Tune H. Pers

Integrative annotation of variants from 1092 humans: application to cancer genomics.

Introduction Plummeting sequencing costs have led to a great increase in the number of personal genomes. Interpreting the large number of variants in them, particularly in noncoding regions, is a current challenge. This is especially the case for somatic variants in cancer genomes, a large proportion of which are noncoding. Prioritization of candidate noncoding cancer drivers based on patterns of selection. (Step 1) Filter somatic variants to exclude 1000 Genomes polymorphisms; (2) retain variants in noncoding annotations; (3) retain those in “sensitive” regions; (4) prioritize those disrupting a transcription-factor binding motif and (5) residing near the center of a biological network; (6) prioritize ones in annotation blocks mutated in multiple cancer samples. Methods We investigated patterns of selection in DNA elements from the ENCODE project using the full spectrum of variants from 1092 individuals in the 1000 Genomes Project (Phase 1), including single-nucleotide variants (SNVs), short insertions and deletions (indels), and structural variants (SVs). Although we analyzed broad functional annotations, such as all transcription-factor binding sites, we focused more on highly specific categories such as distal binding sites of factor ZNF274. The greater statistical power of the Phase 1 data set compared with earlier ones allowed us to differentiate the selective constraints on these categories. We also used connectivity information between elements from protein-protein-interaction and regulatory networks. We integrated all the information on selection to develop a workflow (FunSeq) to prioritize personal-genome variants on the basis of their deleterious impact. As a proof of principle, we experimentally validated and characterized a few candidate variants. Results We identified a specific subgroup of noncoding categories with almost as much selective constraint as coding genes: “ultrasensitive” regions. We also uncovered a number of clear patterns of selection. Elements more consistently active across tissues and both maternal and paternal alleles (in terms of allele-specific activity) are under stronger selection. Variants disruptive because of mechanistic effects on transcription-factor binding (i.e. “motif-breakers”) are selected against. Higher network connectivity (i.e. for hubs) is associated with higher constraint. Additionally, many hub promoters and regulatory elements show evidence of recent positive selection. Overall, indels and SVs follow the same pattern as SNVs; however, there are notable exceptions. For instance, enhancers are enriched for SVs formed by nonallelic homologous recombination. We integrated these patterns of selection into the FunSeq prioritization workflow and applied it to cancer variants, because they present a strong contrast to inherited polymorphisms. In particular, application to ~90 cancer genomes (breast, prostate and medulloblastoma) reveals nearly a hundred candidate noncoding drivers. Discussion Our approach can be readily used to prioritize variants in cancer and is immediately applicable in a precision-medicine context. It can be further improved by incorporation of larger-scale population sequencing, better annotations, and expression data from large cohorts. Identifying Important Identifiers Each of us has millions of sequence variations in our genomes. Signatures of purifying or negative selection should help identify which of those variations is functionally important. Khurana et al. (1235587) used sequence polymorphisms from 1092 humans across 14 populations to identify patterns of selection, especially in noncoding regulatory regions. Noncoding regions under very strong negative selection included binding sites of some chromatin and general transcription factors (TFs) and core motifs of some important TF families. Positive selection in TF binding sites tended to occur in network hub promoters. Many recurrent somatic cancer variants occurred in noncoding regulatory regions and thus might indicate mutations that drive cancer. Regions under strong selection in the human genome identify noncoding regulatory elements with possible roles in disease. Interpreting variants, especially noncoding ones, in the increasing number of personal genomes is challenging. We used patterns of polymorphisms in functionally annotated regions in 1092 humans to identify deleterious variants; then we experimentally validated candidates. We analyzed both coding and noncoding regions, with the former corroborating the latter. We found regions particularly sensitive to mutations (“ultrasensitive”) and variants that are disruptive because of mechanistic effects on transcription-factor binding (that is, “motif-breakers”). We also found variants in regions with higher network centrality tend to be deleterious. Insertions and deletions followed a similar pattern to single-nucleotide variants, with some notable exceptions (e.g., certain deletions and enhancers). On the basis of these patterns, we developed a computational tool (FunSeq), whose application to ~90 cancer genomes reveals nearly a hundred candidate noncoding drivers.

