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Ageing phenotypes, such as years lived in good health (healthspan), total years lived (lifespan), and survival until an exceptional old age (longevity), are of interest to us all but require exceptionally large sample sizes to study genetically. Here we combine existing genome-wide association summary statistics for healthspan, parental lifespan, and longevity in a multivariate framework, increasing statistical power, and identify 10 genomic loci which influence all three phenotypes, of which five (near FOXO3 , SLC4A7 , LINC02513 , ZW10 , and FGD6 ) have not been reported previously at genome-wide significance. The majority of these 10 loci are associated with cardiovascular disease and some affect the expression of genes known to change their activity with age. In total, we implicate 78 genes, and find these to be enriched for ageing pathways previously highlighted in model organisms, such as the response to DNA damage, apoptosis, and homeostasis. Finally, we identify a pathway worthy of further study: haem metabolism. Ageing phenotypes are of great interest but are difficult to study genetically, partly due to the sample sizes required. Here, the authors present a multivariate framework to combine GWAS summary statistics and increase statistical power, identifying additional loci enriched for aging.

Male pattern baldness (MPB) or androgenetic alopecia is one of the most common conditions affecting men, reaching a prevalence of ~50% by the age of 50; however, the known genes explain little of the heritability. Here, we present the results of a genome-wide association study including more than 70,000 men, identifying 71 independently replicated loci, of which 30 are novel. These loci explain 38% of the risk, suggesting that MPB is less genetically complex than other complex traits. We show that many of these loci contain genes that are relevant to the pathology and highlight pathways and functions underlying baldness. Finally, despite only showing genome-wide genetic correlation with height, pathway-specific genetic correlations are significant for traits including lifespan and cancer. Our study not only greatly increases the number of MPB loci, illuminating the genetic architecture, but also provides a new approach to disentangling the shared biological pathways underlying complex diseases. Male pattern baldness (MBP) is a complex trait that has genetic associations. Here, Pirastu and colleagues perform a genome-wide association study to show 71 susceptibility loci associated with MBP — 30 of which are novel — and that these loci can explain 38% of heritability.

Biological age captures physiological deterioration better than chronological age and is amenable to interventions. Blood-based biomarkers have been identified as suitable candidates for biological age estimation. This study aims to improve biological age estimation using machine learning models and a feature-set of 60 circulating biomarkers available from the UK Biobank ( n  = 306,116). We implement an Elastic-Net derived Cox model with 25 selected biomarkers to predict mortality risk (C-Index = 0.778; 95% CI [0.767–0.788]), which outperforms the well-known blood-biomarker based PhenoAge model (C-Index = 0.750; 95% CI [0.739–0.761]), providing a C-Index lift of 0.028 representing an 11% relative increase in predictive value. Importantly, we then show that using common clinical assay panels, with few biomarkers, alongside imputation and the model derived on the full set of biomarkers, does not substantially degrade predictive accuracy from the theoretical maximum achievable for the available biomarkers. Biological age is estimated as the equivalent age within the same-sex population which corresponds to an individual’s mortality risk. Values ranged between 20-years younger and 20-years older than individuals’ chronological age, exposing the magnitude of ageing signals contained in blood markers. Thus, we demonstrate a practical and cost-efficient method of estimating an improved measure of Biological Age, available to the general population. An improved biological age estimation machine learning model uses readily available blood biomarkers, offering a practical and cost-efficient method of estimating biological age with pre-existing clinical assays, available to the general population.

