Genome-wide characterization of circulating metabolic biomarkers.

Genome-wide association analyses using high-throughput metabolomics platforms have led to novel insights into the biology of human metabolism1-7. This detailed knowledge of the genetic determinants of systemic metabolism has been pivotal for uncovering how genetic pathways influence biological mechanisms and complex diseases8-11. Here we present a genome-wide association study for 233 circulating metabolic traits quantified by nuclear magnetic resonance spectroscopy in up to 136,016 participants from 33 cohorts. We identify more than 400 independent loci and assign probable causal genes at two-thirds of these using manual curation of plausible biological candidates. We highlight the importance of sample and participant characteristics that can have significant effects on genetic associations. We use detailed metabolic profiling of lipoprotein- and lipid-associated variants to better characterize how known lipid loci and novel loci affect lipoprotein metabolism at a granular level. We demonstrate the translational utility of comprehensively phenotyped molecular data, characterizing the metabolic associations of intrahepatic cholestasis of pregnancy. Finally, we observe substantial genetic pleiotropy for multiple metabolic pathways and illustrate the importance of careful instrument selection in Mendelian randomization analysis, revealing a putative causal relationship between acetone and hypertension. Our publicly available results provide a foundational resource for the community to examine the role of metabolism across diverse diseases.

Quality control and removal of technical variation of NMR metabolic biomarker data in ~120,000 UK Biobank participants.

Metabolic biomarker data quantified by nuclear magnetic resonance (NMR) spectroscopy in approximately 121,000 UK Biobank participants has recently been released as a community resource, comprising absolute concentrations and ratios of 249 circulating metabolites, lipids, and lipoprotein sub-fractions. Here we identify and characterise additional sources of unwanted technical variation influencing individual biomarkers in the data available to download from UK Biobank. These included sample preparation time, shipping plate well, spectrometer batch effects, drift over time within spectrometer, and outlier shipping plates. We developed a procedure for removing this unwanted technical variation, and demonstrate that it increases signal for genetic and epidemiological studies of the NMR metabolic biomarker data in UK Biobank. We subsequently developed an R package, ukbnmr, which we make available to the wider research community to enhance the utility of the UK Biobank NMR metabolic biomarker data and to facilitate rapid analysis.

Systematic Mendelian randomization using the human plasma proteome to discover potential therapeutic targets for stroke.

Stroke is the second leading cause of death with substantial unmet therapeutic needs. To identify potential stroke therapeutic targets, we estimate the causal effects of 308 plasma proteins on stroke outcomes in a two-sample Mendelian randomization framework and assess mediation effects by stroke risk factors. We find associations between genetically predicted plasma levels of six proteins and stroke (P ≤ 1.62 × 10-4). The genetic associations with stroke colocalize (Posterior Probability >0.7) with the genetic associations of four proteins (TFPI, TMPRSS5, CD6, CD40). Mendelian randomization supports atrial fibrillation, body mass index, smoking, blood pressure, white matter hyperintensities and type 2 diabetes as stroke risk factors (P ≤ 0.0071). Body mass index, white matter hyperintensity and atrial fibrillation appear to mediate the TFPI, IL6RA, TMPRSS5 associations with stroke. Furthermore, thirty-six proteins are associated with one or more of these risk factors using Mendelian randomization. Our results highlight causal pathways and potential therapeutic targets for stroke.

An atlas of mitochondrial DNA genotype-phenotype associations in the UK Biobank.

