Plasma proteomic associations with genetics and health in the UK Biobank.

The Pharma Proteomics Project is a precompetitive biopharmaceutical consortium characterizing the plasma proteomic profiles of 54,219 UK Biobank participants. Here we provide a detailed summary of this initiative, including technical and biological validations, insights into proteomic disease signatures, and prediction modelling for various demographic and health indicators. We present comprehensive protein quantitative trait locus (pQTL) mapping of 2,923 proteins that identifies 14,287 primary genetic associations, of which 81% are previously undescribed, alongside ancestry-specific pQTL mapping in non-European individuals. The study provides an updated characterization of the genetic architecture of the plasma proteome, contextualized with projected pQTL discovery rates as sample sizes and proteomic assay coverages increase over time. We offer extensive insights into trans pQTLs across multiple biological domains, highlight genetic influences on ligand-receptor interactions and pathway perturbations across a diverse collection of cytokines and complement networks, and illustrate long-range epistatic effects of ABO blood group and FUT2 secretor status on proteins with gastrointestinal tissue-enriched expression. We demonstrate the utility of these data for drug discovery by extending the genetic proxied effects of protein targets, such as PCSK9, on additional endpoints, and disentangle specific genes and proteins perturbed at loci associated with COVID-19 susceptibility. This public-private partnership provides the scientific community with an open-access proteomics resource of considerable breadth and depth to help to elucidate the biological mechanisms underlying proteo-genomic discoveries and accelerate the development of biomarkers, predictive models and therapeutics1.

Identifying drug targets for neurological and psychiatric disease via genetics and the brain transcriptome.

Discovering drugs that efficiently treat brain diseases has been challenging. Genetic variants that modulate the expression of potential drug targets can be utilized to assess the efficacy of therapeutic interventions. We therefore employed Mendelian Randomization (MR) on gene expression measured in brain tissue to identify drug targets involved in neurological and psychiatric diseases. We conducted a two-sample MR using cis-acting brain-derived expression quantitative trait loci (eQTLs) from the Accelerating Medicines Partnership for Alzheimer's Disease consortium (AMP-AD) and the CommonMind Consortium (CMC) meta-analysis study (n = 1,286) as genetic instruments to predict the effects of 7,137 genes on 12 neurological and psychiatric disorders. We conducted Bayesian colocalization analysis on the top MR findings (using P<6x10-7 as evidence threshold, Bonferroni-corrected for 80,557 MR tests) to confirm sharing of the same causal variants between gene expression and trait in each genomic region. We then intersected the colocalized genes with known monogenic disease genes recorded in Online Mendelian Inheritance in Man (OMIM) and with genes annotated as drug targets in the Open Targets platform to identify promising drug targets. 80 eQTLs showed MR evidence of a causal effect, from which we prioritised 47 genes based on colocalization with the trait. We causally linked the expression of 23 genes with schizophrenia and a single gene each with anorexia, bipolar disorder and major depressive disorder within the psychiatric diseases and 9 genes with Alzheimer's disease, 6 genes with Parkinson's disease, 4 genes with multiple sclerosis and two genes with amyotrophic lateral sclerosis within the neurological diseases we tested. From these we identified five genes (ACE, GPNMB, KCNQ5, RERE and SUOX) as attractive drug targets that may warrant follow-up in functional studies and clinical trials, demonstrating the value of this study design for discovering drug targets in neuropsychiatric diseases.

HAPRAP: a haplotype-based iterative method for statistical fine mapping using GWAS summary statistics.

MOTIVATION: Fine mapping is a widely used approach for identifying the causal variant(s) at disease-associated loci. Standard methods (e.g. multiple regression) require individual level genotypes. Recent fine mapping methods using summary-level data require the pairwise correlation coefficients ([Formula: see text]) of the variants. However, haplotypes rather than pairwise [Formula: see text], are the true biological representation of linkage disequilibrium (LD) among multiple loci. In this article, we present an empirical iterative method, HAPlotype Regional Association analysis Program (HAPRAP), that enables fine mapping using summary statistics and haplotype information from an individual-level reference panel. RESULTS: Simulations with individual-level genotypes show that the results of HAPRAP and multiple regression are highly consistent. In simulation with summary-level data, we demonstrate that HAPRAP is less sensitive to poor LD estimates. In a parametric simulation using Genetic Investigation of ANthropometric Traits height data, HAPRAP performs well with a small training sample size (N < 2000) while other methods become suboptimal. Moreover, HAPRAP's performance is not affected substantially by single nucleotide polymorphisms (SNPs) with low minor allele frequencies. We applied the method to existing quantitative trait and binary outcome meta-analyses (human height, QTc interval and gallbladder disease); all previous reported association signals were replicated and two additional variants were independently associated with human height. Due to the growing availability of summary level data, the value of HAPRAP is likely to increase markedly for future analyses (e.g. functional prediction and identification of instruments for Mendelian randomization). AVAILABILITY AND IMPLEMENTATION: The HAPRAP package and documentation are available at http://apps.biocompute.org.uk/haprap/ CONTACT: : jie.zheng@bristol.ac.uk or tom.gaunt@bristol.ac.ukSupplementary information: Supplementary data are available at Bioinformatics online.

