Oral Presentation 9th GeneMappers Conference 2012

Genome-wide association study of white matter lesions in older adults (#45)

Arezoo Assareh 1 2 3 , Karen A. Mather 2 , John B.J. Kwok 4 5 , Wei Wen 2 6 , Nicola Armstrong 7 8 , Henry Brodaty 2 3 9 , Peter R. Schofield 4 5 , Perminder S. Sachdev 2 10
  1. Neuroscience Research Australia, Sydney, NSW, Australia
  2. Centre for Healthy Brain Ageing, UNSW Australia, Sydney, NSW, Australia
  3. Dementia Collaborative Research Centre, University of New South Wales, Sydney, NSW, Australia
  4. Neuroscience Research Australia, Sydney, NSW, Australia
  5. School of Medical Sciences, University of New South Wales, Sydney, NSW, Australia
  6. Neuropsychiatric Institute, Prince of Wales Hospital, Sydney, NSW, Australia
  7. Cancer Research Program, Garvan Institute of Medical Research, Sydney, NSW, Australia
  8. School of Mathematics and Statistics, University of New South Wales, Sydney, NSW, Australia
  9. Academic Dept for Old Age Psychiatry, Prince of Wales Hospital, Sydney, NSW, Australia
  10. Neuropsychiatric Institute, Prince of Wales Hospital, Sydney, NSW, Australia

Background: White matter lesions (WMLs), seen as hyperintensities on T2-weighted MRI brain scans, are often associated with cognitive and functional impairments. WMLs also show strong correlation with a range of neurodegenerative and neuropsychiatric disorders. In addition to age and vascular risk factors, genetic factors have been implicated in WMLs. To identify genetic susceptibility loci for WMLs, we conducted a genome-wide association study (GWAS) on a cohort of older Australians.
Methods: The sample comprised 542 individuals, aged 70-90, who were participants in the Sydney Memory and Ageing Study and had brain MRI scans. Total, deep and periventricular white matter lesion volumes were quantified using an automated method. DNA was genotyped using the Affymetrix Genome-wide Human SNP Array 6.0. After quality control checks, 499 samples and 734,518 markers remained in the analysis. Analysis of the association between SNPs and WMLs was undertaken using PLINK including the covariates age, sex and intracranial volume.
Results: No association reached the genome-wide significance threshold for a GWAS (p<10-7). However, 47 SNPs showed suggestive association with either periventricular, deep or total WML measures when using the less stringent significance threshold of p<10-5. Twenty three out of the 47 SNPs were located within known genes. Some of these genes have been implicated in neuronal survival (CREB5, p<4.9x10-6), neuronal development (T1AM1, p=3.5x10-6) myelination (SH3TC2, p=6.5x10-6), postsynaptic differentiation (PHLDB2,), and atherosclerosis (LTA4H, p=1.1x10-6). In addition, six intergenic SNPs in linkage disequilibrium (LD) were found near the PTPDC1 gene (p<2.5x10-7), a signalling molecule involved in the regulation of a wide variety of biological processes.
Conclusions: This study has identified novel putative susceptibility genes and intergenic loci for WMLs. The identified genes have putative functions that have plausible roles in the pathological mechanisms of WMLs. If replicated in other cohorts, these findings can help advance our understanding of the development of WMLs in the elderly.