Fetal alleles predisposing to metabolically favorable adiposity are associated with higher birth weight.

BACKGROUND: Higher birthweight is associated with higher adult body mass index (BMI). Alleles that predispose to greater adult adiposity might act in fetal life to increase fetal growth and birthweight. Whether there are fetal effects of recently identified adult metabolically favorable adiposity alleles on birthweight is unknown. AIM: We aimed to test the effect on birthweight of fetal genetic predisposition to higher metabolically favorable adult adiposity and compare that with the effect of fetal genetic predisposition to higher adult BMI. METHODS: We used published genome wide association study data (n = upto 406 063) to estimate fetal effects on birthweight (adjusting for maternal genotype) of alleles known to raise metabolically favorable adult adiposity or BMI. We combined summary data across single nucleotide polymorphisms (SNPs) with random effects meta-analyses. We performed weighted linear regression of SNP-birthweight effects against SNP-adult adiposity effects to test for a dose-dependent association. RESULTS: Fetal genetic predisposition to higher metabolically favorable adult adiposity and higher adult BMI were both associated with higher birthweight (3 g per effect allele (95% CI: 1-5) averaged over 14 SNPs; P = 0.002; 0.5 g per effect allele (95% CI: 0-1) averaged over 76 SNPs; P = 0.042, respectively). SNPs with greater effects on metabolically favorable adiposity tended to have greater effects on birthweight (R2 = 0.2912, P = 0.027). There was no dose-dependent association for BMI (R2 = -0.0019, P = 0.602). CONCLUSIONS: Fetal genetic predisposition to both higher adult metabolically favorable adiposity and BMI is associated with birthweight. Fetal effects of metabolically favorable adiposity-raising alleles on birthweight are modestly proportional to their effects on future adiposity, but those of BMI-raising alleles are not.

Differing genetic variants associated with liver fat and their contrasting relationships with cardiovascular diseases and cancer.

BACKGROUND: The underlying mechanisms for the link between steatotic liver disease and cardiovascular and cancer outcomes are poorly understood. We aimed to use MRI-derived measures of liver fat and genetics to investigate causal mechanisms that link higher liver fat to various health outcomes. METHODS: We conducted a genome-wide association study on 37,358 UK Biobank participants to identify genetic variants associated with liver fat measured from MRI scans. We used Mendelian randomization approach to investigate the causal effect of liver fat on health outcomes independent of BMI, alcohol consumption and lipids using data from published GWAS and FinnGen. RESULTS: We identified 13 genetic variants associated with liver fat that showed differing risks to health outcomes. Genetic variants associated with impaired hepatic triglyceride export showed liver fat-increasing alleles to be correlated with a reduced risk of coronary artery disease and myocardial infarction but an elevated risk of type 2 diabetes; and variants associated with enhanced de novo lipogenesis showed liver fat-increasing alleles to be linked to a higher risk of myocardial infarction and coronary artery disease. Genetically higher liver fat content increased the risk of non-alcohol liver cirrhosis, hepatocellular and Intrahepatic bile ducts and gallbladder cancers, exhibiting a dose-dependent relationship, irrespective of the mechanism. CONCLUSION: This study provides fresh insight into the heterogeneous effect of liver fat on health outcomes. It challenges the notion that liver fat per se is an independent risk factor for cardiovascular disease, underscoring the dependency of this association on the specific mechanisms that drive fat accumulation in the liver. However, excess liver fat, regardless of how achieved, appears to be causally linked to liver cirrhosis and cancers in a dose dependent manner. IMPACT AND IMPLICATION: This research advances our understanding of the heterogeneity in mechanisms influencing liver fat accumulation, providing new insights into how liver fat accumulation may impact various health outcomes. The findings challenge the notion that liver fat is an independent risk factor for cardiovascular disease and highlight the mechanistic effect of some genetic variants on fat accumulation and the development of cardiovascular diseases. This study is of particular importance for healthcare professionals including physicians and researchers as well as patients as it allows for more targeted and personalised treatment by understanding the relationship between liver fat and various health outcomes. The findings emphasise the need for a personalised management approach and a reshaping of risk assessment criteria. It also provides room for prioritising a clinical intervention aimed at reducing liver fat content (likely by intentional weight loss, however, achieved) that could help protect against liver related fibrosis and cancer.

