To identify genetic loci influencing central obesity and fat distribution, we performed a meta-analysis of 16 genome-wide association studies (GWAS, N = 38,580) informative for adult waist circumference (WC) and waist-hip ratio (WHR). We selected 26 SNPs for follow-up, for which the evidence of association with measures of central adiposity (WC and/or WHR) was strong and disproportionate to that for overall adiposity or height. Follow-up studies in a maximum of 70,689 individuals identified two loci strongly associated with measures of central adiposity; these map near TFAP2B (WC, P = 1.9x10(-11)) and MSRA (WC, P = 8.9x10(-9)). A third locus, near LYPLAL1, was associated with WHR in women only (P = 2.6x10(-8)). The variants near TFAP2B appear to influence central adiposity through an effect on overall obesity/fat-mass, whereas LYPLAL1 displays a strong female-only association with fat distribution. By focusing on anthropometric measures of central obesity and fat distribution, we have identified three loci implicated in the regulation of human adiposity.

Women become infertile approximately 10 years before menopause, and as more women delay childbirth into their 30s, the number of women who experience infertility is likely to increase. Tests that predict the timing of menopause would allow women to make informed reproductive decisions. Current predictors are only effective just prior to menopause, and there are no long-range indicators. Age at menopause and early menopause (EM) are highly heritable, suggesting a genetic aetiology. Recent genome-wide scans have identified four loci associated with variation in the age of normal menopause (40-60 years). We aimed to determine whether theses loci are also risk factors for EM. We tested the four menopause-associated genetic variants in a cohort of approximately 2000 women with menopause≤45 years from the Breakthrough Generations Study (BGS). All four variants significantly increased the odds of having EM. Comparing the 4.5% of individuals with the lowest number of risk alleles (two or three) with the 3.0% with the highest number (eight risk alleles), the odds ratio was 4.1 (95% CI 2.4-7.1, P=4.0×10(-7)). In combination, the four variants discriminated EM cases with a receiver operator characteristic area under the curve of 0.6. Four common genetic variants identified by genome-wide association studies, had a significant impact on the odds of having EM in an independent cohort from the BGS. The discriminative power is still limited, but as more variants are discovered they may be useful for predicting reproductive lifespan.

Hundreds of genes reside in structurally complex, poorly understood regions of the human genome. One such region contains the three amylase genes (AMY2B, AMY2A and AMY1) responsible for digesting starch into sugar. Copy number of AMY1 is reported to be the largest genomic influence on obesity, although genome-wide association studies for obesity have found this locus unremarkable. Using whole-genome sequence analysis, droplet digital PCR and genome mapping, we identified eight common structural haplotypes of the amylase locus that suggest its mutational history. We found that the AMY1 copy number in an individual's genome is generally even (rather than odd) and partially correlates with nearby SNPs, which do not associate with body mass index (BMI). We measured amylase gene copy number in 1,000 obese or lean Estonians and in 2 other cohorts totaling ∼3,500 individuals. We had 99% power to detect the lower bound of the reported effects on BMI, yet found no association.

Previous studies have suggested that modern obesogenic environments accentuate the genetic risk of obesity. However, these studies have proven controversial as to which, if any, measures of the environment accentuate genetic susceptibility to high body mass index (BMI).

Is the length of FMR1 repeat alleles within the normal range associated with the risk of early menopause?

The role of genes in normal birth-weight variation is poorly understood, and it has been suggested that the genetic component of fetal growth is small. Type 2 diabetes genes may influence birth weight through maternal genotype, by increasing maternal glycemia in pregnancy, or through fetal genotype, by altering fetal insulin secretion. We aimed to assess the role of the recently described type 2 diabetes gene TCF7L2 in birth weight. We genotyped the polymorphism rs7903146 in 15,709 individuals whose birth weight was available from six studies and in 8,344 mothers from three studies. Each fetal copy of the predisposing allele was associated with an 18-g (95% confidence interval [CI] 7-29 g) increase in birth weight (P=.001) and each maternal copy with a 30-g (95% CI 15-45 g) increase in offspring birth weight (P=2.8x10-5). Stratification by fetal genotype suggested that the association was driven by maternal genotype (31-g [95% CI 9-48 g] increase per allele; corrected P=.003). Analysis of diabetes-related traits in 10,314 nondiabetic individuals suggested the most likely mechanism is that the risk allele reduces maternal insulin secretion (disposition index reduced by ~0.15 standard deviation; P=1x10-4), which results in increased maternal glycemia in pregnancy and hence increased offspring birth weight. We combined information with the other common variant known to alter fetal growth, the -30G-->A polymorphism of glucokinase (rs1799884). The 4% of offspring born to mothers carrying three or four risk alleles were 119 g (95% CI 62-172 g) heavier than were the 32% born to mothers with none (for overall trend, P=2x10-7), comparable to the impact of maternal smoking during pregnancy. In conclusion, we have identified the first type 2 diabetes-susceptibility allele to be reproducibly associated with birth weight. Common gene variants can substantially influence normal birth-weight variation.

