Biology subjects

McCarthy, M. I.

Publications and source records attributed to McCarthy, M. I..

11 recordsLinked to original sources

bioRxiv2018

Loss of ZnT8 function protects against diabetes by enhanced insulin secretion

A rare loss-of-function variant p.Arg138* in SLC30A8 encoding the zinc transporter 8 (ZnT8) enriched in Western Finland protects against type 2 diabetes (T2D). We recruited relatives of the identified carriers and showed that protection was associated with better insulin secretion due to enhanced glucose responsiveness and proinsulin conversion, especially compared with individuals matched for the genotype of a common T2D risk variant in SLC30A8, p.Arg325. In genome-edited human IPS-derived {beta}-like cells, we establish that the p.Arg138* variant results in reduced SLC30A8 expression due to haploinsufficiency. In human {beta}-cells loss of SLC30A8 leads to increased glucose responsiveness and reduced KATP channel function, which was also seen in isolated islets from carriers of the T2D-protective allele p.Trp325. These data position ZnT8 as an appealing target for treatment aiming at maintaining insulin secretion capacity in T2D.

genetics↗

bioRxiv2018

Variation in the plasma membrane monoamine transporter (PMAT, encoded in SLC29A4) and organic cation transporter 1 (OCT1, encoded in SLC22A1) and gastrointestinal intolerance to metformin in type 2 diabetes: an IMI DIRECT study

Objectives20-30% of patients with metformin treated type 2 diabetes experience gastrointestinal side effects leading to premature discontinuation in 5-10% of the cases. Gastrointestinal intolerance may reflect localised high concentrations of metformin in the gut. We hypothesized that reduced transport of metformin into the circulation via the plasma membrane monoamine transporter (PMAT) and organic cation transporter 1 (OCT1) could increase the risk of severe GI side effects.\n\nResearch Design and MethodsThe study included 286 severe metformin intolerant and 1128 tolerant individuals from the IMI DIRECT consortium. We assessed the association of patient characteristics, concomitant medication and the burden of mutations in the SLC29A4 and SLC22A1, genes that encode PMAT and OCT1, respectively, on odds of metformin intolerance using a logistic regression model.\n\nResultsWomen (p < 0.001) and older people (p < 0.001) were more likely to develop metformin intolerance. Concomitant use of metformin transporter inhibiting drugs increased the odds of intolerance by more than 70% (OR = 1.72 [1.26-2.32], p < 0.001). In a logistic regression model adjusted for age, sex, weight and population substructure, the G allele at rs3889348 (SLC29A4) was associated with GI intolerance (OR = 1.34[1.09-1.65], p = 0.005). rs3889348 is the top cis-eQTL for SLC29A4 in gut tissue where carriers of the G allele had reduced expression. Homozygous carriers of the G allele treated with metformin transporter inhibiting drugs had over three times higher odds of intolerance compared to carriers of no G allele and not treated with inhibiting drugs (OR = 3.23 [1.71-6.39], p < 0.001). Using a genetic risk score (GRS) derived from SLC29A4 (rs3889348) and previously reported SLC22A1 variants (M420del, R61C, G401S), the odds of intolerance was more than twice in individuals who carry three or more risk alleles compared with those carrying none (OR = 2.15 [1.20-4.12], p = 0.01).\n\nConclusionsThese results suggest that intestinal metformin transporters and concomitant medications play an important role in gastrointestinal side effects of metformin.

genetics↗

bioRxiv2018

Cohort Profile: East London Genes & Health (ELGH), a community based population genomics and health study in people of British-Bangladeshi and -Pakistani heritage.

Cohort profile in a nutshellO_LIEast London Genes & Health (ELGH) is a large scale, community genomics and health study (to date >34,000 volunteers; target 100,000 volunteers). C_LIO_LIELGH was set up in 2015 to gain deeper understanding of health and disease, and underlying genetic influences, in British-Bangladeshi and British-Pakistani people living in east London. C_LIO_LIELGH prioritises studies in areas important to, and identified by, the community it represents. Current priorities include cardiometabolic diseases and mental illness, these being of notably high prevalence and severity. However studies in any scientific area are possible, subject to community advisory group and ethical approval. C_LIO_LIELGH combines health data science (using linked UK National Health Service (NHS) electronic health record data) with exome sequencing and SNP array genotyping to elucidate the genetic influence on health and disease, including the contribution from high rates of parental relatedness on rare genetic variation and homozygosity (autozygosity), in two understudied ethnic groups. Linkage to longitudinal health record data enables both retrospective and prospective analyses. C_LIO_LIThrough Stage 2 studies, ELGH offers researchers the opportunity to undertake recall-by-genotype and/or recall-by-phenotype studies on volunteers. Sub-cohort, trial-within-cohort, and other study designs are possible. C_LIO_LIELGH is a fully collaborative, open access resource, open to academic and life sciences industry scientific research partners. C_LI

genomics↗

bioRxiv2018

Variants in the fetal genome near pro-inflammatory cytokine genes on 2q13 are associated with gestational duration