Higher adiposity and mental health: causal inference using Mendelian randomization.

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.

Identifying obesity subtypes: A review of studies utilising clinical biomarkers and genetic data.

Obesity is a complex and multifactorial condition that poses significant health risks. Recent advancements in our understanding of obesity have highlighted the heterogeneity within this disorder. Identifying distinct subtypes of obesity is crucial for personalised treatment and intervention strategies. This review paper aims to examine studies that have utilised clinical biomarkers and genetic data to identify clusters or subtypes of obesity. The findings of these studies may provide valuable insights into the underlying mechanisms and potential targeted approaches for managing obesity-related health issues such as type 2 diabetes.

Genetic scores associated with favourable and unfavourable adiposity have consistent effect on metabolic profile and disease risk across diverse ethnic groups.

AIM: This study aims to investigate the associations between genetic risk scores (GRS) for favourable and unfavourable adiposity and a wide range of adiposity-related outcomes across diverse populations. METHODS: We utilised previously identified variants associated with favourable (36 variants) and unfavourable (38 variants) adiposity to create GRS for each adiposity phenotype. We used summary statistics from 39 outcomes generated by the Pan-UKB genome-wide association studies Version 0.3, incorporating covariates such as age, sex and principal components in six populations: European (n = 420,531), African (6636), American (980), Central/South Asian (8876), East Asian (2709) and Middle Eastern (1599). RESULTS: The favourable adiposity GRS was associated with a healthy metabolic profile, including lower risk of type 2 diabetes, lower liver enzyme levels, lower blood pressure, higher HDL-cholesterol, lower triglycerides, higher apolipoprotein A, lower apolipoprotein B, higher testosterone, lower calcium and lower insulin-like growth factor 1 generally consistently across all the populations. In contrast, the unfavourable adiposity GRS was associated with an adverse metabolic profile, including higher risk of type 2 diabetes, higher random glucose levels, higher HbA1c, lower HDL-cholesterol, higher triglycerides, higher liver enzyme levels, lower testosterone, and higher C-reactive protein generally consistently across all the populations. CONCLUSION: The study provides evidence that the genetic scores associated with favourable and unfavourable adiposity have consistent effects on metabolic profiles and disease risk across diverse ethnic groups. These findings deepen our understanding of distinct adiposity subtypes and their impact on metabolic health.

Exome-Derived Adiponectin-Associated Variants Implicate Obesity and Lipid Biology.

Circulating levels of adiponectin, an adipocyte-secreted protein associated with cardiovascular and metabolic risk, are highly heritable. To gain insights into the biology that regulates adiponectin levels, we performed an exome array meta-analysis of 265,780 genetic variants in 67,739 individuals of European, Hispanic, African American, and East Asian ancestry. We identified 20 loci associated with adiponectin, including 11 that had been reported previously (p < 2 × 10-7). Comparison of exome array variants to regional linkage disequilibrium (LD) patterns and prior genome-wide association study (GWAS) results detected candidate variants (r2 > .60) spanning as much as 900 kb. To identify potential genes and mechanisms through which the previously unreported association signals act to affect adiponectin levels, we assessed cross-trait associations, expression quantitative trait loci in subcutaneous adipose, and biological pathways of nearby genes. Eight of the nine loci were also associated (p < 1 × 10-4) with at least one obesity or lipid trait. Candidate genes include PRKAR2A, PTH1R, and HDAC9, which have been suggested to play roles in adipocyte differentiation or bone marrow adipose tissue. Taken together, these findings provide further insights into the processes that influence circulating adiponectin levels.

Advancing a causal role of type 2 diabetes and its components in developing macro- and microvascular complications via genetic studies.