Meta-analysis of genome-wide association studies identifies ten loci influencing allergic sensitization

Allergen-specific immunoglobulin E (present in allergic sensitization) has a central role in the pathogenesis of allergic disease. We performed the first large-scale genome-wide association study (GWAS) of allergic sensitization in 5,789 affected individuals and 10,056 controls and followed up the top SNP at each of 26 loci in 6,114 affected individuals and 9,920 controls. We increased the number of susceptibility loci with genome-wide significant association with allergic sensitization from three to ten, including SNPs in or near TLR6, C11orf30, STAT6, SLC25A46, HLA-DQB1, IL1RL1, LPP, MYC, IL2 and HLA-B. All the top SNPs were associated with allergic symptoms in an independent study. Risk-associated variants at these ten loci were estimated to account for at least 25% of allergic sensitization and allergic rhinitis. Understanding the molecular mechanisms underlying these associations may provide new insights into the etiology of allergic disease.

Macrophages and adipocytes in human obesity: adipose tissue gene expression and insulin sensitivity during calorie restriction and weight stabilization.

OBJECTIVE We investigated the regulation of adipose tissue gene expression during different phases of a dietary weight loss program and its relation with insulin sensitivity. RESEARCH DESIGN AND METHODS Twenty-two obese women followed a dietary intervention program composed of an energy restriction phase with a 4-week very-low-calorie diet and a weight stabilization period composed of a 2-month low-calorie diet followed by 3–4 months of a weight maintenance diet. At each time point, a euglycemic-hyperinsulinemic clamp and subcutaneous adipose tissue biopsies were performed. Adipose tissue gene expression profiling was performed using a DNA microarray in a subgroup of eight women. RT–quantitative PCR was used for determination of mRNA levels of 31 adipose tissue macrophage markers (n = 22). RESULTS Body weight, fat mass, and C-reactive protein level decreased and glucose disposal rate increased during the dietary intervention program. Transcriptome profiling revealed two main patterns of variations. The first involved 464 mostly adipocyte genes involved in metabolism that were downregulated during energy restriction, upregulated during weight stabilization, and unchanged during the dietary intervention. The second comprised 511 mainly macrophage genes involved in inflammatory pathways that were not changed or upregulated during energy restriction and downregulated during weight stabilization and dietary intervention. Accordingly, macrophage markers were upregulated during energy restriction and downregulated during weight stabilization and dietary intervention. The increase in glucose disposal rates in each dietary phase was associated with variation in expression of sets of 80–110 genes that differed among energy restriction, weight stabilization, and dietary intervention. CONCLUSIONS Adipose tissue macrophages and adipocytes show distinct patterns of gene regulation and association with insulin sensitivity during the various phases of a dietary weight loss program.

The impact of low-frequency and rare variants on lipid levels

Using a genome-wide screen of 9.6 million genetic variants achieved through 1000 Genomes Project imputation in 62,166 samples, we identify association to lipid traits in 93 loci, including 79 previously identified loci with new lead SNPs and 10 new loci, 15 loci with a low-frequency lead SNP and 10 loci with a missense lead SNP, and 2 loci with an accumulation of rare variants. In six loci, SNPs with established function in lipid genetics (CELSR2, GCKR, LIPC and APOE) or candidate missense mutations with predicted damaging function (CD300LG and TM6SF2) explained the locus associations. The low-frequency variants increased the proportion of variance explained, particularly for low-density lipoprotein cholesterol and total cholesterol. Altogether, our results highlight the impact of low-frequency variants in complex traits and show that imputation offers a cost-effective alternative to resequencing.