Mitochondrial DNA (mtDNA) variation in common diseases has been underexplored, partly due to a lack of genotype calling and quality-control procedures. Developing an at-scale workflow for mtDNA variant analyses, we show correlations between nuclear and mitochondrial genomic structures within subpopulations of Great Britain and establish a UK Biobank reference atlas of mtDNA-phenotype associations. A total of 260 mtDNA-phenotype associations were new (P < 1 × 10-5), including rs2853822 /m.8655 C>T (MT-ATP6) with type 2 diabetes, rs878966690 /m.13117 A>G (MT-ND5) with multiple sclerosis, 6 mtDNA associations with adult height, 24 mtDNA associations with 2 liver biomarkers and 16 mtDNA associations with parameters of renal function. Rare-variant gene-based tests implicated complex I genes modulating mean corpuscular volume and mean corpuscular hemoglobin. Seven traits had both rare and common mtDNA associations, where rare variants tended to have larger effects than common variants. Our work illustrates the value of studying mtDNA variants in common complex diseases and lays foundations for future large-scale mtDNA association studies.

Discovery of rare variants associated with blood pressure regulation through meta-analysis of 1.3 million individuals.

Genetic studies of blood pressure (BP) to date have mainly analyzed common variants (minor allele frequency > 0.05). In a meta-analysis of up to ~1.3 million participants, we discovered 106 new BP-associated genomic regions and 87 rare (minor allele frequency ≤ 0.01) variant BP associations (P < 5 × 10-8), of which 32 were in new BP-associated loci and 55 were independent BP-associated single-nucleotide variants within known BP-associated regions. Average effects of rare variants (44% coding) were ~8 times larger than common variant effects and indicate potential candidate causal genes at new and known loci (for example, GATA5 and PLCB3). BP-associated variants (including rare and common) were enriched in regions of active chromatin in fetal tissues, potentially linking fetal development with BP regulation in later life. Multivariable Mendelian randomization suggested possible inverse effects of elevated systolic and diastolic BP on large artery stroke. Our study demonstrates the utility of rare-variant analyses for identifying candidate genes and the results highlight potential therapeutic targets.

The influence of rare variants in circulating metabolic biomarkers.

Circulating metabolite levels are biomarkers for cardiovascular disease (CVD). Here we studied, association of rare variants and 226 serum lipoproteins, lipids and amino acids in 7,142 (discovery plus follow-up) healthy participants. We leveraged the information from multiple metabolite measurements on the same participants to improve discovery in rare variant association analyses for gene-based and gene-set tests by incorporating correlated metabolites as covariates in the validation stage. Gene-based analysis corrected for the effective number of tests performed, confirmed established associations at APOB, APOC3, PAH, HAL and PCSK (p<1.32x10-7) and identified novel gene-trait associations at a lower stringency threshold with ACSL1, MYCN, FBXO36 and B4GALNT3 (p<2.5x10-6). Regulation of the pyruvate dehydrogenase (PDH) complex was associated for the first time, in gene-set analyses also corrected for effective number of tests, with IDL and LDL parameters, as well as circulating cholesterol (pMETASKAT<2.41x10-6). In conclusion, using an approach that leverages metabolite measurements obtained in the same participants, we identified novel loci and pathways involved in the regulation of these important metabolic biomarkers. As large-scale biobanks continue to amass sequencing and phenotypic information, analytical approaches such as ours will be useful to fully exploit the copious amounts of biological data generated in these efforts.

Assessing the causal association of glycine with risk of cardio-metabolic diseases.

Circulating levels of glycine have previously been associated with lower incidence of coronary heart disease (CHD) and type 2 diabetes (T2D) but it remains uncertain if glycine plays an aetiological role. We present a meta-analysis of genome-wide association studies for glycine in 80,003 participants and investigate the causality and potential mechanisms of the association between glycine and cardio-metabolic diseases using genetic approaches. We identify 27 genetic loci, of which 22 have not previously been reported for glycine. We show that glycine is genetically associated with lower CHD risk and find that this may be partly driven by blood pressure. Evidence for a genetic association of glycine with T2D is weaker, but we find a strong inverse genetic effect of hyperinsulinaemia on glycine. Our findings strengthen evidence for a protective effect of glycine on CHD and show that the glycine-T2D association may be driven by a glycine-lowering effect of insulin resistance.

Trans-ethnic association study of blood pressure determinants in over 750,000 individuals.

