Explore the vast range of titles available.

Testosterone supplementation is commonly used for its effects on sexual function, bone health and body composition, yet its effects on disease outcomes are unknown. To better understand this, we identified genetic determinants of testosterone levels and related sex hormone traits in 425,097 UK Biobank study participants. Using 2,571 genome-wide significant associations, we demonstrate that the genetic determinants of testosterone levels are substantially different between sexes and that genetically higher testosterone is harmful for metabolic diseases in women but beneficial in men. For example, a genetically determined 1 s.d. higher testosterone increases the risks of type 2 diabetes (odds ratio (OR) = 1.37 (95% confidence interval (95% CI): 1.22–1.53)) and polycystic ovary syndrome (OR = 1.51 (95% CI: 1.33–1.72)) in women, but reduces type 2 diabetes risk in men (OR = 0.86 (95% CI: 0.76–0.98)). We also show adverse effects of higher testosterone on breast and endometrial cancers in women and prostate cancer in men. Our findings provide insights into the disease impacts of testosterone and highlight the importance of sex-specific genetic analyses. Genetic analysis of data from over 400,000 participants in the UK Biobank Study shows that circulating testosterone levels have sex-specific implications for cardiometabolic diseases and cancer outcomes.

Hilary Finucane, Brendan Bulik-Sullivan, Benjamin Neale, Alkes Price and colleagues introduce a new method, called stratified LD score regression, for partitioning heritability by functional category using genome-wide association study summary statistics. They observe a substantial enrichment of heritability in conserved regions and illustrate how this approach can provide insights into the biological basis of disease and direction for functional follow-up. Recent work has demonstrated that some functional categories of the genome contribute disproportionately to the heritability of complex diseases. Here we analyze a broad set of functional elements, including cell type–specific elements, to estimate their polygenic contributions to heritability in genome-wide association studies (GWAS) of 17 complex diseases and traits with an average sample size of 73,599. To enable this analysis, we introduce a new method, stratified LD score regression, for partitioning heritability from GWAS summary statistics while accounting for linked markers. This new method is computationally tractable at very large sample sizes and leverages genome-wide information. Our findings include a large enrichment of heritability in conserved regions across many traits, a very large immunological disease–specific enrichment of heritability in FANTOM5 enhancers and many cell type–specific enrichments, including significant enrichment of central nervous system cell types in the heritability of body mass index, age at menarche, educational attainment and smoking behavior.

Higher cardiorespiratory fitness is associated with lower risk of type 2 diabetes. However, the causality of this relationship and the biological mechanisms that underlie it are unclear. Here, we examine genetic determinants of cardiorespiratory fitness in 450k European-ancestry individuals in UK Biobank, by leveraging the genetic overlap between fitness measured by an exercise test and resting heart rate. We identified 160 fitness-associated loci which we validated in an independent cohort, the Fenland study. Gene-based analyses prioritised candidate genes, such as CACNA1C, SCN10A, MYH11 and MYH6 , that are enriched in biological processes related to cardiac muscle development and muscle contractility. In a Mendelian Randomisation framework, we demonstrate that higher genetically predicted fitness is causally associated with lower risk of type 2 diabetes independent of adiposity. Integration with proteomic data identified N-terminal pro B-type natriuretic peptide, hepatocyte growth factor-like protein and sex hormone-binding globulin as potential mediators of this relationship. Collectively, our findings provide insights into the biological mechanisms underpinning cardiorespiratory fitness and highlight the importance of improving fitness for diabetes prevention. Being fit has been linked to a lower risk of type 2 diabetes, but it is unclear whether this relationship is causal. Using large scale studies with genetic data and measurements of cardiorespiratory fitness, the authors show evidence that higher genetically predicted fitness is causally associated with lower risk of type 2 diabetes independent of adiposity.

Jian Yang and colleagues present a method, GREML-LDMS, to estimate heritability for complex human traits using whole-genome sequencing data or imputation with the 1000 Genomes Project reference panel. Using the heritability estimates from GREML-LDMS, they find that there is negligible missing heritability for human height and BMI. We propose a method (GREML-LDMS) to estimate heritability for human complex traits in unrelated individuals using whole-genome sequencing data. We demonstrate using simulations based on whole-genome sequencing data that ∼97% and ∼68% of variation at common and rare variants, respectively, can be captured by imputation. Using the GREML-LDMS method, we estimate from 44,126 unrelated individuals that all ∼17 million imputed variants explain 56% (standard error (s.e.) = 2.3%) of variance for height and 27% (s.e. = 2.5%) of variance for body mass index (BMI), and we find evidence that height- and BMI-associated variants have been under natural selection. Considering the imperfect tagging of imputation and potential overestimation of heritability from previous family-based studies, heritability is likely to be 60–70% for height and 30–40% for BMI. Therefore, the missing heritability is small for both traits. For further discovery of genes associated with complex traits, a study design with SNP arrays followed by imputation is more cost-effective than whole-genome sequencing at current prices.

