Polygenic inheritance, GWAS, polygenic risk scores, and the search for functional variants

The reconciliation between Mendelian inheritance of discrete traits and the genetically based correlation between relatives for quantitative traits was Fisher's infinitesimal model of a large number of genetic variants, each with very small effects, whose causal effects could not be individually identified. The development of genome-wide genetic association studies (GWAS) raised the hope that it would be possible to identify single polymorphic variants with identifiable functional effects on complex traits. It soon became clear that, with larger and larger GWAS on more and more complex traits, most of the significant associations had such small effects, that identifying their individual functional effects was essentially hopeless. Polygenic risk scores that provide an overall estimate of the genetic propensity to a trait at the individual level have been developed using GWAS data. These provide useful identification of groups of individuals with substantially increased risks, which can lead to recommendations of medical treatments or behavioral modifications to reduce risks. However, each such claim will require extensive investigation to justify its practical application. The challenge now is to use limited genetic association studies to find individually identifiable variants of significant functional effect that can help to understand the molecular basis of complex diseases and traits, and so lead to improved disease prevention and treatment. This can best be achieved by 1) the study of rare variants, often chosen by careful candidate assessment, and 2) the careful choice of phenotypes, often extremes of a quantitative variable, or traits with relatively high heritability

Somatic selection of poorly differentiating variant stem cell clones could be a key to human ageing

Any replicating system in which heritable variants with differing replicative potentials can arise is subject to a Darwinian evolutionary process. The continually replicating adult tissue stem cells that control the integrity of many tissues of long-lived, multicellular, complex vertebrate organisms, including humans, constitute such a replicating system. Our suggestion is that somatic selection for mutations (or stable epigenetic changes) that cause an increased rate of adult tissue stem cell proliferation, and their long-term persistence, at the expense of normal differentiation, is a major key to the ageing process. Once an organism has passed the reproductive age, there is no longer any significant counterselection at the organismal level to this inevitable cellular level Darwinian process

Genetic variants predisposing most strongly to type 1 diabetes diagnosed under age 7 years lie near candidate genes that function in the immune system and in pancreatic β-cells

OBJECTIVE: Immunohistological analyses of pancreata from patients with type 1 diabetes suggest distinct autoimmune islet β-cell pathology between those diagnosed at <7 years (<7 group) and those diagnosed at age ≥13 years (≥13 group), with both B- and T-lymphocyte islet inflammation common in children in the <7 group, whereas B cells are rare in the ≥13 group. Based on these observations, we sought to identify differences in genetic susceptibility between these prespecified age-at-diagnosis groups to inform on the etiology of the most aggressive form of type 1 diabetes that initiates in the first years of life. RESEARCH DESIGN AND METHODS: Using multinomial logistic regression models, we tested if known type 1 diabetes loci (17 within the HLA and 55 non-HLA loci had significantly stronger effect sizes in the <7 group compared with the ≥13 group, using genotype data from 27,071 individuals (18,485 control subjects and 3,121 case subjects diagnosed at <7 years, 3,757 at 7-13 years, and 1,708 at ≥13 years). RESULTS: Six HLA haplotypes/classical alleles and six non-HLA regions, one of which functions specifically in β-cells (GLIS3) and the other five likely affecting key T-cell (IL2RA, IL10, IKZF3, and THEMIS), thymus (THEMIS), and B-cell development/functions (IKZF3 and IL10) or in both immune and β-cells (CTSH), showed evidence for stronger effects in the <7 group. CONCLUSIONS: A subset of type 1 diabetes-associated variants are more prevalent in children diagnosed under the age of 7 years and are near candidate genes that act in both pancreatic β- and immune cells

Extending Non-negative Matrix Factorisation to 3D Registered Data

The use of non-negative matrix factorisation (NMF) on 2D face images has been shown to result in sparse feature vectors that encode for local patches on the face, and thus provides a statistically justified approach to learning parts from wholes. However successful on 2D images, the method has so far not been extended to 3D images. The main reason for this is that 3D space is a continuum and so it is not apparent how to represent 3D coordinates in a non-negative fashion. This work compares different non-negative representations for spatial coordinates, and demonstrates that not all non-negative representations are suitable. We analyse the representational properties that make NMF a successful method to learn sparse 3D facial features. Using our proposed representation, the factorisation results in sparse and interpretable facial features