In this trans-ethnic multi-omic study, we reinterpret the genetic architecture of blood pressure to identify genes, tissues, phenomes and medication contexts of blood pressure homeostasis. We discovered 208 novel common blood pressure SNPs and 53 rare variants in genome-wide association studies of systolic, diastolic and pulse pressure in up to 776,078 participants from the Million Veteran Program (MVP) and collaborating studies, with analysis of the blood pressure clinical phenome in MVP. Our transcriptome-wide association study detected 4,043 blood pressure associations with genetically predicted gene expression of 840 genes in 45 tissues, and mouse renal single-cell RNA sequencing identified upregulated blood pressure genes in kidney tubule cells.

Association of LPA Variants With Risk of Coronary Disease and the Implications for Lipoprotein(a)-Lowering Therapies: A Mendelian Randomization Analysis.

Importance: Human genetic studies have indicated that plasma lipoprotein(a) (Lp[a]) is causally associated with the risk of coronary heart disease (CHD), but randomized trials of several therapies that reduce Lp(a) levels by 25% to 35% have not provided any evidence that lowering Lp(a) level reduces CHD risk. Objective: To estimate the magnitude of the change in plasma Lp(a) levels needed to have the same evidence of an association with CHD risk as a 38.67-mg/dL (ie, 1-mmol/L) change in low-density lipoprotein cholesterol (LDL-C) level, a change that has been shown to produce a clinically meaningful reduction in the risk of CHD. Design, Setting, and Participants: A mendelian randomization analysis was conducted using individual participant data from 5 studies and with external validation using summarized data from 48 studies. Population-based prospective cohort and case-control studies featured 20 793 individuals with CHD and 27 540 controls with individual participant data, whereas summarized data included 62 240 patients with CHD and 127 299 controls. Data were analyzed from November 2016 to March 2018. Exposures: Genetic LPA score and plasma Lp(a) mass concentration. Main Outcomes and Measures: Coronary heart disease. Results: Of the included study participants, 53% were men, all were of white European ancestry, and the mean age was 57.5 years. The association of genetically predicted Lp(a) with CHD risk was linearly proportional to the absolute change in Lp(a) concentration. A 10-mg/dL lower genetically predicted Lp(a) concentration was associated with a 5.8% lower CHD risk (odds ratio [OR], 0.942; 95% CI, 0.933-0.951; P = 3 × 10-37), whereas a 10-mg/dL lower genetically predicted LDL-C level estimated using an LDL-C genetic score was associated with a 14.5% lower CHD risk (OR, 0.855; 95% CI, 0.818-0.893; P = 2 × 10-12). Thus, a 101.5-mg/dL change (95% CI, 71.0-137.0) in Lp(a) concentration had the same association with CHD risk as a 38.67-mg/dL change in LDL-C level. The association of genetically predicted Lp(a) concentration with CHD risk appeared to be independent of changes in LDL-C level owing to genetic variants that mimic the relationship of statins, PCSK9 inhibitors, and ezetimibe with CHD risk. Conclusions and Relevance: The clinical benefit of lowering Lp(a) is likely to be proportional to the absolute reduction in Lp(a) concentration. Large absolute reductions in Lp(a) of approximately 100 mg/dL may be required to produce a clinically meaningful reduction in the risk of CHD similar in magnitude to what can be achieved by lowering LDL-C level by 38.67 mg/dL (ie, 1 mmol/L).

Genetic predictors of testosterone and their associations with cardiovascular disease and risk factors: A Mendelian randomization investigation.