The duration of pregnancy is influenced by fetal and maternal genetic and non-genetic factors. We conducted a fetal genome-wide association meta-analysis of gestational duration, and early preterm, preterm, and postterm birth in 84,689 infants. One locus on chromosome 2q13 was associated with gestational duration; the association was replicated in 9,291 additional infants (combined P = 3.96 x 10-14). Analysis of 15,536 mother-child pairs showed that the association was driven by fetal rather than maternal genotype. Functional experiments showed that the lead SNP, rs7594852, alters the binding of the HIC1 transcriptional repressor. Genes at the locus include several interleukin 1 family members with roles in pro-inflammatory pathways that are central to the process of parturition. Further understanding of the underlying mechanisms will be of great public health importance, since giving birth either before or after the window of term gestation is associated with increased morbidity and mortality.

genetics↗

bioRxiv2018

Genome-wide scan and fine-mapping of rare nonsynonymous associations implicates intracellular lipolysis genes in fat distribution and cardio-metabolic risk

Difficulties in identifying causal variants and genes underlying genetic associations have limited the translational potential of genetic studies of body fat distribution, an important, partly-heritable risk factor for cardio-metabolic disease. Rare variant associations facilitate fine-mapping of causal alleles, but their contribution to fat distribution is understudied. We performed a genome-wide scan of rare nonsynonymous variants for body mass index-adjusted waist-to-hip-ratio (BMI-adjusted WHR; a widely-used measure of fat distribution) in 450,562 European ancestry individuals, followed by systematic Bayesian fine-mapping at six genome-wide (p<5x10-08; main-analysis) and two subthreshold signals (significant at a Bonferroni-corrected p<1.3x10-06). We found strong statistical evidence of causal association for nonsynonymous alleles in CALCRL (p.L87P, pconditional=5.9x10-12; posterior-probability of association [PPA]=52%), PLIN1 (p.L90P, pconditional=5.5x10-13; PPA>99%), PDE3B (p.R783X, pconditional=6.2x10-15; PPA>99%), ACVR1C (p.I195T; pconditional=5.4x10-12; PPA>99%), and FGF1 (p.G21E, pconditional=1.6x10-07; PPA=98%). Alleles at the four likely-causal main-analysis genes affected fat distribution primarily via larger hip-rather than smaller waist-circumference and six of nine conditionally-independent WHR-lowering index-variants were associated with protection from cardiovascular or metabolic disease. All four genes are expressed in adipose tissue and have been linked with the regulation of intracellular lipolysis, which controls fat retention in mature cells. Targeted follow-up analyses of key intracellular-lipolysis genes revealed associations for a variant in the initiator of intracellular lipolysis PNPLA2 (p.N252K) with higher BMI-adjusted-WHR and higher cardio-metabolic risk. This study provides human genetic evidence of a link between intracellular lipolysis, fat-distribution and its cardio-metabolic complications in the general population.

genetics↗

bioRxiv2018

Developing a network view of type 2 diabetes risk pathways through integration of genetic, genomic and functional data

