etiological link between obesity, fat distribution and T2D presents new opportunities for the management, and ultimately treatment, of these diseases.
Unravelling the genetic architecture of common diseases is a continuing challenge in human genetics. While genome-wide association studies (GWAS) have proven to be successful in identifying many new ...disease susceptibility loci, the extension of these studies beyond single-SNP methods of analysis has been limited. The incorporation of multi-locus methods of analysis may, however, increase the power of GWAS to detect genes of smaller effect size, as well as genes that interact with each other and the environment. This investigation carried out large-scale simulations of four multi-locus model selection techniques; namely forward and backward selection, Bayesian model averaging (BMA) and least angle regression with a lasso modification (lasso), in order to compare the type I error rates and power of each method. At a type I error rate of ~5%, lasso showed the highest power across varied effect sizes, disease frequencies and genetic models. Lasso penalized regression was then used to perform three different types of analysis on GWAS data. Firstly, lasso was applied to the Wellcome Trust Case Control Consortium (WTCCC) data and identified many of the WTCCC SNPs that had a moderate-strong association (p<10-5) type 2 diabetes (T2D), as well as some of the moderate WTCCC associations (p<10-4) that have since been replicated in a large-scale meta-analysis. Secondly, lasso was used to fine-map the 17q21 childhood asthma risk locus and identified putative secondary signals in the 17q21 region, that may further contribute to childhood asthma risk. Finally, lasso identified three potential interaction effects potentially contributing towards coronary artery disease (CAD) risk. While the validity of these findings hinges on their replication in follow-up studies, the results suggest that lasso may provide scientists with exciting new methods of dissecting, and ultimately understanding, the complex genetic framework underlying common human diseases.
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