Large-Scale Mendelian Randomization Study Reveals Circulating Blood-based Proteomic Biomarkers for Psychopathology and Cognitive Task Performance.

Background: Research on peripheral (e.g., blood-based) biomarkers for psychiatric illness has typically been low-throughput in terms of both the number of subjects and the range of assays performed. Moreover, traditional case-control studies examining blood-based biomarkers are subject to potential confounds of treatment and other exposures common to patients with psychiatric illnesses. Our research addresses these challenges by leveraging large-scale, high-throughput proteomics data and Mendelian Randomization (MR) to examine the causal impact of circulating proteins on psychiatric phenotypes and cognitive task performance. Methods: We utilized plasma proteomics data from the UK Biobank (3,072 proteins assayed in 34,557 European-ancestry individuals) and deCODE Genetics (4,719 proteins measured across 35,559 Icelandic individuals). Significant proteomic quantitative trait loci (both cis-pQTLs and trans-pQTLs) served as MR instruments, with the most recent GWAS for schizophrenia, bipolar disorder, major depressive disorder, and cognitive task performance (all excluding overlapping UK Biobank participants) as phenotypic outcomes. Results: MR revealed 109 Bonferroni-corrected causal associations (44 novel) involving 88 proteins across the four phenotypes. Several immune-related proteins, including interleukins and complement factors, stood out as pleiotropic across multiple outcome phenotypes. Drug target enrichment analysis identified several novel potential pharmacologic repurposing opportunities, including anti-inflammatory agents for schizophrenia and bipolar disorder and duloxetine for cognitive performance. Conclusions: Identification of causal effects for these circulating proteins suggests potential biomarkers for these conditions and offers insights for developing innovative therapeutic strategies. The findings also indicate substantial evidence for the pleiotropic effects of many proteins across different phenotypes, shedding light on the shared etiology among psychiatric conditions and cognitive ability.

Causal effects of maternal circulating amino acids on offspring birthweight: a Mendelian randomisation study.

BACKGROUND: Amino acids are key to protein synthesis, energy metabolism, cell signaling and gene expression; however, the contribution of specific maternal amino acids to fetal growth is unclear. METHODS: We explored the effect of maternal circulating amino acids on fetal growth, proxied by birthweight, using two-sample Mendelian randomisation (MR) and summary data from a genome-wide association study (GWAS) of serum amino acids levels (sample 1, n = 86,507) and a maternal GWAS of offspring birthweight in UK Biobank and Early Growth Genetics Consortium, adjusting for fetal genotype effects (sample 2, n = 406,063 with maternal and/or fetal genotype effect estimates). A total of 106 independent single nucleotide polymorphisms robustly associated with 19 amino acids (p < 4.9 × 10-10) were used as genetic instrumental variables (IV). Wald ratio and inverse variance weighted methods were used in MR main analysis. A series of sensitivity analyses were performed to explore IV assumption violations. FINDINGS: Our results provide evidence that maternal circulating glutamine (59 g offspring birthweight increase per standard deviation increase in maternal amino acid level, 95% CI: 7, 110) and serine (27 g, 95% CI: 9, 46) raise, while leucine (-59 g, 95% CI: -106, -11) and phenylalanine (-25 g, 95% CI: -47, -4) lower offspring birthweight. These findings are supported by sensitivity analyses. INTERPRETATION: Our findings strengthen evidence for key roles of maternal circulating amino acids during pregnancy in healthy fetal growth. FUNDING: A full list of funding bodies that contributed to this study can be found under Acknowledgments.

The genetic regulation of protein expression in cerebrospinal fluid.

