Genetic Variation Determines PPARγ Function and Anti-diabetic Drug Response In Vivo.

SNPs affecting disease risk often reside in non-coding genomic regions. Here, we show that SNPs are highly enriched at mouse strain-selective adipose tissue binding sites for PPARγ, a nuclear receptor for anti-diabetic drugs. Many such SNPs alter binding motifs for PPARγ or cooperating factors and functionally regulate nearby genes whose expression is strain selective and imbalanced in heterozygous F1 mice. Moreover, genetically determined binding of PPARγ accounts for mouse strain-specific transcriptional effects of TZD drugs, providing proof of concept for personalized medicine related to nuclear receptor genomic occupancy. In human fat, motif-altering SNPs cause differential PPARγ binding, provide a molecular mechanism for some expression quantitative trait loci, and are risk factors for dysmetabolic traits in genome-wide association studies. One PPARγ motif-altering SNP is associated with HDL levels and other metabolic syndrome parameters. Thus, natural genetic variation in PPARγ genomic occupancy determines individual disease risk and drug response.

Regularized sequence-context mutational trees capture variation in mutation rates across the human genome.

Germline mutation is the mechanism by which genetic variation in a population is created. Inferences derived from mutation rate models are fundamental to many population genetics methods. Previous models have demonstrated that nucleotides flanking polymorphic sites-the local sequence context-explain variation in the probability that a site is polymorphic. However, limitations to these models exist as the size of the local sequence context window expands. These include a lack of robustness to data sparsity at typical sample sizes, lack of regularization to generate parsimonious models and lack of quantified uncertainty in estimated rates to facilitate comparison between models. To address these limitations, we developed Baymer, a regularized Bayesian hierarchical tree model that captures the heterogeneous effect of sequence contexts on polymorphism probabilities. Baymer implements an adaptive Metropolis-within-Gibbs Markov Chain Monte Carlo sampling scheme to estimate the posterior distributions of sequence-context based probabilities that a site is polymorphic. We show that Baymer accurately infers polymorphism probabilities and well-calibrated posterior distributions, robustly handles data sparsity, appropriately regularizes to return parsimonious models, and scales computationally at least up to 9-mer context windows. We demonstrate application of Baymer in three ways-first, identifying differences in polymorphism probabilities between continental populations in the 1000 Genomes Phase 3 dataset, second, in a sparse data setting to examine the use of polymorphism models as a proxy for de novo mutation probabilities as a function of variant age, sequence context window size, and demographic history, and third, comparing model concordance between different great ape species. We find a shared context-dependent mutation rate architecture underlying our models, enabling a transfer-learning inspired strategy for modeling germline mutations. In summary, Baymer is an accurate polymorphism probability estimation algorithm that automatically adapts to data sparsity at different sequence context levels, thereby making efficient use of the available data.

Evaluation of Plasma Biomarkers for Causal Association With Peripheral Artery Disease.

BACKGROUND: Hundreds of biomarkers for peripheral artery disease (PAD) have been reported in the literature; however, the observational nature of these studies limits causal inference due to the potential of reverse causality and residual confounding. We sought to evaluate the potential causal impact of putative PAD biomarkers identified in human observational studies through genetic causal inference methods. METHODS: Putative circulating PAD biomarkers were identified from human observational studies through a comprehensive literature search based on terms related to PAD using PubMed, Cochrane, and Embase. Genetic instruments were generated from publicly available genome-wide association studies of circulating biomarkers. Two-sample Mendelian randomization was used to test the association of genetically determined biomarker levels with PAD using summary statistics from a genome-wide association study of 31 307 individuals with and 211 753 individuals without PAD in the Veterans Affairs Million Veteran Program and replicated in data from FinnGen comprised of 11 924 individuals with and 288 638 individuals without PAD. RESULTS: We identified 204 unique circulating biomarkers for PAD from the observational literature, of which 173 were genetically instrumented using genome-wide association study results. After accounting for multiple testing (false discovery rate, <0.05), 10 of 173 (5.8%) biomarkers had significant associations with PAD. These 10 biomarkers represented categories including plasma lipoprotein regulation, lipid homeostasis, and protein-lipid complex remodeling. Observational literature highlighted different pathways including inflammatory response, negative regulation of multicellular organismal processes, and regulation of response to external stimuli. CONCLUSIONS: Integrating human observational studies and genetic causal inference highlights several key pathways in PAD pathophysiology. This work demonstrates that a substantial portion of biomarkers identified in observational studies are not well supported by human genetic evidence and emphasizes the importance of triangulating evidence to understand PAD pathophysiology. Although the identified biomarkers offer insights into atherosclerotic development in the lower limb, their specificity to PAD compared with more widespread atherosclerosis requires further study.

