Association analysis of 29,956 individuals confirms that a low-frequency variant at CCND2 halves the risk of type 2 diabetes by enhancing insulin secretion

Whole-genome sequencing study to identify candidate markers indicating susceptibility to type 2 diabetes in Bama miniature pigs

BACKGROUND

Hundreds of single-nucleotide polymorphism (SNP) sites have been found to be potential genetic markers of type 2 diabetes mellitus (T2DM). However, SNPs related to T2DM in minipigs have been less reported. This study aimed to screen the T2DM-susceptible candidate SNP loci in Bama minipigs so as to improve the success rate of the minipig T2DM model.

METHODS

The genomic DNAs of three Bama minipigs with T2DM, six sibling low-susceptibility minipigs with T2DM, and three normal control minipigs were compared by whole-genome sequencing. The T2DM Bama minipig-specific loci were obtained, and their functions were annotated. Meanwhile, the Biomart software was used to perform homology alignment with T2DM-related loci obtained from the human genome-wide association study to screen candidate SNP markers for T2DM in Bama miniature pigs.

RESULTS

Whole-genome resequencing detected 6960 specific loci in the minipigs with T2DM, and 13 loci corresponding to 9 diabetes-related genes were selected. Further, a set of 122 specific loci in 69 orthologous genes of human T2DM candidate genes were obtained in the pigs. Collectively, a batch of T2DM-susceptible candidate SNP markers in Bama minipigs, covering 16 genes and 135 loci, was established.

CONCLUSIONS

Whole-genome sequencing and comparative genomics analysis of the orthologous genes in pigs that corresponded to the human T2DM-related variant loci successfully screened out T2DM-susceptible candidate markers in Bama miniature pigs. Using these loci to predict the susceptibility of the pigs before constructing an animal model of T2DM may help to establish an ideal animal model.

Identification of key pathways and genes in polycystic ovary syndrome via integrated bioinformatics analysis and prediction of small therapeutic molecules

To enhance understanding of polycystic ovary syndrome (PCOS) at the molecular level; this investigation intends to examine the genes and pathways associated with PCOS by using an integrated bioinformatics analysis. Based on the expression profiling by high throughput sequencing data GSE84958 derived from the Gene Expression Omnibus (GEO) database, the differentially expressed genes (DEGs) between PCOS samples and normal controls were identified. We performed a functional enrichment analysis. A protein-protein interaction (PPI) network, miRNA- target genes and TF - target gene networks, were constructed and visualized, with which the hub gene nodes were identified. Validation of hub genes was performed by using receiver operating characteristic (ROC) and RT-PCR. Small drug molecules were predicted by using molecular docking. A total of 739 DEGs were identified, of which 360 genes were up regulated and 379 genes were down regulated. GO enrichment analysis revealed that up regulated genes were mainly involved in peptide metabolic process, organelle envelope and RNA binding and the down regulated genes were significantly enriched in plasma membrane bounded cell projection organization, neuron projection and DNA-binding transcription factor activity, RNA polymerase II-specific. REACTOME pathway enrichment analysis revealed that the up regulated genes were mainly enriched in translation and respiratory electron transport and the down regulated genes were mainly enriched in generic transcription pathway and transmembrane transport of small molecules. The top 10 hub genes (SAA1, ADCY6, POLR2K, RPS15, RPS15A, CTNND1, ESR1, NEDD4L, KNTC1 and NGFR) were identified from PPI network, miRNA - target gene network and TF - target gene network. The modules analysis showed that genes in modules were mainly associated with the transport of respiratory electrons and signaling NGF, respectively. We find a series of crucial genes along with the pathways that were most closely related with PCOS initiation and advancement. Our investigations provide a more detailed molecular mechanism for the progression of PCOS, detail information on the potential biomarkers and therapeutic targets.