Studies of the genetic regulation of cerebrospinal fluid (CSF) proteins may reveal pathways for treatment of neurological diseases. 398 proteins in CSF were measured in 1,591 participants from the BioFINDER study. Protein quantitative trait loci (pQTL) were identified as associations between genetic variants and proteins, with 176 pQTLs for 145 CSF proteins (P < 1.25 × 10-10 , 117 cis-pQTLs and 59 trans-pQTLs). Ventricular volume (measured with brain magnetic resonance imaging) was a confounder for several pQTLs. pQTLs for CSF and plasma proteins were overall correlated, but CSF-specific pQTLs were also observed. Mendelian randomization analyses suggested causal roles for several proteins, for example, ApoE, CD33, and GRN in Alzheimer's disease, MMP-10 in preclinical Alzheimer's disease, SIGLEC9 in amyotrophic lateral sclerosis, and CD38, GPNMB, and ADAM15 in Parkinson's disease. CSF levels of GRN, MMP-10, and GPNMB were altered in Alzheimer's disease, preclinical Alzheimer's disease, and Parkinson's disease, respectively. These findings point to pathways to be explored for novel therapies. The novel finding that ventricular volume confounded pQTLs has implications for design of future studies of the genetic regulation of the CSF proteome.

Brain expression quantitative trait locus and network analyses reveal downstream effects and putative drivers for brain-related diseases.

Identification of therapeutic targets from genome-wide association studies (GWAS) requires insights into downstream functional consequences. We harmonized 8,613 RNA-sequencing samples from 14 brain datasets to create the MetaBrain resource and performed cis- and trans-expression quantitative trait locus (eQTL) meta-analyses in multiple brain region- and ancestry-specific datasets (n ≤ 2,759). Many of the 16,169 cortex cis-eQTLs were tissue-dependent when compared with blood cis-eQTLs. We inferred brain cell types for 3,549 cis-eQTLs by interaction analysis. We prioritized 186 cis-eQTLs for 31 brain-related traits using Mendelian randomization and co-localization including 40 cis-eQTLs with an inferred cell type, such as a neuron-specific cis-eQTL (CYP24A1) for multiple sclerosis. We further describe 737 trans-eQTLs for 526 unique variants and 108 unique genes. We used brain-specific gene-co-regulation networks to link GWAS loci and prioritize additional genes for five central nervous system diseases. This study represents a valuable resource for post-GWAS research on central nervous system diseases.

Multi-ancestry Mendelian randomization of omics traits revealing drug targets of COVID-19 severity.

BACKGROUND: Recent omic studies prioritised several drug targets associated with coronavirus disease 2019 (COVID-19) severity. However, little evidence was provided to systematically estimate the effect of drug targets on COVID-19 severity in multiple ancestries. METHODS: In this study, we applied Mendelian randomization (MR) and colocalization approaches to understand the putative causal effects of 16,059 transcripts and 1608 proteins on COVID-19 severity in European and effects of 610 proteins on COVID-19 severity in African ancestry. We further integrated genetics, clinical and literature evidence to prioritise drug targets. Additional sensitivity analyses including multi-trait colocalization and phenome-wide MR were conducted to test for MR assumptions. FINDINGS: MR and colocalization prioritized four protein targets, FCRL3, ICAM5, ENTPD5 and OAS1 that showed effect on COVID-19 severity in European ancestry. One protein target, SERPINA1 showed a stronger effect in African ancestry but much weaker effect in European ancestry (odds ratio [OR] in Africans=0.369, 95%CI=0.203 to 0.668, P = 9.96 × 10-4; OR in Europeans=1.021, 95%CI=0.901 to 1.157, P = 0.745), which suggested that increased level of SERPINA1 will reduce COVID-19 risk in African ancestry. One protein, ICAM1 showed suggestive effect on COVID-19 severity in both ancestries (OR in Europeans=1.152, 95%CI=1.063 to 1.249, P = 5.94 × 10-4; OR in Africans=1.481, 95%CI=1.008 to 2.176; P = 0.045). The OAS1, SERPINA1 and ICAM1 effects were replicated using updated COVID-19 severity data in the two ancestries respectively, where alternative splicing events in OAS1 and ICAM1 also showed marginal effects on COVID-19 severity in Europeans. The phenome-wide MR of the prioritised targets on 622 complex traits provided information on potential beneficial effects on other diseases and suggested little evidence of adverse effects on major complications. INTERPRETATION: Our study identified six proteins as showing putative causal effects on COVID-19 severity. OAS1 and SERPINA1 were targets of existing drugs in trials as potential COVID-19 treatments. ICAM1, ICAM5 and FCRL3 are related to the immune system. Across the six targets, OAS1 has no reliable instrument in African ancestry; SERPINA1, FCRL3, ICAM5 and ENTPD5 showed a different level of putative causal evidence in European and African ancestries, which highlights the importance of more powerful ancestry-specific GWAS and value of multi-ancestry MR in informing the effects of drug targets on COVID-19 across different populations. This study provides a first step towards clinical investigation of beneficial and adverse effects of COVID-19 drug targets. FUNDING: No.

