Genes show different sensitivities in expression corresponding to various biological conditions. Systematical study of this concept is required because of its important implications in microarray analysis etc. J.H. Ohn et al. first studied this gene property with yeast transcriptional profiling data.

To identify genetic loci influencing central obesity and fat distribution, we performed a meta-analysis of 16 genome-wide association studies (GWAS, N = 38,580) informative for adult waist circumference (WC) and waist-hip ratio (WHR). We selected 26 SNPs for follow-up, for which the evidence of association with measures of central adiposity (WC and/or WHR) was strong and disproportionate to that for overall adiposity or height. Follow-up studies in a maximum of 70,689 individuals identified two loci strongly associated with measures of central adiposity; these map near TFAP2B (WC, P = 1.9x10(-11)) and MSRA (WC, P = 8.9x10(-9)). A third locus, near LYPLAL1, was associated with WHR in women only (P = 2.6x10(-8)). The variants near TFAP2B appear to influence central adiposity through an effect on overall obesity/fat-mass, whereas LYPLAL1 displays a strong female-only association with fat distribution. By focusing on anthropometric measures of central obesity and fat distribution, we have identified three loci implicated in the regulation of human adiposity.

Elevated levels of acute-phase serum amyloid A (A-SAA) cause amyloidosis and are a risk factor for atherosclerosis and its clinical complications, type 2 diabetes, as well as various malignancies. To investigate the genetic basis of A-SAA levels, we conducted the first genome-wide association study on baseline A-SAA concentrations in three population-based studies (KORA, TwinsUK, Sorbs) and one prospective case cohort study (LURIC), including a total of 4,212 participants of European descent, and identified two novel genetic susceptibility regions at 11p15.5-p13 and 1p31. The region at 11p15.5-p13 (rs4150642; p = 3.20×10(-111)) contains serum amyloid A1 (SAA1) and the adjacent general transcription factor 2 H1 (GTF2H1), Hermansky-Pudlak Syndrome 5 (HPS5), lactate dehydrogenase A (LDHA), and lactate dehydrogenase C (LDHC). This region explains 10.84% of the total variation of A-SAA levels in our data, which makes up 18.37% of the total estimated heritability. The second region encloses the leptin receptor (LEPR) gene at 1p31 (rs12753193; p = 1.22×10(-11)) and has been found to be associated with CRP and fibrinogen in previous studies. Our findings demonstrate a key role of the 11p15.5-p13 region in the regulation of baseline A-SAA levels and provide confirmative evidence of the importance of the 1p31 region for inflammatory processes and the close interplay between A-SAA, leptin, and other acute-phase proteins.

We carried out a meta-analysis of two recent psoriasis genome-wide association studies with a combined discovery sample of 1,831 affected individuals (cases) and 2,546 controls. One hundred and two loci selected based on P value rankings were followed up in a three-stage replication study including 4,064 cases and 4,685 controls from Michigan, Toronto, Newfoundland and Germany. In the combined meta-analysis, we identified three new susceptibility loci, including one at NOS2 (rs4795067, combined P = 4 × 10⁻¹¹), one at FBXL19 (rs10782001, combined P = 9 × 10⁻¹⁰) and one near PSMA6-NFKBIA (rs12586317, combined P = 2 × 10⁻⁸). All three loci were also associated with psoriatic arthritis (rs4795067, combined P = 1 × 10⁻⁵; rs10782001, combined P = 4 × 10⁻⁸; and rs12586317, combined P = 6 × 1⁻⁵) and purely cutaneous psoriasis (rs4795067, combined P = 1 × 10⁻⁸; rs10782001, combined P = 2 × 10⁻⁶; and rs12586317, combined P = 1 × 10⁻⁶). We also replicated a recently identified association signal near RNF114 (rs495337, combined P = 2 × 10⁻⁷).

