High-resolution African HLA resource uncovers HLA-DRB1 expression effects underlying vaccine response.

How human genetic variation contributes to vaccine effectiveness in infants is unclear, and data are limited on these relationships in populations with African ancestries. We undertook genetic analyses of vaccine antibody responses in infants from Uganda (n = 1391), Burkina Faso (n = 353) and South Africa (n = 755), identifying associations between human leukocyte antigen (HLA) and antibody response for five of eight tested antigens spanning pertussis, diphtheria and hepatitis B vaccines. In addition, through HLA typing 1,702 individuals from 11 populations of African ancestry derived predominantly from the 1000 Genomes Project, we constructed an imputation resource, fine-mapping class II HLA-DR and DQ associations explaining up to 10% of antibody response variance in our infant cohorts. We observed differences in the genetic architecture of pertussis antibody response between the cohorts with African ancestries and an independent cohort with European ancestry, but found no in silico evidence of differences in HLA peptide binding affinity or breadth. Using immune cell expression quantitative trait loci datasets derived from African-ancestry samples from the 1000 Genomes Project, we found evidence of differential HLA-DRB1 expression correlating with inferred protection from pertussis following vaccination. This work suggests that HLA-DRB1 expression may play a role in vaccine response and should be considered alongside peptide selection to improve vaccine design.

Variation in the SERPINA6/SERPINA1 locus alters morning plasma cortisol, hepatic corticosteroid binding globulin expression, gene expression in peripheral tissues, and risk of cardiovascular disease.

The stress hormone cortisol modulates fuel metabolism, cardiovascular homoeostasis, mood, inflammation and cognition. The CORtisol NETwork (CORNET) consortium previously identified a single locus associated with morning plasma cortisol. Identifying additional genetic variants that explain more of the variance in cortisol could provide new insights into cortisol biology and provide statistical power to test the causative role of cortisol in common diseases. The CORNET consortium extended its genome-wide association meta-analysis for morning plasma cortisol from 12,597 to 25,314 subjects and from ~2.2 M to ~7 M SNPs, in 17 population-based cohorts of European ancestries. We confirmed the genetic association with SERPINA6/SERPINA1. This locus contains genes encoding corticosteroid binding globulin (CBG) and α1-antitrypsin. Expression quantitative trait loci (eQTL) analyses undertaken in the STARNET cohort of 600 individuals showed that specific genetic variants within the SERPINA6/SERPINA1 locus influence expression of SERPINA6 rather than SERPINA1 in the liver. Moreover, trans-eQTL analysis demonstrated effects on adipose tissue gene expression, suggesting that variations in CBG levels have an effect on delivery of cortisol to peripheral tissues. Two-sample Mendelian randomisation analyses provided evidence that each genetically-determined standard deviation (SD) increase in morning plasma cortisol was associated with increased odds of chronic ischaemic heart disease (0.32, 95% CI 0.06-0.59) and myocardial infarction (0.21, 95% CI 0.00-0.43) in UK Biobank and similarly in CARDIoGRAMplusC4D. These findings reveal a causative pathway for CBG in determining cortisol action in peripheral tissues and thereby contributing to the aetiology of cardiovascular disease.

Morning plasma cortisol as a cardiovascular risk factor: findings from prospective cohort and Mendelian randomization studies.

OBJECTIVE: The identification of new causal risk factors has the potential to improve cardiovascular disease (CVD) risk prediction and the development of new treatments to reduce CVD deaths. In the general population, we sought to determine whether cortisol is a causal risk factor for CVD and coronary heart disease (CHD). DESIGN AND METHODS: Three approaches were adopted to investigate the association between cortisol and CVD/CHD. First, we used multivariable regression in two prospective nested case-control studies (total 798 participants, 313 incident CVD/CHD with complete data). Second, a random-effects meta-analysis of these data and previously published prospective associations was performed (total 6680 controls, 696 incident CVD/CHD). Finally, one- and two-sample Mendelian randomization analyses were performed (122,737 CHD cases, 547,261 controls for two-sample analyses). RESULTS: In the two prospective nested case-control studies, logistic regression adjusting for sex, age, BMI, smoking and time of sampling, demonstrated a positive association between morning plasma cortisol and incident CVD (OR: 1.28 per 1 SD higher cortisol, 95% CI: 1.06-1.54). In the meta-analysis of prospective studies, the equivalent result was OR: 1.18, 95% CI: 1.06-1.31. Results from the two-sample Mendelian randomization were consistent with these positive associations: OR: 1.06, 95% CI: 0.98-1.15. CONCLUSIONS: All three approaches demonstrated a positive association between morning plasma cortisol and incident CVD. Together, these findings suggest that elevated morning cortisol is a causal risk factor for CVD. The current data suggest strategies targeted at lowering cortisol action should be evaluated for their effects on CVD.

The influence of obesity-related factors in the etiology of renal cell carcinoma-A mendelian randomization study.