Genome-wide study of DNA methylation shows alterations in metabolic, inflammatory, and cholesterol pathways in ALS.

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease with an estimated heritability between 40 and 50%. DNA methylation patterns can serve as proxies of (past) exposures and disease progression, as well as providing a potential mechanism that mediates genetic or environmental risk. Here, we present a blood-based epigenome-wide association study meta-analysis in 9706 samples passing stringent quality control (6763 patients, 2943 controls). We identified a total of 45 differentially methylated positions (DMPs) annotated to 42 genes, which are enriched for pathways and traits related to metabolism, cholesterol biosynthesis, and immunity. We then tested 39 DNA methylation-based proxies of putative ALS risk factors and found that high-density lipoprotein cholesterol, body mass index, white blood cell proportions, and alcohol intake were independently associated with ALS. Integration of these results with our latest genome-wide association study showed that cholesterol biosynthesis was potentially causally related to ALS. Last, DNA methylation at several DMPs and blood cell proportion estimates derived from DNA methylation data were associated with survival rate in patients, suggesting that they might represent indicators of underlying disease processes potentially amenable to therapeutic interventions.

The influence of adult hip shape genetic variants on adolescent hip shape: Findings from a population-based DXA study.

OBJECTIVE: Hip shape is a well-recognized risk factor for hip osteoarthritis (OA) and hip fracture. We aimed to investigate whether the genetic variants known to be associated with adult hip shape were also associated with adolescent hip shape. METHODS: Hip DXA scans, obtained in offspring from the Avon Longitudinal Study of Parents and Children (ALSPAC) at two time points (mean ages 13.8 and 17.8 years), were used to quantify hip morphology using a 53-point Statistical Shape Model (SSM). Principal component analysis was used to generate hip shape modes (HSMs). Genetic variants which had previously shown genome-wide significant association with specific HSMs in adults were tested for association with the same HSMs in adolescents (at each timepoint separately) using SNPTEST v2. RESULTS: Complete genotypic and phenotypic data were available for 3550 and 3175 individuals at 14 and 18 years, respectively. The strongest evidence for association with adolescent hip shape was for a variant located near SOX9 (rs2158915) with consistent effects across both time points for HSM1 (age 14: beta -0.05, p = 9.9 × 10-8; age 18: -0.05, p = 3.3 × 10-6) and HSM5 (age 14: beta -0.07, p = 1.6 × 10-4; age 18: -0.1, p = 2.7 × 10-6). There was also strong evidence of association between rs10743612 (near PTHLH) and HSM1 (age 14: 0.05, p = 1.1 × 10-5; age 18: 0.04, p = 0.003) and between rs6537291 (near HHIP) and HSM2 (age 14: -0.06, p = 0.001; age 18: -0.07, p = 0.001) across both time points. The genes with the strongest associations with hip shape in adolescents, (SOX9, PTHLH and HHIP) are known to be involved in endochondral bone formation. HSM1 indicates narrower aspect ratio of the upper femur, whereas both HSM2 and HSM5 reflect variation in the femoral head size and femoral neck width, features previously found to be related to the risk of OA in later life. The SOX9 locus has previously been found to associate with increased risk of hip fracture. CONCLUSION: In conclusion, variants implicated in endochondral bone formation appear to consistently influence hip shape between adolescence and adulthood, including those aspects related to risk of hip OA and/or fracture in later life.

Genome-wide association study implicates novel loci and reveals candidate effector genes for longitudinal pediatric bone accrual.

