Biology subjects

Shaw, R. J.

Publications and source records attributed to Shaw, R. J..

2 recordsLinked to original sources

bioRxiv2018

Novel genome-wide associations for suicidality in UK Biobank, genetic correlation with psychiatric disorders and polygenic association with completed suicide.

AbstractBackground: Suicide is a major issue for global public health. Suicidality describes a broad clinical spectrum of thoughts and behaviours, some of which are common in the general population.\n\nMethods: UK Biobank recruited [~]0{middle dot}5 million middle age individuals from the UK, of whom 157,000 completed an assessment of suicidality. Mutually exclusive groups were assessed in an ordinal genome-wide association study of suicidality: no suicidality controls (N=83,557); thoughts that life was not worth living (N=21,063); ever contemplated self-harm (N=13,038); an act of deliberate self-harm in the past (N=2,498); and a previous suicide attempt (N=2,666). Linkage of UK Biobank to death certification records identified a small sub-group of completed suicide (N=137).\n\nOutcomes: We identified three novel genome-wide significant loci for suicidality (on Chromosomes 9, 11 and 13) and moderate-to-strong genetic correlations between suicidality and a range of psychiatric disorders, most notably depression (rg 0{middle dot}81). Higher polygenic risk scores for suicidality were associated with increased risk of completed suicide relative to controls in an independent sub-group (N=137 vs N=5,330, OR 1{middle dot}23, 95%CI 1{middle dot}06 to 1{middle dot}41, p=0.03). Rs598046-G (chromosome 11) demonstrated a similar effect size and direction (p=0{middle dot}05) within a Danish suicidality study.\n\nInterpretation: These findings have significant implications for our understanding of genetic vulnerability to suicidal thoughts and behaviours. Future work should assess the extent to which polygenic risk scores for suicidality, in combination with non-genetic risk factors, may be useful for stratified approaches to suicide prevention at a population level.\n\nFunding: UKRI Innovation-HDR-UK Fellowship (MR/S003061/1). MRC Mental Health Data Pathfinder Award (MC_PC_17217).

genetics↗

bioRxiv2016

Detection of long repeat expansions from PCR-free whole-genome sequence data

Identifying large repeat expansions such as those that cause amyotrophic lateral sclerosis (ALS) and Fragile X syndrome is challenging for short-read (100-150 bp) whole genome sequencing (WGS) data. A solution to this problem is an important step towards integrating WGS into precision medicine. We have developed a software tool called ExpansionHunter that, using PCR-free WGS short-read data, can genotype repeats at the locus of interest, even if the expanded repeat is larger than the read length. We applied our algorithm to WGS data from 3,001 ALS patients who have been tested for the presence of the C9orf72 repeat expansion with repeat-primed PCR (RP-PCR). Taking the RP-PCR calls as the ground truth, our WGS-based method identified pathogenic repeat expansions with 98.1% sensitivity and 99.7% specificity. Further inspection identified that all 11 conflicts were resolved as errors in the original RP-PCR results. Compared against this updated result, ExpansionHunter correctly classified all (212/212) of the expanded samples as either expansions (208) or potential expansions (4). Additionally, 99.9% (2,786/2,789) of the wild type samples were correctly classified as wild type by this method with the remaining two identified as possible expansions. We further applied our algorithm to a set of 144 samples where every sample had one of eight different pathogenic repeat expansions including examples associated with fragile X syndrome, Friedreichs ataxia and Huntingtons disease and correctly flagged all of the known repeat expansions. Finally, we tested the accuracy of our method for short repeats by comparing our genotypes with results from 860 samples sized using fragment length analysis and determined that our calls were >95% accurate. ExpansionHunter can be used to accurately detect known pathogenic repeat expansions and provides researchers with a tool that can be used to identify new pathogenic repeat expansions.

bioinformatics↗