Identification of Key Pathways and Genes in Obesity Using Bioinformatics Analysis and Molecular Docking Studies

Obesity is an excess accumulation of body fat. Its progression rate has remained high in recent years. Therefore, the aim of this study was to diagnose important differentially expressed genes (DEGs) associated in its development, which may be used as novel biomarkers or potential therapeutic targets for obesity. The gene expression profile of E-MTAB-6728 was downloaded from the database. After screening DEGs in each ArrayExpress dataset, we further used the robust rank aggregation method to diagnose 876 significant DEGs including 438 up regulated and 438 down regulated genes. Functional enrichment analysis was performed. These DEGs were shown to be significantly enriched in different obesity related pathways and GO functions. Then protein-protein interaction network, target genes - miRNA regulatory network and target genes - TF regulatory network were constructed and analyzed. The module analysis was performed based on the whole PPI network. We finally filtered out STAT3, CORO1C, SERPINH1, MVP, ITGB5, PCM1, SIRT1, EEF1G, PTEN and RPS2 hub genes. Hub genes were validated by ICH analysis, receiver operating curve (ROC) analysis and RT-PCR. Finally a molecular docking study was performed to find small drug molecules. The robust DEGs linked with the development of obesity were screened through the expression profile, and integrated bioinformatics analysis was conducted. Our study provides reliable molecular biomarkers for screening and diagnosis, prognosis as well as novel therapeutic targets for obesity.

Bayesian shrinkage estimation of high dimensional causal mediation effects in omics studies

Causal mediation analysis aims to examine the role of a mediator or a group of mediators that lie in the pathway between an exposure and an outcome. Recent biomedical studies often involve a large number of potential mediators based on high-throughput technologies. Most of the current analytic methods focus on settings with one or a moderate number of potential mediators. With the expanding growth of -omics data, joint analysis of molecular-level genomics data with epidemiological data through mediation analysis is becoming more common. However, such joint analysis requires methods that can simultaneously accommodate high-dimensional mediators and that are currently lacking. To address this problem, we develop a Bayesian inference method using continuous shrinkage priors to extend previous causal mediation analysis techniques to a high-dimensional setting. Simulations demonstrate that our method improves the power of global mediation analysis compared to simpler alternatives and has decent performance to identify true nonnull contributions to the mediation effects of the pathway. The Bayesian method also helps us to understand the structure of the composite null cases for inactive mediators in the pathway. We applied our method to Multi-Ethnic Study of Atherosclerosis and identified DNA methylation regions that may actively mediate the effect of socioeconomic status on cardiometabolic outcomes.

Reaffirmation of known major genes and the identification of novel candidate genes associated with carcass-related metrics based on whole genome sequence within a large multi-breed cattle population

Background

The high narrow sense heritability of carcass traits suggests that the underlying additive genetic potential of an individual should be strongly correlated with both animal carcass quality and quantity, and therefore, by extension, carcass value. Therefore, the objective of the present study was to detect genomic regions associated with three carcass traits, namely carcass weight, conformation and fat cover, using imputed whole genome sequence in 28,470 dairy and beef sires from six breeds with a total of 2,199,926 phenotyped progeny.

Results

Major genes previously associated with carcass performance were identified, as well as several putative novel candidate genes that likely operate both within and across breeds. The role of MSTN in carcass performance was re-affirmed with the segregating Q204X mutation explaining 1.21, 1.11 and 5.95% of the genetic variance in carcass weight, fat and conformation, respectively in the Charolais population. In addition, a genomic region on BTA6 encompassing the NCAPG/LCORL locus, which is a known candidate locus associated with body size, was associated with carcass weight in Angus, Charolais and Limousin. Novel candidate genes identified included ZFAT in Angus, and SLC40A1 and the olfactory gene cluster on BTA15 in Charolais. Although the majority of associations were breed specific, associations that operated across breeds included SORCS1 on BTA26, MCTP2 on BTA21 and ARL15 on BTA20; these are of particular interest due to their potential informativeness in across-breed genomic evaluations. Genomic regions affecting all three carcass traits were identified in each of the breeds, although these were mainly concentrated on BTA2 and BTA6, surrounding MSTN and NCAPG/LCORL, respectively. This suggests that although major genes may be associated with all three carcass traits, the majority of genes containing significant variants (unadjusted p-value < 10^− 4) may be trait specific associations of small effect.

