Biology subjects

Montgomery, G. W.

Publications and source records attributed to Montgomery, G. W..

9 recordsLinked to original sources

bioRxiv2018

The effect of X-linked dosage compensation on complex trait variation

Quantitative genetics theory predicts that X-chromosome dosage compensation between sexes will have a detectable effect on the amount of genetic and therefore phenotypic trait variances at associated loci in males and females. Here, we systematically examine the role of dosage compensation in complex trait variation in humans in 20 complex traits in a sample of more than 450,000 individuals from the UK Biobank and in 1,600 gene expression traits from a sample of 2,000 individuals as well as across-tissue gene expression from the GTEx resource. We find, on average, twice as much genetic variation for complex traits due to X-linked loci in males compared to females, consistent with a negligible effect of predicted escape from X-inactivation on complex trait variation across traits and also detect biologically relevant X-linked heterogeneity between the sexes for a number of complex traits.

genetics↗

bioRxiv2018

Trans-ethnic genome-wide association study provides insight into effector genes and molecular mechanisms for kidney function and highlights a causal effect on kidney-specific disease aetiologies

Chronic kidney disease (CKD) affects [~]10% of the global population, with considerable ethnic differences in prevalence and aetiology. We assembled genome-wide association studies (GWAS)1-3 of estimated glomerular filtration rate (eGFR), a measure of kidney function that defines CKD, in 312,468 individuals from four ancestry groups. We identified 93 loci (20 novel), which were delineated to 127 distinct association signals. These signals were homogenous across ancestries, and were enriched for protein-coding exons, kidney-specific histone modifications, and transcription factor binding sites for HDAC2 and EZH2. Fine-mapping revealed 40 high-confidence variants driving eGFR associations and highlighted potential causal genes with cell-type specific expression in glomerulus, and proximal and distal nephron. Mendelian randomisation (MR) supported causal effects of eGFR on overall and cause-specific CKD, kidney stone formation, diastolic blood pressure (DBP) and hypertension. These results define novel molecular mechanisms and effector genes for eGFR, offering insight into clinical outcomes and routes to CKD treatment development.

genetics↗

bioRxiv2018

Genome-wide association analysis identifies 27 novel loci associated with uterine leiomyomata revealing common genetic origins with endometriosis

Uterine leiomyomata (UL), also known as uterine fibroids, are the most common neoplasms of the reproductive tract and the primary cause for hysterectomy, leading to considerable impact on womens lives as well as high economic burden1,2. Genetic epidemiologic studies indicate that heritable risk factors contribute to UL pathogenesis3. Previous genome-wide association studies (GWAS) identified five loci associated with UL at genome-wide significance (P < 5 x 10-8)4-6. We conducted GWAS meta-analysis in 20,406 cases and 223,918 female controls of white European ancestry, identifying 24 genome-wide significant independent loci; 17 replicated in an unrelated cohort of 15,068 additional cases and 43,587 female controls. Aggregation of discovery and replication studies (35,474 cases and 267,505 female controls) revealed six additional significant loci. Interestingly, four of the 17 loci identified and replicated in these analyses have also been associated with risk for endometriosis - another common gynecologic disorder. These findings increase our understanding of the biological mechanisms underlying UL development, and suggest overlapping genetic origins with endometriosis.

genomics↗

bioRxiv2018

Identifying gene targets for brain-related traits using transcriptomic and methylomic data from blood

Understanding the difference in genetic regulation of gene expression between brain and blood is important for discovering genes associated with brain-related traits and disorders. Here, we estimate the correlation of genetic effects at the top associated cis-expression (cis-eQTLs or cis-mQTLs) between brain and blood for genes expressed (or CpG sites methylated) in both tissues, while accounting for errors in their estimated effects (rb). Using publicly available data (n = 72 to l,366), we find that the genetic effects of cis-eQTLs (PeQTL < 5x10-8) or mQTLs (PmQTL < 1x10-10) are highly correlated between independent brain and blood samples ([Formula] with SE = 0.015 for cis-eQTL and [Formula] with SE = 0.006 for cis-mQTLs). Using meta-analyzed brain eQTL/mQTL data (n = 526 to 1,194), we identify 61 genes and 167 DNA methylation (DNAm) sites associated with 4 brain-related traits and disorders. Most of these associations are a subset of the discoveries (97 genes and 295 DNAm sites) using data from blood with larger sample sizes (n = l,980 to 14,115). We further find that cis-eQTLs with tissue-specific effects are approximately uniformly distributed across all the functional annotation categories, and that mean difference in gene expression level between brain and blood is almost independent of the difference in the corresponding cis-eQTL effect. Our results demonstrate the gain of power in gene discovery for brain-related phenotypes using blood cis-eQTL or cis-mQTL data with large sample sizes.

genetics↗

bioRxiv2018

Genome-wide association analysis of lifetime cannabis use (N=184,765) identifies new risk loci, genetic overlap with mental health, and a causal influence of schizophrenia on cannabis use

Cannabis use is a heritable trait [1] that has been associated with adverse mental health outcomes. To identify risk variants and improve our knowledge of the genetic etiology of cannabis use, we performed the largest genome-wide association study (GWAS) meta-analysis for lifetime cannabis use (N=184,765) to date. We identified 4 independent loci containing genome-wide significant SNP associations. Gene-based tests revealed 29 genome-wide significant genes located in these 4 loci and 8 additional regions. All SNPs combined explained 10% of the variance in lifetime cannabis use. The most significantly associated gene, CADM2, has previously been associated with substance use and risk-taking phenotypes [2-4]. We used S-PrediXcan to explore gene expression levels and found 11 unique eGenes. LD-score regression uncovered genetic correlations with smoking, alcohol use and mental health outcomes, including schizophrenia and bipolar disorder. Mendelian randomisation analysis provided evidence for a causal positive influence of schizophrenia risk on lifetime cannabis use.