The role of diabetes in developing microvascular and macrovascular complications has been subject to extensive research. Despite multiple observational and genetic studies, the causal inference of diabetes (and associated risk factors) on those complications remains incomplete. In this review, we focused on type 2 diabetes, as the major form of diabetes, and investigated the evidence of causality provided by observational and genetic studies. We found that genetic studies based on Mendelian randomization provided consistent evidence of causal inference of type 2 diabetes on macrovascular complications; however, the evidence for causal inference on microvascular complications has been somewhat limited. We also noted high BMI could be causal for several diabetes complications, notable given high BMI is commonly upstream of type 2 diabetes and the recent calls to target weight loss more aggressively. We emphasize the need for further studies to identify type 2 diabetes components that mostly drive the risk of those complications. Even so, the genetic evidence summarized broadly concurs with the need for a multifactorial risk reduction approach in type 2 diabetes, including addressing excess adiposity.

Higher maternal adiposity reduces offspring birthweight if associated with a metabolically favourable profile.

AIMS/HYPOTHESIS: Higher maternal BMI during pregnancy is associated with higher offspring birthweight, but it is not known whether this is solely the result of adverse metabolic consequences of higher maternal adiposity, such as maternal insulin resistance and fetal exposure to higher glucose levels, or whether there is any effect of raised adiposity through non-metabolic (e.g. mechanical) factors. We aimed to use genetic variants known to predispose to higher adiposity, coupled with a favourable metabolic profile, in a Mendelian randomisation (MR) study comparing the effect of maternal 'metabolically favourable adiposity' on offspring birthweight with the effect of maternal general adiposity (as indexed by BMI). METHODS: To test the causal effects of maternal metabolically favourable adiposity or general adiposity on offspring birthweight, we performed two-sample MR. We used variants identified in large, published genetic-association studies as being associated with either higher adiposity and a favourable metabolic profile, or higher BMI (n = 442,278 and n = 322,154 for metabolically favourable adiposity and BMI, respectively). We then extracted data on the metabolically favourable adiposity and BMI variants from a large, published genetic-association study of maternal genotype and offspring birthweight controlling for fetal genetic effects (n = 406,063 with maternal and/or fetal genotype effect estimates). We used several sensitivity analyses to test the reliability of the results. As secondary analyses, we used data from four cohorts (total n = 9323 mother-child pairs) to test the effects of maternal metabolically favourable adiposity or BMI on maternal gestational glucose, anthropometric components of birthweight and cord-blood biomarkers. RESULTS: Higher maternal adiposity with a favourable metabolic profile was associated with lower offspring birthweight (-94 [95% CI -150, -38] g per 1 SD [6.5%] higher maternal metabolically favourable adiposity, p = 0.001). By contrast, higher maternal BMI was associated with higher offspring birthweight (35 [95% CI 16, 53] g per 1 SD [4 kg/m2] higher maternal BMI, p = 0.0002). Sensitivity analyses were broadly consistent with the main results. There was evidence of outlier SNPs for both exposures; their removal slightly strengthened the metabolically favourable adiposity estimate and made no difference to the BMI estimate. Our secondary analyses found evidence to suggest that a higher maternal metabolically favourable adiposity decreases pregnancy fasting glucose levels while a higher maternal BMI increases them. The effects on neonatal anthropometric traits were consistent with the overall effect on birthweight but the smaller sample sizes for these analyses meant that the effects were imprecisely estimated. We also found evidence to suggest that higher maternal metabolically favourable adiposity decreases cord-blood leptin while higher maternal BMI increases it. CONCLUSIONS/INTERPRETATION: Our results show that higher adiposity in mothers does not necessarily lead to higher offspring birthweight. Higher maternal adiposity can lead to lower offspring birthweight if accompanied by a favourable metabolic profile. DATA AVAILABILITY: The data for the genome-wide association studies (GWAS) of BMI are available at https://portals.broadinstitute.org/collaboration/giant/index.php/GIANT_consortium_data_files . The data for the GWAS of body fat percentage are available at https://walker05.u.hpc.mssm.edu .

Meta-analysis of genome-wide association studies for body fat distribution in 694 649 individuals of European ancestry.

More than one in three adults worldwide is either overweight or obese. Epidemiological studies indicate that the location and distribution of excess fat, rather than general adiposity, are more informative for predicting risk of obesity sequelae, including cardiometabolic disease and cancer. We performed a genome-wide association study meta-analysis of body fat distribution, measured by waist-to-hip ratio (WHR) adjusted for body mass index (WHRadjBMI), and identified 463 signals in 346 loci. Heritability and variant effects were generally stronger in women than men, and we found approximately one-third of all signals to be sexually dimorphic. The 5% of individuals carrying the most WHRadjBMI-increasing alleles were 1.62 times more likely than the bottom 5% to have a WHR above the thresholds used for metabolic syndrome. These data, made publicly available, will inform the biology of body fat distribution and its relationship with disease.

