Genomic analysis of male puberty timing highlights shared genetic basis with hair colour and lifespan.

The timing of puberty is highly variable and is associated with long-term health outcomes. To date, understanding of the genetic control of puberty timing is based largely on studies in women. Here, we report a multi-trait genome-wide association study for male puberty timing with an effective sample size of 205,354 men. We find moderately strong genomic correlation in puberty timing between sexes (rg = 0.68) and identify 76 independent signals for male puberty timing. Implicated mechanisms include an unexpected link between puberty timing and natural hair colour, possibly reflecting common effects of pituitary hormones on puberty and pigmentation. Earlier male puberty timing is genetically correlated with several adverse health outcomes and Mendelian randomization analyses show a genetic association between male puberty timing and shorter lifespan. These findings highlight the relationships between puberty timing and health outcomes, and demonstrate the value of genetic studies of puberty timing in both sexes

Genomic analysis of male puberty timing highlights shared genetic basis with hair colour and lifespan

Abstract: The timing of puberty is highly variable and is associated with long-term health outcomes. To date, understanding of the genetic control of puberty timing is based largely on studies in women. Here, we report a multi-trait genome-wide association study for male puberty timing with an effective sample size of 205,354 men. We find moderately strong genomic correlation in puberty timing between sexes (rg = 0.68) and identify 76 independent signals for male puberty timing. Implicated mechanisms include an unexpected link between puberty timing and natural hair colour, possibly reflecting common effects of pituitary hormones on puberty and pigmentation. Earlier male puberty timing is genetically correlated with several adverse health outcomes and Mendelian randomization analyses show a genetic association between male puberty timing and shorter lifespan. These findings highlight the relationships between puberty timing and health outcomes, and demonstrate the value of genetic studies of puberty timing in both sexes

Limited Effect of Y Chromosome Variation on Coronary Artery Disease and Mortality in UK Biobank—Brief Report

Background: The effect of genetic variation in the male-specific region of the Y chromosome (MSY) on coronary artery disease and cardiovascular risk factors has been disputed. In this study, we systematically assessed the association of MSY genetic variation on these traits using a kin-cohort analysis of family disease history in the largest sample to date. Methods: We tested 90 MSY haplogroups against coronary artery disease, hypertension, blood pressure, classical lipid levels, and all-cause mortality in up to 152 186 unrelated, genomically British individuals from UK Biobank. Unlike previous studies, we did not adjust for heritable lifestyle factors (to avoid collider bias) and instead adjusted for geographic variables and socioeconomic deprivation, given the link between MSY haplogroups and geography. For family history traits, subject MSY haplogroups were tested against father and mother disease as validation and negative control, respectively. Results: Our models find little evidence for an effect of any MSY haplogroup on cardiovascular risk in participants. Parental models confirm these findings. Conclusions: Kin-cohort analysis of the Y chromosome uniquely allows for discoveries in subjects to be validated using family history data. Despite our large sample size, improved models, and parental validation, there is little evidence to suggest cardiovascular risk in UK Biobank is influenced by genetic variation in MSY. </jats:sec

Comparative analysis of mammal genomes unveils key genomic variability for human life span

The enormous mammal's lifespan variation is the result of each species' adaptations to their own biological trade-offs and ecological conditions. Comparative genomics have demonstrated that genomic factors underlying both, species lifespans and longevity of individuals, are in part shared across the tree of life. Here, we compared protein-coding regions across the mammalian phylogeny to detect individual amino acid (AA) changes shared by the most long-lived mammals and genes whose rates of protein evolution correlate with longevity. We discovered a total of 2,737 AA in 2,004 genes that distinguish long- and short-lived mammals, significantly more than expected by chance (P = 0.003). These genes belong to pathways involved in regulating lifespan, such as inflammatory response and hemostasis. Among them, a total 1,157 AA showed a significant association with maximum lifespan in a phylogenetic test. Interestingly, most of the detected AA positions do not vary in extant human populations (81.2%) or have allele frequencies below 1% (99.78%). Consequently, almost none of these putatively important variants could have been detected by genome-wide association studies. Additionally, we identified four more genes whose rate of protein evolution correlated with longevity in mammals. Crucially, SNPs located in the detected genes explain a larger fraction of human lifespan heritability than expected, successfully demonstrating for the first time that comparative genomics can be used to enhance interpretation of human genome-wide association studies. Finally, we show that the human longevity-associated proteins are significantly more stable than the orthologous proteins from short-lived mammals, strongly suggesting that general protein stability is linked to increased lifespan.This work was supported by AEI-PGC2018-101927-BI00 (FEDER/UE), the Spanish National Institute of Bioinformatics of the Instituto de Salud Carlos III (PT17/0009/0020), FEDER (Fondo Europeo de Desarrollo Regional)/FSE (Fondo Social Europeo), “Unidad de Excelencia María de Maeztu,” funded by the AEI (CEX2018-000792-M) and Secretaria d’Universitats i Recerca and CERCA Programme del Departament d’Economia i Coneixement de la Generalitat de Catalunya (GRC 2017 SGR 880)

