Knowledge-driven binning approach for rare variant association analysis: application to neuroimaging biomarkers in Alzheimer’s disease

Abstract

BackgroundRapid advancement of next generation sequencing technologies such as whole genome sequencing (WGS) has facilitated the search for genetic factors that influence disease risk in the field of human genetics. To identify rare variants associated with human diseases or traits, an efficient genome-wide binning approach is needed. In this study we developed a novel biological knowledge-based binning approach for rare-variant association analysis and then applied the approach to structural neuroimaging endophenotypes related to late-onset Alzheimer’s disease (LOAD).MethodsFor rare-variant analysis, we used the knowledge-driven binning approach implemented in Bin-KAT, an automated tool, that provides 1) binning/collapsing methods for multi-level variant aggregation with a flexible, biologically informed binning strategy and 2) an option of performing unified collapsing and statistical rare variant analyses in one tool. A total of 750 non-Hispanic Caucasian participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort who had both WGS data and magnetic resonance imaging (MRI) scans were used in this study. Mean bilateral cortical thickness of the entorhinal cortex extracted from MRI scans was used as an AD-related neuroimaging endophenotype. SKAT was used for a genome-wide gene- and region-based association analysis of rare variants (MAF (minor allele frequency) < 0.05) and potential confounding factors (age, gender, years of education, intracranial volume (ICV) and MRI field strength) for entorhinal cortex thickness were used as covariates. Significant associations were determined using FDR adjustment for multiple comparisons.ResultsOur knowledge-driven binning approach identified 16 functional exonic rare variants in FANCC significantly associated with entorhinal cortex thickness (FDR-corrected p-value < 0.05). In addition, the approach identified 7 evolutionary conserved regions, which were mapped to FAF1, RFX7, LYPLAL1 and GOLGA3, significantly associated with entorhinal cortex thickness (FDR-corrected p-value < 0.05). In further analysis, the functional exonic rare variants in FANCC were also significantly associated with hippocampal volume and cerebrospinal fluid (CSF) Aβ1–42 (p-value < 0.05).ConclusionsOur novel binning approach identified rare variants in FANCC as well as 7 evolutionary conserved regions significantly associated with a LOAD-related neuroimaging endophenotype. FANCC (fanconi anemia complementation group C) has been shown to modulate TLR and p38 MAPK-dependent expression of IL-1β in macrophages. Our results warrant further investigation in a larger independent cohort and demonstrate that the biological knowledge-driven binning approach is a powerful strategy to identify rare variants associated with AD and other complex disease.

Codon bias among synonymous rare variants is associated with Alzheimer’s disease imaging biomarker

“…Samples were collected as described previously [6]. There was a total of 750 non-Hispanic Caucasian participants (425 were male and 325 female).…”

“…This strategy has several strengths: first it increases the detection power by aggregating association signals in the variants in the bin; secondly, it reduces the multiple testing burden by not testing every variant, thus increasing the power to detect a significant association. In addition to binning by a specific region, filtering for a specific type of variant, such as non-synonymous changes, have important benefits in addition to reducing the testing burden by focusing the association on variants that are more likely to influence the phenotype and provide easier interpretation of the results [6]. …”

FANCC localizes with UNC5A at neurite outgrowth and promotes neuritogenesis

“…The fact that Unc5A protein levels are reduced in FancC − / − brains also suggest that FANCC with UNC5A may have critical implications in neuronal tissues. Interestingly, the FANCC gene has been associated with entorhinal cortex thickness, a region that is affected early in the progression of Alzheimer’s disease (AD) [ 28 ].…”

Rare variants in the splicing regulatory elements of EXOC3L4 are associated with brain glucose metabolism in Alzheimer’s disease

“…This strategy will accumulate effects of rare variants within a knowledge-driven region and reduce the number of statistical tests, thereby increasing the power to detect an association. Additionally, focusing on specific types of variants, such as those that lead to non-synonymous changes can also reduce the multiple testing burden and provide a potential explanation for the gene associated with the phenotype [ 17 ]. An attractive category of variant for studying AD are those that impact splicing.…”