Crouch, D. J. M. | bioRxiv Science

bioRxiv2021

Enhanced genetic analysis of type 1 diabetes by selecting variants on both effect size and significance, and by integration with autoimmune thyroid disease

For polygenic traits, associations with genetic variants can be detected over many chromosome regions, owing to the availability oflarge sample sizes. Most variants, however, have small effects on disease risk and, therefore, unravelling the causal variants, target genes, and biology of these variants is challenging. Here, we define the Bigger or False Discovery Rate (BFDR) as the probability that either a variant is a false-positive or a randomly drawn, true-positive association exceeds it in effect size. Using the BFDR, we identified 302 previously unreported signals with larger effect associations with type 1 diabetes and autoimmune thyroid disease. Out of 239 genome-wide significant signals in both diseases, only 66 (28%) show evidence for having a large effect using the BFDR, further demonstrating how using a combination of effect size and significance, rather than significance alone, is important in identifying SNPs and candidate genes for further investigation.

genetics↗

bioRxiv2020

Divergent genetic effects for type 1 and type 2 diabetes at overlapping association signals

Aims/hypothesisGiven 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 colocalises with divergent effect directions, it could provide valuable biological insight into how the association affects the two diseases differently. MethodsUsing publicly available type 2 diabetes summary statistics from a genomewide 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 (7,467 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 colocalisation of each type 1 diabetes signal with each type 2 diabetes signal using coloc. Any association with a colocalisation posterior probability of [≥]0.9 was considered a genuine shared association with both diseases. ResultsOf the 81 association signals from 42 genetic regions that showed association with both type 1 and type 2 diabetes, four association signals colocalised between both diseases (posterior probability [≥]0.9): (i) chromosome 16q23.1, near Chymotripsinogen B1 (CTRB1) / Breast Cancer Anti-Estrogen Resistance Protein 1 (BCAR1), which has been previously identified; (ii) chromosome 11p15.5, near the Insulin (INS) gene; (iii) chromosome 4p16.3, near Transmembrane protein 129 (TMEM129), and (iv) chromosome 1p31.3, near Phosphoglucomutase 1 (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 colocalisation on chromosome 9p24.2, near the GLIS Family Zinc Finger Protein 3 (GLIS3) gene, in this case with a concordant direction of effect. Conclusions/interpretationThat four of five association signals that colocalise between type 1 diabetes and type 2 diabetes are in opposite directions suggests a complex genetic relationship between the two diseases. Research in ContextWhat is already known about this subject? O_LIOther than insulin, there are currently no treatments for both type 1 and type 2 diabetes. C_LIO_LIFindings that genetic variants near the GLIS3 gene increase risk of both type 1 and type 2 diabetes have indicated shared genetic mechanisms at the level of the pancreatic {beta} cell. C_LI What is the key question? O_LIBy examining chromosome regions associated with both diseases, are there any more variants that affect risk of both diseases and could support common mechanisms and repositioning of therapeutics between the diseases? C_LI What are the new findings? O_LIAt current sample sizes, there is evidence that five genetic variants in different chromosome regions impact risk of developing both diseases. C_LIO_LIHowever, four of these variants have the opposite direction of effect in type 1 diabetes compared to type 2 diabetes, with only one, near GLIS3, having a concordant direction of effect. C_LI How might this impact on clinical practise in the foreseeable future? O_LIGenetic findings have furthered research in type 1 and type 2 diabetes independently, and suggest therapeutic strategies. However, our current investigation into their shared genetics suggests that repositioning of current type 2 diabetes treatments into type 1 diabetes may not be straightforward. C_LI

genetics↗

bioRxiv2019

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. Author ContributionsWFB conceived the project. WFB and DJMC wrote the manuscript.

cell biology↗