BACKGROUND: Bone accrual impacts lifelong skeletal health, but genetic discovery has been primarily limited to cross-sectional study designs and hampered by uncertainty about target effector genes. Here, we capture this dynamic phenotype by modeling longitudinal bone accrual across 11,000 bone scans in a cohort of healthy children and adolescents, followed by genome-wide association studies (GWAS) and variant-to-gene mapping with functional follow-up. RESULTS: We identify 40 loci, 35 not previously reported, with various degrees of supportive evidence, half residing in topological associated domains harboring known bone genes. Of several loci potentially associated with later-life fracture risk, a candidate SNP lookup provides the most compelling evidence for rs11195210 (SMC3). Variant-to-gene mapping combining ATAC-seq to assay open chromatin with high-resolution promoter-focused Capture C identifies contacts between GWAS loci and nearby gene promoters. siRNA knockdown of gene expression supports the putative effector gene at three specific loci in two osteoblast cell models. Finally, using CRISPR-Cas9 genome editing, we confirm that the immediate genomic region harboring the putative causal SNP influences PRPF38A expression, a location which is predicted to coincide with a set of binding sites for relevant transcription factors. CONCLUSIONS: Using a new longitudinal approach, we expand the number of genetic loci putatively associated with pediatric bone gain. Functional follow-up in appropriate cell models finds novel candidate genes impacting bone accrual. Our data also raise the possibility that the cell fate decision between osteogenic and adipogenic lineages is important in normal bone accrual.

Common and rare variant association analyses in amyotrophic lateral sclerosis identify 15 risk loci with distinct genetic architectures and neuron-specific biology.

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease with a lifetime risk of one in 350 people and an unmet need for disease-modifying therapies. We conducted a cross-ancestry genome-wide association study (GWAS) including 29,612 patients with ALS and 122,656 controls, which identified 15 risk loci. When combined with 8,953 individuals with whole-genome sequencing (6,538 patients, 2,415 controls) and a large cortex-derived expression quantitative trait locus (eQTL) dataset (MetaBrain), analyses revealed locus-specific genetic architectures in which we prioritized genes either through rare variants, short tandem repeats or regulatory effects. ALS-associated risk loci were shared with multiple traits within the neurodegenerative spectrum but with distinct enrichment patterns across brain regions and cell types. Of the environmental and lifestyle risk factors obtained from the literature, Mendelian randomization analyses indicated a causal role for high cholesterol levels. The combination of all ALS-associated signals reveals a role for perturbations in vesicle-mediated transport and autophagy and provides evidence for cell-autonomous disease initiation in glutamatergic neurons.

Phenome-wide Mendelian randomization mapping the influence of the plasma proteome on complex diseases.

The human proteome is a major source of therapeutic targets. Recent genetic association analyses of the plasma proteome enable systematic evaluation of the causal consequences of variation in plasma protein levels. Here we estimated the effects of 1,002 proteins on 225 phenotypes using two-sample Mendelian randomization (MR) and colocalization. Of 413 associations supported by evidence from MR, 130 (31.5%) were not supported by results of colocalization analyses, suggesting that genetic confounding due to linkage disequilibrium is widespread in naïve phenome-wide association studies of proteins. Combining MR and colocalization evidence in cis-only analyses, we identified 111 putatively causal effects between 65 proteins and 52 disease-related phenotypes ( https://www.epigraphdb.org/pqtl/ ). Evaluation of data from historic drug development programs showed that target-indication pairs with MR and colocalization support were more likely to be approved, evidencing the value of this approach in identifying and prioritizing potential therapeutic targets.

Mendelian Randomization Analysis Reveals a Causal Influence of Circulating Sclerostin Levels on Bone Mineral Density and Fractures.

