Two common genetic variants near nuclear-encoded OXPHOS genes are associated with insulin secretion in vivo

Context Mitochondrial ATP production is important in the regulation of glucose-stimulated insulin secretion. Genetic factors may modulate the capacity of the β-cells to secrete insulin and thereby contribute to the risk of type 2 diabetes. OBJECTIVE: The aim of this study was to identify genetic loci in or adjacent to nuclear encoded genes of the oxidative phosphorylation (OXPHOS) pathway that are associated with insulin secretion in vivo. DESIGN AND METHODS: To find polymorphisms associated with glucose-stimulated insulin secretion, data from a genome-wide association study (GWAS) of 1467 non-diabetic individuals, the Diabetes Genetic Initiative (DGI), was examined. 413 single nucleotide polymorphisms (SNPs) with a minor allele frequency (MAF) ≥0.05 located in or adjacent to 76 OXPHOS genes were included in the DGI GWAS. A more extensive population based study of 4323 non-diabetics, the PPP-Botnia, was used as a replication cohort. Insulinogenic index during an oral glucose tolerance test (OGTT) was used as a surrogate marker of glucose-stimulated insulin secretion. Multivariate linear regression analyses were used to test genotype-phenotype associations. RESULTS: Two common variants were indentified in the DGI, where the major C-allele of rs606164, adjacent to NDUFC2 (NADH dehyrogenase (ubiqinone) 1 subunit C2), and the minor G-allele of rs1323070, adjacent to COX7A2 (cythochrome c oxidase subunit VIIa polypeptide 2), showed nominal associations with decreased glucose-stimulated insulin secretion (p=0.0009 respective p=0.003). These associations were replicated in PPP-Botnia (p=0.002 and p=0.05). CONCLUSION: Our study shows that genetic variation near genes involved in oxidative phosphorylation may influence glucose-stimulated insulin secretion in vivo

Fine mapping the KLK3 locus on chromosome 19q13.33 associated with prostate cancer susceptibility and PSA levels

Measurements of serum prostate-specific antigen (PSA) protein levels form the basis for a widely used test to screen men for prostate cancer. Germline variants in the gene that encodes the PSA protein (KLK3) have been shown to be associated with both serum PSA levels and prostate cancer. Based on a resequencing analysis of a 56 kb region on chromosome 19q13.33, centered on the KLK3 gene, we fine mapped this locus by genotyping tag SNPs in 3,522 prostate cancer cases and 3,338 controls from five case–control studies. We did not observe a strong association with the KLK3 variant, reported in previous studies to confer risk for prostate cancer (rs2735839; P = 0.20) but did observe three highly correlated SNPs (rs17632542, rs62113212 and rs62113214) associated with prostate cancer [P = 3.41 × 10−4, per-allele trend odds ratio (OR) = 0.77, 95% CI = 0.67–0.89]. The signal was apparent only for nonaggressive prostate cancer cases with Gleason score <7 and disease stage <III (P = 4.72 × 10−5, per-allele trend OR = 0.68, 95% CI = 0.57–0.82) and not for advanced cases with Gleason score >8 or stage ≥III (P = 0.31, per-allele trend OR = 1.12, 95% CI = 0.90–1.40). One of the three highly correlated SNPs, rs17632542, introduces a non-synonymous amino acid change in the KLK3 protein with a predicted benign or neutral functional impact. Baseline PSA levels were 43.7% higher in control subjects with no minor alleles (1.61 ng/ml, 95% CI = 1.49–1.72) than in those with one or more minor alleles at any one of the three SNPs (1.12 ng/ml, 95% CI = 0.96–1.28) (P = 9.70 × 10−5). Together our results suggest that germline KLK3 variants could influence the diagnosis of nonaggressive prostate cancer by influencing the likelihood of biopsy

A comprehensive resequence analysis of the KLK15–KLK3–KLK2 locus on chromosome 19q13.33

Single nucleotide polymorphisms (SNPs) in the KLK3 gene on chromosome 19q13.33 are associated with serum prostate-specific antigen (PSA) levels. Recent genome wide association studies of prostate cancer have yielded conflicting results for association of the same SNPs with prostate cancer risk. Since the KLK3 gene encodes the PSA protein that forms the basis for a widely used screening test for prostate cancer, it is critical to fully characterize genetic variation in this region and assess its relationship with the risk of prostate cancer. We have conducted a next-generation sequence analysis in 78 individuals of European ancestry to characterize common (minor allele frequency, MAF >1%) genetic variation in a 56 kb region on chromosome 19q13.33 centered on the KLK3 gene (chr19:56,019,829–56,076,043 bps). We identified 555 polymorphic loci in the process including 116 novel SNPs and 182 novel insertion/deletion polymorphisms (indels). Based on tagging analysis, 144 loci are necessary to tag the region at an r2 threshold of 0.8 and MAF of 1% or higher, while 86 loci are required to tag the region at an r2 threshold of 0.8 and MAF >5%. Our sequence data augments coverage by 35 and 78% as compared to variants in dbSNP and HapMap, respectively. We observed six non-synonymous amino acid or frame shift changes in the KLK3 gene and three changes in each of the neighboring genes, KLK15 and KLK2. Our study has generated a detailed map of common genetic variation in the genomic region surrounding the KLK3 gene, which should be useful for fine-mapping the association signal as well as determining the contribution of this locus to prostate cancer risk and/or regulation of PSA expression

