Tissue-specific multi-omics analysis of atrial fibrillation.

Genome-wide association studies (GWAS) for atrial fibrillation (AF) have uncovered numerous disease-associated variants. Their underlying molecular mechanisms, especially consequences for mRNA and protein expression remain largely elusive. Thus, refined multi-omics approaches are needed for deciphering the underlying molecular networks. Here, we integrate genomics, transcriptomics, and proteomics of human atrial tissue in a cross-sectional study to identify widespread effects of genetic variants on both transcript (cis-eQTL) and protein (cis-pQTL) abundance. We further establish a novel targeted trans-QTL approach based on polygenic risk scores to determine candidates for AF core genes. Using this approach, we identify two trans-eQTLs and five trans-pQTLs for AF GWAS hits, and elucidate the role of the transcription factor NKX2-5 as a link between the GWAS SNP rs9481842 and AF. Altogether, we present an integrative multi-omics method to uncover trans-acting networks in small datasets and provide a rich resource of atrial tissue-specific regulatory variants for transcript and protein levels for cardiovascular disease gene prioritization.

Assessment of causality of natriuretic peptides and atrial fibrillation and heart failure: a Mendelian randomization study in the FINRISK cohort.

AIMS: Natriuretic peptides are extensively studied biomarkers for atrial fibrillation (AF) and heart failure (HF). Their role in the pathogenesis of both diseases is not entirely understood and previous studies several single-nucleotide polymorphisms (SNPs) at the NPPA-NPPB locus associated with natriuretic peptides have been identified. We investigated the causal relationship between natriuretic peptides and AF as well as HF using a Mendelian randomization approach. METHODS AND RESULTS: N-terminal pro B-type natriuretic peptide (NT-proBNP) (N = 6669), B-type natriuretic peptide (BNP) (N = 6674), and mid-regional pro atrial natriuretic peptide (MR-proANP) (N = 6813) were measured in the FINRISK 1997 cohort. N = 30 common SNPs related to NT-proBNP, BNP, and MR-proANP were selected from studies. We performed six Mendelian randomizations for all three natriuretic peptide biomarkers and for both outcomes, AF and HF, separately. Polygenic risk scores (PRSs) based on multiple SNPs were used as genetic instrumental variable in Mendelian randomizations. Polygenic risk scores were significantly associated with the three natriuretic peptides. Polygenic risk scores were not significantly associated with incident AF nor HF. Most cardiovascular risk factors showed significant confounding percentages, but no association with PRS. A causal relation except for small causal betas is unlikely. CONCLUSION: In our Mendelian randomization approach, we confirmed an association between common genetic variation at the NPPA-NPPB locus and natriuretic peptides. A strong causal relationship between natriuretic peptides and incidence of AF as well as HF at the community-level was ruled out. Therapeutic approaches targeting natriuretic peptides will therefore very likely work through indirect mechanisms.

Performance of prognostic modelling of high and low ovarian response to ovarian stimulation for IVF.

STUDY QUESTION: What is the performance of previously established regression models in predicting low and high ovarian response to 150 µg corifollitropin alfa/GnRH-antagonist ovarian stimulation in an independent dataset? SUMMARY ANSWER: The outcome of ovarian stimulation with 150 µg corifollitropin alfa in a fixed, multiple dose GnRH-antagonist protocol can be validly predicted using logistic regression models with AMH being of paramount importance. WHAT IS KNOWN ALREADY: Predictors of ovarian response have been identified in FSH/GnRH agonist protocols as well as ovarian stimulation with corifollitropin alfa/GnRH-antagonist. Multivariable response models have been established already, however, external validation of model performance has so far been lacking. STUDY DESIGN, SIZE, DURATION: Data from a prospective, multi-centre (n = 5), multi-national, investigator-initiated, observational cohort study were analysed. Infertile women (n = 211), body weight >60 kg, were undergoing ovarian stimulation with 150 µg corifollitropin alfa in a GnRH-antagonist multiple dose protocol for transvaginal oocyte retrieval for IVF. Demographic, sonographic and endocrine parameters were prospectively assessed on cycle Day 2 or 3 of spontaneous menstruation before ovarian stimulation. Main outcomes were low (<6 oocytes) and high (>18 oocytes) ovarian response. PARTICIPANTS/MATERIALS, SETTING, METHODS: Firstly, previously established prediction models for low ovarian response (LOR) and high ovarian response (HOR) were tested using the original parameters. Secondly, re-estimated parameters generated from the present data were tested on the established models. Thirdly, for the development of new predictive models of both LOR and HOR, several logistic regression models were estimated. Resulting prediction models were compared by means of the area under the receiver operating characteristic curve (AUC) and bias-corrected Akaike's Information Criterion (AICc) to identify the most reasonable model for each scenario. MAIN RESULTS AND THE ROLE OF CHANCE: The previously established prediction models for low and high response performed remarkably well on this dataset (low response AUC 0.8879 (95% CI: 0.8185-0.9573) and high response AUC 0.8909 (95% CI: 0.8251-0.9568)). A newly developed simplified model for LOR with log-transformed AMH values and only age as another covariate showed an AUC of 0.8920 (95% CI: 0.8237-0.9603) with the lowest AICc of all models compared. For predicting HOR, we suggest a simplified model using AMH, FSH and AFC (AUC of 0.8976, 95% CI: 0.8206-0.9746). LIMITATIONS, REASONS FOR CAUTION: All analyses were done on data from women with a body weight >60 kg. The newly developed simplified models may suffer from overfitting and need to be tested in further independent data sets. WIDER IMPLICATIONS OF THE FINDINGS: Patient selection for ovarian stimulation with corifollitropin alfa should utilize established response prediction models. The clinical impact of this needs to be evaluated in future studies. STUDY FUNDING/COMPETING INTEREST(S): The study was funded by university funds. M.O.S., T.L. and I.R.K. have nothing to declare. G.G. has received personal fees and non-financial support from MSD, Ferring, Merck-Serono, Finox, TEVA, IBSA, Glycotope, Abbott, Marckryl Pharma, VitroLife, NMC Healthcare, ReprodWissen, ZIVA and BioSilu. TRIAL REGISTRATION NUMBER: Not applicable.