Metabolomics is the rapidly evolving field of the comprehensive measurement of ideally all endogenous metabolites in a biological fluid. However, no single analytic technique covers the entire spectrum of the human metabolome. Here we present results from a multiplatform study, in which we investigate what kind of results can presently be obtained in the field of diabetes research when combining metabolomics data collected on a complementary set of analytical platforms in the framework of an epidemiological study.

Rare mutations of the low-density lipoprotein receptor gene (LDLR) cause familial hypercholesterolemia, which increases the risk for coronary artery disease (CAD). Less is known about the implications of common genetic variation in the LDLR gene regarding the variability of cholesterol levels and risk of CAD.

The rapidly evolving field of metabolomics aims at a comprehensive measurement of ideally all endogenous metabolites in a cell or body fluid. It thereby provides a functional readout of the physiological state of the human body. Genetic variants that associate with changes in the homeostasis of key lipids, carbohydrates, or amino acids are not only expected to display much larger effect sizes due to their direct involvement in metabolite conversion modification, but should also provide access to the biochemical context of such variations, in particular when enzyme coding genes are concerned. To test this hypothesis, we conducted what is, to the best of our knowledge, the first GWA study with metabolomics based on the quantitative measurement of 363 metabolites in serum of 284 male participants of the KORA study. We found associations of frequent single nucleotide polymorphisms (SNPs) with considerable differences in the metabolic homeostasis of the human body, explaining up to 12% of the observed variance. Using ratios of certain metabolite concentrations as a proxy for enzymatic activity, up to 28% of the variance can be explained (p-values 10(-16) to 10(-21)). We identified four genetic variants in genes coding for enzymes (FADS1, LIPC, SCAD, MCAD) where the corresponding metabolic phenotype (metabotype) clearly matches the biochemical pathways in which these enzymes are active. Our results suggest that common genetic polymorphisms induce major differentiations in the metabolic make-up of the human population. This may lead to a novel approach to personalized health care based on a combination of genotyping and metabolic characterization. These genetically determined metabotypes may subscribe the risk for a certain medical phenotype, the response to a given drug treatment, or the reaction to a nutritional intervention or environmental challenge.

Mean platelet volume (MPV) is increased in myocardial and cerebral infarction and is an independent and strong predictor for postevent morbidity and mortality. We conducted a genome-wide association study (GWAS), the KORA (Kooperative Gesundheitsforschung in der Region Augsburg) F3 500K study, and found MPV to be strongly associated with three common single-nucleotide polymorphisms (SNPs): rs7961894 located within intron 3 of WDR66 on chromosome 12q24.31, rs12485738 upstream of the ARHGEF3 on chromosome 3p13-p21, and rs2138852 located upstream of TAOK1 on chromosome 17q11.2. We replicated all three SNPs in another GWAS from the UK and in two population-based samples from Germany. In a combined analysis including 10,048 subjects, the SNPs had p values of 7.24 x 10(-48) for rs7961894, 3.81 x 10(-27) for rs12485738, and 7.19 x 10(-28) for rs2138852. These three quantitative trait loci together accounted for 4%-5% of the variance in MPV. In-depth sequence analysis of WDR66 in 382 samples from the extremes revealed 20 new variants and a haplotype with three coding SNPs and one SNP at the transcription start site associated with MPV (p = 6.8 x 10(-5)). In addition, expression analysis indicated a direct correlation of WDR66 transcripts and MPV. These findings may not only enhance our understanding of platelet activation and function, but may also provide a focus for several novel research avenues.

The global prevalence of obesity has increased significantly in recent decades, mainly due to excess calorie intake and increasingly sedentary lifestyle. Here, we test the association between obesity measured by body mass index (BMI) and one of the best-known genetic variants showing strong selective pressure: the functional variant in the cis-regulatory element of the lactase gene. We tested this variant since it is presumed to provide nutritional advantage in specific physical and cultural environments. We genetically defined lactase persistence (LP) in 31 720 individuals from eight European population-based studies and one family study by genotyping or imputing the European LP variant (rs4988235). We performed a meta-analysis by pooling the beta-coefficient estimates of the relationship between rs4988235 and BMI from the nine studies and found that the carriers of the allele responsible for LP among Europeans showed higher BMI (P = 7.9 x 10(-5)). Since this locus has been shown to be prone to population stratification, we paid special attention to reveal any population substructure which might be responsible for the association signal. The best evidence of exclusion of stratification came from the Dutch family sample which is robust for stratification. In this study, we highlight issues in model selection in the genome-wide association studies and problems in imputation of these special genomic regions.

