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.
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FZAB, GEOZS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
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 <5 g/m3, an absolute humidity of 5–10 g/m3 was associated with a 23% (95% CI: 6–42%) higher rate of COVID-19 cases, while absolute humidity >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.
•A global study from 206 countries/regions to examine the effects of climatic factors on COVID-19.•We adjusted for a range of climatic and structural factors in the analysis.•No association with 7-day lagged climatic variables and COVID-19.•Positive association between COVID-19 and 14-day lagged temperature.•Negative association between COVID-19 and 14-day lagged wind speed.•Consistently higher rate of COVID-19 cases in absolute humidity of 5–10 g/m3.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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•Gene-air pollution interaction effects on lung function are poorly understood.•Seven new potential gene-air pollution interaction signals identified.•Interaction effects identified ...are large enough to be clinically relevant.•Observed interactions include one previously identified lung function signal.•High-risk genetic subgroups potentially more susceptible to outdoor air pollution.
Impaired lung function is predictive of mortality and is a key component of chronic obstructive pulmonary disease. Lung function has a strong genetic component but is also affected by environmental factors such as increased exposure to air pollution, but the effect of their interactions is not well understood.
To identify interactions between genetic variants and air pollution measures which affect COPD risk and lung function. Additionally, to determine whether previously identified lung function genetic association signals showed evidence of interaction with air pollution, considering both individual effects and combined effects using a genetic risk score (GRS).
We conducted a genome-wide gene-air pollution interaction analysis of spirometry measures with three measures of air pollution at home address: particulate matter (PM2.5 & PM10) and nitrogen dioxide (NO2), in approximately 300,000 unrelated European individuals from UK Biobank. We explored air pollution interactions with previously identified lung function signals and determined their combined interaction effect using a GRS.
We identified seven new genome-wide interaction signals (P<5×10-8), and a further ten suggestive interaction signals (P<5×10-7). Additionally, we found statistical evidence of interaction for FEV1/FVC between PM2.5 and previously identified lung function signal, rs10841302, near AEBP2, suggesting increased susceptibility as copies of the G allele increased (but size of the impact was small - interaction beta: -0.363 percentage points, 95% CI: -0.523, -0.203 per 5 µg/m3). There was no observed interaction between air pollutants and the weighted GRS.
We carried out the largest genome-wide gene-air pollution interaction study of lung function and identified potential effects of clinically relevant size and significance. We observed up to 440 ml lower lung function for certain genotypes when exposed to mean levels of outdoor air pollution, which is approximately equivalent to nine years of average normal loss of lung function in adults.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
LD score regression is a reliable and efficient method of using genome-wide association study (GWAS) summary-level results data to estimate the SNP heritability of complex traits and diseases, ...partition this heritability into functional categories, and estimate the genetic correlation between different phenotypes. Because the method relies on summary level results data, LD score regression is computationally tractable even for very large sample sizes. However, publicly available GWAS summary-level data are typically stored in different databases and have different formats, making it difficult to apply LD score regression to estimate genetic correlations across many different traits simultaneously.
In this manuscript, we describe LD Hub - a centralized database of summary-level GWAS results for 173 diseases/traits from different publicly available resources/consortia and a web interface that automates the LD score regression analysis pipeline. To demonstrate functionality and validate our software, we replicated previously reported LD score regression analyses of 49 traits/diseases using LD Hub; and estimated SNP heritability and the genetic correlation across the different phenotypes. We also present new results obtained by uploading a recent atopic dermatitis GWAS meta-analysis to examine the genetic correlation between the condition and other potentially related traits. In response to the growing availability of publicly accessible GWAS summary-level results data, our database and the accompanying web interface will ensure maximal uptake of the LD score regression methodology, provide a useful database for the public dissemination of GWAS results, and provide a method for easily screening hundreds of traits for overlapping genetic aetiologies.
