43 research outputs found
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The Y Chromosome: A Complex Locus for Genetic Analyses of Complex Human Traits.
The Human Y chromosome (ChrY) has been demonstrated to be a powerful tool for phylogenetics, population genetics, genetic genealogy and forensics. However, the importance of ChrY genetic variation in relation to human complex traits is less clear. In this review, we summarise existing evidence about the inherent complexities of ChrY variation and their use in association studies of human complex traits. We present and discuss the specific particularities of ChrY genetic variation, including Y chromosomal haplogroups, that need to be considered in the design and interpretation of genetic epidemiological studies involving ChrY
Proxy Molecular Diagnosis from Whole-Exome Sequencing Reveals Papillon-Lefevre Syndrome Caused by a Missense Mutation in CTSC
Using Y-Chromosomal Haplogroups in Genetic Association Studies and Suggested Implications
Y-chromosomal (Y-DNA) haplogroups are more widely used in population genetics than in genetic epidemiology, although associations between Y-DNA haplogroups and several traits, including cardiometabolic traits, have been reported. In apparently homogeneous populations defined by principal component analyses, there is still Y-DNA haplogroup variation which will result from population history. Therefore, hidden stratification and/or differential phenotypic effects by Y-DNA haplogroups could exist. To test this, we hypothesised that stratifying individuals according to their Y-DNA haplogroups before testing for associations between autosomal single nucleotide polymorphisms (SNPs) and phenotypes will yield difference in association. For proof of concept, we derived Y-DNA haplogroups from 6537 males from two epidemiological cohorts, Avon Longitudinal Study of Parents and Children (ALSPAC) (n = 5080; 816 Y-DNA SNPs) and the 1958 Birth Cohort (n = 1457; 1849 Y-DNA SNPs), and studied the robust associations between 32 SNPs and body mass index (BMI), including SNPs in or near Fat Mass and Obesity-associated protein (FTO) which yield the strongest effects. Overall, no association was replicated in both cohorts when Y-DNA haplogroups were considered and this suggests that, for BMI at least, there is little evidence of differences in phenotype or SNP association by Y-DNA structure. Further studies using other traits, phenome-wide association studies (PheWAS), other haplogroups and/or autosomal SNPs are required to test the generalisability and utility of this approach.</p
Y Chromosome, Mitochondrial DNA and Childhood Behavioural Traits
Abstract Many psychiatric traits are sexually dimorphic in terms of prevalence, age of onset, progression and prognosis; sex chromosomes could play a role in these differences. In this study we evaluated the association between Y chromosome and mitochondrial DNA haplogroups with sexually-dimorphic behavioural and psychiatric traits. The study sample included 4,211 males and 4,009 females with mitochondrial DNA haplogroups and 4,788 males with Y chromosome haplogroups who are part of the Avon Longitudinal Study of Parents and Children (ALSPAC) based in the United Kingdom. Different subsets of these populations were assessed using measures of behavioural and psychiatric traits with logistic regression being used to measure the association between haplogroups and the traits. The majority of behavioural traits in our cohort differed between males and females; however Y chromosome and mitochondrial DNA haplogroups were not associated with any of the variables. These findings suggest that if there is common variation on the Y chromosome and mitochondrial DNA associated with behavioural and psychiatric trait variation, it has a small effect
COVID-19 and climatic factors: A global analysis.
BACKGROUND: It is unknown if COVID-19 will exhibit seasonal pattern as other diseases e.g., seasonal influenza. Similarly, some environmental factors (e.g., temperature, humidity) have been shown to be associated with transmission of SARS-CoV and MERS-CoV, but global data on their association with COVID-19 are scarce. OBJECTIVE: To examine the association between climatic factors and COVID-19. METHODS: We used multilevel mixed-effects (two-level random-intercepts) negative binomial regression models to examine the association between 7- and 14-day-lagged temperature, humidity (relative and absolute), wind speed and UV index and COVID-19 cases, adjusting for Gross Domestic Products, Global Health Security Index, cloud cover (%), precipitation (mm), sea-level air-pressure (mb), and daytime length. The effects estimates are reported as adjusted rate ratio (aRR) and their corresponding 95% confidence interval (CI). RESULTS: Data from 206 countries/regions (until April 20, 2020) with ≥100 reported cases showed no association between COVID-19 cases and 7-day-lagged temperature, relative humidity, UV index, and wind speed, after adjusting for potential confounders, but a positive association with 14-day-lagged temperature and a negative association with 14-day-lagged wind speed. Compared to an absolute humidity of 10 g/m3 did not have a significant effect. These findings were robust in the 14-day-lagged analysis. CONCLUSION: Our results of higher COVID-19 cases (through April 20) at absolute humidity of 5-10 g/m3 may be suggestive of a 'sweet point' for viral transmission, however only controlled laboratory experiments can decisively prove it
Meta-analysis of up to 622,409 individuals identifies 40 novel smoking behaviour associated genetic 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
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Genetically Predicted Glucose-Dependent Insulinotropic Polypeptide (GIP) Levels and Cardiovascular Disease Risk Are Driven by Distinct Causal Variants in the GIPR Region.