Isolated dystonia is a disorder characterized by involuntary twisting postures arising from sustained muscle contractions. Although autosomal-dominant mutations in TOR1A, THAP1, and GNAL have been found in some cases, the molecular mechanisms underlying isolated dystonia are largely unknown. In addition, although emphasis has been placed on dominant isolated dystonia, the disorder is also transmitted as a recessive trait, for which no mutations have been defined. Using whole-exome sequencing in a recessive isolated dystonia-affected kindred, we identified disease-segregating compound heterozygous mutations in COL6A3, a collagen VI gene associated previously with muscular dystrophy. Genetic screening of a further 367 isolated dystonia subjects revealed two additional recessive pedigrees harboring compound heterozygous mutations in COL6A3. Strikingly, all affected individuals had at least one pathogenic allele in exon 41, including an exon-skipping mutation that induced an in-frame deletion. We tested the hypothesis that disruption of this exon is pathognomonic for isolated dystonia by inducing a series of in-frame deletions in zebrafish embryos. Consistent with our human genetics data, suppression of the exon 41 ortholog caused deficits in axonal outgrowth, whereas suppression of other exons phenocopied collagen deposition mutants. All recessive mutation carriers demonstrated early-onset segmental isolated dystonia without muscular disease. Finally, we show that Col6a3 is expressed in neurons, with relevant mRNA levels detectable throughout the adult mouse brain. Taken together, our data indicate that loss-of-function mutations affecting a specific region of COL6A3 cause recessive isolated dystonia with underlying neurodevelopmental deficits and highlight the brain extracellular matrix as a contributor to dystonia pathogenesis.

Biological systems consist of multiple organizational levels all densely interacting with each other to ensure function and flexibility of the system. Simultaneous analysis of cross-sectional multi-omics data from large population studies is a powerful tool to comprehensively characterize the underlying molecular mechanisms on a physiological scale. In this study, we systematically analyzed the relationship between fasting serum metabolomics and whole blood transcriptomics data from 712 individuals of the German KORA F4 cohort. Correlation-based analysis identified 1,109 significant associations between 522 transcripts and 114 metabolites summarized in an integrated network, the 'human blood metabolome-transcriptome interface' (BMTI). Bidirectional causality analysis using Mendelian randomization did not yield any statistically significant causal associations between transcripts and metabolites. A knowledge-based interpretation and integration with a genome-scale human metabolic reconstruction revealed systematic signatures of signaling, transport and metabolic processes, i.e. metabolic reactions mainly belonging to lipid, energy and amino acid metabolism. Moreover, the construction of a network based on functional categories illustrated the cross-talk between the biological layers at a pathway level. Using a transcription factor binding site enrichment analysis, this pathway cross-talk was further confirmed at a regulatory level. Finally, we demonstrated how the constructed networks can be used to gain novel insights into molecular mechanisms associated to intermediate clinical traits. Overall, our results demonstrate the utility of a multi-omics integrative approach to understand the molecular mechanisms underlying both normal physiology and disease.