Lifespan is a trait of enormous personal interest. Research into the biological basis of human lifespan, however, is hampered by the long time to death. Using a novel approach of regressing (272,081) parental lifespans beyond age 40 years on participant genotype in a new large data set (UK Biobank), we here show that common variants near the apolipoprotein E and nicotinic acetylcholine receptor subunit alpha 5 genes are associated with lifespan. The effects are strongly sex and age dependent, with APOE ɛ4 differentially influencing maternal lifespan ( P =4.2 × 10 −15 , effect −1.24 years of maternal life per imputed risk allele in parent; sex difference, P =0.011), and a locus near CHRNA3/5 differentially affecting paternal lifespan ( P =4.8 × 10 −11 , effect −0.86 years per allele; sex difference P =0.075). Rare homozygous carriers of the risk alleles at both loci are predicted to have 3.3–3.7 years shorter lives. Understanding the genetic influences on human aging requires a large number of subjects for a study of sufficient power. Here, Jim Wilson and colleagues use information on parental ages at death to show that common variants near the genes for apolipoprotein E and nicotinic acetylcholine receptor subunit alpha 5 are associated with longer lifespan.

To efficiently transform genetic associations into drug targets requires evidence that a particular gene, and its encoded protein, contribute causally to a disease. To achieve this, we employ a three-step proteome-by-phenome Mendelian Randomization (MR) approach. In step one, 154 protein quantitative trait loci (pQTLs) were identified and independently replicated. From these pQTLs, 64 replicated locally-acting variants were used as instrumental variables for proteome-by-phenome MR across 846 traits (step two). When its assumptions are met, proteome-by-phenome MR, is equivalent to simultaneously running many randomized controlled trials. Step 2 yielded 38 proteins that significantly predicted variation in traits and diseases in 509 instances. Step 3 revealed that amongst the 271 instances from GeneAtlas (UK Biobank), 77 showed little evidence of pleiotropy (HEIDI), and 92 evidence of colocalization (eCAVIAR). Results were wide ranging: including, for example, new evidence for a causal role of tyrosine-protein phosphatase non-receptor type substrate 1 (SHPS1; SIRPA) in schizophrenia, and a new finding that intestinal fatty acid binding protein (FABP2) abundance contributes to the pathogenesis of cardiovascular disease. We also demonstrated confirmatory evidence for the causal role of four further proteins (FGF5, IL6R, LPL, LTA) in cardiovascular disease risk.

The enormous variation in human lifespan is in part due to a myriad of sequence variants, only a few of which have been revealed to date. Since many life-shortening events are related to diseases, we developed a Mendelian randomization-based method combining 58 disease-related GWA studies to derive longevity priors for all HapMap SNPs. A Bayesian association scan, informed by these priors, for parental age of death in the UK Biobank study ( n =116,279) revealed 16 independent SNPs with significant Bayes factor at a 5% false discovery rate (FDR). Eleven of them replicate (5% FDR) in five independent longevity studies combined; all but three are depleted of the life-shortening alleles in older Biobank participants. Further analysis revealed that brain expression levels of nearby genes ( RBM6, SULT1A1 and CHRNA5 ) might be causally implicated in longevity. Gene expression and caloric restriction experiments in model organisms confirm the conserved role for RBM6 and SULT1A1 in modulating lifespan. Along with various environmental factors, a complex genetic architecture influences human lifespan. Here, McDaid and colleagues reveal novel loci associated with human lifespan and linked biomarkers by Bayesian association scan.

Length and quality of life are important to us all, yet identification of promising drug targets for human aging using genetics has had limited success. In the present study, we combine six European-ancestry genome-wide association studies of human aging traits—healthspan, father and mother lifespan, exceptional longevity, frailty index and self-rated health—in a principal component framework that maximizes their shared genetic architecture. The first principal component (aging-GIP1) captures both length of life and indices of mental and physical wellbeing. We identify 27 genomic regions associated with aging-GIP1, and provide additional, independent evidence for an effect on human aging for loci near HTT and MAML3 using a study of Finnish and Japanese survival. Using proteome-wide, two-sample, Mendelian randomization and colocalization, we provide robust evidence for a detrimental effect of blood levels of apolipoprotein(a) and vascular cell adhesion molecule 1 on aging-GIP1. Together, our results demonstrate that combining multiple aging traits using genetic principal components enhances the power to detect biological targets for human aging. Many aging-related phenotypes share a common genetic component, but to disentangle disease-specific variants from aging-specific ones has been challenging. Here Timmers et al. combined several genetics studies of aging-related traits to identify common underlying genetic factors that contribute to aging.