An individual’s nutritional status has a powerful effect on sexual maturation. Puberty onset is delayed in response to chronic energy insufficiency and is advanced under energy abundance. The consequences of altered pubertal timing for human health are profound. Late puberty increases the chances of cardiometabolic, musculoskeletal and neurocognitive disorders, whereas early puberty is associated with increased risks of adult obesity, type 2 diabetes mellitus, cardiovascular diseases and various cancers, such as breast, endometrial and prostate cancer. Kennedy and Mitra’s trailblazing studies, published in 1963 and using experimental models, were the first to demonstrate that nutrition is a key factor in puberty onset. Building on this work, the field has advanced substantially in the past decade, which is largely due to the impressive development of molecular tools for experimentation and population genetics. In this Review, we discuss the latest advances in basic and translational sciences underlying the nutritional and metabolic control of pubertal development, with a focus on perspectives and future directions. Key papers on the role of nutrition and metabolic cues in puberty timing were published in 1963, and there have been many advances in this field in the subsequent 60 years. This Review discusses the latest advances in basic and translational sciences underlying the nutritional and metabolic control of pubertal development. Key points In 1963, Kennedy and Mitra published a seminal study in rats demonstrating that body weight is a major determinant of pubertal timing. An increasing incidence of earlier ages at puberty has been documented; early pubertal timing favours the occurrence of type 2 diabetes mellitus, cardiovascular diseases and certain cancers in adulthood. Macronutrients and hormones that modulate growth and/or signal adipose tissue mass serve as metabolic cues conveying the nutritional status and stored energy available for sexual maturation, differentiation and growth. The effect of metabolic cues on puberty is mediated by neural targets upstream of GnRH neurons; considerable progress in defining the neuronal circuitry and glial components has been achieved. A number of molecular pathways and epigenetic mechanisms have been identified as primary components in the modulation of pubertal timing by hormones and nutritional cues.

A number of open questions in human evolutionary genetics would become tractable if we were able to directly measure evolutionary fitness. As a step towards this goal, we developed a method to examine whether individual genetic variants, or sets of genetic variants, currently influence viability. The approach consists in testing whether the frequency of an allele varies across ages, accounting for variation in ancestry. We applied it to the Genetic Epidemiology Research on Adult Health and Aging (GERA) cohort and to the parents of participants in the UK Biobank. Across the genome, we found only a few common variants with large effects on age-specific mortality: tagging the APOE ε4 allele and near CHRNA3. These results suggest that when large, even late-onset effects are kept at low frequency by purifying selection. Testing viability effects of sets of genetic variants that jointly influence 1 of 42 traits, we detected a number of strong signals. In participants of the UK Biobank of British ancestry, we found that variants that delay puberty timing are associated with a longer parental life span (P~6.2 × 10-6 for fathers and P~2.0 × 10-3 for mothers), consistent with epidemiological studies. Similarly, variants associated with later age at first birth are associated with a longer maternal life span (P~1.4 × 10-3). Signals are also observed for variants influencing cholesterol levels, risk of coronary artery disease (CAD), body mass index, as well as risk of asthma. These signals exhibit consistent effects in the GERA cohort and among participants of the UK Biobank of non-British ancestry. We also found marked differences between males and females, most notably at the CHRNA3 locus, and variants associated with risk of CAD and cholesterol levels. Beyond our findings, the analysis serves as a proof of principle for how upcoming biomedical data sets can be used to learn about selection effects in contemporary humans.

Loss-of-function of DDX3X is a leading cause of neurodevelopmental disorders (NDD) in females. DDX3X is also a somatically mutated cancer driver gene proposed to have tumour promoting and suppressing effects. We perform saturation genome editing of DDX3X , testing in vitro the functional impact of 12,776 nucleotide variants. We identify 3432 functionally abnormal variants, in three distinct classes. We train a machine learning classifier to identify functionally abnormal variants of NDD-relevance. This classifier has at least 97% sensitivity and 99% specificity to detect variants pathogenic for NDD, substantially out-performing in silico predictors, and resolving up to 93% of variants of uncertain significance. Moreover, functionally-abnormal variants can account for almost all of the excess nonsynonymous DDX3X somatic mutations seen in DDX3X -driven cancers. Systematic maps of variant effects generated in experimentally tractable cell types have the potential to transform clinical interpretation of both germline and somatic disease-associated variation. Pathogenic variants of DDX3X are associated with neurodevelopmental disorders (NDD) and cancer. Here, the authors perform saturation genome editing of DDX3X to test the functional impact of 12,776 variants, develop a machine learning classifier to identify variants relevant for NDD, and show that DDX3X predominantly acts as a tumour suppressor in cancer.

