Assessment of the genetic similarity between two phenotypes can provide insight into a common genetic aetiology and inform the use of pleiotropy-informed, cross-phenotype analytical methods to identify novel genetic associations. The genetic correlation is a well-known means of quantifying and testing for genetic similarity between traits, but its estimates are subject to comparatively large sampling error. This makes it unsuitable for use in a small-sample context. We discuss the use of a previously published nonparametric test of genetic similarity for application to GWAS summary statistics. We establish that the null distribution of the test statistic is modelled better by an extreme value distribution than a transformation of the standard exponential distribution. We show with simulation studies and real data from GWAS of 18 phenotypes from the UK Biobank that the test is to be preferred for use with small sample sizes, particularly when genetic effects are few and large, outperforming the genetic correlation and another nonparametric statistical test of independence. We find the test suitable for the detection of genetic similarity in the rare disease context.

Genome-wide association studies (GWAS) have identified thousands of genetic variants that are associated with complex traits. However, a stringent significance threshold is required to identify robust genetic associations. Leveraging relevant auxiliary covariates has the potential to boost statistical power to exceed the significance threshold. Particularly, abundant pleiotropy and the non-random distribution of SNPs across various functional categories suggests that leveraging GWAS test statistics from related traits and/or functional genomic data may boost GWAS discovery. While type 1 error rate control has become standard in GWAS, control of the false discovery rate can be a more powerful approach. The conditional false discovery rate (cFDR) extends the standard FDR framework by conditioning on auxiliary data to call significant associations, but current implementations are restricted to auxiliary data satisfying specific parametric distributions, typically GWASp-values for related traits. We relax these distributional assumptions, enabling an extension of the cFDR framework that supports auxiliary covariates from arbitrary continuous distributions (“Flexible cFDR”). Our method can be applied iteratively, thereby supporting multi-dimensional covariate data. Through simulations we show that Flexible cFDR increases sensitivity whilst controlling FDR after one or several iterations. We further demonstrate its practical potential through application to an asthma GWAS, leveraging various functional genomic data to find additional genetic associations for asthma, which we validate in the larger, independent, UK Biobank data resource.

Coeliac disease (CeD) is a gastrointestinal enteropathy triggered by the consumption of gluten in predisposed individuals. A recent study showed that individuals were at more than 10% risk of having CeD if a first-degree relative also had the disease. However, only around 50% of CeD genetic heritability is attributable to specific loci, with the majority of this heritable risk attributed to the HLA loci, while the remaining 50% of disease risk is currently unidentified. We investigated the butyrophilin family of immunomodulators as novel CeD risk loci. We sequenced the butyrophilin loci of 48 CeD and 46 control patients and carried out gene-based burden testing on the captured single-nucleotide polymorphisms (SNPs). We found a significantly increased BTN2A1 gene burden in CeD patients. To validate these results, the SNP data of 3094 CeD patients and 29,762 control participants from the UK Biobank database were subjected to single-variant analyses. Fourteen BTN2A1, ten BTN3A1, and thirteen BTN3A2 SNPs were significantly associated with CeD status. These results are interesting, as BTN2A1 and BTN3A2 have not been associated with CeD risk previously but are known to modulate the activation of Vγ9+ γδ T cells and NK cells. Twenty of the 37 SNPs above were associated with CeD status independent of the risk-associated HLA genotypes. All twenty of these SNPs, alongside a novel SNP not included in the above SNPs, were associated with CeD in HLA-DQ2.5-matched case-control groups. We reaffirm the association of the BTN3A2 locus with CeD risk and identify BTN2A1 and BTN3A1 as putative novel CeD risk loci.

Selective IgA deficiency (SIgAD) is the most common inborn error of immunity (IEI). Unlike many IEIs, evidence of a role for highly penetrant rare variants in SIgAD is lacking. Previous SIgAD studies have had limited power to identify common variants due to their small sample size. We overcame this problem first through meta-analysis of two existing GWAS. This identified four novel common-variant associations and enrichment of SIgAD-associated variants in genes linked to Mendelian IEIs. SIgAD showed evidence of shared genetic architecture with serum IgA and a number of immune-mediated diseases. We leveraged this pleiotropy through the conditional false discovery rate procedure, conditioning our SIgAD meta-analysis on large GWAS of asthma and rheumatoid arthritis, and our own meta-analysis of serum IgA. This identified an additional 18 variants, increasing the number of known SIgAD-associated variants to 27 and strengthening the evidence for a polygenic, common-variant aetiology for SIgAD.

Assessment of the genetic similarity between two phenotypes can provide insight into a common genetic aetiology and inform the use of pleiotropy-informed, cross-phenotype analytical methods to identify novel genetic associations. The genetic correlation is a well-known means of quantifying and testing for genetic similarity between traits, but its estimates are subject to comparatively large sampling error. This makes it unsuitable for use in a small-sample context. We discuss the use of a non-parametric test of genetic similarity first introduced by Li et al. for application to GWAS summary statistics. We establish that the null distribution of the test statistic is modelled better by an extreme value distribution than a transformation of the standard exponential distribution as originally recommended by Li and colleagues. We show with simulation studies and real data from GWAS of 18 phenotypes from the UK Biobank that the test is to be preferred for use with small sample sizes, particularly when genetic effects are few and large, outperforming the genetic correlation and another nonparametric statistical test of independence. We find the test suitable for the detection of genetic similarity in the rare disease context. Author summary The genome-wide association study (GWAS) is a method used to identify genetic variants which contribute to the risk of developing disease. These genetic variants are frequently shared between conditions, such that the study of the genetic basis of one disease can be informed by knowledge of another, similar disease. This approach can be productive where the disease in question is rare such that a GWAS has less power to associate variants with the disease, but there exist larger GWAS of similar diseases. Existing methods do not measure genetic similarity precisely when patients are few. Here we assess a previously published method of testing for genetic similarity between pairs of diseases using GWAS data, the ‘GPS’ test, against three other methods with the use of real and simulated data. We present a new computational procedure for carrying out the test and show that the GPS test is superior to its comparators in identifying genetic similarity when the sample size is small and when the genetic similarity signal is less strong. Use of the test will enable accurate detection of genetic similarity and the study of rarer conditions using data from better-characterised diseases.

Novel open reading frames (nORFs) with coding potential may arise from noncoding DNA. Not much is known about their emergence, functional role, fixation in a population or contribution to adaptive radiation. Cichlids fishes exhibit extensive phenotypic diversification and speciation. Encounters with new environments alone are not sufficient to explain this striking diversity of cichlid radiation because other taxa coexistent with the Cichlidae demonstrate lower species richness. Wagner et al analyzed cichlid diversification in 46 African lakes and reported that both extrinsic environmental factors and intrinsic lineage-specific traits related to sexual selection have strongly influenced the cichlid radiation ¹ , which indicates the existence of unknown molecular mechanisms responsible for rapid phenotypic diversification, such as emergence of novel open reading frames (nORFs). In this study, we integrated transcriptomic and proteomic signatures from two tissues of two cichlids species, identified nORFs and performed evolutionary analysis on these nORF regions. Our results suggest that the time scale of speciation of the two species and evolutionary divergence of these nORF genomic regions are similar and indicate a potential role for these nORFs in speciation of the cichlid fishes.