In bone, sclerostin is mainly osteocyte-derived and plays an important local role in adaptive responses to mechanical loading. Whether circulating levels of sclerostin also play a functional role is currently unclear, which we aimed to examine by two-sample Mendelian randomization (MR). A genetic instrument for circulating sclerostin, derived from a genomewide association study (GWAS) meta-analysis of serum sclerostin in 10,584 European-descent individuals, was examined in relation to femoral neck bone mineral density (BMD; n = 32,744) in GEFOS and estimated bone mineral density (eBMD) by heel ultrasound (n = 426,824) and fracture risk (n = 426,795) in UK Biobank. Our GWAS identified two novel serum sclerostin loci, B4GALNT3 (standard deviation [SD]) change in sclerostin per A allele (ß = 0.20, p = 4.6 × 10-49 ) and GALNT1 (ß = 0.11 per G allele, p = 4.4 × 10-11 ). B4GALNT3 is an N-acetyl-galactosaminyltransferase, adding a terminal LacdiNAc disaccharide to target glycocoproteins, found to be predominantly expressed in kidney, whereas GALNT1 is an enzyme causing mucin-type O-linked glycosylation. Using these two single-nucleotide polymorphisms (SNPs) as genetic instruments, MR revealed an inverse causal relationship between serum sclerostin and femoral neck BMD (ß = -0.12, 95% confidence interval [CI] -0.20 to -0.05) and eBMD (ß = -0.12, 95% CI -0.14 to -0.10), and a positive relationship with fracture risk (ß = 0.11, 95% CI 0.01 to 0.21). Colocalization analysis demonstrated common genetic signals within the B4GALNT3 locus for higher sclerostin, lower eBMD, and greater B4GALNT3 expression in arterial tissue (probability >99%). Our findings suggest that higher sclerostin levels are causally related to lower BMD and greater fracture risk. Hence, strategies for reducing circulating sclerostin, for example by targeting glycosylation enzymes as suggested by our GWAS results, may prove valuable in treating osteoporosis. © 2019 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals, Inc.

Identification of Novel Loci Associated With Hip Shape: A Meta-Analysis of Genomewide Association Studies.

We aimed to report the first genomewide association study (GWAS) meta-analysis of dual-energy X-ray absorptiometry (DXA)-derived hip shape, which is thought to be related to the risk of both hip osteoarthritis and hip fracture. Ten hip shape modes (HSMs) were derived by statistical shape modeling using SHAPE software, from hip DXA scans in the Avon Longitudinal Study of Parents and Children (ALSPAC; adult females), TwinsUK (mixed sex), Framingham Osteoporosis Study (FOS; mixed), Osteoporotic Fractures in Men study (MrOS), and Study of Osteoporotic Fractures (SOF; females) (total N = 15,934). Associations were adjusted for age, sex, and ancestry. Five genomewide significant (p < 5 × 10-9 , adjusted for 10 independent outcomes) single-nucleotide polymorphisms (SNPs) were associated with HSM1, and three SNPs with HSM2. One SNP, in high linkage disequilibrium with rs2158915 associated with HSM1, was associated with HSM5 at genomewide significance. In a look-up of previous GWASs, three of the identified SNPs were associated with hip osteoarthritis, one with hip fracture, and five with height. Seven SNPs were within 200 kb of genes involved in endochondral bone formation, namely SOX9, PTHrP, RUNX1, NKX3-2, FGFR4, DICER1, and HHIP. The SNP adjacent to DICER1 also showed osteoblast cis-regulatory activity of GSC, in which mutations have previously been reported to cause hip dysplasia. For three of the lead SNPs, SNPs in high LD (r2 > 0.5) were identified, which intersected with open chromatin sites as detected by ATAC-seq performed on embryonic mouse proximal femora. In conclusion, we identified eight SNPs independently associated with hip shape, most of which were associated with height and/or mapped close to endochondral bone formation genes, consistent with a contribution of processes involved in limb growth to hip shape and pathological sequelae. These findings raise the possibility that genetic studies of hip shape might help in understanding potential pathways involved in hip osteoarthritis and hip fracture. © 2018 The Authors. Journal of Bone and Mineral Research Published by Wiley Periodicals, Inc.

Using Y-Chromosomal Haplogroups in Genetic Association Studies and Suggested Implications.

Y-chromosomal (Y-DNA) haplogroups are more widely used in population genetics than in genetic epidemiology, although associations between Y-DNA haplogroups and several traits, including cardiometabolic traits, have been reported. In apparently homogeneous populations defined by principal component analyses, there is still Y-DNA haplogroup variation which will result from population history. Therefore, hidden stratification and/or differential phenotypic effects by Y-DNA haplogroups could exist. To test this, we hypothesised that stratifying individuals according to their Y-DNA haplogroups before testing for associations between autosomal single nucleotide polymorphisms (SNPs) and phenotypes will yield difference in association. For proof of concept, we derived Y-DNA haplogroups from 6537 males from two epidemiological cohorts, Avon Longitudinal Study of Parents and Children (ALSPAC) (n = 5080; 816 Y-DNA SNPs) and the 1958 Birth Cohort (n = 1457; 1849 Y-DNA SNPs), and studied the robust associations between 32 SNPs and body mass index (BMI), including SNPs in or near Fat Mass and Obesity-associated protein (FTO) which yield the strongest effects. Overall, no association was replicated in both cohorts when Y-DNA haplogroups were considered and this suggests that, for BMI at least, there is little evidence of differences in phenotype or SNP association by Y-DNA structure. Further studies using other traits, phenome-wide association studies (PheWAS), other haplogroups and/or autosomal SNPs are required to test the generalisability and utility of this approach.