Genome wide association studies (GWAS) have identified several hundred susceptibility loci for Type 2 Diabetes (T2D). One critical, but unresolved, issue concerns the extent to which the mechanisms through which these diverse signals influencing T2D predisposition converge on a limited set of biological processes. However, the causal variants identified by GWAS mostly fall into non-coding sequence, complicating the task of defining the effector transcripts through which they operate. Here, we describe implementation of an analytical pipeline to address this question. First, we integrate multiple sources of genetic, genomic, and biological data to assign positional candidacy scores to the genes that map to T2D GWAS signals. Second, we introduce genes with high scores as seeds within a network optimization algorithm (the asymmetric prize-collecting Steiner Tree approach) which uses external, experimentally-confirmed protein-protein interaction (PPI) data to generate high confidence subnetworks. Third, we use GWAS data to test the T2D-association enrichment of the \"non-seed\" proteins introduced into the network, as a measure of the overall functional connectivity of the network. We find: (a) non-seed proteins in the T2D protein-interaction network so generated (comprising 705 nodes) are enriched for association to T2D (p=0.0014) but not control traits; (b) stronger T2D-enrichment for islets than other tissues when we use RNA expression data to generate tissue-specific PPI networks; and (c) enhanced enrichment (p=3.9xl0-5) when we combine analysis of the islet-specific PPI network with a focus on the subset of T2D GWAS loci which act through defective insulin secretion. These analyses reveal a pattern of non-random functional connectivity between causal candidate genes atT2D GWAS loci, and highlight the products of genes including YWHAG, SMAD4 or CDK2 as contributors to T2D-relevant islet dysfunction. The approach we describe can be applied to other complex genetic and genomic data sets, facilitating integration of diverse data types into disease-associated networks.\n\nAuthor summaryWe were interested in the following question: as we discover more and more genetic variants associated with a complex disease, such as type 2 diabetes, will the biological pathways implicated by those variants proliferate, or will the biology converge onto a more limited set of aetiological processes? To address this, we first took the 1895 genes that map to ~100 type 2 diabetes association signals, and pruned these to a set of 451 for which combined genetic, genomic and biological evidence assigned the strongest candidacy with respect to type 2 diabetes pathogenesis. We then sought to maximally connect these genes within a curated protein-protein interaction network. We found that proteins brought into the resulting diabetes interaction network were themselves enriched for diabetes association signals as compared to appropriate control proteins. Furthermore, when we used tissue-specific RNA abundance data to filter the generic protein-protein network, we found that the enrichment for type 2 diabetes association signals was enhanced within a network filtered for pancreatic islet expression, particularly when we selected the subset of diabetes association signals acting through reduced insulin secretion. Our data demonstrate convergence of the biological processes involved in type 2 diabetes pathogenesis and highlight novel contributors.

bioinformatics↗

bioRxiv2018

Large-Scale Genome-Wide Meta Analysis of Polycystic Ovary Syndrome Suggests Shared Genetic Architecture for Different Diagnosis Criteria.

Polycystic ovary syndrome (PCOS) is a disorder characterized by hyperandrogenism, ovulatory dysfunction and polycystic ovarian morphology. Affected women frequently have metabolic disturbances including insulin resistance and dysregulation of glucose homeostasis. PCOS is diagnosed with two different sets of diagnostic criteria, resulting in a phenotypic spectrum of PCOS cases. The genetic similarities between cases diagnosed with different criteria have been largely unknown. Previous studies in Chinese and European subjects have identified 16 loci associated with risk of PCOS. We report a meta-analysis from 10,074 PCOS cases and 103,164 controls of European ancestry and characterisation of PCOS related traits. We identified 3 novel loci (near PLGRKT, ZBTB16 and MAPRE1), and provide replication of 11 previously reported loci. Identified variants were associated with hyperandrogenism, gonadotropin regulation and testosterone levels in affected women. Genetic correlations with obesity, fasting insulin, type 2 diabetes, lipid levels and coronary artery disease indicate shared genetic architecture between metabolic traits and PCOS. Mendelian randomization analyses suggested variants associated with body mass index, fasting insulin, menopause timing, depression and male-pattern balding play a causal role in PCOS. Only one locus differed in its association by diagnostic criteria, otherwise the genetic architecture was similar between PCOS diagnosed by self-report and PCOS diagnosed by NIH or Rotterdam criteria across common variants at 13 loci.

genetics↗

bioRxiv2018

A general framework for predicting the transcriptomic consequences of non-coding variation

Genome wide association studies (GWASs) for complex traits have implicated thousands of genetic loci. Most GWAS-nominated variants lie in noncoding regions, complicating the systematic translation of these findings into functional understanding. Here, we leverage convolutional neural networks to assist in this challenge. Our computational framework, peaBrain, models the transcriptional machinery of a tissue as a two-stage process: first, predicting the mean tissue specific abundance of all genes and second, incorporating the transcriptomic consequences of genotype variation to predict individual abundance on a subject-by-subject basis. We demonstrate that peaBrain accounts for the majority (>50%) of variance observed in mean transcript abundance across most tissues and outperforms regularized linear models in predicting the consequences of individual genotype variation. We highlight the validity of the peaBrain model by calculating non-coding impact scores that correlate with nucleotide evolutionary constraint that are also predictive of disease-associated variation and allele-specific transcription factor binding. We further show how these tissue-specific peaBrain scores can be leveraged to pinpoint functional tissues underlying complex traits, outperforming methods that depend on colocalization of eQTL and GWAS signals. We subsequently derive continuous dense embeddings of genes for downstream applications, and identify putatively functional eQTLs that are missed by high-throughput experimental approaches.