There is considerable interest in GIPR agonism to enhance the insulinotropic and extrapancreatic effects of GIP, thereby improving glycemic and weight control in type 2 diabetes (T2D) and obesity. Recent genetic epidemiological evidence has implicated higher GIPR-mediated GIP levels in raising coronary artery disease (CAD) risk, a potential safety concern for GIPR agonism. We therefore aimed to quantitatively assess whether the association between higher GIPR-mediated fasting GIP levels and CAD risk is mediated via GIPR or is instead the result of linkage disequilibrium (LD) confounding between variants at the GIPR locus. Using Bayesian multitrait colocalization, we identified a GIPR missense variant, rs1800437 (G allele; E354), as the putatively causal variant shared among fasting GIP levels, glycemic traits, and adiposity-related traits (posterior probability for colocalization [PPcoloc] > 0.97; PP explained by the candidate variant [PPexplained] = 1) that was independent from a cluster of CAD and lipid traits driven by a known missense variant in APOE (rs7412; distance to E354 ∼770 Kb; R 2 with E354 = 0.004; PPcoloc > 0.99; PPexplained = 1). Further, conditioning the association between E354 and CAD on the residual LD with rs7412, we observed slight attenuation in association, but it remained significant (odds ratio [OR] per copy of E354 after adjustment 1.03; 95% CI 1.02, 1.04; P = 0.003). Instead, E354's association with CAD was completely attenuated when conditioning on an additional established CAD signal, rs1964272 (R 2 with E354 = 0.27), an intronic variant in SNRPD2 (OR for E354 after adjustment for rs1964272: 1.01; 95% CI 0.99, 1.03; P = 0.06). We demonstrate that associations with GIP and anthropometric and glycemic traits are driven by genetic signals distinct from those driving CAD and lipid traits in the GIPR region and that higher E354-mediated fasting GIP levels are not associated with CAD risk. These findings provide evidence that the inclusion of GIPR agonism in dual GIPR/GLP1R agonists could potentiate the protective effect of GLP-1 agonists on diabetes without undue CAD risk, an aspect that has yet to be assessed in clinical trials
HAPRAP: a haplotype-based iterative method for statistical fine mapping using GWAS summary statistics
Motivation
Fine mapping is a widely used approach for identifying the causal variant(s) at disease-associated loci. Standard methods (e.g. multiple regression) require individual level genotypes. Recent fine mapping methods using summary-level data require the pairwise correlation coefficients (r2
) of the variants. However, haplotypes rather than pairwise r2
, are the true biological representation of linkage disequilibrium (LD) among multiple loci. In this article, we present an empirical iterative method, HAPlotype Regional Association analysis Program (HAPRAP), that enables fine mapping using summary statistics and haplotype information from an individual-level reference panel.
Results
Simulations with individual-level genotypes show that the results of HAPRAP and multiple regression are highly consistent. In simulation with summary-level data, we demonstrate that HAPRAP is less sensitive to poor LD estimates. In a parametric simulation using Genetic Investigation of ANthropometric Traits height data, HAPRAP performs well with a small training sample size (N < 2000) while other methods become suboptimal. Moreover, HAPRAP’s performance is not affected substantially by single nucleotide polymorphisms (SNPs) with low minor allele frequencies. We applied the method to existing quantitative trait and binary outcome meta-analyses (human height, QTc interval and gallbladder disease); all previous reported association signals were replicated and two additional variants were independently associated with human height. Due to the growing availability of summary level data, the value of HAPRAP is likely to increase markedly for future analyses (e.g. functional prediction and identification of instruments for Mendelian randomization)
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Using human genetics to understand the disease impacts of testosterone in men and women.
Testosterone supplementation is commonly used for its effects on sexual function, bone health and body composition, yet its effects on disease outcomes are unknown. To better understand this, we identified genetic determinants of testosterone levels and related sex hormone traits in 425,097 UK Biobank study participants. Using 2,571 genome-wide significant associations, we demonstrate that the genetic determinants of testosterone levels are substantially different between sexes and that genetically higher testosterone is harmful for metabolic diseases in women but beneficial in men. For example, a genetically determined 1 s.d. higher testosterone increases the risks of type 2 diabetes (odds ratio (OR) = 1.37 (95% confidence interval (95% CI): 1.22-1.53)) and polycystic ovary syndrome (OR = 1.51 (95% CI: 1.33-1.72)) in women, but reduces type 2 diabetes risk in men (OR = 0.86 (95% CI: 0.76-0.98)). We also show adverse effects of higher testosterone on breast and endometrial cancers in women and prostate cancer in men. Our findings provide insights into the disease impacts of testosterone and highlight the importance of sex-specific genetic analyses.A.R.W. and T.M.F. are supported by the European Research Council grant: SZ-245 50371-GLUCOSEGENES-FP7-IDEAS-ERC. R.B. is funded by the Wellcome Trust and Royal Society grant 104150/Z/14/Z. J.T. is supported by the Academy of Medical Sciences Springboard award which is supported by the Wellcome Trust and GCRF [SBF004\1079]. This work was supported by the Medical Research Council [Unit Programme numbers MC_UU_12015/1 and MC_UU_12015/2]