A multi-population phenome-wide association study of genetically-predicted height in the Million Veteran Program.

BACKGROUND: Height has been associated with many clinical traits but whether such associations are causal versus secondary to confounding remains unclear in many cases. To systematically examine this question, we performed a Mendelian Randomization-Phenome-wide association study (MR-PheWAS) using clinical and genetic data from a national healthcare system biobank. METHODS AND FINDINGS: Analyses were performed using data from the US Veterans Affairs (VA) Million Veteran Program in non-Hispanic White (EA, n = 222,300) and non-Hispanic Black (AA, n = 58,151) adults in the US. We estimated height genetic risk based on 3290 height-associated variants from a recent European-ancestry genome-wide meta-analysis. We compared associations of measured and genetically-predicted height with phenome-wide traits derived from the VA electronic health record, adjusting for age, sex, and genetic principal components. We found 345 clinical traits associated with measured height in EA and an additional 17 in AA. Of these, 127 were associated with genetically-predicted height at phenome-wide significance in EA and 2 in AA. These associations were largely independent from body mass index. We confirmed several previously described MR associations between height and cardiovascular disease traits such as hypertension, hyperlipidemia, coronary heart disease (CHD), and atrial fibrillation, and further uncovered MR associations with venous circulatory disorders and peripheral neuropathy in the presence and absence of diabetes. As a number of traits associated with genetically-predicted height frequently co-occur with CHD, we evaluated effect modification by CHD status of genetically-predicted height associations with risk factors for and complications of CHD. We found modification of effects of MR associations by CHD status for atrial fibrillation/flutter but not for hypertension, hyperlipidemia, or venous circulatory disorders. CONCLUSIONS: We conclude that height may be an unrecognized but biologically plausible risk factor for several common conditions in adults. However, more studies are needed to reliably exclude horizontal pleiotropy as a driving force behind at least some of the MR associations observed in this study.

Mendelian Randomization Analysis Reveals a Complex Genetic Interplay among Atopic Dermatitis, Asthma, and Gastroesophageal Reflux Disease.

Rationale: Gastroesophageal reflux disease (GERD) is commonly associated with atopic disorders, but cause-effect relationships remain unclear. Objectives: We applied Mendelian randomization analysis to explore whether GERD is causally related to atopic disorders of the lung (asthma) and/or skin (atopic dermatitis [AD]). Methods: We conducted two-sample bidirectional Mendelian randomization to infer the magnitude and direction of causality between asthma and GERD, using summary statistics from the largest genome-wide association studies conducted on asthma (Ncases = 56,167) and GERD (Ncases = 71,522). In addition, we generated instrumental variables for AD from the latest population-level genome-wide association study meta-analysis (Ncases = 22,474) and assessed their fidelity and confidence of predicting the likely causal pathway(s) leading to asthma and/or GERD. Measurements and Main Results: Applying three different methods, each method revealed similar magnitude of causal estimates that were directionally consistent across the sensitivity analyses. Using an inverse variance-weighted method, the largest effect size was detected for asthma predisposition to AD (odds ratio [OR], 1.46; 95% confidence interval [CI], 1.34-1.59), followed by AD to asthma (OR, 1.34; 95% CI, 1.24-1.45). A significant association was detected for genetically determined asthma on risk of GERD (OR, 1.06; 95% CI, 1.03-1.09) but not genetically determined AD on GERD. In contrast, GERD equally increased risks of asthma (OR, 1.21; 95% CI, 1.09-1.35) and AD (OR, 1.21; 95% CI, 1.07-1.37). Conclusions: This study uncovers previously unrecognized causal pathways that have clinical implications in European-ancestry populations: 1) asthma is a causal risk for AD, and 2) the predisposition to AD, including asthma, can arise from specific pathogenic mechanisms manifested by GERD.