Background The role of metabolism in the variation of age at menarche (AAM) and age at natural menopause (ANM) in the female population is not entirely known. We aimed to investigate the causal role of circulating metabolites in AAM and ANM using Mendelian randomization (MR). Methods We combined MR with genetic colocalization to investigate potential causal associations between 658 metabolites and AAM and between 684 metabolites and ANM. We extracted genetic instruments for our exposures from four genome-wide association studies (GWAS) on circulating metabolites and queried the effects of these variants on the outcomes in two large GWAS from the ReproGen consortium. Additionally, we assessed the mediating role of the body mass index (BMI) in these associations, identified metabolic pathways implicated in AAM and ANM, and sought validation for selected metabolites in the Avon Longitudinal Study of Parents and Children (ALSPAC). Results Our analysis identified 10 candidate metabolites for AAM, but none of them colocalized with AAM. For ANM, 76 metabolites were prioritized (FDR-adjusted MR P -value ≤ 0.05), with 17 colocalizing, primarily in the glycerophosphocholines class, including the omega-3 fatty acid and phosphatidylcholine (PC) categories. Pathway analyses and validation in ALSPAC mothers also highlighted the role of omega and polyunsaturated fatty acids levels in delaying age at menopause. Conclusions Our study suggests that metabolites from the glycerophosphocholine and fatty acid families play a causal role in the timing of both menarche and menopause. This underscores the significance of specific metabolic pathways in the biology of female reproductive longevity.

Clonal hematopoiesis (CH)—age-related expansion of mutated hematopoietic clones—can differ in frequency and cellular fitness by CH type (e.g., mutations in driver genes (CHIP), gains/losses and copy-neutral loss of chromosomal segments (mCAs), and loss of sex chromosomes). Co-occurring CH raises questions as to their origin, selection, and impact. We integrate sequence and genotype array data in up to 482,378 UK Biobank participants to demonstrate shared genetic architecture across CH types. Our analysis suggests a cellular evolutionary trade-off between different types of CH, with LOY occurring at lower rates in individuals carrying mutations in established CHIP genes. We observed co-occurrence of CHIP and mCAs with overlap at TET2 , DNMT3A , and JAK2 , in which CHIP precedes mCA acquisition. Furthermore, individuals carrying overlapping CH had high risk of future lymphoid and myeloid malignancies. Finally, we leverage shared genetic architecture of CH traits to identify 15 novel loci associated with leukemia risk. Types of clonal hematopoiesis (CH) differ in frequency and fitness. These findings uncover shared genetic architecture, suggest evolutionary trade-offs between CH types, and detail elevated leukemia risk in individuals with overlapping types of CH.

Mosaic loss of chromosome Y (LOY) in leukocytes is the most common form of clonal mosaicism, caused by dysregulation in cell-cycle and DNA damage response pathways. Previous genetic studies have focussed on identifying common variants associated with LOY, which we now extend to rarer, protein-coding variation using exome sequences from 82,277 male UK Biobank participants. We find that loss of function of two genes— CHEK2 and GIGYF1 —reach exome-wide significance. Rare alleles in GIGYF1 have not previously been implicated in any complex trait, but here loss-of-function carriers exhibit six-fold higher susceptibility to LOY (OR = 5.99 [3.04–11.81], p = 1.3 × 10 −10 ). These same alleles are also associated with adverse metabolic health, including higher susceptibility to Type 2 Diabetes (OR = 6.10 [3.51–10.61], p  = 1.8 × 10 −12 ), 4 kg higher fat mass ( p  = 1.3 × 10 −4 ), 2.32 nmol/L lower serum IGF1 levels ( p  = 1.5 × 10 −4 ) and 4.5 kg lower handgrip strength ( p  = 4.7 × 10 −7 ) consistent with proposed GIGYF1 enhancement of insulin and IGF-1 receptor signalling. These associations are mirrored by a common variant nearby associated with the expression of GIGYF1 . Our observations highlight a potential direct connection between clonal mosaicism and metabolic health. Mosaic loss of chromosome Y (LOY) is a common form of clonal mosaicism in leukocytes. Here, the authors extend genetic association analyses to rare variation using exome-sequence data from 82,277 males, finding that loss-of-function alleles in GIGYF1 are associated with six-fold higher susceptibility to both LOY and Type 2 Diabetes.