We conducted pooled and meta-analyses of the association of the calpain-10 gene (CAPN10) polymorphisms SNP-43, Indel-19 and SNP-63 individually and as haplotypes with type 2 diabetes (T2D) in 3237 patients and 2935 controls of European ancestry. In the pooled analyses, the common SNP-43*G allele was associated with modest but statistically significant increased risk of T2D (odds ratio (OR)=1.11 (95% confidence interval (CI), 1.02-1.20), P=0.01). Two haplotype combinations were associated with increased risk of T2D (1-2-1/1-2-1, OR=1.20 (1.03-1.41), P=0.02; and 1-1-2/1-2-1, OR=1.26 (1.01-1.59), P=0.04) and one with decreased risk (1-1-1/2-2-1, OR=0.86 (0.75-0.99), P=0.03). The meta-analysis also showed a significant effect of the 1-2-1/1-2-1 haplogenotype on risk (OR=1.25 (1.05-1.50), P=0.01). However, there was evidence for heterogeneity with respect to this effect (P=0.06). The heterogeneity appeared to be due to data sets in which the cases were selected from samples used in linkage studies of T2D. Using only the population-based case-control samples removed the heterogeneity (P=0.89) and strengthened the evidence for association with T2D in both the pooled (SNP-43*G, OR=1.19 (1.07-1.32), P=0.001; 1-2-1/1-2-1 haplogenotype, OR=1.46 (1.19-1.78), P=0.0003; 1-1-2/1-2-1 haplogenotype, OR=1.52 (1.12-2.06), P=0.007; and 1-1-1/2-2-1 haplogenotype, OR=0.83 (0.70-0.99), P=0.03) and the meta-analysis (SNP-43*G, OR=1.18 (1.05-1.32), P=0.005; 1-2-1/1-2-1 haplogenotype, OR=1.68 (1.33-2.11), P=0.00001). The pooled and meta-analyses as well as the linkage disequilibrium and haplotype diversity studies suggest a role for genetic variation in CAPN10 affecting risk of T2D in Europeans.

Being a morning person is a behavioural indicator of a person's underlying circadian rhythm. Using genome-wide data from 697,828 UK Biobank and 23andMe participants we increase the number of genetic loci associated with being a morning person from 24 to 351. Using data from 85,760 individuals with activity-monitor derived measures of sleep timing we find that the chronotype loci associate with sleep timing: the mean sleep timing of the 5% of individuals carrying the most morningness alleles is 25 min earlier than the 5% carrying the fewest. The loci are enriched for genes involved in circadian regulation, cAMP, glutamate and insulin signalling pathways, and those expressed in the retina, hindbrain, hypothalamus, and pituitary. Using Mendelian Randomisation, we show that being a morning person is causally associated with better mental health but does not affect BMI or risk of Type 2 diabetes. This study offers insights into circadian biology and its links to disease in humans.

Sleep is an essential human function but its regulation is poorly understood. Using accelerometer data from 85,670 UK Biobank participants, we perform a genome-wide association study of 8 derived sleep traits representing sleep quality, quantity and timing, and validate our findings in 5,819 individuals. We identify 47 genetic associations at P < 5 × 10-8, of which 20 reach a stricter threshold of P < 8 × 10-10. These include 26 novel associations with measures of sleep quality and 10 with nocturnal sleep duration. The majority of identified variants associate with a single sleep trait, except for variants previously associated with restless legs syndrome. For sleep duration we identify a missense variant (p.Tyr727Cys) in PDE11A as the likely causal variant. As a group, sleep quality loci are enriched for serotonin processing genes. Although accelerometer-derived measures of sleep are imperfect and may be affected by restless legs syndrome, these findings provide new biological insights into sleep compared to previous efforts based on self-report sleep measures.

Recent analyses have shown educational attainment to be associated with a number of health outcomes. This association may, in part, be due to an effect of educational attainment on smoking behaviour. In this study, we apply a multivariable Mendelian randomisation design to determine whether the effect of educational attainment on smoking behaviour is due to educational attainment or general cognitive ability. We use individual data from the UK Biobank study (N = 120,050) and summary data from large GWA studies of educational attainment, cognitive ability and smoking behaviour. Our results show that more years of education are associated with a reduced likelihood of smoking that is not due to an effect of general cognitive ability on smoking behaviour. Given the considerable physical harms associated with smoking, the effect of educational attainment on smoking is likely to contribute to the health inequalities associated with differences in educational attainment.