Genetically proxied glucose-lowering drug target perturbation and risk of cancer: a Mendelian randomisation analysis.

AIMS/HYPOTHESIS: Epidemiological studies have generated conflicting findings on the relationship between glucose-lowering medication use and cancer risk. Naturally occurring variation in genes encoding glucose-lowering drug targets can be used to investigate the effect of their pharmacological perturbation on cancer risk. METHODS: We developed genetic instruments for three glucose-lowering drug targets (peroxisome proliferator activated receptor γ [PPARG]; sulfonylurea receptor 1 [ATP binding cassette subfamily C member 8 (ABCC8)]; glucagon-like peptide 1 receptor [GLP1R]) using summary genetic association data from a genome-wide association study of type 2 diabetes in 148,726 cases and 965,732 controls in the Million Veteran Program. Genetic instruments were constructed using cis-acting genome-wide significant (p<5×10-8) SNPs permitted to be in weak linkage disequilibrium (r2<0.20). Summary genetic association estimates for these SNPs were obtained from genome-wide association study (GWAS) consortia for the following cancers: breast (122,977 cases, 105,974 controls); colorectal (58,221 cases, 67,694 controls); prostate (79,148 cases, 61,106 controls); and overall (i.e. site-combined) cancer (27,483 cases, 372,016 controls). Inverse-variance weighted random-effects models adjusting for linkage disequilibrium were employed to estimate causal associations between genetically proxied drug target perturbation and cancer risk. Co-localisation analysis was employed to examine robustness of findings to violations of Mendelian randomisation (MR) assumptions. A Bonferroni correction was employed as a heuristic to define associations from MR analyses as 'strong' and 'weak' evidence. RESULTS: In MR analysis, genetically proxied PPARG perturbation was weakly associated with higher risk of prostate cancer (for PPARG perturbation equivalent to a 1 unit decrease in inverse rank normal transformed HbA1c: OR 1.75 [95% CI 1.07, 2.85], p=0.02). In histological subtype-stratified analyses, genetically proxied PPARG perturbation was weakly associated with lower risk of oestrogen receptor-positive breast cancer (OR 0.57 [95% CI 0.38, 0.85], p=6.45×10-3). In co-localisation analysis, however, there was little evidence of shared causal variants for type 2 diabetes liability and cancer endpoints in the PPARG locus, although these analyses were likely underpowered. There was little evidence to support associations between genetically proxied PPARG perturbation and colorectal or overall cancer risk or between genetically proxied ABCC8 or GLP1R perturbation with risk across cancer endpoints. CONCLUSIONS/INTERPRETATION: Our drug target MR analyses did not find consistent evidence to support an association of genetically proxied PPARG, ABCC8 or GLP1R perturbation with breast, colorectal, prostate or overall cancer risk. Further evaluation of these drug targets using alternative molecular epidemiological approaches may help to further corroborate the findings presented in this analysis. DATA AVAILABILITY: Summary genetic association data for select cancer endpoints were obtained from the public domain: breast cancer ( https://bcac.ccge.medschl.cam.ac.uk/bcacdata/ ); and overall prostate cancer ( http://practical.icr.ac.uk/blog/ ). Summary genetic association data for colorectal cancer can be accessed by contacting GECCO (kafdem at fredhutch.org). Summary genetic association data on advanced prostate cancer can be accessed by contacting PRACTICAL (practical at icr.ac.uk). Summary genetic association data on type 2 diabetes from Vujkovic et al (Nat Genet, 2020) can be accessed through dbGAP under accession number phs001672.v3.p1 (pha004945.1 refers to the European-specific summary statistics). UK Biobank data can be accessed by registering with UK Biobank and completing the registration form in the Access Management System (AMS) ( https://www.ukbiobank.ac.uk/enable-your-research/apply-for-access ).

ColocQuiaL: a QTL-GWAS colocalization pipeline.