Genetically defined favourable adiposity is not associated with a clinically meaningful difference in clinical course in people with type 2 diabetes but does associate with a favourable metabolic profile.

AIMS: Change in weight, HbA1c , lipids, blood pressure and cardiometabolic events over time is variable in individuals with type 2 diabetes. We hypothesised that people with a genetic predisposition to a more favourable adiposity distribution could have a less severe clinical course/progression. METHODS: We involved people with type 2 diabetes from two UK-based cohorts: 11,914 individuals with GP follow-up data from the UK Biobank and 723 from Salford. We generated a 'favourable adiposity' genetic score and conducted cross-sectional and longitudinal studies to test its association with weight, BMI, lipids, blood pressure, medication use and risk of myocardial infarction and stroke using 15 follow-up time points with 1-year intervals. RESULTS: The 'favourable adiposity' genetic score was cross-sectionally associated with higher weight (effect size per 1 standard deviation higher genetic score: 0.91 kg [0.59,1.23]) and BMI (0.30 kg/m2 [0.19,0.40]), but higher high-density lipoprotein (0.02 mmol/L [0.01,0.02]) and lower triglycerides (-0.04 mmol/L [-0.07, -0.02]) in the UK Biobank at baseline, and this pattern of association was consistent across follow-up. There was a trend for participants with higher 'favourable adiposity' genetic score to have lower risk of myocardial infarction and/or stroke (odds ratio 0.79 [0.62, 1.00]) compared to those with lower score. A one standard deviation higher score was associated with lower odds of using lipid-lowering (0.91 [0.86, 0.97]) and anti-hypertensive medication (0.95 [0.91, 0.99]). CONCLUSIONS: In individuals with type 2 diabetes, having more 'favourable adiposity' alleles is associated with a marginally better lipid profile long-term and having lower odds of requiring lipid-lowering or anti-hypertensive medication in spite of relatively higher adiposity.

Machine Learning based histology phenotyping to investigate the epidemiologic and genetic basis of adipocyte morphology and cardiometabolic traits.

Genetic studies have recently highlighted the importance of fat distribution, as well as overall adiposity, in the pathogenesis of obesity-associated diseases. Using a large study (n = 1,288) from 4 independent cohorts, we aimed to investigate the relationship between mean adipocyte area and obesity-related traits, and identify genetic factors associated with adipocyte cell size. To perform the first large-scale study of automatic adipocyte phenotyping using both histological and genetic data, we developed a deep learning-based method, the Adipocyte U-Net, to rapidly derive mean adipocyte area estimates from histology images. We validate our method using three state-of-the-art approaches; CellProfiler, Adiposoft and floating adipocytes fractions, all run blindly on two external cohorts. We observe high concordance between our method and the state-of-the-art approaches (Adipocyte U-net vs. CellProfiler: R2visceral = 0.94, P < 2.2 × 10-16, R2subcutaneous = 0.91, P < 2.2 × 10-16), and faster run times (10,000 images: 6mins vs 3.5hrs). We applied the Adipocyte U-Net to 4 cohorts with histology, genetic, and phenotypic data (total N = 820). After meta-analysis, we found that mean adipocyte area positively correlated with body mass index (BMI) (Psubq = 8.13 × 10-69, ßsubq = 0.45; Pvisc = 2.5 × 10-55, ßvisc = 0.49; average R2 across cohorts = 0.49) and that adipocytes in subcutaneous depots are larger than their visceral counterparts (Pmeta = 9.8 × 10-7). Lastly, we performed the largest GWAS and subsequent meta-analysis of mean adipocyte area and intra-individual adipocyte variation (N = 820). Despite having twice the number of samples than any similar study, we found no genome-wide significant associations, suggesting that larger sample sizes and a homogenous collection of adipose tissue are likely needed to identify robust genetic associations.

Using genetics to decipher the link between type 2 diabetes and cancer: shared aetiology or downstream consequence?

