Tom G. Richardson

Influence of adiposity-related genetic markers in a population of saudi arabians where other variables influencing obesity may be reduced.

Large scale studies in Europeans have clearly identified common polymorphism affecting BMI and obesity. We undertook a genotype study to examine the impact of variants, known to influence obesity, in a sample from the Saudi Arabian population, notable for its profound combination of low mean physical activity indices and high energy intake. Anthropometry measures and genotypes were obtained for 367 Saudis, taken from King Saud University and Biomarker Screening Project in Riyadh (Riyadh Cohort). We observed large effect sizes with obesity for rs10767664 (BDNF) (OR = 1.923, P = 0.00072) and rs3751812 (FTO) (OR = 1.523, P = 0.016) in our sample and, using weighted genetic risk scores, we found strong evidence of a cumulative effect using 11 SNPs taken predominantly from loci principally affecting appetite (OR = 2.57, P = 0.00092). We used conditional analyses to discern which of our three highly correlated FTO SNPs were responsible for the observed signal, although we were unable to determine with confidence which best marked the causal site. Our analysis indicates that markers located in loci known to influence fat mass through increased appetite affect obesity in Saudi Arabians to an extent possibly greater than in Europeans. Larger scale studies will be necessary to obtain a precise comparison.

Mendelian Randomization Analysis Identifies CpG Sites as Putative Mediators for Genetic Influences on Cardiovascular Disease Risk

The extent to which genetic influences on cardiovascular disease risk are mediated by changes in DNA methylation levels has not been systematically explored. We developed an analytical framework that integrates genetic fine mapping and Mendelian randomization with epigenome-wide association studies to evaluate the causal relationships between methylation levels and 14 cardiovascular disease traits. We identified ten genetic loci known to influence proximal DNA methylation which were also associated with cardiovascular traits after multiple-testing correction. Bivariate fine mapping provided evidence that the individual variants responsible for the observed effects on cardiovascular traits at the ADCY3 and ADIPOQ loci were potentially mediated through changes in DNA methylation, although we highlight that we are unable to reliably separate causality from horizontal pleiotropy. Estimates of causal effects were replicated with results from large-scale consortia. Genetic variants and CpG sites identified in this study were enriched for histone mark peaks in relevant tissue types and gene promoter regions. Integrating our results with expression quantitative trait loci data, we provide evidence that variation at these regulatory regions is likely to also influence gene expression levels at these loci.

Identifying Highly Penetrant Disease Causal Mutations Using Next Generation Sequencing: Guide to Whole Process

Recent technological advances have created challenges for geneticists and a need to adapt to a wide range of new bioinformatics tools and an expanding wealth of publicly available data (e.g., mutation databases, and software). This wide range of methods and a diversity of file formats used in sequence analysis is a significant issue, with a considerable amount of time spent before anyone can even attempt to analyse the genetic basis of human disorders. Another point to consider that is although many possess “just enough” knowledge to analyse their data, they do not make full use of the tools and databases that are available and also do not fully understand how their data was created. The primary aim of this review is to document some of the key approaches and provide an analysis schema to make the analysis process more efficient and reliable in the context of discovering highly penetrant causal mutations/genes. This review will also compare the methods used to identify highly penetrant variants when data is obtained from consanguineous individuals as opposed to nonconsanguineous; and when Mendelian disorders are analysed as opposed to common-complex disorders.

Structural and Population-Based Evaluations of TBC1D1 p.Arg125Trp

Obesity is now a leading cause of preventable death in the industrialised world. Understanding its genetic influences can enhance insight into molecular pathogenesis and potential therapeutic targets. A non-synonymous polymorphism (rs35859249, p.Arg125Trp) in the N-terminal TBC1D1 phosphotyrosine-binding (PTB) domain has shown a replicated association with familial obesity in women. We investigated these findings in the Avon Longitudinal Study of Parents and Children (ALSPAC), a large European birth cohort of mothers and offspring, and by generating a predicted model of the structure of this domain. Structural prediction involved the use of three separate algorithms; Robetta, HHpred/MODELLER and I-TASSER. We used the transmission disequilibrium test (TDT) to investigate familial association in the ALSPAC study cohort (N = 2,292 mother-offspring pairs). Linear regression models were used to examine the association of genotype with mean measurements of adiposity (Body Mass Index (BMI), waist circumference and Dual-energy X-ray absorptiometry (DXA) assessed fat mass), and logistic regression was used to examine the association with odds of obesity. Modelling showed that the R125W mutation occurs in a location of the TBC1D1 PTB domain that is predicted to have a function in a putative protein:protein interaction. We did not detect an association between R125W and BMI (mean per allele difference 0.27 kg/m2 (95% Confidence Interval: 0.00, 0.53) P = 0.05) or obesity (odds ratio 1.01 (95% Confidence Interval: 0.77, 1.31, P = 0.96) in offspring after adjusting for multiple comparisons. Furthermore, there was no evidence to suggest that there was familial association between R125W and obesity (χ2 = 0.06, P = 0.80). Our analysis suggests that R125W in TBC1D1 plays a role in the binding of an effector protein, but we find no evidence that the R125W variant is related to mean BMI or odds of obesity in a general population sample.