SUMMARY: Identifying genomic features responsible for genome-wide association study (GWAS) signals has proven to be a difficult challenge; many researchers have turned to colocalization analysis of GWAS signals with expression quantitative trait loci (eQTL) and splicing quantitative trait loci (sQTL) to connect GWAS signals to candidate causal genes. The ColocQuiaL pipeline provides a framework to perform these colocalization analyses at scale across the genome and returns summary files and locus visualization plots to allow for detailed review of the results. As an example, we used ColocQuiaL to perform colocalization between a recent type 2 diabetes GWAS and Genotype-Tissue Expression (GTEx) v8 single-tissue eQTL and sQTL data. AVAILABILITY AND IMPLEMENTATION: ColocQuiaL is primarily written in R and is freely available on GitHub: https://github.com/bvoightlab/ColocQuiaL.

Estimating the effect size of a hidden causal factor between SNPs and a continuous trait: a mediation model approach.

BACKGROUND: Observational studies and Mendelian randomization experiments have been used to identify many causal factors for complex traits in humans. Given a set of causal factors, it is important to understand the extent to which these causal factors explain some, all, or none of the genetic heritability, as measured by single-nucleotide polymorphisms (SNPs) that are associated with the trait. Using the mediation model framework with SNPs as the exposure, a trait of interest as the outcome, and the known causal factors as the mediators, we hypothesize that any unexplained association between the SNPs and the outcome trait is mediated by an additional unobserved, hidden causal factor. RESULTS: We propose a method to infer the effect size of this hidden mediating causal factor on the outcome trait by utilizing the estimated associations between a continuous outcome trait, the known causal factors, and the SNPs. The proposed method consists of three steps and, in the end, implements Markov chain Monte Carlo to obtain a posterior distribution for the effect size of the hidden mediator. We evaluate our proposed method via extensive simulations and show that when model assumptions hold, our method estimates the effect size of the hidden mediator well and controls type I error rate if the hidden mediator does not exist. In addition, we apply the method to the UK Biobank data and estimate parameters for a potential hidden mediator for waist-hip ratio beyond body mass index (BMI), and find that the hidden mediator has a large effect size relatively to the effect size of the known mediator BMI. CONCLUSIONS: We develop a framework to infer the effect of potential, hidden mediators influencing complex traits. This framework can be used to place boundaries on unexplained risk factors contributing to complex traits.

Separating the direct effects of traits on atherosclerotic cardiovascular disease from those mediated by type 2 diabetes.

AIMS/HYPOTHESIS: Type 2 diabetes and atherosclerotic CVD share many risk factors. This study aimed to systematically assess a broad range of continuous traits to separate their direct effects on coronary and peripheral artery disease from those mediated by type 2 diabetes. METHODS: Our main analysis was a two-step Mendelian randomisation for mediation to quantify the extent to which the associations observed between continuous traits and liability to atherosclerotic CVD were mediated by liability to type 2 diabetes. To support this analysis, we performed several univariate Mendelian randomisation analyses to examine the associations between our continuous traits, liability to type 2 diabetes and liability to atherosclerotic CVD. RESULTS: Eight traits were eligible for the two-step Mendelian randomisation with liability to coronary artery disease as the outcome and we found similar direct and total effects in most cases. Exceptions included fasting insulin and hip circumference where the proportion mediated by liability to type 2 diabetes was estimated as 56% and 52%, respectively. Six traits were eligible for the analysis with liability to peripheral artery disease as the outcome. Again, we found limited evidence to support mediation by liability to type 2 diabetes for all traits apart from fasting insulin (proportion mediated: 70%). CONCLUSIONS/INTERPRETATION: Most traits were found to affect liability to atherosclerotic CVD independently of their relationship with liability to type 2 diabetes. These traits are therefore important for understanding atherosclerotic CVD risk regardless of an individual's liability to type 2 diabetes.

Prioritizing the Role of Major Lipoproteins and Subfractions as Risk Factors for Peripheral Artery Disease.

