Background: High-throughput genotyping technologies has allowed for numerous genetic studies to be undertaken in Alzheimer's disease (AD) populations ...
Poster Presentations: P4 Background: High-throughput genotyping technologies has allowed for numerous genetic studies to be undertaken in Alzheimer’s disease (AD) populations resulting in the identification of new risk factors. The importance of both genetics and environment in brain function is well known, as is the role of neuroimaging in revealing brain dysfunction, neuronal loss and neocortical Ab burden (NAB) through positron emission tomography (PET) using a radioactive tracer (e.g. PiB). Thus, the synergy of integrating genetics with brain imaging carries clear advantages. These quantitative trait (QT) analyses can be further extended to include phenotypes such as clinical and cognitive standardized assessments and other disease biomarkers. Here, we report on gene associations, both independent and combinatorial, with AD risk and QTs in the Australian Imaging, Biomarkers and Lifestyle (AIBL) study cohort. Methods: This study reports on the recently completed screen of candidate genes in the Australian Imaging, Biomarkers and Lifestyle (AIBL) study (942 individuals). Single Nucleotide Polymorphisms (SNPs) were selected from the top candidate genes identified in AD association and QT studies (specific hits and fine-mapping). Genotyping was via a custom Illumina GoldenGate assay, with failed SNPs and additional supplemental SNPs genotyped using the OpenArray Platform. Genotype data that passed quality control were then analysed with respect to clinical classification of disease and QT traits including, but not limited to; plasma Ab levels, hippocampal volume, NAB, and measures of cognitive performance. Novel machine learning algorithms were utilised to identify combinations of SNPs that classified AD or allowed for the prediction of high NAB (defined as a greater than 1.3 Standardized Uptake Value Ratio (SUVR) determined via PiB-PET). Results: A total of over 1525 SNPs passed quality control and 935 individuals (call rate greater than 98%) were included in analyses. Single marker and haplotype associations with AD risk and QTs were identified using GoldenHelix SVS7 software. Novel ML models have identified a combination of SNPs able to classify AD or high NAB. Conclusions: The use of a quantitative phenotype in combination with genetic studies provides many advantages over a case-control design, both in terms of power and in terms of physiological understanding of the underlying cognitive and pathological processes. P4-136
EXOME SEQUENCING OF EXTENDED LATE-ONSET ALZHEIMER’S DISEASE FAMILIES IDENTIFIES A VARIANT IN THE TTC3 GENE
Stephan Zuchner1, Martin Kohli1, Adam Naj1, Kara Hamilton1, Ruchita Rajbhandary1, Timothy Plitnik1, Krista John-Williams1, Patrice L. Whitehead1, John Gilbert1, Eden Martin1, Gary Beecham1, Jonathan Haines2, Margaret Pericak-Vance1, 1University of Miami, Miami, Florida, United States; 2Vanderbilt University Medical Center, Nashville, Tennessee, United States. Background: Over the past five years, identifying risk genes for Alzheimer’s disease (AD) has focused on testing the ‘common diseasecommon variant’ (CDCV) hypothesis. While common variants like the E4 allele of APOE clearly play a role in AD, there is a growing realization that the CDCV hypothesis is unlikely to explain all the genetic effect underlying AD. One alternative hypothesis invokes multiple rare variants (RV) in one or more genes, each with stronger individual effects than common variants in genes identified under CDCV. Methods: We identified a subset of 6 pedigrees from our collection of 61 extended multi-generational, late-onset AD (LOAD) families to test the rare variant hypothesis. The pedigrees have on average six AD-affected individuals, and have been screened to exclude known familial AD mutations in APp, PS1, and PS2 genes. All affected individuals underwent genome-wide high-density genotyping. Whole-exome sequencing was performed on 4-9 affected individuals per pedigree comprising 2-6 cousin or avuncular pairs. Results: Based on genome-wide genotyping results we calculated identity-by-descent (IBD) across the genome. This identifies regions of complete IBD sharing among affected samples, indicating regions most likely to harbor segregating LOAD variants. For example, in family 1229 IBD filtering left us