Restless legs syndrome (RLS) is a common neurologic condition characterized by nocturnal dysesthesias and an urge to move, affecting the legs. RLS is a complex trait, for which genome-wide association studies (GWASs) have identified common susceptibility alleles of modest (OR 1.2-1.7) risk at six genomic loci. Among these, variants in MEIS1 have emerged as the largest risk factors for RLS, suggesting that perturbations in this transcription factor might be causally related to RLS susceptibility. To establish this causality, direction of effect, and total genetic burden of MEIS1, we interrogated 188 case subjects and 182 control subjects for rare alleles not captured by previous GWASs, followed by genotyping of ∼3,000 case subjects and 3,000 control subjects, and concluded with systematic functionalization of all discovered variants using a previously established in vivo model of neurogenesis. We observed a significant excess of rare MEIS1 variants in individuals with RLS. Subsequent assessment of all nonsynonymous variants by in vivo complementation revealed an excess of loss-of-function alleles in individuals with RLS. Strikingly, these alleles compromised the function of the canonical MEIS1 splice isoform but were irrelevant to an isoform known to utilize an alternative 3' sequence. Our data link MEIS1 loss of function to the etiopathology of RLS, highlight how combined sequencing and systematic functional annotation of rare variation at GWAS loci can detect risk burden, and offer a plausible explanation for the specificity of phenotypic expressivity of loss-of-function alleles at a locus broadly necessary for neurogenesis and neurodevelopment.

The aim was to characterise associations between circulating thyroid hormones-free thyroxine (FT4) and thyrotropin (TSH)-and the metabolite profiles in serum samples from participants of the German population-based KORA F4 study. Analyses were based on the metabolite profile of 1463 euthyroid subjects. In serum samples, obtained after overnight fasting (≥8), 151 different metabolites were quantified in a targeted approach including amino acids, acylcarnitines (ACs), and phosphatidylcholines (PCs). Associations between metabolites and thyroid hormone concentrations were analysed using adjusted linear regression models. To draw conclusions on thyroid hormone related pathways, intra-class metabolite ratios were additionally explored. We discovered 154 significant associations (Bonferroni p < 1.75 × 10-04) between FT4 and various metabolites and metabolite ratios belonging to AC and PC groups. Significant associations with TSH were lacking. High FT4 levels were associated with increased concentrations of many ACs and various sums of ACs of different chain length, and the ratio of C2 by C0. The inverse associations observed between FT4 and many serum PCs reflected the general decrease in PC concentrations. Similar results were found in subgroup analyses, e.g., in weight-stable subjects or in obese subjects. Further, results were independent of different parameters for liver or kidney function, or inflammation, which supports the notion of an independent FT4 effect. In fasting euthyroid adults, higher serum FT4 levels are associated with increased serum AC concentrations and an increased ratio of C2 by C0 which is indicative of an overall enhanced fatty acyl mitochondrial transport and β-oxidation of fatty acids.

Approximately 1.5 billion people worldwide are overweight or affected by obesity, and are at risk of developing type 2 diabetes, cardiovascular disease and related metabolic and inflammatory disturbances. Although the mechanisms linking adiposity to associated clinical conditions are poorly understood, recent studies suggest that adiposity may influence DNA methylation, a key regulator of gene expression and molecular phenotype. Here we use epigenome-wide association to show that body mass index (BMI; a key measure of adiposity) is associated with widespread changes in DNA methylation (187 genetic loci with P < 1 × 10-7, range P = 9.2 × 10-8 to 6.0 × 10-46; n = 10,261 samples). Genetic association analyses demonstrate that the alterations in DNA methylation are predominantly the consequence of adiposity, rather than the cause. We find that methylation loci are enriched for functional genomic features in multiple tissues (P < 0.05), and show that sentinel methylation markers identify gene expression signatures at 38 loci (P < 9.0 × 10-6, range P = 5.5 × 10-6 to 6.1 × 10-35, n = 1,785 samples). The methylation loci identify genes involved in lipid and lipoprotein metabolism, substrate transport and inflammatory pathways. Finally, we show that the disturbances in DNA methylation predict future development of type 2 diabetes (relative risk per 1 standard deviation increase in methylation risk score: 2.3 (2.07-2.56); P = 1.1 × 10-54). Our results provide new insights into the biologic pathways influenced by adiposity, and may enable development of new strategies for prediction and prevention of type 2 diabetes and other adverse clinical consequences of obesity.