BACKGROUND: Lipoprotein-related traits have been consistently identified as risk factors for atherosclerotic cardiovascular disease, largely on the basis of studies of coronary artery disease (CAD). The relative contributions of specific lipoproteins to the risk of peripheral artery disease (PAD) have not been well defined. We leveraged large-scale genetic association data to investigate the effects of circulating lipoprotein-related traits on PAD risk. METHODS: Genome-wide association study summary statistics for circulating lipoprotein-related traits were used in the mendelian randomization bayesian model averaging framework to prioritize the most likely causal major lipoprotein and subfraction risk factors for PAD and CAD. Mendelian randomization was used to estimate the effect of apolipoprotein B (ApoB) lowering on PAD risk using gene regions proxying lipid-lowering drug targets. Genes relevant to prioritized lipoprotein subfractions were identified with transcriptome-wide association studies. RESULTS: ApoB was identified as the most likely causal lipoprotein-related risk factor for both PAD (marginal inclusion probability, 0.86; P=0.003) and CAD (marginal inclusion probability, 0.92; P=0.005). Genetic proxies for ApoB-lowering medications were associated with reduced risk of both PAD (odds ratio,0.87 per 1-SD decrease in ApoB [95% CI, 0.84-0.91]; P=9×10-10) and CAD (odds ratio,0.66 [95% CI, 0.63-0.69]; P=4×10-73), with a stronger predicted effect of ApoB lowering on CAD (ratio of effects, 3.09 [95% CI, 2.29-4.60]; P<1×10-6). Extra-small very-low-density lipoprotein particle concentration was identified as the most likely subfraction associated with PAD risk (marginal inclusion probability, 0.91; P=2.3×10-4), whereas large low-density lipoprotein particle concentration was the most likely subfraction associated with CAD risk (marginal inclusion probability, 0.95; P=0.011). Genes associated with extra-small very-low-density lipoprotein particle and large low-density lipoprotein particle concentration included canonical ApoB pathway components, although gene-specific effects were variable. Lipoprotein(a) was associated with increased risk of PAD independently of ApoB (odds ratio, 1.04 [95% CI, 1.03-1.04]; P=1.0×10-33). CONCLUSIONS: ApoB was prioritized as the major lipoprotein fraction causally responsible for both PAD and CAD risk. However, ApoB-lowering drug targets and ApoB-containing lipoprotein subfractions had diverse associations with atherosclerotic cardiovascular disease, and distinct subfraction-associated genes suggest possible differences in the role of lipoproteins in the pathogenesis of PAD and CAD.

TSABL: Trait Specific Annotation Based Locus predictor.

BACKGROUND: The majority of Genome Wide Associate Study (GWAS) loci fall in the non-coding genome, making causal variants difficult to identify and study. We hypothesized that the regulatory features underlying causal variants are biologically specific, identifiable from data, and that the regulatory architecture that influences one trait is distinct compared to biologically unrelated traits. RESULTS: To better characterize and identify these variants, we used publicly available GWAS loci and genomic annotations to build 17 Trait Specific Annotation Based Locus (TSABL) predictors to identify differences between GWAS loci associated with different phenotypic trait groups. We used a penalized binomial logistic regression model to select trait relevant annotations and tested all models on a holdout set of loci not used for training in any trait. We were able to successfully build models for autoimmune, electrocardiogram, lipid, platelet, red blood cell, and white blood cell trait groups. We used these models both to prioritize variants in existing loci and to identify new genomic regions of interest. CONCLUSIONS: We found that TSABL models identified biologically relevant regulatory features, and anticipate their future use to enhance the design and interpretation of genetic studies.

Genetic determinants of increased body mass index mediate the effect of smoking on increased risk for type 2 diabetes but not coronary artery disease.

Clinical observations have linked tobacco smoking with increased type 2 diabetes risk. Mendelian randomization analysis has recently suggested smoking may be a causal risk factor for type 2 diabetes. However, this association could be mediated by additional risk factors correlated with smoking behavior, which have not been investigated. We hypothesized that body mass index (BMI) could help to explain the association between smoking and diabetes risk. First, we confirmed that genetic determinants of smoking initiation increased risk for type 2 diabetes (OR 1.21, 95% CI: 1.15-1.27, P = 1 × 10-12) and coronary artery disease (CAD; OR 1.21, 95% CI: 1.16-1.26, P = 2 × 10-20). Additionally, 2-fold increased smoking risk was positively associated with increased BMI (~0.8 kg/m2, 95% CI: 0.54-0.98 kg/m2, P = 1.8 × 10-11). Multivariable Mendelian randomization analyses showed that BMI accounted for nearly all the risk smoking exerted on type 2 diabetes (OR 1.06, 95% CI: 1.01-1.11, P = 0.03). In contrast, the independent effect of smoking on increased CAD risk persisted (OR 1.12, 95% CI: 1.08-1.17, P = 3 × 10-8). Causal mediation analyses agreed with these estimates. Furthermore, analysis using individual-level data from the Million Veteran Program independently replicated the association of smoking behavior with CAD (OR 1.24, 95% CI: 1.12-1.37, P = 2 × 10-5), but not type 2 diabetes (OR 0.98, 95% CI: 0.89-1.08, P = 0.69), after controlling for BMI. Our findings support a model whereby genetic determinants of smoking increase type 2 diabetes risk indirectly through their relationship with obesity. Smokers should be advised to stop smoking to limit type 2 diabetes and CAD risk. Therapeutic efforts should consider pathophysiology relating smoking and obesity.