Exome sequencing reveals aggregates of rare variants in glycosyltransferase and other genes influencing immunoglobulin G and transferrin glycosylation

AbstractIt is often difficult to be certain which genes underlie the effects seen in association studies. However, variants that disrupt the protein, such as predicted loss of function (pLoF) and missense variants, provide a shortcut to identify genes with a clear biological link to the phenotype of interest. Glycosylation is one of the most common post-translationalmodifications of proteins, and an important biomarker of both disease and its progression. Here, we utilised the power of genetic isolates, gene-based aggregation tests and intermediate phenotypes to assess the effect of rare (MAF&lt;5%) pLoF and missense variants from whole exome sequencing on the N-glycome of plasma transferrin (N=1907) and immunoglobulin G (N=4912), and their effect on diseases. We identified significant gene-based associations for transferrin glycosylation at 5 genes (p&lt;8.06×10−8) and for IgG glycan traits at 4 genes (p&lt;1.19×10−7). Associations in three of these genes (FUT8, MGAT3andRFXAP) are driven by multiple rare variants simultaneously contributing to protein glycosylation. Association atST6GAL1, with a 300-fold up-drifted variant in the Orkney Islands, was detectable by a single-point exome-wide association analysis. Glycome-associated aggregate associations are located in genes already known to have a biological link to protein glycosylation (FUT6, FUT8for transferrin;FUT8, MGAT3andST6GAL1for IgG) but also in genes which have not been previously reported (e.g.RFXAPfor IgG). To assess the potential impact of rare variants associated with glycosylation on other traits, we queried public repositories of gene-based tests, discovering a potential connection between transferrin glycosylation,MSR1, galectin-3, insulin-like growth factor 1 and diabetes. However, the exact mechanism behind these connections requires further elucidation.</jats:p

Clinical implications of bone marrow adiposity identified using deep learning, phenome-wide association, and Mendelian randomization in the UK Biobank

Bone marrow adipose tissue (BMAT) is a normal feature of mammalian anatomy that increases with ageing and in osteoporosis, type 2 diabetes, and other diverse clinical contexts. However, the full scope of diseases associated with altered bone marrow adiposity remains to be determined, and whether BMAT directly contributes to human disease is unknown. To address these critical gaps in knowledge, we previously used deep learning to measure the bone marrow fat fraction (BMFF) of the femoral head, total hip, femoral diaphysis, and spine of over 44,000 participants in the UK Biobank, followed by genome-wide association meta-analyses to identify the genetic architecture of altered BMFF. Here, we use these data for phenome-wide association studies (PheWAS) to systematically investigate the diseases associated with BMFF at each site. First, we conduct a PheWAS using measured BMFF and find that it is associated with 47 incident diseases across 12 disease categories. These include not only osteoporosis, fracture, and type 2 diabetes, but also diseases not previously linked to BMAT, such as cardiovascular diseases, several cancers, and other conditions with a substantial worldwide burden on human health. Sex-stratified PheWASes further demonstrate that BMFF-associated diseases differ between males and females. We then establish polygenic risk scores (PRSs) and use PRS-PheWAS and Mendelian Randomization to explore potential causal association between BMFF and disease outcomes. These reveal that increased spine BMFF is associated with fractures but not osteoporosis, whereas genetic predisposition to increased BMFF at each femoral site is positively associated with both diseases. Intriguingly, genetic predisposition to increased spine BMFF is positively associated with type 2 diabetes, whereas this association is negative for BMFF at the total hip and diaphysis. Together, our findings substantially advance understanding of the impact of BMAT on human health and establish bone marrow adiposity as a promising biomarker and/or potential therapeutic target for improved prevention and treatment of human diseases

Clinical implications of bone marrow adiposity identified using deep learning, phenome-wide association, and Mendelian randomization in the UK Biobank