Raised albumin-creatinine ratio (ACR) is an indicator of microvascular damage and renal disease. We aimed to identify genetic variants associated with raised ACR and study the implications of carrying multiple ACR-raising alleles with metabolic and vascular-related disease. We performed a genome-wide association study of ACR using 437 027 individuals from the UK Biobank in the discovery phase, 54 527 more than previous studies, and followed up our findings in independent studies. We identified 62 independent associations with ACR across 56 loci (P < 5 × 10-8), of which 20 were not previously reported. Pathway analyses and the identification of 20 of the 62 variants (at r2 > 0.8) coinciding with signals for at least 16 related metabolic and vascular traits, suggested multiple pathways leading to raised ACR levels. After excluding variants at the CUBN locus, known to alter ACR via effects on renal absorption, an ACR genetic risk score was associated with a higher risk of hypertension, and less strongly, type 2 diabetes and stroke. For some rare genotype combinations at the CUBN locus, most individuals had ACR levels above the microalbuminuria clinical threshold. Contrary to our hypothesis, individuals carrying more CUBN ACR-raising alleles, and above the clinical threshold, had a higher frequency of vascular disease. The CUBN allele effects on ACR were twice as strong in people with diabetes-a result robust to an optimization-algorithm approach to simulating interactions, validating previously reported gene-diabetes interactions (P ≤ 4 × 10-5). In conclusion, a variety of genetic mechanisms and traits contribute to variation in ACR.

Over a decade of genome-wide association studies (GWAS) have led to the finding of extreme polygenicity of complex traits. The phenomenon that "all genes affect every complex trait" complicates Mendelian Randomization (MR) studies, where natural genetic variations are used as instruments to infer the causal effect of heritable risk factors. We reexamine the assumptions of existing MR methods and show how they need to be clarified to allow for pervasive horizontal pleiotropy and heterogeneous effect sizes. We propose a comprehensive framework GRAPPLE to analyze the causal effect of target risk factors with heterogeneous genetic instruments and identify possible pleiotropic patterns from data. By using GWAS summary statistics, GRAPPLE can efficiently use both strong and weak genetic instruments, detect the existence of multiple pleiotropic pathways, determine the causal direction and perform multivariable MR to adjust for confounding risk factors. With GRAPPLE, we analyze the effect of blood lipids, body mass index, and systolic blood pressure on 25 disease outcomes, gaining new information on their causal relationships and potential pleiotropic pathways involved.

With genome-wide association data for many exposures and outcomes now available from large biobanks, one-sample Mendelian randomization (MR) is increasingly used to investigate causal relationships. Many robust MR methods are available to address pleiotropy, but these assume independence between the gene-exposure and gene-outcome association estimates. Unlike in two-sample MR, in one-sample MR the two estimates are obtained from the same individuals, and the assumption of independence does not hold in the presence of confounding.

Current clinical guidelines for childhood-onset monogenic diabetes outside infancy are mainly focused on identifying and testing for dominantly inherited, predominantly MODY genes. There are no systematic studies of the recessively inherited causes of monogenic diabetes that are likely to be more common in populations with high rates of consanguinity. We aimed to determine the contribution of recessive causes of monogenic diabetes in paediatric diabetes clinics and to identify clinical criteria by which to select individuals for recessive monogenic diabetes testing.

Findings from genome-wide association studies have facilitated the generation of genetic predictors for many common human phenotypes. Stratifying individuals misaligned to a genetic predictor based on common variants may be important for follow-up studies that aim to identify alternative causal factors. Using genome-wide imputed genetic data, we aimed to classify 158,951 unrelated individuals from the UK Biobank as either concordant or deviating from two well-measured phenotypes. We first applied our methods to standing height: our primary analysis classified 244 individuals (0.15%) as misaligned to their genetically predicted height. We show that these individuals are enriched for self-reporting being shorter or taller than average at age 10, diagnosed congenital malformations, and rare loss-of-function variants in genes previously catalogued as causal for growth disorders. Secondly, we apply our methods to LDL cholesterol. We classified 156 (0.12%) individuals as misaligned to their genetically predicted LDL cholesterol and show that these individuals were enriched for both clinically actionable cardiovascular risk factors and rare genetic variants in genes previously shown to be involved in metabolic processes. Individuals whose LDL-C was higher than expected based on the genetic predictor were also at higher risk of developing coronary artery disease and type-two diabetes, even after adjustment for measured LDL-C, BMI and age, suggesting upward deviation from genetically predicted LDL-C is indicative of generally poor health. Our results remained broadly consistent when performing sensitivity analysis based on a variety of parametric and non-parametric methods to define individuals deviating from polygenic expectation. Our analyses demonstrate the potential importance of quantitatively identifying individuals for further follow-up based on deviation from genetic predictions.