Association Between Genetic Variation in Blood Pressure and Increased Lifetime Risk of Peripheral Artery Disease.

[Figure: see text].

Natural human genetic variation determines basal and inducible expression of PM20D1, an obesity-associated gene.

PM20D1 is a candidate thermogenic enzyme in mouse fat, with its expression cold-induced and enriched in brown versus white adipocytes. Thiazolidinedione (TZD) antidiabetic drugs, which activate the peroxisome proliferator-activated receptor-γ (PPARγ) nuclear receptor, are potent stimuli for adipocyte browning yet fail to induce Pm20d1 expression in mouse adipocytes. In contrast, PM20D1 is one of the most strongly TZD-induced transcripts in human adipocytes, although not in cells from all individuals. Two putative PPARγ binding sites exist near the gene's transcription start site (TSS) in human but not mouse adipocytes. The -4 kb upstream site falls in a segmental duplication of a nearly identical intronic region +2.5 kb downstream of the TSS, and this duplication occurred in the primate lineage and not in other mammals, like mice. PPARγ binding and gene activation occur via this upstream duplicated site, thus explaining the species difference. Furthermore, this functional upstream PPARγ site exhibits genetic variation among people, with 1 SNP allele disrupting a PPAR response element and giving less activation by PPARγ and TZDs. In addition to this upstream variant that determines PPARγ regulation of PM20D1 in adipocytes, distinct variants downstream of the TSS have strong effects on PM20D1 expression in human fat as well as other tissues. A haplotype of 7 tightly linked downstream SNP alleles is associated with very low PMD201 expression and correspondingly high DNA methylation at the TSS. These PM20D1 low-expression variants may account for human genetic associations in this region with obesity as well as neurodegenerative diseases.

Type 1 diabetes in Africa: an immunogenetic study in the Amhara of North-West Ethiopia.

AIMS/HYPOTHESIS: We aimed to characterise the immunogenic background of insulin-dependent diabetes in a resource-poor rural African community. The study was initiated because reports of low autoantibody prevalence and phenotypic differences from European-origin cases with type 1 diabetes have raised doubts as to the role of autoimmunity in this and similar populations. METHODS: A study of consecutive, unselected cases of recently diagnosed, insulin-dependent diabetes (n = 236, ≤35 years) and control participants (n = 200) was carried out in the ethnic Amhara of rural North-West Ethiopia. We assessed their demographic and socioeconomic characteristics, and measured non-fasting C-peptide, diabetes-associated autoantibodies and HLA-DRB1 alleles. Leveraging genome-wide genotyping, we performed both a principal component analysis and, given the relatively modest sample size, a provisional genome-wide association study. Type 1 diabetes genetic risk scores were calculated to compare their genetic background with known European type 1 diabetes determinants. RESULTS: Patients presented with stunted growth and low BMI, and were insulin sensitive; only 15.3% had diabetes onset at ≤15 years. C-peptide levels were low but not absent. With clinical diabetes onset at ≤15, 16-25 and 26-35 years, 86.1%, 59.7% and 50.0% were autoantibody positive, respectively. Most had autoantibodies to GAD (GADA) as a single antibody; the prevalence of positivity for autoantibodies to IA-2 (IA-2A) and ZnT8 (ZnT8A) was low in all age groups. Principal component analysis showed that the Amhara genomes were distinct from modern European and other African genomes. HLA-DRB1*03:01 (p = 0.0014) and HLA-DRB1*04 (p = 0.0001) were positively associated with this form of diabetes, while HLA-DRB1*15 was protective (p < 0.0001). The mean type 1 diabetes genetic risk score (derived from European data) was higher in patients than control participants (p = 1.60 × 10-7). Interestingly, despite the modest sample size, autoantibody-positive patients revealed evidence of association with SNPs in the well-characterised MHC region, already known to explain half of type 1 diabetes heritability in Europeans. CONCLUSIONS/INTERPRETATION: The majority of patients with insulin-dependent diabetes in rural North-West Ethiopia have the immunogenetic characteristics of autoimmune type 1 diabetes. Phenotypic differences between type 1 diabetes in rural North-West Ethiopia and the industrialised world remain unexplained.