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with a single missense variant (Ser1038Cys) in the gene tetratricopeptide repeat domain 3 (TTC3).This variant is at a phylogenetically highly-conserved site (PhastCons¼1; GERP¼4.9) and has not been reported in w10,000 control alleles. TTC3, which encodes an akt- kinase-regulated factor, is located in the Down syndrome critical region, and has been implicated in neuronal differentiation and learning and memory impairment. Functional follow-up is currently under way. Conclusions: We have identified a potentially damaging missense change in TTC3, a novel biological candidate for AD that warrants further genetic and functional examination. Challenges and confounding factors for this type of analysis in LOAD include incomplete segregation, reduced penetrance, and the potential rarity of any candidate allele. We show however, that whole exome sequencing of extended LOAD families leads to the identification of novel candidate genes that have the potential to help close the gap of missing heritability in LOAD. P4-137
PIN1 GENE EXPRESSION IS ASSOCIATED WITH MCI AND ALZHEIMER’S DISEASE
Suk Ling Ma, Nelson Leung Sang Tang, Linda Lam, The Chinese University of Hong Kong, Hong Kong, Hong Kong. Background: Alzheimer’s disease (AD) is the commonest neurodegenerative disease and it affected over 30 million people worldwide. Mild cognitive impairment (MCI) is regarded as a preclinical stage of AD and identifying subjects with MCI is a possible way in identifying subjects at risk for developing AD. Our previous study reported the association of Pin1 polymorphism rs2287839 and earlier age-at-onset for AD patients. However, the genotypes of rs2287839 were not associated with diagnosis of AD. In this study, we investigated the association of gene expression level of Pin1 and AD or MCI. Methods: Three hundred and thirty-three Chinese subjects (95 AD patients, 68 subjects with MCI and 170 normal controls) were included in the study. Gene expression of Pin1 was quantified by real-time qPCR and genotyping of polymorphism rs2287839 was performed. The result of gene expression and genotyping was associated with the diagnosis. Results: Significantly lower level of Pin1 gene expression was found in AD patients when compared to MCI subjects and normal controls (P ¼ 0.02 and P< 0.001 respectively). In addition, MCI subjects showed significantly lower level of Pin1 expression when compared to normal controls (P ¼ 0.042). The result suggested the gene expression levels of Pin1 were associated with the severity of the disease. Interestingly, the genotypes of rs2287839 were associated with gene expression level of Pin1 in AD patients and MCI subjects but not in normal controls. Conclusions: Our result suggested the gene expression level of Pin1 was associated with risk of AD. The result was consistent with previous reports suggesting the decreased level of Pin1 in AD patients’ brain. This finding may be useful in developing diagnostic test in identifying subjects with high risk of AD and facilitate the commencement of treatment in an earlier time. P4-138
PAIRWISE GENE-PROTEIN ASSOCIATION ANALYSIS ON CEREBROSPINAL FLUID PROTEOMICS IN THE ADNI-1 COHORT
Sungeun Kim1, Shanker Swaminathan2, Kwangsik Ngo1, Shannon Risacher1, Li Shen2, Tatiana Foroud1, Leslie Shaw3, John Trojanowski4, Michael Weiner5, Andrew Saykin2, Alzheimer’s Disease Neuroimaging Initiative1, 1Indiana University, Indianapolis, Indiana, United States; 2Indiana University School of Medicine, Indianapolis, Indiana, United States; 3University of Pennsylvania Medical Center, Philadelphia, Pennsylvania, United States; 4University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, United States; 5University of California, San Francisco, San Francisco, California, United States. Background: Early detection of Alzheimer’s disease (AD) before or during prodromal stages is critically important for development of effective treatment or prevention. Recent efforts seeking novel measures for early