Recent developments in the field of genetics have accelerated our understanding of the aetiology of complex diseases. Type 2 diabetes mellitus and cancer are no exception, with large-scale genome-wide association studies (GWAS) facilitating exploration of the underlying pathology. Here, we discuss how genetics studies can be used to investigate the relationship between these complex diseases. Observational epidemiological studies consistently report that people with type 2 diabetes have a higher risk of several types of cancer. Indeed, type 2 diabetes and cancer share many common risk factors, such as obesity, ageing, poor diet and low levels of physical activity. However, questions remain regarding the biological mechanisms that link these two diseases. Large-scale GWAS of type 2 diabetes and cancer allow us to consider the evidence for shared genetic architecture. Several shared susceptibility genes have been identified, yet tissue specificity and direction of effect must be taken into account when considering common genetic aetiology. We also consider how GWAS, and associated techniques such as Mendelian randomisation, allow us to dissect the link between the two diseases and address questions such as 'Does type 2 diabetes cause cancer or is the increased risk observed driven by higher adiposity or another associated metabolic feature?' Graphical abstract.

DNA methylation and inflammation marker profiles associated with a history of depression.

Depression is a common and disabling disorder, representing a major social and economic health issue. Moreover, depression is associated with the progression of diseases with an inflammatory etiology including many inflammatory-related disorders. At the molecular level, the mechanisms by which depression might promote the onset of these diseases and associated immune-dysfunction are not well understood. In this study we assessed genome-wide patterns of DNA methylation in whole blood-derived DNA obtained from individuals with a self-reported history of depression (n = 100) and individuals without a history of depression (n = 100) using the Illumina 450K microarray. Our analysis identified six significant (Sidák corrected P < 0.05) depression-associated differentially methylated regions (DMRs); the top-ranked DMR was located in exon 1 of the LTB4R2 gene (Sidák corrected P = 1.27 × 10-14). Polygenic risk scores (PRS) for depression were generated and known biological markers of inflammation, telomere length (TL) and IL-6, were measured in DNA and serum samples, respectively. Next, we employed a systems-level approach to identify networks of co-methylated loci associated with a history of depression, in addition to depression PRS, TL and IL-6 levels. Our analysis identified one depression-associated co-methylation module (P = 0.04). Interestingly, the depression-associated module was highly enriched for pathways related to immune function and was also associated with TL and IL-6 cytokine levels. In summary, our genome-wide DNA methylation analysis of individuals with and without a self-reported history of depression identified several candidate DMRs of potential relevance to the pathogenesis of depression and its associated immune-dysfunction phenotype.

Genome-wide and Mendelian randomisation studies of liver MRI yield insights into the pathogenesis of steatohepatitis.

BACKGROUND & AIMS: MRI-based corrected T1 (cT1) is a non-invasive method to grade the severity of steatohepatitis and liver fibrosis. We aimed to identify genetic variants influencing liver cT1 and use genetics to understand mechanisms underlying liver fibroinflammatory disease and its link with other metabolic traits and diseases. METHODS: First, we performed a genome-wide association study (GWAS) in 14,440 Europeans, with liver cT1 measures, from the UK Biobank. Second, we explored the effects of the cT1 variants on liver blood tests, and a range of metabolic traits and diseases. Third, we used Mendelian randomisation to test the causal effects of 24 predominantly metabolic traits on liver cT1 measures. RESULTS: We identified 6 independent genetic variants associated with liver cT1 that reached the GWAS significance threshold (p <5×10-8). Four of the variants (rs759359281 in SLC30A10, rs13107325 in SLC39A8, rs58542926 in TM6SF2, rs738409 in PNPLA3) were also associated with elevated aminotransferases and had variable effects on liver fat and other metabolic traits. Insulin resistance, type 2 diabetes, non-alcoholic fatty liver and body mass index were causally associated with elevated cT1, whilst favourable adiposity (instrumented by variants associated with higher adiposity but lower risk of cardiometabolic disease and lower liver fat) was found to be protective. CONCLUSION: The association between 2 metal ion transporters and cT1 indicates an important new mechanism in steatohepatitis. Future studies are needed to determine whether interventions targeting the identified transporters might prevent liver disease in at-risk individuals. LAY SUMMARY: We estimated levels of liver inflammation and scarring based on magnetic resonance imaging of 14,440 UK Biobank participants. We performed a genetic study and identified variations in 6 genes associated with levels of liver inflammation and scarring. Participants with variations in 4 of these genes also had higher levels of markers of liver cell injury in blood samples, further validating their role in liver health. Two identified genes are involved in the transport of metal ions in our body. Further investigation of these variations may lead to better detection, assessment, and/or treatment of liver inflammation and scarring.