Signals of Variation in Human Mutation Rate at Multiple Levels of Sequence Context.

Our understanding of the human mutation rate helps us build evolutionary models and interpret patterns of genetic variation observed in human populations. Recent work indicates that the frequencies of specific polymorphism types have been elevated in Europe, and that many more, subtler signatures of global polymorphism variation may yet remain unidentified. Here, we present an analysis of the 1000 Genomes Project supported by analysis in the Simons Genome Diversity Panel, suggesting additional putative signatures of mutation rate variation across populations and the extent to which they are shaped by local sequence context. First, we compiled a list of the most significantly variable polymorphism types in a cross-continental statistical test. Clustering polymorphisms together, we observe three sets that showed distinct shared patterns of relative enrichment among ancestral populations, and we characterize each one of these putative "signatures" of polymorphism variation. For three of these signatures, we found that a single flanking base pair of sequence context was sufficient to determine the majority of enrichment or depletion of a polymorphism type. However, local genetic context up to 2-3 bp away contributes additional variability and may help to interpret a previously noted enrichment of certain polymorphism types in some East Asian groups. Moreover, considering broader local genetic context highlights patterns of polymorphism variation, which were not captured by previous approaches. Building our understanding of mutation rate in this way can help us to construct more accurate evolutionary models and better understand the mechanisms that underlie genetic change.

The relationship between circulating lipids and breast cancer risk: A Mendelian randomization study.

BACKGROUND: A number of epidemiological and genetic studies have attempted to determine whether levels of circulating lipids are associated with risks of various cancers, including breast cancer (BC). However, it remains unclear whether a causal relationship exists between lipids and BC. If alteration of lipid levels also reduced risk of BC, this could present a target for disease prevention. This study aimed to assess a potential causal relationship between genetic variants associated with plasma lipid traits (high-density lipoprotein, HDL; low-density lipoprotein, LDL; triglycerides, TGs) with risk for BC using Mendelian randomization (MR). METHODS AND FINDINGS: Data from genome-wide association studies in up to 215,551 participants from the Million Veteran Program (MVP) were used to construct genetic instruments for plasma lipid traits. The effect of these instruments on BC risk was evaluated using genetic data from the BCAC (Breast Cancer Association Consortium) based on 122,977 BC cases and 105,974 controls. Using MR, we observed that a 1-standard-deviation genetically determined increase in HDL levels is associated with an increased risk for all BCs (HDL: OR [odds ratio] = 1.08, 95% confidence interval [CI] = 1.04-1.13, P < 0.001). Multivariable MR analysis, which adjusted for the effects of LDL, TGs, body mass index (BMI), and age at menarche, corroborated this observation for HDL (OR = 1.06, 95% CI = 1.03-1.10, P = 4.9 × 10-4) and also found a relationship between LDL and BC risk (OR = 1.03, 95% CI = 1.01-1.07, P = 0.02). We did not observe a difference in these relationships when stratified by breast tumor estrogen receptor (ER) status. We repeated this analysis using genetic variants independent of the leading association at core HDL pathway genes and found that these variants were also associated with risk for BCs (OR = 1.11, 95% CI = 1.06-1.16, P = 1.5 × 10-6), including locus-specific associations at ABCA1 (ATP Binding Cassette Subfamily A Member 1), APOE-APOC1-APOC4-APOC2 (Apolipoproteins E, C1, C4, and C2), and CETP (Cholesteryl Ester Transfer Protein). In addition, we found evidence that genetic variation at the ABO locus is associated with both lipid levels and BC. Through multiple statistical approaches, we minimized and tested for the confounding effects of pleiotropy and population stratification on our analysis; however, the possible existence of residual pleiotropy and stratification remains a limitation of this study. CONCLUSIONS: We observed that genetically elevated plasma HDL and LDL levels appear to be associated with increased BC risk. Future studies are required to understand the mechanism underlying this putative causal relationship, with the goal of developing potential therapeutic strategies aimed at altering the cholesterol-mediated effect on BC risk.