Incorporating Non-Coding Annotations into Rare Variant Analysis

Background The success of collapsing methods which investigate the combined effect of rare variants on complex traits has so far been limited. The manner in which variants within a gene are selected prior to analysis has a crucial impact on this success, which has resulted in analyses conventionally filtering variants according to their consequence. This study investigates whether an alternative approach to filtering, using annotations from recently developed bioinformatics tools, can aid these types of analyses in comparison to conventional approaches. Methods & Results We conducted a candidate gene analysis using the UK10K sequence and lipids data, filtering according to functional annotations using the resource CADD (Combined Annotation-Dependent Depletion) and contrasting results with ‘nonsynonymous’ and ‘loss of function’ consequence analyses. Using CADD allowed the inclusion of potentially deleterious intronic variants, which was not possible when filtering by consequence. Overall, different filtering approaches provided similar evidence of association, although filtering according to CADD identified evidence of association between ANGPTL4 and High Density Lipoproteins (P = 0.02, N = 3,210) which was not observed in the other analyses. We also undertook genome-wide analyses to determine how filtering in this manner compared to conventional approaches for gene regions. Results suggested that filtering by annotations according to CADD, as well as other tools known as FATHMM-MKL and DANN, identified association signals not detected when filtering by variant consequence and vice versa. Conclusion Incorporating variant annotations from non-coding bioinformatics tools should prove to be a valuable asset for rare variant analyses in the future. Filtering by variant consequence is only possible in coding regions of the genome, whereas utilising non-coding bioinformatics annotations provides an opportunity to discover unknown causal variants in non-coding regions as well. This should allow studies to uncover a greater number of causal variants for complex traits and help elucidate their functional role in disease.

PhenoSpD: an integrated toolkit for phenotypic correlation estimation and multiple testing correction using GWAS summary statistics

Abstract Background Identifying phenotypic correlations between complex traits and diseases can provide useful etiological insights. Restricted access to much individual-level phenotype data makes it difficult to estimate large-scale phenotypic correlation across the human phenome. Two state-of-the-art methods, metaCCA and LD score regression, provide an alternative approach to estimate phenotypic correlation using only genome-wide association study (GWAS) summary results. Results Here, we present an integrated R toolkit, PhenoSpD, to use LD score regression to estimate phenotypic correlations using GWAS summary statistics and to utilize the estimated phenotypic correlations to inform correction of multiple testing for complex human traits using the spectral decomposition of matrices (SpD). The simulations suggest that it is possible to identify nonindependence of phenotypes using samples with partial overlap; as overlap decreases, the estimated phenotypic correlations will attenuate toward zero and multiple testing correction will be more stringent than in perfectly overlapping samples. Also, in contrast to LD score regression, metaCCA will provide approximate genetic correlations rather than phenotypic correlation, which limits its application for multiple testing correction. In a case study, PhenoSpD using UK Biobank GWAS results suggested 399.6 independent tests among 487 human traits, which is close to the 352.4 independent tests estimated using true phenotypic correlation. We further applied PhenoSpD to an estimated 5,618 pair-wise phenotypic correlations among 107 metabolites using GWAS summary statistics from Kettunens publication and PhenoSpD suggested the equivalent of 33.5 independent tests for these metabolites. Conclusions PhenoSpD extends the use of summary-level results, providing a simple and conservative way to reduce dimensionality for complex human traits using GWAS summary statistics. This is particularly valuable in the age of large-scale biobank and consortia studies, where GWAS results are much more accessible than individual-level data.