The Maori and Pacific specific CREBRF variant and adult height.

BACKGROUND: The CREBRF missense variant (p.Arg457Gln) is paradoxically associated with lower risk of type 2 diabetes, yet higher body mass index (BMI). Here we sought to determine whether this CREBRF variant might be associated with adult height. METHODS: Linear regression was used to analyse the association of the CREBRF minor (A) allele with height in 2286 Maori and Pacific adults living in Aotearoa/New Zealand. A potential type 2 diabetes index event was corrected to account for a bias that may be the cause of paradoxical association between the CREBRF diabetes-protective allele and higher BMI and height. RESULTS: The CREBRF protective allele was associated with increased adult height (ß = 1.25 cm, P = 3.9 × 10-6), with the effect being more pronounced in males. The lower odds of diabetes remained similar when analyses were adjusted for height (OR = 0.67-0.65). We found no evidence of a diabetes index event bias to explain the paradoxical effect of CREBRF with either BMI or height and diabetes. The orthologous CREBRF p.Arg457Gln variant was created in knock-in mice to independently assess the effect of the variant, and length was found to be greater in male mice at 8 weeks of age. CONCLUSION: These data taken together indicate that CREBRF p.Arg457Gln is associated with taller stature in Maori and Pacific adults.

Quantifying the extent to which index event biases influence large genetic association studies.

As genetic association studies increase in size to 100 000s of individuals, subtle biases may influence conclusions. One possible bias is 'index event bias' (IEB) that appears due to the stratification by, or enrichment for, disease status when testing associations between genetic variants and a disease-associated trait. We aimed to test the extent to which IEB influences some known trait associations in a range of study designs and provide a statistical framework for assessing future associations. Analyzing data from 113 203 non-diabetic UK Biobank participants, we observed three (near TCF7L2, CDKN2AB and CDKAL1) overestimated (body mass index (BMI) decreasing) and one (near MTNR1B) underestimated (BMI increasing) associations among 11 type 2 diabetes risk alleles (at P < 0.05). IEB became even stronger when we tested a type 2 diabetes genetic risk score composed of these 11 variants (-0.010 standard deviations BMI per allele, P = 5 × 10- 4), which was confirmed in four additional independent studies. Similar results emerged when examining the effect of blood pressure increasing alleles on BMI in normotensive UK Biobank samples. Furthermore, we demonstrated that, under realistic scenarios, common disease alleles would become associated at P < 5 × 10- 8 with disease-related traits through IEB alone, if disease prevalence in the sample differs appreciably from the background population prevalence. For example, some hypertension and type 2 diabetes alleles will be associated with BMI in sample sizes of >500 000 if the prevalence of those diseases differs by >10% from the background population. In conclusion, IEB may result in false positive or negative genetic associations in very large studies stratified or strongly enriched for/against disease cases.

Genetic studies of abdominal MRI data identify genes regulating hepcidin as major determinants of liver iron concentration.

