Phenome-wide association studies across large population cohorts support drug target validation.

Phenome-wide association studies (PheWAS) have been proposed as a possible aid in drug development through elucidating mechanisms of action, identifying alternative indications, or predicting adverse drug events (ADEs). Here, we select 25 single nucleotide polymorphisms (SNPs) linked through genome-wide association studies (GWAS) to 19 candidate drug targets for common disease indications. We interrogate these SNPs by PheWAS in four large cohorts with extensive health information (23andMe, UK Biobank, FINRISK, CHOP) for association with 1683 binary endpoints in up to 697,815 individuals and conduct meta-analyses for 145 mapped disease endpoints. Our analyses replicate 75% of known GWAS associations (P < 0.05) and identify nine study-wide significant novel associations (of 71 with FDR < 0.1). We describe associations that may predict ADEs, e.g., acne, high cholesterol, gout, and gallstones with rs738409 (p.I148M) in PNPLA3 and asthma with rs1990760 (p.T946A) in IFIH1. Our results demonstrate PheWAS as a powerful addition to the toolkit for drug discovery.

Smoking Gun or Circumstantial Evidence? Comparison of Statistical Learning Methods using Functional Annotations for Prioritizing Risk Variants.

Although technology has triumphed in facilitating routine genome sequencing, new challenges have been created for the data-analyst. Genome-scale surveys of human variation generate volumes of data that far exceed capabilities for laboratory characterization. By incorporating functional annotations as predictors, statistical learning has been widely investigated for prioritizing genetic variants likely to be associated with complex disease. We compared three published prioritization procedures, which use different statistical learning algorithms and different predictors with regard to the quantity, type and coding. We also explored different combinations of algorithm and annotation set. As an application, we tested which methodology performed best for prioritizing variants using data from a large schizophrenia meta-analysis by the Psychiatric Genomics Consortium. Results suggest that all methods have considerable (and similar) predictive accuracies (AUCs 0.64-0.71) in test set data, but there is more variability in the application to the schizophrenia GWAS. In conclusion, a variety of algorithms and annotations seem to have a similar potential to effectively enrich true risk variants in genome-scale datasets, however none offer more than incremental improvement in prediction. We discuss how methods might be evolved for risk variant prediction to address the impending bottleneck of the new generation of genome re-sequencing studies.

Recursive splicing in long vertebrate genes.

It is generally believed that splicing removes introns as single units from precursor messenger RNA transcripts. However, some long Drosophila melanogaster introns contain a cryptic site, known as a recursive splice site (RS-site), that enables a multi-step process of intron removal termed recursive splicing. The extent to which recursive splicing occurs in other species and its mechanistic basis have not been examined. Here we identify highly conserved RS-sites in genes expressed in the mammalian brain that encode proteins functioning in neuronal development. Moreover, the RS-sites are found in some of the longest introns across vertebrates. We find that vertebrate recursive splicing requires initial definition of an 'RS-exon' that follows the RS-site. The RS-exon is then excluded from the dominant mRNA isoform owing to competition with a reconstituted 5' splice site formed at the RS-site after the first splicing step. Conversely, the RS-exon is included when preceded by cryptic promoters or exons that fail to reconstitute an efficient 5' splice site. Most RS-exons contain a premature stop codon such that their inclusion can decrease mRNA stability. Thus, by establishing a binary splicing switch, RS-sites demarcate different mRNA isoforms emerging from long genes by coupling cryptic elements with inclusion of RS-exons.

Pooled sequencing of 531 genes in inflammatory bowel disease identifies an associated rare variant in BTNL2 and implicates other immune related genes.

The contribution of rare coding sequence variants to genetic susceptibility in complex disorders is an important but unresolved question. Most studies thus far have investigated a limited number of genes from regions which contain common disease associated variants. Here we investigate this in inflammatory bowel disease by sequencing the exons and proximal promoters of 531 genes selected from both genome-wide association studies and pathway analysis in pooled DNA panels from 474 cases of Crohn's disease and 480 controls. 80 variants with evidence of association in the sequencing experiment or with potential functional significance were selected for follow up genotyping in 6,507 IBD cases and 3,064 population controls. The top 5 disease associated variants were genotyped in an extension panel of 3,662 IBD cases and 3,639 controls, and tested for association in a combined analysis of 10,147 IBD cases and 7,008 controls. A rare coding variant p.G454C in the BTNL2 gene within the major histocompatibility complex was significantly associated with increased risk for IBD (p = 9.65x10-10, OR = 2.3[95% CI = 1.75-3.04]), but was independent of the known common associated CD and UC variants at this locus. Rare (<1%) and low frequency (1-5%) variants in 3 additional genes showed suggestive association (p<0.005) with either an increased risk (ARIH2 c.338-6C>T) or decreased risk (IL12B p.V298F, and NICN p.H191R) of IBD. These results provide additional insights into the involvement of the inhibition of T cell activation in the development of both sub-phenotypes of inflammatory bowel disease. We suggest that although rare coding variants may make a modest overall contribution to complex disease susceptibility, they can inform our understanding of the molecular pathways that contribute to pathogenesis.

Genome-scale methods converge on key mitochondrial genes for the survival of human cardiomyocytes in hypoxia.