Genetics of height and risk of atrial fibrillation: A Mendelian randomization study.

BACKGROUND: Observational studies have identified height as a strong risk factor for atrial fibrillation, but this finding may be limited by residual confounding. We aimed to examine genetic variation in height within the Mendelian randomization (MR) framework to determine whether height has a causal effect on risk of atrial fibrillation. METHODS AND FINDINGS: In summary-level analyses, MR was performed using summary statistics from genome-wide association studies of height (GIANT/UK Biobank; 693,529 individuals) and atrial fibrillation (AFGen; 65,446 cases and 522,744 controls), finding that each 1-SD increase in genetically predicted height increased the odds of atrial fibrillation (odds ratio [OR] 1.34; 95% CI 1.29 to 1.40; p = 5 × 10-42). This result remained consistent in sensitivity analyses with MR methods that make different assumptions about the presence of pleiotropy, and when accounting for the effects of traditional cardiovascular risk factors on atrial fibrillation. Individual-level phenome-wide association studies of height and a height genetic risk score were performed among 6,567 European-ancestry participants of the Penn Medicine Biobank (median age at enrollment 63 years, interquartile range 55-72; 38% female; recruitment 2008-2015), confirming prior observational associations between height and atrial fibrillation. Individual-level MR confirmed that each 1-SD increase in height increased the odds of atrial fibrillation, including adjustment for clinical and echocardiographic confounders (OR 1.89; 95% CI 1.50 to 2.40; p = 0.007). The main limitations of this study include potential bias from pleiotropic effects of genetic variants, and lack of generalizability of individual-level findings to non-European populations. CONCLUSIONS: In this study, we observed evidence that height is likely a positive causal risk factor for atrial fibrillation. Further study is needed to determine whether risk prediction tools including height or anthropometric risk factors can be used to improve screening and primary prevention of atrial fibrillation, and whether biological pathways involved in height may offer new targets for treatment of atrial fibrillation.

Large-Scale trans-eQTLs Affect Hundreds of Transcripts and Mediate Patterns of Transcriptional Co-regulation.

Efforts to decipher the causal relationships between differences in gene regulation and corresponding differences in phenotype have been stymied by several basic technical challenges. Although detecting local, cis-eQTLs is now routine, trans-eQTLs, which are distant from the genes of origin, are far more difficult to find because millions of SNPs must currently be compared to thousands of transcripts. Here, we demonstrate an alternative approach: we looked for SNPs associated with the expression of many genes simultaneously and found that hundreds of trans-eQTLs each affect hundreds of transcripts in lymphoblastoid cell lines across three African populations. These trans-eQTLs target the same genes across the three populations and show the same direction of effect. We discovered that target transcripts of a high-confidence set of trans-eQTLs encode proteins that interact more frequently than expected by chance, are bound by the same transcription factors, and are enriched for pathway annotations indicative of roles in basic cell homeostasis. We thus demonstrate that our approach can uncover trans-acting transcriptional control circuits that affect co-regulated groups of genes: a key to understanding how cellular pathways and processes are orchestrated.

Single-cell analysis reveals that noncoding RNAs contribute to clonal heterogeneity by modulating transcription factor recruitment.

Mechanisms through which long intergenic noncoding RNAs (ncRNAs) exert regulatory effects on eukaryotic biological processes remain largely elusive. Most studies of these phenomena rely on methods that measure average behaviors in cell populations, lacking resolution to observe the effects of ncRNA transcription on gene expression in a single cell. Here, we combine quantitative single-molecule RNA FISH experiments with yeast genetics and computational modeling to gain mechanistic insights into the regulation of the Saccharomyces cerevisiae protein-coding gene FLO11 by two intergenic ncRNAs, ICR1 and PWR1. Direct detection of FLO11 mRNA and these ncRNAs in thousands of individual cells revealed alternative expression states and provides evidence that ICR1 and PWR1 contribute to FLO11's variegated transcription, resulting in Flo11-dependent phenotypic heterogeneity in clonal cell populations by modulating recruitment of key transcription factors to the FLO11 promoter.