BACKGROUND & AIMS: Excess liver iron content is common and is linked to the risk of hepatic and extrahepatic diseases. We aimed to identify genetic variants influencing liver iron content and use genetics to understand its link to other traits and diseases. METHODS: First, we performed a genome-wide association study (GWAS) in 8,289 individuals from UK Biobank, whose liver iron level had been quantified by magnetic resonance imaging, before validating our findings in an independent cohort (n = 1,513 from IMI DIRECT). Second, we used Mendelian randomisation to test the causal effects of 25 predominantly metabolic traits on liver iron content. Third, we tested phenome-wide associations between liver iron variants and 770 traits and disease outcomes. RESULTS: We identified 3 independent genetic variants (rs1800562 [C282Y] and rs1799945 [H63D] in HFE and rs855791 [V736A] in TMPRSS6) associated with liver iron content that reached the GWAS significance threshold (p <5 × 10-8). The 2 HFE variants account for ∼85% of all cases of hereditary haemochromatosis. Mendelian randomisation analysis provided evidence that higher central obesity plays a causal role in increased liver iron content. Phenome-wide association analysis demonstrated shared aetiopathogenic mechanisms for elevated liver iron, high blood pressure, cirrhosis, malignancies, neuropsychiatric and rheumatological conditions, while also highlighting inverse associations with anaemias, lipidaemias and ischaemic heart disease. CONCLUSION: Our study provides genetic evidence that mechanisms underlying higher liver iron content are likely systemic rather than organ specific, that higher central obesity is causally associated with higher liver iron, and that liver iron shares common aetiology with multiple metabolic and non-metabolic diseases. LAY SUMMARY: Excess liver iron content is common and is associated with liver diseases and metabolic diseases including diabetes, high blood pressure, and heart disease. We identified 3 genetic variants that are linked to an increased risk of developing higher liver iron content. We show that the same genetic variants are linked to higher risk of many diseases, but they may also be associated with some health advantages. Finally, we use genetic variants associated with waist-to-hip ratio as a tool to show that central obesity is causally associated with increased liver iron content.

Mosaic Turner syndrome shows reduced penetrance in an adult population study.

PURPOSE: Many women with X chromosome aneuploidy undergo lifetime clinical monitoring for possible complications. However, ascertainment of cases in the clinic may mean that the penetrance has been overestimated. METHODS: We characterized the prevalence and phenotypic consequences of X chromosome aneuploidy in a population of 244,848 women over 40 years of age from UK Biobank, using single-nucleotide polymorphism (SNP) array data. RESULTS: We detected 30 women with 45,X; 186 with mosaic 45,X/46,XX; and 110 with 47,XXX. The prevalence of nonmosaic 45,X (12/100,000) and 47,XXX (45/100,000) was lower than expected, but was higher for mosaic 45,X/46,XX (76/100,000). The characteristics of women with 45,X were consistent with the characteristics of a clinically recognized Turner syndrome phenotype, including short stature and primary amenorrhea. In contrast, women with mosaic 45,X/46,XX were less short, had a normal reproductive lifespan and birth rate, and no reported cardiovascular complications. The phenotype of women with 47,XXX included taller stature (5.3 cm; SD = 5.52 cm; P = 5.8 × 10-20) and earlier menopause age (5.12 years; SD = 5.1 years; P = 1.2 × 10-14). CONCLUSION: Our results suggest that the clinical management of women with 45,X/46,XX mosaicism should be minimal, particularly those identified incidentally.

Genome-Wide Association Analyses in 128,266 Individuals Identifies New Morningness and Sleep Duration Loci.

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.

Testing the role of predicted gene knockouts in human anthropometric trait variation.

Although the role of complete gene inactivation by two loss-of-function mutations inherited in trans is well-established in recessive Mendelian diseases, we have not yet explored how such gene knockouts (KOs) could influence complex human phenotypes. Here, we developed a statistical framework to test the association between gene KOs and quantitative human traits. Our method is flexible, publicly available, and compatible with common genotype format files (e.g. PLINK and vcf). We characterized gene KOs in 4498 participants from the NHLBI Exome Sequence Project (ESP) sequenced at high coverage (>100×), 1976 French Canadians from the Montreal Heart Institute Biobank sequenced at low coverage (5.7×), and >100 000 participants from the Genetic Investigation of ANthropometric Traits (GIANT) Consortium genotyped on an exome array. We tested associations between gene KOs and three anthropometric traits: body mass index (BMI), height and BMI-adjusted waist-to-hip ratio (WHR). Despite our large sample size and multiple datasets available, we could not detect robust associations between specific gene KOs and quantitative anthropometric traits. Our results highlight several limitations and challenges for future gene KO studies in humans, in particular when there is no prior knowledge on the phenotypes that might be affected by the tested gene KOs. They also suggest that gene KOs identified with current DNA sequencing methodologies probably do not strongly influence normal variation in BMI, height, and WHR in the general human population.