A molecular census of arcuate hypothalamus and median eminence cell types

The hypothalamic arcuate–median eminence complex (Arc-ME) controls energy balance, fertility and growth through molecularly distinct cell types, many of which remain unknown. To catalog cell types in an unbiased way, we profiled gene expression in 20,921 individual cells in and around the adult mouse Arc-ME using Drop-seq. We identify 50 transcriptionally distinct Arc-ME cell populations, including a rare tanycyte population at the Arc-ME diffusion barrier, a new leptin-sensing neuron population, multiple agouti-related peptide (AgRP) and pro-opiomelanocortin (POMC) subtypes, and an orexigenic somatostatin neuron population. We extended Drop-seq to detect dynamic expression changes across relevant physiological perturbations, revealing cell type–specific responses to energy status, including distinct responses in AgRP and POMC neuron subtypes. Finally, integrating our data with human genome-wide association study data implicates two previously unknown neuron populations in the genetic control of obesity. This resource will accelerate biological discovery by providing insights into molecular and cell type diversity from which function can be inferred.

A genome-wide association study of men with symptoms of testicular dysgenesis syndrome and its network biology interpretation

Background Testicular dysgenesis syndrome (TDS) is a common disease that links testicular germ cell cancer, cryptorchidism and some cases of hypospadias and male infertility with impaired development of the testis. The incidence of these disorders has increased over the last few decades, and testicular cancer now affects 1% of the Danish and Norwegian male population. Methods To identify genetic variants that span the four TDS phenotypes, the authors performed a genome-wide association study (GWAS) using Affymetrix Human SNP Array 6.0 to screen 488 patients with symptoms of TDS and 439 selected controls with excellent reproductive health. Furthermore, they developed a novel integrative method that combines GWAS data with other TDS-relevant data types and identified additional TDS markers. The most significant findings were replicated in an independent cohort of 671 Nordic men. Results Markers located in the region of TGFBR3 and BMP7 showed association with all TDS phenotypes in both the discovery and replication cohorts. An immunohistochemistry investigation confirmed the presence of transforming growth factor β receptor type III (TGFBR3) in peritubular and Leydig cells, in both fetal and adult testis. Single-nucleotide polymorphisms in the KITLG gene showed significant associations, but only with testicular cancer. Conclusions The association of single-nucleotide polymorphisms in the TGFBR3 and BMP7 genes, which belong to the transforming growth factor β signalling pathway, suggests a role for this pathway in the pathogenesis of TDS. Integrating data from multiple layers can highlight findings in GWAS that are biologically relevant despite having border significance at currently accepted statistical levels.

Metabolic Network Topology Reveals Transcriptional Regulatory Signatures of Type 2 Diabetes

Type 2 diabetes mellitus (T2DM) is a disorder characterized by both insulin resistance and impaired insulin secretion. Recent transcriptomics studies related to T2DM have revealed changes in expression of a large number of metabolic genes in a variety of tissues. Identification of the molecular mechanisms underlying these transcriptional changes and their impact on the cellular metabolic phenotype is a challenging task due to the complexity of transcriptional regulation and the highly interconnected nature of the metabolic network. In this study we integrate skeletal muscle gene expression datasets with human metabolic network reconstructions to identify key metabolic regulatory features of T2DM. These features include reporter metabolites—metabolites with significant collective transcriptional response in the associated enzyme-coding genes, and transcription factors with significant enrichment of binding sites in the promoter regions of these genes. In addition to metabolites from TCA cycle, oxidative phosphorylation, and lipid metabolism (known to be associated with T2DM), we identified several reporter metabolites representing novel biomarker candidates. For example, the highly connected metabolites NAD+/NADH and ATP/ADP were also identified as reporter metabolites that are potentially contributing to the widespread gene expression changes observed in T2DM. An algorithm based on the analysis of the promoter regions of the genes associated with reporter metabolites revealed a transcription factor regulatory network connecting several parts of metabolism. The identified transcription factors include members of the CREB, NRF1 and PPAR family, among others, and represent regulatory targets for further experimental analysis. Overall, our results provide a holistic picture of key metabolic and regulatory nodes potentially involved in the pathogenesis of T2DM.