Britain and Ireland are known to show population genetic structure; however, large swathes of Scotland, in particular, have yet to be described. Delineating the structure and ancestry of these populations will allow variant discovery efforts to focus efficiently on areas not represented in existing cohorts. Thus, we assembled genotype data for 2,554 individuals from across the entire archipelago with geographically restricted ancestry, and performed population structure analyses and comparisons to ancient DNA. Extensive geographic structuring is revealed, from broad scales such as a NE to SW divide in mainland Scotland, through to the finest scale observed to date: across 3 km in the Northern Isles. Many genetic boundaries are consistent with Dark Age kingdoms of Gaels, Picts, Britons, and Norse. Populations in the Hebrides, the Highlands, Argyll, Donegal, and the Isle of Man show characteristics of isolation. We document a pole of Norwegian ancestry in the north of the archipelago (reaching 23 to 28% in Shetland) which complements previously described poles of Germanic ancestry in the east, and “Celtic” to the west. This modern genetic structure suggests a northwestern British or Irish source population for the ancient Gaels that contributed to the founding of Iceland. As rarer variants, often with larger effect sizes, become the focus of complex trait genetics, more diverse rural cohorts may be required to optimize discoveries in British and Irish populations and their considerable global diaspora.

Long runs of homozygosity (ROH) arise when identical haplotypes are inherited from each parent and thus a long tract of genotypes is homozygous. Cousin marriage or inbreeding gives rise to such autozygosity; however, genome-wide data reveal that ROH are universally common in human genomes even among outbred individuals. The number and length of ROH reflect individual demographic history, while the homozygosity burden can be used to investigate the genetic architecture of complex disease. We discuss how to identify ROH in genome-wide microarray and sequence data, their distribution in human populations and their application to the understanding of inbreeding depression and disease risk.

Genome‐wide association studies (GWAS) have identified several risk loci for nonalcoholic fatty liver disease (NAFLD). Previous studies have largely relied on small sample sizes and have assessed quantitative traits. We performed a case‐control GWAS in the UK Biobank using recorded diagnosis of NAFLD based on diagnostic codes recommended in recent consensus guidelines. We performed a GWAS of 4,761 cases of NAFLD and 373,227 healthy controls without evidence of NAFLD. Sensitivity analyses were performed excluding other co‐existing hepatic pathology, adjusting for body mass index (BMI) and adjusting for alcohol intake. A total of 9,723,654 variants were assessed by logistic regression adjusted for age, sex, genetic principal components, and genotyping batch. We performed a GWAS meta‐analysis using available summary association statistics. Six risk loci were identified (P < 5*10−8) (apolipoprotein E [APOE], patatin‐like phospholipase domain containing 3 [PNPLA3, transmembrane 6 superfamily member 2 [TM6SF2], glucokinase regulator [GCKR], mitochondrial amidoxime reducing component 1 [MARC1], and tribbles pseudokinase 1 [TRIB1]). All loci retained significance in sensitivity analyses without co‐existent hepatic pathology and after adjustment for BMI. PNPLA3 and TM6SF2 remained significant after adjustment for alcohol (alcohol intake was known in only 158,388 individuals), with others demonstrating consistent direction and magnitude of effect. All six loci were significant on meta‐analysis. Rs429358 (P = 2.17*10−11) is a missense variant within the APOE gene determining ϵ4 versus ϵ2/ϵ3 alleles. The ϵ4 allele of APOE offered protection against NAFLD (odds ratio for heterozygotes 0.84 [95% confidence interval 0.78‐0.90] and homozygotes 0.64 [0.50‐0.79]). Conclusion: This GWAS replicates six known NAFLD‐susceptibility loci and confirms that the ϵ4 allele of APOE is associated with protection against NAFLD. The results are consistent with published GWAS using histological and radiological measures of NAFLD, confirming that NAFLD identified through diagnostic codes from consensus guidelines is a valid alternative to more invasive and costly approaches.