Investigation of the Relationship Between Susceptibility Loci for Hip Osteoarthritis and Dual X-Ray Absorptiometry-Derived Hip Shape in a Population-Based Cohort of Perimenopausal Women.

OBJECTIVE: To examine relationships between known osteoarthritis (OA) susceptibility loci and hip shape in a population-based cohort of perimenopausal women in order to investigate whether hip shape contributes to OA development. METHODS: Hip shape was measured, using statistical shape modeling, on dual x-ray absorptiometry scans of the hip from mothers in the Avon Longitudinal Study of Parents and Children (ALSPAC). The proximal femur and superior acetabulum were outlined, and independent hip shape modes were generated. In a subregional model, points were restricted to the acetabulum and superior femoral head. Associations between 11 OA-related single-nucleotide polymorphisms, identified by literature search, and shape modes were analyzed in a multivariate canonical correlation analysis. RESULTS: A total of 3,111 women (mean age 48 years) had genetic and hip shape data. The KLHDC5/PTHLH rs10492367 OA risk allele was associated with a wider upper femur in the whole shape model (P = 1 × 10-5 ). The DOT1L rs12982744 OA risk allele was associated with reduced superior joint space in the subregional shape model (P = 2 × 10-3 ). The COL11A1 rs4907986 OA risk allele was associated with lateral displacement of the femoral head relative to the acetabulum in the subregional shape model (P = 5 × 10-4 ). Regional association plots identified an additional COL11A1 locus in moderate linkage disequilibrium with rs4907986, which was more strongly associated with hip shape (rs10047217; P = 3 × 10-6 ). Colocalization analysis indicated sharing of genetic signals for hip shape and hip OA for the KLHDC5/PTHLH and COL11A1 loci. CONCLUSION: Hip OA susceptibility loci were associated with shape in this study, suggesting that these loci (and potentially yet-to-be-identified hip OA loci) could contribute to hip OA in later life via perturbing biologic pathways that mediate morphology development.

The MR-Base platform supports systematic causal inference across the human phenome.

Results from genome-wide association studies (GWAS) can be used to infer causal relationships between phenotypes, using a strategy known as 2-sample Mendelian randomization (2SMR) and bypassing the need for individual-level data. However, 2SMR methods are evolving rapidly and GWAS results are often insufficiently curated, undermining efficient implementation of the approach. We therefore developed MR-Base (http://www.mrbase.org): a platform that integrates a curated database of complete GWAS results (no restrictions according to statistical significance) with an application programming interface, web app and R packages that automate 2SMR. The software includes several sensitivity analyses for assessing the impact of horizontal pleiotropy and other violations of assumptions. The database currently comprises 11 billion single nucleotide polymorphism-trait associations from 1673 GWAS and is updated on a regular basis. Integrating data with software ensures more rigorous application of hypothesis-driven analyses and allows millions of potential causal relationships to be efficiently evaluated in phenome-wide association studies.

Recent Developments in Mendelian Randomization Studies.

PURPOSE OF REVIEW: Mendelian randomization (MR) is a strategy for evaluating causality in observational epidemiological studies. MR exploits the fact that genotypes are not generally susceptible to reverse causation and confounding, due to their fixed nature and Mendel's First and Second Laws of Inheritance. MR has the potential to provide information on causality in many situations where randomized controlled trials are not possible, but the results of MR studies must be interpreted carefully to avoid drawing erroneous conclusions. RECENT FINDINGS: In this review, we outline the principles behind MR, as well as assumptions and limitations of the method. Extensions to the basic approach are discussed, including two-sample MR, bidirectional MR, two-step MR, multivariable MR, and factorial MR. We also consider some new applications and recent developments in the methodology, including its ability to inform drug development, automation of the method using tools such as MR-Base, and phenome-wide and hypothesis-free MR. SUMMARY: In conjunction with the growing availability of large-scale genomic databases, higher level of automation and increased robustness of the methods, MR promises to be a valuable strategy to examine causality in complex biological/omics networks, inform drug development and prioritize intervention targets for disease prevention in the future.