Analysis of overlapping genetic association in type 1 and type 2 diabetes

Aims/hypothesis Given the potential shared aetiology between type 1 and type 2 diabetes, we aimed to identify any genetic regions associated with both diseases. For associations where there is a shared signal and the allele that increases risk to one disease also increases risk to the other, inference about shared aetiology could be made, with the potential to develop therapeutic strategies to treat or prevent both diseases simultaneously. Alternatively, if a genetic signal co-localises with divergent effect directions, it could provide valuable biological insight into how the association affects the two diseases differently. Methods Using publicly available type 2 diabetes summary statistics from a genome-wide association study (GWAS) meta-analysis of European ancestry individuals (74,124 cases and 824,006 controls) and type 1 diabetes GWAS summary statistics from a meta-analysis of studies on individuals from the UK and Sardinia (7467 cases and 10,218 controls), we identified all regions of 0.5 Mb that contained variants associated with both diseases (false discovery rate <0.01). In each region, we performed forward stepwise logistic regression to identify independent association signals, then examined co-localisation of each type 1 diabetes signal with each type 2 diabetes signal using coloc. Any association with a co-localisation posterior probability of ≥0.9 was considered a genuine shared association with both diseases. Results Of the 81 association signals from 42 genetic regions that showed association with both type 1 and type 2 diabetes, four association signals co-localised between both diseases (posterior probability ≥0.9): (1) chromosome 16q23.1, near CTRB1/BCAR1, which has been previously identified; (2) chromosome 11p15.5, near the INS gene; (3) chromosome 4p16.3, near TMEM129 and (4) chromosome 1p31.3, near PGM1. In each of these regions, the effect of genetic variants on type 1 diabetes was in the opposite direction to the effect on type 2 diabetes. Use of additional datasets also supported the previously identified co-localisation on chromosome 9p24.2, near the GLIS3 gene, in this case with a concordant direction of effect. Conclusions/interpretation Four of five association signals that co-localise between type 1 diabetes and type 2 diabetes are in opposite directions, suggesting a complex genetic relationship between the two diseases

Genetics of the human face: Identification of large-effect single gene variants

To discover specific variants with relatively large effects on the human face, we have devised an approach to identifying facial features with high heritability. This is based on using twin data to estimate the additive genetic value of each point on a face, as provided by a 3D camera system. In addition, we have used the ethnic difference between East Asian and European faces as a further source of face genetic variation. We use principal components (PCs) analysis to provide a fine definition of the surface features of human faces around the eyes and of the profile, and chose upper and lower 10% extremes of the most heritable PCs for looking for genetic associations. Using this strategy for the analysis of 3D images of 1,832 unique volunteers from the well-characterized People of the British Isles study and 1,567 unique twin images from the TwinsUK cohort, together with genetic data for 500,000 SNPs, we have identified three specific genetic variants with notable effects on facial profiles and eyes

Genes predict village of origin in rural Europe

The genetic structure of human populations is important in population genetics, forensics and medicine. Using genome-wide scans and individuals with all four grandparents born in the same settlement, we here demonstrate remarkable geographical structure across 8-30 km in three different parts of rural Europe. After excluding close kin and inbreeding, village of origin could still be predicted correctly on the basis of genetic data for 89-100% of individuals. European Journal of Human Genetics (2010) 18, 1269-1270; doi: 10.1038/ejhg.2010.92; published online 23 June 201

Genetic regulation of RNA splicing in human pancreatic islets

Background: Non‑coding genetic variants that influence gene transcription in pancreatic islets play a major role in the susceptibility to type 2 diabetes (T2D), and likely also contribute to type 1 diabetes (T1D) risk. For many loci, however, the mechanisms through which non‑coding variants influence diabetes susceptibility are unknown. Results: We examine splicing QTLs (sQTLs) in pancreatic islets from 399 human donors and observe that genetic variation has a widespread influence on splicing of genes with established roles in islet biology and diabetes. In parallel, we profile expression QTLs (eQTLs) and use transcriptome‑wide association as well as genetic co‑localization studies to assign islet sQTLs or eQTLs to T2D and T1D susceptibility signals, many of which lack candidate effector genes. This analysis reveals biologically plausible mechanisms, including the association of T2D with an sQTL that creates a nonsense isoform in ERO1B, a regulator of ER‑stress and proinsulin biosynthesis. The expanded list of T2D risk effector genes reveals overrepresented pathways, including regulators of G‑protein‑mediated cAMP production. The analysis of sQTLs also reveals candidate effector genes for T1D susceptibility such as DCLRE1B, a senescence regulator, and lncRNA MEG3. Conclusions: These data expose widespread effects of common genetic variants on RNA splicing in pancreatic islets. The results support a role for splicing variation in diabetes susceptibility, and offer a new set of genetic targets with potential therapeutic benefit