Identifying copy number variants (CNVs) can provide diagnoses to patients and provide important biological insights into human health and disease. Current exome and targeted sequencing approaches cannot detect clinically and biologically-relevant CNVs outside their target area. We present SavvyCNV, a tool which uses off-target read data from exome and targeted sequencing data to call germline CNVs genome-wide. Up to 70% of sequencing reads from exome and targeted sequencing fall outside the targeted regions. We have developed a new tool, SavvyCNV, to exploit this 'free data' to call CNVs across the genome. We benchmarked SavvyCNV against five state-of-the-art CNV callers using truth sets generated from genome sequencing data and Multiplex Ligation-dependent Probe Amplification assays. SavvyCNV called CNVs with high precision and recall, outperforming the five other tools at calling CNVs genome-wide, using off-target or on-target reads from targeted panel and exome sequencing. We then applied SavvyCNV to clinical samples sequenced using a targeted panel and were able to call previously undetected clinically-relevant CNVs, highlighting the utility of this tool within the diagnostic setting. SavvyCNV outperforms existing tools for calling CNVs from off-target reads. It can call CNVs genome-wide from targeted panel and exome data, increasing the utility and diagnostic yield of these tests. SavvyCNV is freely available at https://github.com/rdemolgen/SavvySuite.

Pharmacogenetic variants impact dihydropyridine calcium-channel blockers (dCCBs; e.g., amlodipine) treatment efficacy, yet evidence on clinical outcomes in routine primary care is limited. Reported associations in pharmacogenomics knowledge base PharmGKB have weak supporting evidence. We aimed to estimate associations between reported pharmacogenetic variants and incident adverse events in a community-based cohort prescribed dCCB.

Telomere attrition is one of biological aging hallmarks and may be intervened to target multiple aging-related diseases, including Alzheimer's disease and Alzheimer's disease related dementias (AD/ADRD). The objective of this study was to assess associations of leukocyte telomere length (TL) with AD/ADRD and early markers of AD/ADRD, including cognitive performance and brain magnetic resonance imaging (MRI) phenotypes. Data from European-ancestry participants in the UK Biobank (n = 435,046) were used to evaluate whether mid-life leukocyte TL is associated with incident AD/ADRD over a mean follow-up of 12.2 years. In a subsample without AD/ADRD and with brain imaging data (n = 43,390), we associated TL with brain MRI phenotypes related to AD or vascular dementia pathology. Longer TL was associated with a lower risk of incident AD/ADRD (adjusted Hazard Ratio [aHR] per SD = 0.93, 95% CI 0.90-0.96, p = 3.37 × 10-7 ). Longer TL also was associated with better cognitive performance in specific cognitive domains, larger hippocampus volume, lower total volume of white matter hyperintensities, and higher fractional anisotropy and lower mean diffusivity in the fornix. In conclusion, longer TL is inversely associated with AD/ADRD, cognitive impairment, and brain structural lesions toward the development of AD/ADRD. However, the relationships between genetically determined TL and the outcomes above were not statistically significant based on the results from Mendelian randomization analysis results. Our findings add to the literature of prioritizing risk for AD/ADRD. The causality needs to be ascertained in mechanistic studies.

Iterative advances in understanding of the genetics of type 1 diabetes have identified >70 genetic regions associated with risk of the disease, including strong associations across the HLA class II region that account for >50% of heritability. The increased availability of genetic data combined with the decreased costs of generating these data, have facilitated the development of polygenic scores that aggregate risk variants from associated loci into a single number: either a genetic risk score (GRS) or a polygenic risk score (PRS). PRSs incorporate the risk of many possibly correlated variants from across the genome, even if they do not reach genome-wide significance, whereas GRSs estimate the cumulative contribution of a smaller subset of genetic variants that reach genome-wide significance. Type 1 diabetes GRSs have utility in diabetes classification, aiding discrimination between type 1 diabetes, type 2 diabetes and MODY. Type 1 diabetes GRSs are also being used in newborn screening studies to identify infants at risk of future presentation of the disease. Most early studies of type 1 diabetes genetics have been conducted in European ancestry populations, but, to develop accurate GRSs across diverse ancestries, large case-control cohorts from non-European populations are still needed. The current barriers to GRS implementation within healthcare are mainly related to a lack of guidance and knowledge on integration with other biomarkers and clinical variables. Once these limitations are addressed, there is huge potential for 'test and treat' approaches to be used to tailor care for individuals with type 1 diabetes.

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.