Biology subjects

Sinnott-Armstrong, N.

Publications and source records attributed to Sinnott-Armstrong, N..

3 recordsLinked to original sources

bioRxiv2018

Narcolepsy risk loci are enriched in immune cells and suggest autoimmune modulation of the T cell receptor repertoire

Type 1 narcolepsy (T1N) is a neurological condition, in which the death of hypocretin-producing neurons in the lateral hypothalamus leads to excessive daytime sleepiness and symptoms of abnormal Rapid Eye Movement (REM) sleep. Known triggers for narcolepsy are influenza-A infection and associated immunization during the 2009 H1N1 influenza pandemic. Here, we genotyped all remaining consented narcolepsy cases worldwide and assembled this with the existing genotyped individuals. We used this multi-ethnic sample in genome wide association study (GWAS) to dissect disease mechanisms and interactions with environmental triggers (5,339 cases and 20,518 controls). Overall, we found significant associations with HLA (2 GWA significant subloci) and 11 other loci. Six of these other loci have been previously reported (TRA, TRB, CTSH, IFNAR1, ZNF365 and P2RY11) and five are new (PRF1, CD207, SIRPG, IL27 and ZFAND2A). Strikingly, in vaccination-related cases GWA significant effects were found in HLA, TRA, and in a novel variant near SIRPB1. Furthermore, IFNAR1 associated polymorphisms regulated dendritic cell response to influenza-A infection in vitro (p-value =1.92*10-25). A partitioned heritability analysis indicated specific enrichment of functional elements active in cytotoxic and helper T cells. Furthermore, functional analysis showed the genetic variants in TRA and TRB loci act as remarkable strong chain usage QTLs for TRAJ*24 (p-value = 0.0017), TRAJ*28 (p-value = 1.36*10-10) and TRBV*4-2 (p-value = 3.71*10-117). This was further validated in TCR sequencing of 60 narcolepsy cases and 60 DQB1*06:02 positive controls, where chain usage effects were further accentuated. Together these findings show that the autoimmune component in narcolepsy is defined by antigen presentation, mediated through specific T cell receptor chains, and modulated by influenza-A as a critical trigger.

genetics↗

bioRxiv2018

Reduced signal for polygenic adaptation of height in UK Biobank

Several recent papers have reported strong signals of selection on European polygenic height scores. These analyses used height effect estimates from the GIANT consortium and replication studies. Here, we describe a new analysis based on the the UK Biobank (UKB), a large, independent dataset. We find that the signals of selection using UKB effect-size estimates for height are strongly attenuated or absent. We also provide evidence that previous analyses were confounded by population stratification Therefore, the conclusion of strong polygenic adaptation now lacks support. Moreover, these discrepancies highlight (1) that methods for correcting for population stratification in GWAS may not always be sufficient for polygenic trait analyses, and (2) that claims of differences in polygenic scores between populations should be treated with caution until these issues are better understood.

evolutionary biology↗

bioRxiv2017

Vulnerabilities of transcriptome-wide association studies

Transcriptome-wide association studies (TWAS) integrate GWAS and gene expression datasets to find gene-trait associations. In this Perspective, we explore properties of TWAS as a potential approach to prioritize causal genes, using simulations and case studies of literature-curated candidate causal genes for schizophrenia, LDL cholesterol and Crohns disease. We explore risk loci where TWAS accurately prioritizes the likely causal gene, as well as loci where TWAS prioritizes multiple genes, some of which are unlikely to be causal, because they share the same variants as eQTLs. We illustrate that TWAS is especially prone to spurious prioritization when using expression data from tissues or cell types that are less related to the trait, due to substantial variation in both expression levels and eQTL strengths across cell types. Nonetheless, TWAS prioritizes candidate causal genes at GWAS loci more accurately than simple baselines based on proximity to lead GWAS variant and expression in trait-related tissue. We discuss current strategies and future opportunities for improving the performance of TWAS for causal gene prioritization. Our results showcase the strengths and limitations of using expression variation across individuals to determine causal genes at GWAS loci and provide guidelines and best practices when using TWAS to prioritize candidate causal genes.

genetics↗