Adrenal cortex expression quantitative trait loci in a German Holstein × Charolais cross.

BACKGROUND: The importance of the adrenal gland in regard to lactation and reproduction in cattle has been recognized early. Caused by interest in animal welfare and the impact of stress on economically important traits in farm animals the adrenal gland and its function within the stress response is of increasing interest. However, the molecular mechanisms and pathways involved in stress-related effects on economically important traits in farm animals are not fully understood. Gene expression is an important mechanism underlying complex traits, and genetic variants affecting the transcript abundance are thought to influence the manifestation of an expressed phenotype. We therefore investigated the genetic background of adrenocortical gene expression by applying an adaptive linear rank test to identify genome-wide expression quantitative trait loci (eQTL) for adrenal cortex transcripts in cattle. RESULTS: A total of 10,986 adrenal cortex transcripts and 37,204 single nucleotide polymorphisms (SNPs) were analysed in 145 F2 cows of a Charolais × German Holstein cross. We identified 505 SNPs that were associated with the abundance of 129 transcripts, comprising 482 cis effects and 17 trans effects. These SNPs were located on all chromosomes but X, 16, 24 and 28. Associated genes are mainly involved in molecular and cellular functions comprising free radical scavenging, cellular compromise, cell morphology and lipid metabolism, including genes such as CYP27A1 and LHCGR that have been shown to affect economically important traits in cattle. CONCLUSIONS: In this study we showed that adrenocortical eQTL affect the expression of genes known to contribute to the phenotypic manifestation in cattle. Furthermore, some of the identified genes and related molecular pathways were previously shown to contribute to the phenotypic variation of behaviour, temperament and growth at the onset of puberty in the same population investigated here. We conclude that eQTL analysis appears to be a useful approach providing insight into the molecular and genetic background of complex traits in cattle and will help to understand molecular networks involved.

Novel multiple sclerosis susceptibility loci implicated in epigenetic regulation.

We conducted a genome-wide association study (GWAS) on multiple sclerosis (MS) susceptibility in German cohorts with 4888 cases and 10,395 controls. In addition to associations within the major histocompatibility complex (MHC) region, 15 non-MHC loci reached genome-wide significance. Four of these loci are novel MS susceptibility loci. They map to the genes L3MBTL3, MAZ, ERG, and SHMT1. The lead variant at SHMT1 was replicated in an independent Sardinian cohort. Products of the genes L3MBTL3, MAZ, and ERG play important roles in immune cell regulation. SHMT1 encodes a serine hydroxymethyltransferase catalyzing the transfer of a carbon unit to the folate cycle. This reaction is required for regulation of methylation homeostasis, which is important for establishment and maintenance of epigenetic signatures. Our GWAS approach in a defined population with limited genetic substructure detected associations not found in larger, more heterogeneous cohorts, thus providing new clues regarding MS pathogenesis.

Adaptive linear rank tests for eQTL studies.

Expression quantitative trait loci (eQTL) studies are performed to identify single-nucleotide polymorphisms that modify average expression values of genes, proteins, or metabolites, depending on the genotype. As expression values are often not normally distributed, statistical methods for eQTL studies should be valid and powerful in these situations. Adaptive tests are promising alternatives to standard approaches, such as the analysis of variance or the Kruskal-Wallis test. In a two-stage procedure, skewness and tail length of the distributions are estimated and used to select one of several linear rank tests. In this study, we compare two adaptive tests that were proposed in the literature using extensive Monte Carlo simulations of a wide range of different symmetric and skewed distributions. We derive a new adaptive test that combines the advantages of both literature-based approaches. The new test does not require the user to specify a distribution. It is slightly less powerful than the locally most powerful rank test for the correct distribution and at least as powerful as the maximin efficiency robust rank test. We illustrate the application of all tests using two examples from different eQTL studies.