Previously generated genetic risk scores (GRSs) for type 1 diabetes (T1D) have not captured all known information at non-HLA loci or, particularly, at HLA risk loci. We aimed to more completely ...incorporate HLA alleles, their interactions, and recently discovered non-HLA loci into an improved T1D GRS (termed the "T1D GRS2") to better discriminate diabetes subtypes and to predict T1D in newborn screening studies.
In 6,481 case and 9,247 control subjects from the Type 1 Diabetes Genetics Consortium, we analyzed variants associated with T1D both in the HLA region and across the genome. We modeled interactions between variants marking strongly associated HLA haplotypes and generated odds ratios to create the improved GRS, the T1D GRS2. We validated our findings in UK Biobank. We assessed the impact of the T1D GRS2 in newborn screening and diabetes classification and sought to provide a framework for comparison with previous scores.
The T1D GRS2 used 67 single nucleotide polymorphisms (SNPs) and accounted for interactions between 18 HLA DR-DQ haplotype combinations. The T1D GRS2 was highly discriminative for all T1D (area under the curve AUC 0.92;
< 0.0001 vs. older scores) and even more discriminative for early-onset T1D (AUC 0.96). In simulated newborn screening, the T1D GRS2 was nearly twice as efficient as HLA genotyping alone and 50% better than current genetic scores in general population T1D prediction.
An improved T1D GRS, the T1D GRS2, is highly useful for classifying adult incident diabetes type and improving newborn screening. Given the cost-effectiveness of SNP genotyping, this approach has great clinical and research potential in T1D.
Sleep is an essential state of decreased activity and alertness but molecular factors regulating sleep duration remain unknown. Through genome-wide association analysis in 446,118 adults of European ...ancestry from the UK Biobank, we identify 78 loci for self-reported habitual sleep duration (p < 5 × 10
; 43 loci at p < 6 × 10
). Replication is observed for PAX8, VRK2, and FBXL12/UBL5/PIN1 loci in the CHARGE study (n = 47,180; p < 6.3 × 10
), and 55 signals show sign-concordant effects. The 78 loci further associate with accelerometer-derived sleep duration, daytime inactivity, sleep efficiency and number of sleep bouts in secondary analysis (n = 85,499). Loci are enriched for pathways including striatum and subpallium development, mechanosensory response, dopamine binding, synaptic neurotransmission and plasticity, among others. Genetic correlation indicates shared links with anthropometric, cognitive, metabolic, and psychiatric traits and two-sample Mendelian randomization highlights a bidirectional causal link with schizophrenia. This work provides insights into the genetic basis for inter-individual variation in sleep duration implicating multiple biological pathways.
Disrupted circadian rhythms and reduced sleep duration are associated with several human diseases, particularly obesity and type 2 diabetes, but until recently, little was known about the genetic ...factors influencing these heritable traits. We performed genome-wide association studies of self-reported chronotype (morning/evening person) and self-reported sleep duration in 128,266 white British individuals from the UK Biobank study. Sixteen variants were associated with chronotype (P<5x10-8), including variants near the known circadian rhythm genes RGS16 (1.21 odds of morningness, 95% CI 1.15, 1.27, P = 3x10-12) and PER2 (1.09 odds of morningness, 95% CI 1.06, 1.12, P = 4x10-10). The PER2 signal has previously been associated with iris function. We sought replication using self-reported data from 89,283 23andMe participants; thirteen of the chronotype signals remained associated at P<5x10-8 on meta-analysis and eleven of these reached P<0.05 in the same direction in the 23andMe study. We also replicated 9 additional variants identified when the 23andMe study was used as a discovery GWAS of chronotype (all P<0.05 and meta-analysis P<5x10-8). For sleep duration, we replicated one known signal in PAX8 (2.6 minutes per allele, 95% CI 1.9, 3.2, P = 5.7x10-16) and identified and replicated two novel associations at VRK2 (2.0 minutes per allele, 95% CI 1.3, 2.7, P = 1.2x10-9; and 1.6 minutes per allele, 95% CI 1.1, 2.2, P = 7.6x10-9). Although we found genetic correlation between chronotype and BMI (rG = 0.056, P = 0.05); undersleeping and BMI (rG = 0.147, P = 1x10-5) and oversleeping and BMI (rG = 0.097, P = 0.04), Mendelian Randomisation analyses, with limited power, provided no consistent evidence of causal associations between BMI or type 2 diabetes and chronotype or sleep duration. Our study brings the total number of loci associated with chronotype to 22 and with sleep duration to three, and provides new insights into the biology of sleep and circadian rhythms in humans.