Recent genome-wide association studies (GWAS) with metabolomics data linked genetic variation in the human genome to differences in individual metabolite levels. A strong relevance of this metabolic individuality for biomedical and pharmaceutical research has been reported. However, a considerable amount of the molecules currently quantified by modern metabolomics techniques are chemically unidentified. The identification of these "unknown metabolites" is still a demanding and intricate task, limiting their usability as functional markers of metabolic processes. As a consequence, previous GWAS largely ignored unknown metabolites as metabolic traits for the analysis. Here we present a systems-level approach that combines genome-wide association analysis and Gaussian graphical modeling with metabolomics to predict the identity of the unknown metabolites. We apply our method to original data of 517 metabolic traits, of which 225 are unknowns, and genotyping information on 655,658 genetic variants, measured in 1,768 human blood samples. We report previously undescribed genotype-metabotype associations for six distinct gene loci (SLC22A2, COMT, CYP3A5, CYP2C18, GBA3, UGT3A1) and one locus not related to any known gene (rs12413935). Overlaying the inferred genetic associations, metabolic networks, and knowledge-based pathway information, we derive testable hypotheses on the biochemical identities of 106 unknown metabolites. As a proof of principle, we experimentally confirm nine concrete predictions. We demonstrate the benefit of our method for the functional interpretation of previous metabolomics biomarker studies on liver detoxification, hypertension, and insulin resistance. Our approach is generic in nature and can be directly transferred to metabolomics data from different experimental platforms.

Several infrequent genetic polymorphisms in the SERPINA1 gene are known to substantially reduce concentration of alpha1-antitrypsin (AAT) in the blood. Since low AAT serum levels fail to protect pulmonary tissue from enzymatic degradation, these polymorphisms also increase the risk for early onset chronic obstructive pulmonary disease (COPD). The role of more common SERPINA1 single nucleotide polymorphisms (SNPs) in respiratory health remains poorly understood. We present here an agnostic investigation of genetic determinants of circulating AAT levels in a general population sample by performing a genome-wide association study (GWAS) in 1392 individuals of the SAPALDIA cohort. Five common SNPs, defined by showing minor allele frequencies (MAFs) >5%, reached genome-wide significance, all located in the SERPINA gene cluster at 14q32.13. The top-ranking genotyped SNP rs4905179 was associated with an estimated effect of β = -0.068 g/L per minor allele (P = 1.20*10(-12)). But denser SERPINA1 locus genotyping in 5569 participants with subsequent stepwise conditional analysis, as well as exon-sequencing in a subsample (N = 410), suggested that AAT serum level is causally determined at this locus by rare (MAF<1%) and low-frequent (MAF 1-5%) variants only, in particular by the well-documented protein inhibitor S and Z (PI S, PI Z) variants. Replication of the association of rs4905179 with AAT serum levels in the Copenhagen City Heart Study (N = 8273) was successful (P<0.0001), as was the replication of its synthetic nature (the effect disappeared after adjusting for PI S and Z, P = 0.57). Extending the analysis to lung function revealed a more complex situation. Only in individuals with severely compromised pulmonary health (N = 397), associations of common SNPs at this locus with lung function were driven by rarer PI S or Z variants. Overall, our meta-analysis of lung function in ever-smokers does not support a functional role of common SNPs in the SERPINA gene cluster in the general population.