BACKGROUND: Testosterone supplementation has been linked to increased cardiovascular disease risk in some observational studies. The causal role of testosterone can be investigated using a Mendelian randomization approach. METHODS AND RESULTS: We assessed genetic associations of variants in two gene regions (SHBG and JMJD1C) with several cardiovascular risk factors (lipids, adiponectin, blood pressure, anthropometric traits) plus male pattern baldness, including control outcomes and potential mediators. We assessed genetic associations with coronary artery disease (CAD) risk in the CARDIoGRAMplusC4D consortium (171,191 individuals including 60,801 cases), and associations with CAD and ischaemic stroke risk in the UK Biobank (367,643 individuals including 25,352 CAD cases and 3650 ischaemic stroke cases). Genetic predictors of increased serum testosterone were associated with lipids, blood pressure, and height. There was some evidence of an association with risk of CAD (SHBG gene region: odds ratio (OR) 0.95 per 1 unit increase in log-transformed testosterone [95% confidence interval: 0.81-1.12, p = 0.55]; JMJD1C gene region: OR 1.24 [1.01-1.51, p = 0.04]) and ischaemic stroke both overall (SHBG: OR 1.05 [0.64, 1.73, p = 0.83]; JMJD1C: OR 2.52 [1.33, 4.77, p = 0.005]) and in men. However, associations with some control outcomes were in the opposite direction to that expected. CONCLUSIONS: Sex hormone-related mechanisms appear to be relevant to cardiovascular risk factors and for stroke (particularly for men). However, the extent that these findings are specifically informative about endogenous testosterone or testosterone supplementation is unclear. These findings underline a fundamental limitation for the use of Mendelian randomization where biological knowledge about the function of genetic variants is uncertain.

hg19K: addressing a significant lacuna in hg19-based variant calling.

BACKGROUND: The hg19 assembly of the human genome is the most heavily annotated and most commonly used reference to make variant calls for individual genomes. Based on the phase 3 report of the 1000 genomes project (1000G), it is now well known that many positions in the hg19 genome represent minor alleles. Since commonly used variant call methods are developed under the assumption that hg19 reference harbors major alleles at all the ~3 billion positions, these methods mask the calls whenever an individual is homozygous to the minor allele at the respective positions. Hence, it is important to address the extent and impact of these minor alleles in hg19 from the point of view of individual genomes. METHOD: We have created a reference genome, hg19K, in which all the positions in hg19 reference harboring minor allele were replaced by those from the phase 3 report of the 1000 genomes project. The genomes of five individuals, downloaded from the public repository, were analyzed using both hg19 and hg19K and compared. RESULTS: Out of the 81 million SNPs in phase 3 report from the 1000 genomes project, 1.9 million positions were found to be major alleles compared to hg19 with many having an allele frequency of >0.9. We observed that ~30% of the SNVs found in individual genomes are confined to the 1.9 million positions. Also, there are ~8% unique SNVs predicted using hg19K-based approach, which are also confined to the 1.9 million positions. CONCLUSION: We report that the presence of minor alleles in hg19 alone results in ~8% false negatives and ~30% false positives during variant calls. Also, among the variant calls unique to hg19K-based methods, which are missed in individuals homozygous to the minor alleles in hg19-based prediction, some are deleterious missense mutations at sites conserved across diverse species.

The African Genome Variation Project shapes medical genetics in Africa.

Given the importance of Africa to studies of human origins and disease susceptibility, detailed characterization of African genetic diversity is needed. The African Genome Variation Project provides a resource with which to design, implement and interpret genomic studies in sub-Saharan Africa and worldwide. The African Genome Variation Project represents dense genotypes from 1,481 individuals and whole-genome sequences from 320 individuals across sub-Saharan Africa. Using this resource, we find novel evidence of complex, regionally distinct hunter-gatherer and Eurasian admixture across sub-Saharan Africa. We identify new loci under selection, including loci related to malaria susceptibility and hypertension. We show that modern imputation panels (sets of reference genotypes from which unobserved or missing genotypes in study sets can be inferred) can identify association signals at highly differentiated loci across populations in sub-Saharan Africa. Using whole-genome sequencing, we demonstrate further improvements in imputation accuracy, strengthening the case for large-scale sequencing efforts of diverse African haplotypes. Finally, we present an efficient genotype array design capturing common genetic variation in Africa.