Doctor of Philosophy, University Of Edinburgh (2018)
Master of Science, Leiden University (2014)
Bachelor of Applied Science, Unlisted School (2011)
Lower performances in cognitive ability in individuals with Major Depressive Disorder (MDD) have been observed on multiple occasions. Understanding cognitive performance in MDD could provide a wider insight in the aetiology of MDD as a whole. Using a large, well characterised cohort (N = 7012), we tested for: differences in cognitive performance by MDD status and a gene (single SNP or polygenic score) by MDD interaction effect on cognitive performance. Linear regression was used to assess the association between cognitive performance and MDD status in a case-control, single-episode-recurrent MDD and control-recurrent MDD study design. Test scores on verbal declarative memory, executive functioning, vocabulary, and processing speed were examined. Cognitive performance measures showing a significant difference between groups were subsequently analysed for genetic associations. Those with recurrent MDD have lower processing speed versus controls and single-episode MDD (β = -2.44, p = 3.6 × 10-04; β = -2.86, p = 1.8 × 10-03, respectively). There were significantly higher vocabulary scores in MDD cases versus controls (β = 0.79, p = 2.0 × 10-06), and for recurrent MDD versus controls (β = 0.95, p = 5.8 × 10-05). Observed differences could not be linked to significant single-locus associations. Polygenic scores created from a processing speed meta-analysis GWAS explained 1% of variation in processing speed performance in the single-episode versus recurrent MDD study (p = 1.7 × 10-03) and 0.5% of variation in the control versus recurrent MDD study (p = 1.6 × 10-10). Individuals with recurrent MDD showed lower processing speed and executive function while showing higher vocabulary performance. Within MDD, persons with recurrent episodes show lower processing speed and executive function scores relative to individuals experiencing a single episode.
View details for DOI 10.1038/s41398-018-0111-0
View details for Web of Science ID 000428350700003
View details for PubMedID 29531327
View details for PubMedCentralID PMC5847617
The genomic architecture of human complex diseases is thought to be attributable to single markers, polygenic components and epistatic components. No study has examined the ability of tree-based methods to detect epistasis in the presence of a polygenic signal. We sought to apply decision tree-based methods, C5.0 and logic regression, to detect epistasis under several simulated conditions, varying strength of interaction and linkage disequilibrium (LD) structure. We, then applied the same methods to the phenotype of educational attainment in a large population cohort.LD pruning improved the power and reduced the type I error. C5.0 had a conservative type I error rate whereas logic regression had a type I error rate that exceeded 5%. Despite the more conservative type I error, C5.0 was observed to have higher power than logic regression across several conditions. In the presence of a polygenic signal, power was generally reduced. Applying both methods on educational attainment in a large population cohort yielded numerous interacting SNPs; notably a SNP in RCAN3 which is associated with reading and spelling and a SNP in NPAS3 a neurodevelopmental gene.All methods used are implemented and freely available in R.Supplementary Data are available at Bioinformatics online.
View details for DOI 10.1093/bioinformatics/bty462
View details for PubMedID 29931044