Asbestos exposure is the main risk factor for malignant pleural mesothelioma (MPM), a rare aggressive tumor. Nevertheless, only 5-17% of those exposed to asbestos develop MPM, suggesting the involvement of other environmental and genetic risk factors. To identify the genetic risk factors that may contribute to the development of MPM, we conducted a genome-wide association study (GWAS; 370,000 genotyped SNPs, 5 million imputed SNPs) in Italy, among 407 MPM cases and 389 controls with a complete history of asbestos exposure. A replication study was also undertaken and included 428 MPM cases and 1269 controls from Australia. Although no single marker reached the genome-wide significance threshold, several associations were supported by haplotype-, chromosomal region-, gene- and gene-ontology process-based analyses. Most of these SNPs were located in regions reported to harbor aberrant alterations in mesothelioma (SLC7A14, THRB, CEBP350, ADAMTS2, ETV1, PVT1 and MMP14 genes), causing at most a 2-3-fold increase in MPM risk. The Australian replication study showed significant associations in five of these chromosomal regions (3q26.2, 4q32.1, 7p22.2, 14q11.2, 15q14). Multivariate analysis suggested an independent contribution of 10 genetic variants, with an Area Under the ROC Curve (AUC) of 0.76 when only exposure and covariates were included in the model, and of 0.86 when the genetic component was also included, with a substantial increase of asbestos exposure risk estimation (odds ratio, OR: 45.28, 95% confidence interval, CI: 21.52-95.28). These results showed that genetic risk factors may play an additional role in the development of MPM, and that these should be taken into account to better estimate individual MPM risk in individuals who have been exposed to asbestos.

Age-related macular degeneration (AMD) is a common cause of blindness in older individuals. To accelerate the understanding of AMD biology and help design new therapies, we executed a collaborative genome-wide association study, including >17,100 advanced AMD cases and >60,000 controls of European and Asian ancestry. We identified 19 loci associated at P < 5 × 10(-8). These loci show enrichment for genes involved in the regulation of complement activity, lipid metabolism, extracellular matrix remodeling and angiogenesis. Our results include seven loci with associations reaching P < 5 × 10(-8) for the first time, near the genes COL8A1-FILIP1L, IER3-DDR1, SLC16A8, TGFBR1, RAD51B, ADAMTS9 and B3GALTL. A genetic risk score combining SNP genotypes from all loci showed similar ability to distinguish cases and controls in all samples examined. Our findings provide new directions for biological, genetic and therapeutic studies of AMD.

Economic variables such as income, education, and occupation are known to affect mortality and morbidity, such as cardiovascular disease, and have also been shown to be partly heritable. However, very little is known about which genes influence economic variables, although these genes may have both a direct and an indirect effect on health. We report results from the first large-scale collaboration that studies the molecular genetic architecture of an economic variable-entrepreneurship-that was operationalized using self-employment, a widely-available proxy. Our results suggest that common SNPs when considered jointly explain about half of the narrow-sense heritability of self-employment estimated in twin data (σ(g)(2)/σ(P)(2) = 25%, h(2) = 55%). However, a meta-analysis of genome-wide association studies across sixteen studies comprising 50,627 participants did not identify genome-wide significant SNPs. 58 SNPs with p<10(-5) were tested in a replication sample (n = 3,271), but none replicated. Furthermore, a gene-based test shows that none of the genes that were previously suggested in the literature to influence entrepreneurship reveal significant associations. Finally, SNP-based genetic scores that use results from the meta-analysis capture less than 0.2% of the variance in self-employment in an independent sample (p≥0.039). Our results are consistent with a highly polygenic molecular genetic architecture of self-employment, with many genetic variants of small effect. Although self-employment is a multi-faceted, heavily environmentally influenced, and biologically distal trait, our results are similar to those for other genetically complex and biologically more proximate outcomes, such as height, intelligence, personality, and several diseases.

Genetic association studies have identified 21 loci associated with atopic dermatitis risk predominantly in populations of European ancestry. To identify further susceptibility loci for this common, complex skin disease, we performed a meta-analysis of >15 million genetic variants in 21,399 cases and 95,464 controls from populations of European, African, Japanese and Latino ancestry, followed by replication in 32,059 cases and 228,628 controls from 18 studies. We identified ten new risk loci, bringing the total number of known atopic dermatitis risk loci to 31 (with new secondary signals at four of these loci). Notably, the new loci include candidate genes with roles in the regulation of innate host defenses and T cell function, underscoring the important contribution of (auto)immune mechanisms to atopic dermatitis pathogenesis.