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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
More than 100,000 genetic variants are classified as disease causing in public databases. However, the true penetrance of many of these rare alleles is uncertain and might be over-estimated by ...clinical ascertainment. Here, we use data from 379,768 UK Biobank (UKB) participants of European ancestry to assess the pathogenicity and penetrance of putatively clinically important rare variants. Although rare variants are harder to genotype accurately than common variants, we were able to classify as high quality 1,244 of 4,585 (27%) putatively clinically relevant rare (MAF < 1%) variants genotyped on the UKB microarray. We defined as “clinically relevant” variants that were classified as either pathogenic or likely pathogenic in ClinVar or are in genes known to cause two specific monogenic diseases: maturity-onset diabetes of the young (MODY) and severe developmental disorders (DDs). We assessed the penetrance and pathogenicity of these high-quality variants by testing their association with 401 clinically relevant traits. 27 of the variants were associated with a UKB trait, and we were able to refine the penetrance estimate for some of the variants. For example, the HNF4A c.340C>T (p.Arg114Trp) (GenBank: NM_175914.4) variant associated with diabetes is <10% penetrant by the time an individual is 40 years old. We also observed associations with relevant traits for heterozygous carriers of some rare recessive conditions, e.g., heterozygous carriers of the ERCC4 c.2395C>T (p.Arg799Trp) variant that causes Xeroderma pigmentosum were more susceptible to sunburn. Finally, we refute the previous disease association of RNF135 in developmental disorders. In conclusion, this study shows that very large population-based studies will help refine our understanding of the pathogenicity of rare genetic variants.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Frozen shoulder is a painful condition that often requires surgery and affects up to 5% of individuals aged 40-60 years. Little is known about the causes of the condition, but diabetes is a strong ...risk factor. To begin to understand the biological mechanisms involved, we aimed to identify genetic variants associated with frozen shoulder and to use Mendelian randomization to test the causal role of diabetes. We performed a genome-wide association study (GWAS) of frozen shoulder in the UK Biobank using data from 10,104 cases identified from inpatient, surgical and primary care codes. We used data from FinnGen for replication and meta-analysis. We used one-sample and two-sample Mendelian randomization approaches to test for a causal association of diabetes with frozen shoulder. We identified five genome-wide significant loci. The most significant locus (lead SNP rs28971325; OR = 1.20, 95% CI: 1.16-1.24, p = 5x10-29) contained WNT7B. This variant was also associated with Dupuytren's disease (OR = 2.31 2.24, 2.39, p<1x10-300) as were a further two of the frozen shoulder associated variants. The Mendelian randomization results provided evidence that type 1 diabetes is a causal risk factor for frozen shoulder (OR = 1.03 1.02-1.05, p = 3x10-6). There was no evidence that obesity was causally associated with frozen shoulder, suggesting that diabetes influences risk of the condition through glycemic rather than mechanical effects. We have identified genetic loci associated with frozen shoulder. There is a large overlap with Dupuytren's disease associated loci. Diabetes is a likely causal risk factor. Our results provide evidence of biological mechanisms involved in this common painful condition.
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Gene panels with a series of strict variant filtering rules are often used for clinical analysis of exomes and genomes. Panel sizes vary, affecting the test’s sensitivity and specificity. We ...investigated the background rate of candidate variants in a population setting using gene panels developed to diagnose a range of heterogeneous monogenic diseases.
We used the Gene2Phenotype database with the Variant Effect Predictor plugin to identify rare nonsynonymous variants in exome sequence data from 200,643 individuals in UK Biobank. We evaluated 5 clinically curated gene panels of varying sizes (50-1700 genes).
Bigger gene panels resulted in more prioritized variants, varying from an average of approximately 0.3 to 3.5 variants per person. The number of individuals with prioritized variants varied linearly with coding sequence length for monoallelic genes (∼300 individuals per 1000 base pairs) and quadratically for biallelic genes, with notable outliers.
Although large gene panels may be the best strategy to maximize diagnostic yield in genetically heterogeneous diseases, they frequently prioritize likely benign variants requiring follow up. Most individuals have ≥1 rare nonsynonymous variant in panels containing >500 disease genes. Extreme caution should be applied when interpreting candidate variants, particularly in the absence of relevant phenotypes.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Abstract
Background
The incidence of thyroid cancer is rising, and relatively little is known about modifiable risk factors for the condition. Observational studies have suggested a link between ...adiposity and thyroid cancer; however, these are subject to confounding and reverse causality. Here, we used data from the UK Biobank and Mendelian randomization approaches to investigate whether adiposity causes benign nodular thyroid disease and differentiated thyroid cancer.