Causal epigenome-wide association study identifies CpG sites that influence cardiovascular disease risk

The extent to which genetic influences on complex traits and disease are mediated by changes in DNA methylation levels has not been systematically explored. We developed an analytical framework that integrates genetic fine mapping and Mendelian randomization with epigenome-wide association studies to evaluate the causal relationships between methylation levels and 14 cardiovascular disease traits. We identified 10 genetic loci known to influence proximal DNA methylation which were also associated with cardiovascular traits (P < 3.83×10-08). Bivariate fine mapping suggested that the individual variants responsible for the observed effects on cardiovascular traits at the ABO, ADCY3, ADIPOQ, APOA1 and IL6R loci were likely mediated through changes in DNA methylation. Causal effect estimates on cardiovascular traits ranged between 0.109-0.992 per standard deviation change in DNA methylation and were replicated using results from large-scale consortia. Functional informatics suggests that the causal variants and CpG sites identified in this study were enriched for histone mark peaks in adipose tissue and gene promoter regions. Integrating our results with expression quantitative trait loci data we provide evidence that variation at these regulatory regions is likely to also influence gene expression at these loci.

A Pathway-centric Approach to Rare Variant Association Analysis

Current endeavours in rare variant analysis are typically underpowered when investigating association signals from individual genes. We undertook an approach to rare variant analysis which utilises biological pathway information to analyse functionally relevant genes together. Conventional filtering approaches for rare variant analysis are based on variant consequence and are therefore confined to coding regions of the genome. Therefore, we undertook a novel approach to this process by obtaining functional annotations from the Combined Annotation Dependent Depletion (CADD) tool, which allowed potentially deleterious variants from intronic regions of genes to be incorporated into analyses. This work was undertaken using whole-genome sequencing data from the UK10K project. Rare variants from the KEGG pathway for arginine and proline metabolism were collectively associated with systolic blood pressure (P=3.32x10−5) based on analyses using the optimal sequence kernel association test. Variants along this pathway also showed evidence of replication using imputed data from the Avon Longitudinal Study of Parents and Children cohort (P=0.02). Subsequent analyses found that the strength of evidence diminished when analysing genes in this pathway individually, suggesting that they would have been overlooked in a conventional gene-based analysis. Future studies that adopt similar approaches to investigate polygenic effects should yield value in better understanding the genetic architecture of complex disease.

Collapsed Methylation Quantitative Trait Loci analysis for Low Frequency and Rare variants

Background: Single variant approaches have been successful in identifying DNA methylation quantitative trait loci (mQTL), although as with complex traits they lack the statistical power to identify the effects from rare genetic variants. We have undertaken extensive analyses to identify regions of low frequency and rare variants that are associated with DNA methylation levels. Methods: We used repeated measurements of DNA methylation from five different life stages in human blood, taken from the Avon Longitudinal Study of Parents and Children (ALSPAC) cohort. Variants were collapsed across CpG islands and their flanking regions to identify variants collectively associated with methylation, where no single variant was individually responsible for the observed signal. All analyses were undertaken using the sequence kernel association test. Results: For loci where no individual variant mQTL was observed based on a single variant analysis, we identified 95 unique regions where the combined effect of low frequency variants (MAF ≤ 5%) provided strong evidence of association with methylation. For loci where there was previous evidence of an individual variant mQTL, a further 3 regions provided evidence of association between multiple low frequency variants and methylation levels. Effects were observed consistently across 5 different time points in the lifecourse and evidence of replication in the TwinsUK and Exeter cohorts was also identified. Conclusion: We have demonstrated the potential of this novel approach to mQTL analysis by analysing the combined effect of multiple low frequency or rare variants. Future studies should benefit from applying this approach as a complementary follow up to single variant analyses.

Evaluating the role of a galanin enhancer genotype on a range of metabolic, depressive and addictive phenotypes

There is a large body of pre‐clinical and some clinical data to link the neuropeptide galanin to a range of physiological and pathological functions that include metabolism, depression, and addiction. An enhancer region upstream of the human GAL transcriptional start site has previously been characterised. In‐vitro transfection studies in rat hypothalamic neurons demonstrated that the CA allele was 40% less active than the GG allele in driving galanin expression. Our hypothesis was to investigate the effect of this galanin enhancer genotype on a range of variables that relate to the known functions of the galaninergic system in the Avon Longitudinal Study of Parents and Children (ALSPAC) cohort of young adults (N = 169–6,078). Initial findings showed a positive relationship of cannabis usage (OR = 2.070, P = 0.007, N = 406 (individuals who had used cannabis at least once within the last 12 months, total sample size 2731) with the GG haplotype, consistent with the previous published data linking galanin with an increased release of dopamine. As our sample size was relatively small we replicated the analysis in a larger cohort of 2,224 African Americans and 1,840 European Americans, but no discernible trend across genotypes was observed for the relationship with cannabis usage. Further, we found no association of the galanin enhancer genotype with any of the other pathophysiological parameters measured. These findings emphasise that preclinical data does not always predict clinical outcomes in cohort studies, noting that association studies are subject to multiple confounders.