Population genetic differentiation of height and body mass index across Europe

Across-nation differences in the mean values for complex traits are common, but the reasons for these differences are unknown. Here we find that many independent loci contribute to population genetic differences in height and body mass index (BMI) in 9,416 individuals across 14 European countries. Using discovery data on over 250,000 individuals and unbiased effect size estimates from 17,500 sibling pairs, we estimate that 24% (95% credible interval (CI) = 9%, 41%) and 8% (95% CI = 4%, 16%) of the captured additive genetic variance for height and BMI, respectively, reflect population genetic differences. Population genetic divergence differed significantly from that in a null model (height, P < 3.94 × 10−8; BMI, P < 5.95 × 10−4), and we find an among-population genetic correlation for tall and slender individuals (r = −0.80, 95% CI = −0.95, −0.60), consistent with correlated selection for both phenotypes. Observed differences in height among populations reflected the predicted genetic means (r = 0.51; P < 0.001), but environmental differences across Europe masked genetic differentiation for BMI (P < 0.58).

Fatness-Associated FTO Gene Variant Increases Mortality Independent of Fatness – in Cohorts of Danish Men

Background The A-allele of the single nucleotide polymorphism (SNP), rs9939609, in the FTO gene is associated with increased fatness. We hypothesized that the SNP is associated with morbidity and mortality through the effect on fatness. Methodology/Principal Findings In a population of 362,200 Danish young men, examined for military service between 1943 and 1977, all obese (BMI≥31.0 kg/m2) and a random 1% sample of the others were identified. In 1992–94, at an average age of 46 years, 752 of the obese and 876 of the others were re-examined, including measurements of weight, fat mass, height, and waist circumference, and DNA sampling. Hospitalization and death occurring during the following median 13.5 years were ascertained by linkage to national registers. Cox regression analyses were performed using a dominant effect model (TT vs. TA or AA). In total 205 men died. Mortality was 42% lower (p = 0.001) with the TT genotype than in A-allele carriers. This phenomenon was observed in both the obese and the randomly sampled cohort when analysed separately. Adjustment for fatness covariates attenuated the association only slightly. Exploratory analyses of cause-specific mortality and morbidity prior to death suggested a general protective effect of the TT genotype, whereas there were only weak associations with disease incidence, except for diseases of the nervous system. Conclusion Independent of fatness, the A-allele of the FTO SNP appears to increase mortality of a magnitude similar to smoking, but without a particular underlying disease pattern barring an increase in the risk of diseases of the nervous system.

A distinct adipose tissue gene expression response to caloric restriction predicts 6-mo weight maintenance in obese subjects

BACKGROUND Weight loss has been shown to reduce risk factors associated with cardiovascular disease and diabetes; however, successful maintenance of weight loss continues to pose a challenge. OBJECTIVE The present study was designed to assess whether changes in subcutaneous adipose tissue (scAT) gene expression during a low-calorie diet (LCD) could be used to differentiate and predict subjects who experience successful short-term weight maintenance from subjects who experience weight regain. DESIGN Forty white women followed a dietary protocol consisting of an 8-wk LCD phase followed by a 6-mo weight-maintenance phase. Participants were classified as weight maintainers (WMs; 0-10% weight regain) and weight regainers (WRs; 50-100% weight regain) by considering changes in body weight during the 2 phases. Anthropometric measurements, bioclinical variables, and scAT gene expression were studied in all individuals before and after the LCD. Energy intake was estimated by using 3-d dietary records. RESULTS No differences in body weight and fasting insulin were observed between WMs and WRs at baseline or after the LCD period. The LCD resulted in significant decreases in body weight and in several plasma variables in both groups. WMs experienced a significant reduction in insulin secretion in response to an oral-glucose-tolerance test after the LCD; in contrast, no changes in insulin secretion were observed in WRs after the LCD. An ANOVA of scAT gene expression showed that genes regulating fatty acid metabolism, citric acid cycle, oxidative phosphorylation, and apoptosis were regulated differently by the LCD in WM and WR subjects. CONCLUSION This study suggests that LCD-induced changes in insulin secretion and scAT gene expression may have the potential to predict successful short-term weight maintenance. This trial was registered at clinicaltrials.gov as NCT00390637.