Conclusions

Although plausible novel candidate genes were identified, the proportion of variance explained by these candidates was minimal thus reaffirming that while carcass performance may be affected by major genes in the form of MSTN and NCAPG/LCORL, the majority of variance is attributed to the additive (and possibly multiplicative) effect of many polymorphisms of small effect.

Electronic supplementary material

The online version of this article (10.1186/s12864-019-6071-9) contains supplementary material, which is available to authorized users.

The difference in expression of long noncoding RNAs in rat semen induced by high-fat diet was associated with metabolic pathways

Background

Obesity, a common metabolic disease, is a known cause of male infertility due to its associated health risk. Long noncoding RNAs (lncRNAs) have also been reported to be associated with male reproductive diseases; however, their role in the association between high-fat diet-induced obesity (DIO) and male reproduction remains unclear.

Methods

We used microarray analysis to compare the expression levels of lncRNAs and mRNAs in the spermatozoa of rats with DIO and normal rats. We selected a few lncRNAs that were obviously up-regulated or down-regulated, and then used RT-PCR to verify the accuracy of their expression. We then performed a functional enrichment analysis of the differentially expressed mRNAs using gene ontology and pathway analysis. Finally, target gene predictive analysis was used to explore the relationship between lncRNAs and mRNAs.

Results

The results revealed a statistically significant difference in the fasting blood glucose level in rats with DIO and control rats. We found that 973 lncRNAs and 2,994 mRNAs were differentially expressed in the sperm samples of the DIO rats, compared to the controls. GO enrichment analysis revealed 263 biological process terms, 39 cellular component terms, and 40 molecular function terms (p < 0.01) in the differentially expressed mRNAs. The pathway analysis showed that metabolic pathways were most enriched in protein-coding genes.

Discussion

To the best of our knowledge, this is the first report to show differences in the expression levels of lncRNAs and mRNAs in the sperms of rats with DIO and normal rats, and to determine the expression profile of lncRNAs in the sperm of rats with DIO. Our results have revealed a number of lncRNAs and pathways associated with obesity-induced infertility, including metabolic pathways. These pathways could be new candidates that help cope with and investigate the mechanisms behind the progression of obesity-induced male infertility.

Exploration of haplotype research consortium imputation for genome-wide association studies in 20,032 Generation Scotland participants

Background

The Generation Scotland: Scottish Family Health Study (GS:SFHS) is a family-based population cohort with DNA, biological samples, socio-demographic, psychological and clinical data from approximately 24,000 adult volunteers across Scotland. Although data collection was cross-sectional, GS:SFHS became a prospective cohort due to of the ability to link to routine Electronic Health Record (EHR) data. Over 20,000 participants were selected for genotyping using a large genome-wide array.

Methods

GS:SFHS was analysed using genome-wide association studies (GWAS) to test the effects of a large spectrum of variants, imputed using the Haplotype Research Consortium (HRC) dataset, on medically relevant traits measured directly or obtained from EHRs. The HRC dataset is the largest available haplotype reference panel for imputation of variants in populations of European ancestry and allows investigation of variants with low minor allele frequencies within the entire GS:SFHS genotyped cohort.

Results

Genome-wide associations were run on 20,032 individuals using both genotyped and HRC imputed data. We present results for a range of well-studied quantitative traits obtained from clinic visits and for serum urate measures obtained from data linkage to EHRs collected by the Scottish National Health Service. Results replicated known associations and additionally reveal novel findings, mainly with rare variants, validating the use of the HRC imputation panel. For example, we identified two new associations with fasting glucose at variants near to Y_RNA and WDR4 and four new associations with heart rate at SNPs within CSMD1 and ASPH, upstream of HTR1F and between PROKR2 and GPCPD1. All were driven by rare variants (minor allele frequencies in the range of 0.08–1%). Proof of principle for use of EHRs was verification of the highly significant association of urate levels with the well-established urate transporter SLC2A9.

Conclusions

GS:SFHS provides genetic data on over 20,000 participants alongside a range of phenotypes as well as linkage to National Health Service laboratory and clinical records. We have shown that the combination of deeper genotype imputation and extended phenotype availability make GS:SFHS an attractive resource to carry out association studies to gain insight into the genetic architecture of complex traits.