Methods
We analyzed data from 379 708 unrelated participants of European ancestry in the UK Biobank and identified 1812 participants with benign nodular thyroid disease and 425 with differentiated thyroid carcinoma. We tested observational associations with measures of adiposity and type 2 diabetes mellitus. One and 2-sample Mendelian randomization approaches were used to investigate causal relationships.
Results
Observationally, there were positive associations between higher body mass index (odds ratio OR, 1.15; 95% confidence interval CI, 1.08-1.22), higher waist-hip ratio (OR, 1.16; 95% CI, 1.09-1.23), and benign nodular thyroid disease, but not thyroid cancer. Mendelian randomization did not support a causal link for obesity with benign nodular thyroid disease or thyroid cancer, although it did provide some evidence that individuals in the highest quartile for genetic liability of type 2 diabetes had higher odds of thyroid cancer than those in the lowest quartile (OR, 1.45; CI, 1.11-1.90).
Conclusions
Contrary to the findings of observational studies, our results do not confirm a causal role for obesity in benign nodular thyroid disease or thyroid cancer. They do, however, suggest a link between type 2 diabetes and thyroid cancer.
Abstract
Higher adiposity is an established risk factor for psychiatric diseases including depression and anxiety. The associations between adiposity and depression may be explained by the metabolic ...consequences and/or by the psychosocial impact of higher adiposity. We performed one- and two- sample Mendelian randomization (MR) in up to 145 668 European participants from the UK Biobank to test for a causal effect of higher adiposity on 10 well-validated mental health and well-being outcomes derived using the Mental Health Questionnaire (MHQ). We used three sets of adiposity genetic instruments: (a) a set of 72 BMI genetic variants, (b) a set of 36 favourable adiposity variants and (c) a set of 38 unfavourable adiposity variants. We additionally tested causal relationships (1) in men and women separately, (2) in a subset of individuals not taking antidepressants and (3) in non-linear MR models. Two-sample MR provided evidence that a genetically determined one standard deviation (1-SD) higher BMI (4.6 kg/m2) was associated with higher odds of current depression OR: 1.50, 95%CI: 1.15, 1.95 and lower well-being ß: −0.15, 95%CI: −0.26, −0.04. Findings were similar when using the metabolically favourable and unfavourable adiposity variants, with higher adiposity associated with higher odds of depression and lower well-being scores. Our study provides further evidence that higher BMI causes higher odds of depression and lowers well-being. Using genetics to separate out metabolic and psychosocial effects, our study suggests that in the absence of adverse metabolic effects higher adiposity remains causal to depression and lowers well-being.
Mental health conditions represent one of the major groups of non-transmissible diseases. Physical activity (PA) and sedentary time (ST) have been shown to affect mental health outcomes in opposite ...directions. In this study, we use accelerometery-derived measures of PA and ST from the UK Biobank (UKB) and depression, anxiety and well-being data from the UKB mental health questionnaire as well as published summary statistics to explore the causal associations between these phenotypes.
We used MRlap to test if objectively measured PA and ST associate with mental health outcomes using UKB data and summary statistics from published genome-wide association studies. We also tested for bidirectional associations. We performed sex stratified as well as sensitivity analyses.
Genetically instrumented higher PA was associated with lower odds of depression (OR = 0.92; 95% CI: 0.88, 0.97) and depression severity (beta = - 0.11; 95% CI: - 0.18, - 0.04), Genetically instrumented higher ST was associated higher odds of anxiety (OR = 2.59; 95% CI: 1.10, 4.60). PA was associated with higher well-being (beta = 0.11, 95% CI: 0.04; 0.18) and ST with lower well-being (beta = - 0.18; 95% CI: - 0.32, - 0.03). Similar findings were observed when stratifying by sex. There was evidence for a bidirectional relationship, with higher genetic liability to depression associated with lower PA (beta = - 0.25, 95% CI: - 0.42; - 0.08) and higher well-being associated with higher PA (beta = 0.15; 95% CI: 0.05, 0.25).
We have demonstrated the bidirectional effects of both PA and ST on a range of mental health outcomes using objectively measured predictors and MR methods for causal inference. Our findings support a causal role for PA and ST in the development of mental health problems and in affecting well-being.
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