svclassify: a method to establish benchmark structural variant calls

The human genome contains variants ranging in size from small single nucleotide polymorphisms (SNPs) to large structural variants (SVs). High-quality benchmark small variant calls for the pilot National Institute of Standards and Technology (NIST) Reference Material (NA12878) have been developed by the Genome in a Bottle Consortium, but no similar high-quality benchmark SV calls exist for this genome. Since SV callers output highly discordant results, we developed methods to combine multiple forms of evidence from multiple sequencing technologies to classify candidate SVs into likely true or false positives. Our method (svclassify) calculates annotations from one or more aligned bam files from many high-throughput sequencing technologies, and then builds a one-class model using these annotations to classify candidate SVs as likely true or false positives. We first used pedigree analysis to develop a set of high-confidence breakpoint-resolved large deletions. We then used svclassify to cluster and classify these deletions as well as a set of high-confidence deletions from the 1000 Genomes Project and a set of breakpoint-resolved complex insertions from Spiral Genetics. We find that likely SVs cluster separately from likely non-SVs based on our annotations, and that the SVs cluster into different types of deletions. We then developed a supervised one-class classification method that uses a training set of random non-SV regions to determine whether candidate SVs have abnormal annotations different from most of the genome. To test this classification method, we use our pedigree-based breakpoint-resolved SVs, SVs validated by the 1000 Genomes Project, and assembly-based breakpoint-resolved insertions, along with semi-automated visualization using svviz. We find that candidate SVs with high scores from multiple technologies have high concordance with PCR validation and an orthogonal consensus method MetaSV (99.7 % concordant), and candidate SVs with low scores are questionable. We distribute a set of 2676 high-confidence deletions and 68 high-confidence insertions with high svclassify scores from these call sets for benchmarking SV callers. We expect these methods to be particularly useful for establishing high-confidence SV calls for benchmark samples that have been characterized by multiple technologies.https://doi.org/10.1186/s12864-016-2366-

Characterizing cis-regulatory variation in the transcriptome of histologically normal and tumor-derived pancreatic tissues

Objective To elucidate the genetic architecture of gene expression in pancreatic tissues. Design We performed expression quantitative trait locus (eQTL) analysis in histologically normal pancreatic tissue samples (n=95) using RNA-sequencing and the corresponding 1000 Genomes imputed germline genotypes. Data from pancreatic tumor-derived tissue samples (n=115) from The Cancer Genome Atlas (TCGA) was included for comparison. Results We identified 38,615 cis-eQTLs (in 484 genes) in histologically normal tissues and 39,713 cis-eQTL (in 237 genes) in tumor-derived tissues (FDR<0.1), with the strongest effects seen near transcriptional start sites (TSS). Approximately 23% and 42% of genes with significant cis-eQTLs appeared to be specific for tumor and normal derived tissues, respectively. Significant enrichment of cis-eQTL variants was noted in noncoding regulatory regions, in particular for pancreatic tissues (1.53-3.12 fold, P≤0.0001), indicating tissue-specific functional relevance. A common pancreatic cancer risk locus on 9q34.2 (rs687289) was associated with ABO expression in histologically normal (P=5.8x10-8) and tumor-derived (P=8.3x10-5) tissues. The high linkage disequilibrium (LD) between this variant and the O blood group generating deletion variant in ABO (exon 6) suggested that nonsense-mediated decay (NMD) of the “O” mRNA might explain this finding. However, knockdown of crucial NMD regulators did not influence decay of the ABO “O” mRNA, indicating that a gene regulatory element influenced by pancreatic cancer risk alleles may underlie the eQTL. Conclusions We have identified cis-eQTLs representing potential functional regulatory variants in the pancreas and generated a rich dataset for further studies on gene expression and its regulation in pancreatic tissues

A Common Variant in TFB1M Is Associated with Reduced Insulin Secretion and Increased Future Risk of Type 2 Diabetes

Type 2 diabetes (T2D) evolves when insulin secretion fails. Insulin release from the pancreatic β cell is controlled by mitochondrial metabolism, which translates fluctuations in blood glucose into metabolic coupling signals. We identified a common variant (rs950994) in the human transcription factor B1 mitochondrial (TFB1M) gene associated with reduced insulin secretion, elevated postprandial glucose levels, and future risk of T2D. Because islet TFB1M mRNA levels were lower in carriers of the risk allele and correlated with insulin secretion, we examined mice heterozygous for Tfb1m deficiency. These mice displayed lower expression of TFB1M in islets and impaired mitochondrial function and released less insulin in response to glucose in vivo and in vitro. Reducing TFB1M mRNA and protein in clonal β cells by RNA interference impaired complexes of the mitochondrial oxidative phosphorylation system. Consequently, nutrient-stimulated ATP generation was reduced, leading to perturbed insulin secretion. We conclude that a deficiency in TFB1M and impaired mitochondrial function contribute to the pathogenesis of T2D