Electronic supplementary material

The online version of this article (doi:10.1186/s13073-017-0414-4) contains supplementary material, which is available to authorized users.

The Metabolic Syndrome in Men study: a resource for studies of metabolic and cardiovascular diseases

The Metabolic Syndrome in Men (METSIM) study is a population-based study including 10,197 Finnish men examined in 2005-2010. The aim of the study is to investigate nongenetic and genetic factors associated with the risk of T2D and CVD, and with cardiovascular risk factors. The protocol includes a detailed phenotyping of the participants, an oral glucose tolerance test, fasting laboratory measurements including proton NMR measurements, mass spectometry metabolomics, adipose tissue biopsies from 1,400 participants, and a stool sample. In our ongoing follow-up study, we have, to date, reexamined 6,496 participants. Extensive genotyping and exome sequencing have been performed for essentially all METSIM participants, and >2,000 METSIM participants have been whole-genome sequenced. We have identified several nongenetic markers associated with the development of diabetes and cardiovascular events, and participated in several genetic association studies to identify gene variants associated with diabetes, hyperglycemia, and cardiovascular risk factors. The generation of a phenotype and genotype resource in the METSIM study allows us to proceed toward a "systems genetics" approach, which includes statistical methods to quantitate and integrate intermediate phenotypes, such as transcript, protein, or metabolite levels, to provide a global view of the molecular architecture of complex traits.

Epigenetic alterations in blood mirror age-associated DNA methylation and gene expression changes in human liver

AIM

To study the impact of aging on DNA methylation and mRNA expression in human liver.

EXPERIMENTAL PROCEDURES

We analysed genome-wide DNA methylation and gene expression in human liver samples using Illumina 450K and HumanHT12 expression BeadChip arrays.

RESULTS

DNA methylation analysis of ∼455,000 CpG sites in human liver revealed that age was significantly associated with altered DNA methylation of 20,396 CpG sites. Comparison of liver methylation data with published methylation data in other tissues showed that vast majority of the age-associated significant CpG sites overlapped between liver and blood, whereas a smaller overlap was found between liver and pancreatic islets or adipose tissue, respectively. We identified 151 genes whose liver expression also correlated with age.

CONCLUSIONS

We identified age-associated DNA methylation and expression changes in human liver that are partly reflected by epigenetic alterations in blood.

The Common p.R114W HNF4A Mutation Causes a Distinct Clinical Subtype of Monogenic Diabetes

HNF4A mutations cause increased birth weight, transient neonatal hypoglycemia, and maturity onset diabetes of the young (MODY). The most frequently reported HNF4A mutation is p.R114W (previously p.R127W), but functional studies have shown inconsistent results; there is a lack of cosegregation in some pedigrees and an unexpectedly high frequency in public variant databases. We confirm that p.R114W is a pathogenic mutation with an odds ratio of 30.4 (95% CI 9.79-125, P = 2 × 10(-21)) for diabetes in our MODY cohort compared with control subjects. p.R114W heterozygotes did not have the increased birth weight of patients with other HNF4A mutations (3,476 g vs. 4,147 g, P = 0.0004), and fewer patients responded to sulfonylurea treatment (48% vs. 73%, P = 0.038). p.R114W has reduced penetrance; only 54% of heterozygotes developed diabetes by age 30 years compared with 71% for other HNF4A mutations. We redefine p.R114W as a pathogenic mutation that causes a distinct clinical subtype of HNF4A MODY with reduced penetrance, reduced sensitivity to sulfonylurea treatment, and no effect on birth weight. This has implications for diabetes treatment, management of pregnancy, and predictive testing of at-risk relatives. The increasing availability of large-scale sequence data is likely to reveal similar examples of rare, low-penetrance MODY mutations.