Bone marrow adipose tissue (BMAT) is a normal feature of mammalian anatomy that increases with ageing and in osteoporosis, type 2 diabetes, and other diverse clinical contexts. However, the full scope of diseases associated with altered bone marrow adiposity remains to be determined, and whether BMAT directly contributes to human disease is unknown. To address these critical gaps in knowledge, we previously used deep learning to measure the bone marrow fat fraction (BMFF) of the femoral head, total hip, femoral diaphysis, and spine of over 44,000 participants in the UK Biobank, followed by genome-wide association meta-analyses to identify the genetic architecture of altered BMFF. Here, we use these data for phenome-wide association studies (PheWAS) to systematically investigate the diseases associated with BMFF at each site. First, we conduct a PheWAS using measured BMFF and find that it is associated with 47 incident diseases across 12 disease categories. These include not only osteoporosis, fracture, and type 2 diabetes, but also diseases not previously linked to BMAT, such as cardiovascular diseases, several cancers, and other conditions with a substantial worldwide burden on human health. Sex-stratified PheWASes further demonstrate that BMFF-associated diseases differ between males and females. We then establish polygenic risk scores (PRSs) and use PRS-PheWAS and Mendelian Randomization to explore potential causal association between BMFF and disease outcomes. These reveal that increased spine BMFF is associated with fractures but not osteoporosis, whereas genetic predisposition to increased BMFF at each femoral site is positively associated with both diseases. Intriguingly, genetic predisposition to increased spine BMFF is positively associated with type 2 diabetes, whereas this association is negative for BMFF at the total hip and diaphysis. Together, our findings substantially advance understanding of the impact of BMAT on human health and establish bone marrow adiposity as a promising biomarker and/or potential therapeutic target for improved prevention and treatment of human diseases

Genetic architecture of glycomic and lipidomic phenotypes in isolated populations

This dataset contains the extended supplementary tables from the PhD thesis entitled "Genetic architecture of glycomic and lipidomic phenotypes in isolated populations" by Arianna Landini. Understanding how genetics contributes to the variation of complex traits and diseases is one of the key objectives of current medical studies. To date, a large portion of this genetic variation still needs to be identified, especially considering the contribution of low-frequency and rare variants. Omics data, such as proteomics and metabolomics, are extensively employed in genetic association studies as ‘proxies’ for traits or diseases of interest. They are regarded as “intermediate” traits: measurable manifestations of more complex phenotypes (e.g., cholesterol levels for cardiovascular diseases), often more strongly associated with genetic variation and having a clearer functional link than the endpoint or disease of interest. Accordingly, the genetics of omics have the potential to offer insights into relevant biological mechanisms and pathways and point to new drug targets or diagnostic biomarkers. The main goal of the related research is to expand the current knowledge about the genetic architecture of protein glycomics and bile acid lipidomics, two under-studied omic traits, but which are involved in several common diseases. In summary, in my thesis I describe the genetic architecture of the protein glycome and the bile acid lipidome: the former has a higher genetic component, while the latter is largely influenced by environmental factors (e.g., sex, diet, gut flora). Despite the limited sample size, we were able to describe rare variant associations, demonstrating that isolated populations represent a useful strategy to increase statistical power. However, additional statistical power is needed to identify the possible effect of protein glycome and bile acid lipidome on complex disease. A clearer understanding of the genetic architecture of omics traits is crucial to develop informed disease screening tests, to improve disease diagnosis and prognosis, and finally to design innovative and more customised treatment strategies to enhance human health.The dataset contains the following data: Chapter_3_Extended_Supplementary_Tables.xlsx: Extended Supplementary Tables for Chapter 3; Chapter_4_Extended_Supplementary_Tables.xlsx: Extended Supplementary Tables for Chapter 4; Chapter_2_Extended_Supplementary_Tables.xlsx: Extended Supplementary Tables for Chapter 2 The README.txt file contains the related documentation

Genetic architecture of glycomic and lipidomic phenotypes in isolated populations

This dataset contains the extended supplementary tables from the PhD thesis entitled "Genetic architecture of glycomic and lipidomic phenotypes in isolated populations" by Arianna Landini. Understanding how genetics contributes to the variation of complex traits and diseases is one of the key objectives of current medical studies. To date, a large portion of this genetic variation still needs to be identified, especially considering the contribution of low-frequency and rare variants. Omics data, such as proteomics and metabolomics, are extensively employed in genetic association studies as ‘proxies’ for traits or diseases of interest. They are regarded as “intermediate” traits: measurable manifestations of more complex phenotypes (e.g., cholesterol levels for cardiovascular diseases), often more strongly associated with genetic variation and having a clearer functional link than the endpoint or disease of interest. Accordingly, the genetics of omics have the potential to offer insights into relevant biological mechanisms and pathways and point to new drug targets or diagnostic biomarkers. The main goal of the related research is to expand the current knowledge about the genetic architecture of protein glycomics and bile acid lipidomics, two under-studied omic traits, but which are involved in several common diseases. In summary, in my thesis I describe the genetic architecture of the protein glycome and the bile acid lipidome: the former has a higher genetic component, while the latter is largely influenced by environmental factors (e.g., sex, diet, gut flora). Despite the limited sample size, we were able to describe rare variant associations, demonstrating that isolated populations represent a useful strategy to increase statistical power. However, additional statistical power is needed to identify the possible effect of protein glycome and bile acid lipidome on complex disease. A clearer understanding of the genetic architecture of omics traits is crucial to develop informed disease screening tests, to improve disease diagnosis and prognosis, and finally to design innovative and more customised treatment strategies to enhance human health