BACKGROUND: Several obesity-related factors have been associated with renal cell carcinoma (RCC), but it is unclear which individual factors directly influence risk. We addressed this question using genetic markers as proxies for putative risk factors and evaluated their relation to RCC risk in a mendelian randomization (MR) framework. This methodology limits bias due to confounding and is not affected by reverse causation. METHODS AND FINDINGS: Genetic markers associated with obesity measures, blood pressure, lipids, type 2 diabetes, insulin, and glucose were initially identified as instrumental variables, and their association with RCC risk was subsequently evaluated in a genome-wide association study (GWAS) of 10,784 RCC patients and 20,406 control participants in a 2-sample MR framework. The effect on RCC risk was estimated by calculating odds ratios (ORSD) for a standard deviation (SD) increment in each risk factor. The MR analysis indicated that higher body mass index increases the risk of RCC (ORSD: 1.56, 95% confidence interval [CI] 1.44-1.70), with comparable results for waist-to-hip ratio (ORSD: 1.63, 95% CI 1.40-1.90) and body fat percentage (ORSD: 1.66, 95% CI 1.44-1.90). This analysis further indicated that higher fasting insulin (ORSD: 1.82, 95% CI 1.30-2.55) and diastolic blood pressure (DBP; ORSD: 1.28, 95% CI 1.11-1.47), but not systolic blood pressure (ORSD: 0.98, 95% CI 0.84-1.14), increase the risk for RCC. No association with RCC risk was seen for lipids, overall type 2 diabetes, or fasting glucose. CONCLUSIONS: This study provides novel evidence for an etiological role of insulin in RCC, as well as confirmatory evidence that obesity and DBP influence RCC risk.

Meta-analysis of human genome-microbiome association studies: the MiBioGen consortium initiative.

BACKGROUND: In recent years, human microbiota, especially gut microbiota, have emerged as an important yet complex trait influencing human metabolism, immunology, and diseases. Many studies are investigating the forces underlying the observed variation, including the human genetic variants that shape human microbiota. Several preliminary genome-wide association studies (GWAS) have been completed, but more are necessary to achieve a fuller picture. RESULTS: Here, we announce the MiBioGen consortium initiative, which has assembled 18 population-level cohorts and some 19,000 participants. Its aim is to generate new knowledge for the rapidly developing field of microbiota research. Each cohort has surveyed the gut microbiome via 16S rRNA sequencing and genotyped their participants with full-genome SNP arrays. We have standardized the analytical pipelines for both the microbiota phenotypes and genotypes, and all the data have been processed using identical approaches. Our analysis of microbiome composition shows that we can reduce the potential artifacts introduced by technical differences in generating microbiota data. We are now in the process of benchmarking the association tests and performing meta-analyses of genome-wide associations. All pipeline and summary statistics results will be shared using public data repositories. CONCLUSION: We present the largest consortium to date devoted to microbiota-GWAS. We have adapted our analytical pipelines to suit multi-cohort analyses and expect to gain insight into host-microbiota cross-talk at the genome-wide level. And, as an open consortium, we invite more cohorts to join us (by contacting one of the corresponding authors) and to follow the analytical pipeline we have developed.

Fraction of exhaled nitric oxide values in childhood are associated with 17q11.2-q12 and 17q12-q21 variants.

BACKGROUND: The fraction of exhaled nitric oxide (Feno) value is a biomarker of eosinophilic airway inflammation and is associated with childhood asthma. Identification of common genetic variants associated with childhood Feno values might help to define biological mechanisms related to specific asthma phenotypes. OBJECTIVE: We sought to identify the genetic variants associated with childhood Feno values and their relation with asthma. METHODS: Feno values were measured in children age 5 to 15 years. In 14 genome-wide association studies (N = 8,858), we examined the associations of approximately 2.5 million single nucleotide polymorphisms (SNPs) with Feno values. Subsequently, we assessed whether significant SNPs were expression quantitative trait loci in genome-wide expression data sets of lymphoblastoid cell lines (n = 1,830) and were related to asthma in a previously published genome-wide association data set (cases, n = 10,365; control subjects: n = 16,110). RESULTS: We identified 3 SNPs associated with Feno values: rs3751972 in LYR motif containing 9 (LYRM9; P = 1.97 × 10(-10)) and rs944722 in inducible nitric oxide synthase 2 (NOS2; P = 1.28 × 10(-9)), both of which are located at 17q11.2-q12, and rs8069176 near gasdermin B (GSDMB; P = 1.88 × 10(-8)) at 17q12-q21. We found a cis expression quantitative trait locus for the transcript soluble galactoside-binding lectin 9 (LGALS9) that is in linkage disequilibrium with rs944722. rs8069176 was associated with GSDMB and ORM1-like 3 (ORMDL3) expression. rs8069176 at 17q12-q21, but not rs3751972 and rs944722 at 17q11.2-q12, were associated with physician-diagnosed asthma. CONCLUSION: This study identified 3 variants associated with Feno values, explaining 0.95% of the variance. Identification of functional SNPs and haplotypes in these regions might provide novel insight into the regulation of Feno values. This study highlights that both shared and distinct genetic factors affect Feno values and childhood asthma.

Functional gene group analysis reveals a role of synaptic heterotrimeric G proteins in cognitive ability.

Although cognitive ability is a highly heritable complex trait, only a few genes have been identified, explaining relatively low proportions of the observed trait variation. This implies that hundreds of genes of small effect may be of importance for cognitive ability. We applied an innovative method in which we tested for the effect of groups of genes defined according to cellular function (functional gene group analysis). Using an initial sample of 627 subjects, this functional gene group analysis detected that synaptic heterotrimeric guanine nucleotide binding proteins (G proteins) play an important role in cognitive ability (P(EMP) = 1.9 x 10(-4)). The association with heterotrimeric G proteins was validated in an independent population sample of 1507 subjects. Heterotrimeric G proteins are central relay factors between the activation of plasma membrane receptors by extracellular ligands and the cellular responses that these induce, and they can be considered a point of convergence, or a "signaling bottleneck." Although alterations in synaptic signaling processes may not be the exclusive explanation for the association of heterotrimeric G proteins with cognitive ability, such alterations may prominently affect the properties of neuronal networks in the brain in such a manner that impaired cognitive ability and lower intelligence are observed. The reported association of synaptic heterotrimeric G proteins with cognitive ability clearly points to a new direction in the study of the genetic basis of cognitive ability.