genetics↗

bioRxiv2017

GWAS meta-analysis (N=279,930) identifies new genes and functional links to intelligence

Intelligence is highly heritable1 and a major determinant of human health and well-being2. Recent genome-wide meta-analyses have identified 24 genomic loci linked to intelligence3-7, but much about its genetic underpinnings remains to be discovered. Here, we present the largest genetic association study of intelligence to date (N=279,930), identifying 206 genomic loci (191 novel) and implicating 1,041 genes (963 novel) via positional mapping, expression quantitative trait locus (eQTL) mapping, chromatin interaction mapping, and gene-based association analysis. We find enrichment of genetic effects in conserved and coding regions and identify 89 nonsynonymous exonic variants. Associated genes are strongly expressed in the brain and specifically in striatal medium spiny neurons and cortical and hippocampal pyramidal neurons. Gene-set analyses implicate pathways related to neurogenesis, neuron differentiation and synaptic structure. We confirm previous strong genetic correlations with several neuropsychiatric disorders, and Mendelian Randomization results suggest protective effects of intelligence for Alzheimers dementia and ADHD, and bidirectional causation with strong pleiotropy for schizophrenia. These results are a major step forward in understanding the neurobiology of intelligence as well as genetically associated neuropsychiatric traits.

genetics↗

bioRxiv2017

Novel pleiotropic risk loci for melanoma and nevus density implicate multiple biological pathways

The total number of acquired melanocytic nevi on the skin is strongly correlated with melanoma risk. Here we report a meta-analysis of 11 nevus GWAS from Australia, Netherlands, United Kingdom, and United States, comprising a total of 52,506 phenotyped individuals. We confirm known loci including MTAP, PLA2G6, and IRF4, and detect novel SNPs at a genome-wide level of significance in KITLG, DOCK8, and a broad region of 9q32. In a bivariate analysis combining the nevus results with those from a recent melanoma GWAS meta-analysis (12,874 cases, 23,203 controls), SNPs near GPRC5A, CYP1B1, PPARGC1B, HDAC4, FAM208B and SYNE2 reached global significance, and other loci, including MIR146A and OBFC1, reached a suggestive level of significance. Overall, we conclude that most nevus genes affect melanoma risk (KITLG an exception), while many melanoma risk loci do not alter nevus count. For example, variants in TERC and OBFC1 affect both traits, but other telomere length maintenance genes seem to affect melanoma risk only. Our findings implicate multiple pathways in nevogenesis via genes we can show to be expressed under control of the MITF melanocytic cell lineage regulator.

genetics↗

bioRxiv2017

Identification of 55,000 Replicated DNA Methylation QTL

DNA methylation plays an important role in the regulation of transcription. Genetic control of DNA methylation is a potential candidate for explaining the many identified SNP associations with disease that are not found in coding regions. We replicated 52,916 cis and 2,025 trans DNA methylation quantitative trait loci (mQTL) using methylation measured on Illumina HumanMethylation450 arrays in the Brisbane Systems Genetics Study (n=614 from 177 families) and the Lothian Birth Cohorts of 1921 and 1936 (combined n = 1366). The trans mQTL SNPs were found to be over-represented in 1Mbp subtelomeric regions, and on chromosomes 16 and 19. There was a significant increase in trans mQTL DNA methylation sites in upstream and 5 UTR regions. No association was observed between either the SNPs or DNA methylation sites of trans mQTL and telomere length. The genetic heritability of a number of complex traits and diseases was partitioned into components due to mQTL and the remainder of the genome. Significant enrichment was observed for height (p = 2.1x10-10), ulcerative colitis (p = 2x10-5), Crohns disease (p = 6x10-8) and coronary artery disease (p = 5.5x10-6) when compared to a random sample of SNPs with matched minor allele frequency, although this enrichment is explained by the genomic location of the mQTL SNPs.

genomics↗

bioRxiv2016

Constraints on eQTL fine mapping in the presence of multi-site local regulation of gene expression

Expression QTL (eQTL) detection has emerged as an important tool for unravelling of the relationship between genetic risk factors and disease or clinical phenotypes. Most studies use single marker linear regression to discover primary signals, followed by sequential conditional modeling to detect secondary genetic variants affecting gene expression. However, this approach assumes that functional variants are sparsely distributed and that close linkage between them has little impact on estimation of their precise location and magnitude of effects. In this study, we address the prevalence of secondary signals and bias in estimation of their effects by performing multi-site linear regression on two large human cohort peripheral blood gene expression datasets (each greater than 2,500 samples) with accompanying whole genome genotypes, namely the CAGE compendium of Illumina microarray studies, and the Framingham Heart Study Affymetrix data. Stepwise conditional modeling demonstrates that multiple eQTL signals are present for ~40% of over 3500 eGenes in both datasets, and the number of loci with additional signals reduces by approximately two-thirds with each conditioning step. However, the concordance of specific signals between the two studies is only ~30%, indicating that expression profiling platform is a large source of variance in effect estimation. Furthermore, a series of simulation studies imply that in the presence of multi-site regulation, up to 10% of the secondary signals could be artefacts of incomplete tagging, and at least 5% but up to one quarter of credible intervals may not even include the causal site, which is thus mis-localized. Joint multi-site effect estimation recalibrates effect size estimates by just a small amount on average. Presumably similar conclusions apply to most types of quantitative trait. Given the strong empirical evidence that gene expression is commonly regulated by more than one variant, we conclude that the fine-mapping of causal variants needs to be adjusted for multi-site influences, as conditional estimates can be highly biased by interference among linked sites.

genetics↗