The web interface and instructions for using LD Hub are available at http://ldsc.broadinstitute.org/ CONTACT: jie.zheng@bristol.ac.ukSupplementary information: Supplementary data are available at Bioinformatics online.
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.
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
locus. Using Bayesian multitrait colocalization, we identified a
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 PP
> 0.97; PP explained by the candidate variant PP
= 1) that was independent from a cluster of CAD and lipid traits driven by a known missense variant in
(rs7412; distance to E354 ∼770 Kb;
with E354 = 0.004; PP
> 0.99; PP
= 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;
= 0.003). Instead, E354's association with CAD was completely attenuated when conditioning on an additional established CAD signal, rs1964272 (
with E354 = 0.27), an intronic variant in
(OR for E354 after adjustment for rs1964272: 1.01; 95% CI 0.99, 1.03;
= 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
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.
Summary
Consanguineous offspring have elevated levels of homozygosity. Autozygous stretches within their genome are likely to harbour loss of function (LoF) mutations which will lead to complete ...inactivation or dysfunction of genes. Studying consanguineous offspring with clinical phenotypes has been very useful for identifying disease causal mutations. However, at present, most of the genes in the human genome have no disorder associated with them or have unknown function. This is presumably mostly due to the fact that homozygous LoF variants are not observed in outbred populations which are the main focus of large sequencing projects. However, another reason may be that many genes in the genome—even when completely “knocked out,” do not cause a distinct or defined phenotype. Here, we discuss the benefits and implications of studying consanguineous populations, as opposed to the traditional approach of analysing a subset of consanguineous families or individuals with disease. We suggest that studying consanguineous populations “as a whole” can speed up the characterisation of novel gene functions as well as indicating nonessential genes and/or regions in the human genome. We also suggest designing a single nucleotide variant (SNV) array to make the process more efficient.
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DOBA, FZAB, GIS, IJS, IZUM, KILJ, NLZOH, NUK, OILJ, PILJ, PNG, SAZU, SBCE, SBMB, UILJ, UKNU, UL, UM, UPUK
Males have greater cardiometabolic risk than females, though the reasons for this are poorly understood. The aim of this study was to examine the association between common Y chromosomal haplogroups ...and cardiometabolic risk during early life.
In a British birth cohort, we examined the association of Y chromosomal haplogroups with trajectories of cardiometabolic risk factors from birth to 18 years and with carotid-femoral pulse wave velocity, carotid intima media thickness and left ventricular mass index at age 18. Haplogroups were grouped according to their phylogenetic relatedness into categories of R, I, E, J, G and all other haplogroups combined (T, Q, H, L, C, N and O). Risk factors included BMI, fat and lean mass, systolic blood pressure (SBP), diastolic blood pressure, pulse rate, triglycerides, high density lipoprotein cholesterol (HDL-c), non-HDL-c and c-reactive protein. Analyses were performed using multilevel models and linear regression, as appropriate.
Y chromosomal haplogroups were not associated with any cardiometabolic risk factors from birth to 18 years. For example, at age 18, the difference in SBP comparing each haplogroup with haplogroup R was −0.39 mmHg (95% Confidence Interval (CI): −0.75, 1.54) for haplogroup I, 2.56 mmHg (95% CI: −0.76, 5.89) for haplogroup E, −0.02 mmHg (95% CI: −2.87, 2.83) for haplogroup J, 1.28 mmHg (95% CI: −4.70, 2.13) for haplogroup G and −2.75 mmHg (95% CI: −6.38, 0.88) for all other haplogroups combined.
Common Y chromosomal haplogroups are not associated with cardiometabolic risk factors during childhood and adolescence or with subclinical cardiovascular measures at age 18.
•Common Y chromosomal haplogroups are not associated with cardiometabolic risk factors from birth to age 18.•Common Y chromosomal haplogroups are not associated with cardiovascular structure and function at age 18.•Common Y chromosomal haplogroups are not associated with cardiometabolic risk in males during early life.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, SBCE, SBJE, UL, UPCLJ, UPUK, ZRSKP
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