BACKGROUND: Any reduction in myocardial oxygen delivery relative to its demands can impair cardiac contractile performance. Understanding the mitochondrial metabolic response to hypoxia is key to understanding ischemia tolerance in the myocardium. We used a novel combination of 2 genome-scale methods to study key processes underlying human myocardial hypoxia tolerance. In particular, we hypothesized that computational modeling and evolution would identify similar genes as critical to human myocardial hypoxia tolerance. METHODS AND RESULTS: We analyzed a reconstruction of the cardiac mitochondrial metabolic network using constraint-based methods, under conditions of simulated hypoxia. We used flux balance analysis, random sampling, and principal component analysis to explore feasible steady-state solutions. Hypoxia blunted maximal ATP (-17%) and heme (-75%) synthesis and shrank the feasible solution space. Tricarboxylic acid and urea cycle fluxes were also reduced in hypoxia, but phospholipid synthesis was increased. Using mathematical optimization methods, we identified reactions that would be critical to hypoxia tolerance in the human heart. We used data regarding single-nucleotide polymorphism frequency and distribution in the genomes of Tibetans (whose ancestors have resided in persistent high-altitude hypoxia for several millennia). Six reactions were identified by both methods as being critical to mitochondrial ATP production in hypoxia: phosphofructokinase, phosphoglucokinase, complex II, complex IV, aconitase, and fumarase. CONCLUSIONS: Mathematical optimization and evolution converged on similar genes as critical to human myocardial hypoxia tolerance. Our approach is unique and completely novel and demonstrates that genome-scale modeling and genomics can be used in tandem to provide new insights into cardiovascular genetics.

A Bayesian method to incorporate hundreds of functional characteristics with association evidence to improve variant prioritization.

The increasing quantity and quality of functional genomic information motivate the assessment and integration of these data with association data, including data originating from genome-wide association studies (GWAS). We used previously described GWAS signals ("hits") to train a regularized logistic model in order to predict SNP causality on the basis of a large multivariate functional dataset. We show how this model can be used to derive Bayes factors for integrating functional and association data into a combined Bayesian analysis. Functional characteristics were obtained from the Encyclopedia of DNA Elements (ENCODE), from published expression quantitative trait loci (eQTL), and from other sources of genome-wide characteristics. We trained the model using all GWAS signals combined, and also using phenotype specific signals for autoimmune, brain-related, cancer, and cardiovascular disorders. The non-phenotype specific and the autoimmune GWAS signals gave the most reliable results. We found SNPs with higher probabilities of causality from functional characteristics showed an enrichment of more significant p-values compared to all GWAS SNPs in three large GWAS studies of complex traits. We investigated the ability of our Bayesian method to improve the identification of true causal signals in a psoriasis GWAS dataset and found that combining functional data with association data improves the ability to prioritise novel hits. We used the predictions from the penalized logistic regression model to calculate Bayes factors relating to functional characteristics and supply these online alongside resources to integrate these data with association data.

Fine-mapping, gene expression and splicing analysis of the disease associated LRRK2 locus.

Association studies have identified several signals at the LRRK2 locus for Parkinson's disease (PD), Crohn's disease (CD) and leprosy. However, little is known about the molecular mechanisms mediating these effects. To further characterize this locus, we fine-mapped the risk association in 5,802 PD and 5,556 controls using a dense genotyping array (ImmunoChip). Using samples from 134 post-mortem control adult human brains (UK Human Brain Expression Consortium), where up to ten brain regions were available per individual, we studied the regional variation, splicing and regulation of LRRK2. We found convincing evidence for a common variant PD association located outside of the LRRK2 protein coding region (rs117762348, A>G, P = 2.56×10(-8), case/control MAF 0.083/0.074, odds ratio 0.86 for the minor allele with 95% confidence interval [0.80-0.91]). We show that mRNA expression levels are highest in cortical regions and lowest in cerebellum. We find an exon quantitative trait locus (QTL) in brain samples that localizes to exons 32-33 and investigate the molecular basis of this eQTL using RNA-Seq data in n = 8 brain samples. The genotype underlying this eQTL is in strong linkage disequilibrium with the CD associated non-synonymous SNP rs3761863 (M2397T). We found two additional QTLs in liver and monocyte samples but none of these explained the common variant PD association at rs117762348. Our results characterize the LRRK2 locus, and highlight the importance and difficulties of fine-mapping and integration of multiple datasets to delineate pathogenic variants and thus develop an understanding of disease mechanisms.

Gene expression changes with age in skin, adipose tissue, blood and brain.

BACKGROUND: Previous studies have demonstrated that gene expression levels change with age. These changes are hypothesized to influence the aging rate of an individual. We analyzed gene expression changes with age in abdominal skin, subcutaneous adipose tissue and lymphoblastoid cell lines in 856 female twins in the age range of 39-85 years. Additionally, we investigated genotypic variants involved in genotype-by-age interactions to understand how the genomic regulation of gene expression alters with age. RESULTS: Using a linear mixed model, differential expression with age was identified in 1,672 genes in skin and 188 genes in adipose tissue. Only two genes expressed in lymphoblastoid cell lines showed significant changes with age. Genes significantly regulated by age were compared with expression profiles in 10 brain regions from 100 postmortem brains aged 16 to 83 years. We identified only one age-related gene common to the three tissues. There were 12 genes that showed differential expression with age in both skin and brain tissue and three common to adipose and brain tissues. CONCLUSIONS: Skin showed the most age-related gene expression changes of all the tissues investigated, with many of the genes being previously implicated in fatty acid metabolism, mitochondrial activity, cancer and splicing. A significant proportion of age-related changes in gene expression appear to be tissue-specific with only a few genes sharing an age effect in expression across tissues. More research is needed to improve our understanding of the genetic influences on aging and the relationship with age-related diseases.