Islet biology, the CDKN2A/B locus and type 2 diabetes risk

Type 2 diabetes, fuelled by the obesity epidemic, is an escalating worldwide cause of personal hardship and public cost. Diabetes incidence increases with age, and many studies link the classic senescence and ageing protein p16(INK4A) to diabetes pathophysiology via pancreatic islet biology. Genome-wide association studies (GWASs) have unequivocally linked the CDKN2A/B locus, which encodes p16 inhibitor of cyclin-dependent kinase (p16(INK4A)) and three other gene products, p14 alternate reading frame (p14(ARF)), p15(INK4B) and antisense non-coding RNA in the INK4 locus (ANRIL), with human diabetes risk. However, the mechanism by which the CDKN2A/B locus influences diabetes risk remains uncertain. Here, we weigh the evidence that CDKN2A/B polymorphisms impact metabolic health via islet biology vs effects in other tissues. Structured in a bedside-to-bench-to-bedside approach, we begin with a summary of the evidence that the CDKN2A/B locus impacts diabetes risk and a brief review of the basic biology of CDKN2A/B gene products. The main emphasis of this work is an in-depth look at the nuanced roles that CDKN2A/B gene products and related proteins play in the regulation of beta cell mass, proliferation and insulin secretory function, as well as roles in other metabolic tissues. We finish with a synthesis of basic biology and clinical observations, incorporating human physiology data. We conclude that it is likely that the CDKN2A/B locus influences diabetes risk through both islet and non-islet mechanisms.

Type 2 diabetes: genetic data sharing to advance complex disease research

As with other complex diseases, unbiased association studies followed by physiological and experimental characterization have for years formed a paradigm for identifying genes or processes of relevance to type 2 diabetes mellitus (T2D). Recent large-scale common and rare variant genome-wide association studies (GWAS) suggest that substantially larger association studies are needed to identify most T2D loci in the population. To hasten clinical translation of genetic discoveries, new paradigms are also required to aid specialized investigation of nascent hypotheses. We argue for an integrated T2D knowledgebase, designed for a worldwide community to access aggregated large-scale genetic data sets, as one paradigm to catalyse convergence of these efforts.

Genetics of Type 2 Diabetes: the Power of Isolated Populations

Type 2 diabetes (T2D) affects millions of people worldwide. Improving the understanding of the underlying mechanisms and ultimately improving the treatment strategies are, thus, of great interest. To achieve this, identification of genetic variation predisposing to T2D is important. A large number of variants have been identified in large outbred populations, mainly from Europe and Asia. However, to elucidate additional variation, isolated populations have a number of advantageous properties, including increased amounts of linkage disequilibrium, and increased probability for presence of high frequency disease-associated variants due to genetic drift. Collectively, this increases the statistical power to detect association signals in isolated populations compared to large outbred populations. In this review, we elaborate on why isolated populations are a powerful resource for the identification of complex disease variants and describe their contributions to the understanding of the genetics of T2D.

Glucose Induces Mouse β-Cell Proliferation via IRS2, MTOR, and Cyclin D2 but Not the Insulin Receptor

An important goal in diabetes research is to understand the processes that trigger endogenous β-cell proliferation. Hyperglycemia induces β-cell replication, but the mechanism remains debated. A prime candidate is insulin, which acts locally through the insulin receptor. Having previously developed an in vivo mouse hyperglycemia model, we tested whether glucose induces β-cell proliferation through insulin signaling. By using mice lacking insulin signaling intermediate insulin receptor substrate 2 (IRS2), we confirmed that hyperglycemia-induced β-cell proliferation requires IRS2 both in vivo and ex vivo. Of note, insulin receptor activation was not required for glucose-induced proliferation, and insulin itself was not sufficient to drive replication. Glucose and insulin caused similar acute signaling in mouse islets, but chronic signaling differed markedly, with mammalian target of rapamycin (MTOR) and extracellular signal-related kinase (ERK) activation by glucose and AKT activation by insulin. MTOR but not ERK activation was required for glucose-induced proliferation. Cyclin D2 was necessary for glucose-induced β-cell proliferation. Cyclin D2 expression was reduced when either IRS2 or MTOR signaling was lost, and restoring cyclin D2 expression rescued the proliferation defect. Human islets shared many of these regulatory pathways. Taken together, these results support a model in which IRS2, MTOR, and cyclin D2, but not the insulin receptor, mediate glucose-induced proliferation.