genetics↗

bioRxiv2017

Integration of human pancreatic islet genomic data refines regulatory mechanisms at Type 2 Diabetes susceptibility loci

Human genetic studies have emphasised the dominant contribution of pancreatic islet dysfunction to development of Type 2 Diabetes (T2D). However, limited annotation of the islet epigenome has constrained efforts to define the molecular mechanisms mediating the, largely regulatory, signals revealed by Genome-Wide Association Studies (GWAS). We characterised patterns of chromatin accessibility (ATAC-seq, n=17) and DNA methylation (whole-genome bisulphite sequencing, n=10) in human islets, generating high-resolution chromatin state maps through integration with established ChIP-seq marks. We found enrichment of GWAS signals for T2D and fasting glucose was concentrated in subsets of islet enhancers characterised by open chromatin and hypomethylation, with the former annotation predominant. At several loci (including CDC123, ADCY5, KLHDC5) the combination of fine-mapping genetic data and chromatin state enrichment maps, supplemented by allelic imbalance in chromatin accessibility pinpointed likely causal variants. The combination of increasingly-precise genetic and islet epigenomic information accelerates definition of causal mechanisms implicated in T2D pathogenesis.

genomics↗

bioRxiv2017

Refining The Accuracy Of Validated Target Identification Through Coding Variant Fine-Mapping In Type 2 Diabetes

Identification of coding variant associations for complex diseases offers a direct route to biological insight, but is dependent on appropriate inference concerning the causal impact of those variants on disease risk. We aggregated exome-array and exome sequencing data for 81,412 type 2 diabetes (T2D) cases and 370,832 controls of diverse ancestry, identifying 40 distinct coding variant association signals (at 38 loci) reaching significance (p<2.2x10-7). Of these, 16 represent novel associations mapping outside known genome-wide association study (GWAS) signals. We make two important observations. First, despite a threefold increase in sample size over previous efforts, only five of the 40 signals are driven by variants with minor allele frequency <5%, and we find no evidence for low-frequency variants with allelic odds ratio >1.36. Second, we used GWAS data from 50,160 T2D cases and 465,272 controls to fine-map associated coding variants in their regional context, with and without additional weighting, to account for the global enrichment of complex trait association signals in coding exons. We demonstrate convincing support (posterior probability >80% under the \"annotation-weighted\" model) that coding variants are causal for the association at 16 of the 40 signals (including novel signals involving POC5 p.His36Arg, ANKH p.Arg187Gln, WSCD2 p.Thr113Ile, PLCB3 p.Ser778Leu, and PNPLA3 p.Ile148Met). However, one third of coding variant association signals represent \"false leads\" at which naive analysis would have led to an erroneous inference regarding the effector transcript mediating the signal. Accurate identification of validated targets is dependent on correct specification of the contribution of coding and non-coding mediated mechanisms at associated loci.

genetics↗

bioRxiv2017

Causal Analyses, Statistical Efficiency And Phenotypic Precision Through Recall-By-Genotype Study Design

Genome-wide association studies have been useful in identifying common genetic variants related to a variety of complex traits and diseases; however, they are often limited in their ability to inform about underlying biology. Whilst bioinformatics analyses, studies of cells, animal models and applied genetic epidemiology have provided some understanding of genetic associations or causal pathways, there is a need for new genetic studies that elucidate causal relationships and mechanisms in a cost-effective, precise and statistically efficient fashion. We discuss the motivation for and the characteristics of the Recall-by-Genotype (RbG) study design, an approach that enables genotype-directed deep-phenotyping and improvement in drawing causal inferences. Specifically, we present RbG designs using single and multiple variants and discuss the inferential properties, analytical approaches and applications of both. We consider the efficiency of the RbG approach, the likely value of RbG studies for the causal investigation of disease aetiology and the practicalities of incorporating genotypic data into population studies in the context of the RbG study design. Finally, we provide a catalogue of the UK-based resources for such studies, an online tool to aid the design of new RbG studies and discuss future developments of this approach.

genetics↗