RESUMO
OBJECTIVE: Alzheimer's disease (AD) is a neurodegenerative disorder for which more than 20 genetic loci have been implicated to date. However, studies demonstrate not all genetic factors have been identified. Therefore, in this study we seek to identify additional rare variants and novel genes potentially contributing to AD. METHODS: Whole exome sequencing was performed on 23 multi-generational families with an average of eight affected subjects. Exome sequencing was filtered for rare, nonsynonymous and loss-of-function variants. Alterations predicted to have a functional consequence and located within either a previously reported AD gene, a linkage peak (LOD>2), or clustering in the same gene across multiple families, were prioritized. RESULTS: Rare variants were found in known AD risk genes including AKAP9, CD33, CR1, EPHA1, INPP5D, NME8, PSEN1, SORL1, TREM2 and UNC5C. Three families had five variants of interest in linkage regions with LOD>2. Genes with segregating alterations in these peaks include CD163L1 and CLECL1, two genes that have both been implicated in immunity, CTNNA1, which encodes a catenin in the cerebral cortex and MIEF1, a gene that may induce mitochondrial dysfunction and has the potential to damage neurons. Four genes were identified with alterations in more than one family include PLEKHG5, a gene that causes Charcot-Marie-Tooth disease and THBS2, which promotes synaptogenesis. CONCLUSION: Utilizing large families with a heavy burden of disease allowed for the identification of rare variants co-segregating with disease. Variants were identified in both known AD risk genes and in novel genes.
RESUMO
Autism is a heritable neurodevelopmental disorder with substantial genetic heterogeneity. Studies point to possible links between autism and two serotonin related genes: SLC6A4 and ITGB3 with a sex-specific genetic effect and interaction between the genes. Despite positive findings, inconsistent results have complicated interpretation. This study seeks to validate and clarify previous findings in an independent dataset taking into account sex, family-history (FH) and gene-gene effects. Family-based association analysis was performed within each gene. Gene-gene interactions were tested using extended multifactor dimensionality reduction (EMDR) and MDR-phenomics (MDR-P) using sex of affecteds and FH as covariates. No significant associations with individual SNPs were found in the datasets stratified by sex, but associations did emerge when we stratified by family history. While not significant in the overall dataset, nominally significant association was identified at RS2066713 (P = 0.006) within SLC6A4 in family-history negative (FH-) families, at RS2066713 (P = 0.038) in family-history positive (FH+) families but with the opposite risk allele as in the FH- families. For ITGB3, nominally significant association was identified at RS3809865 overall (P = 0.040) and within FH+ families (P = 0.031). However, none of the associations survived the multiple testing correction. MDR-P confirmed gene-gene effects using sex of affecteds (P = 0.023) and family history (P = 0.014, survived the multiple testing corrections) as covariates. Our results indicate the extensive heterogeneity within these two genes among families. The potential interaction between SLC6A4 and ITGB3 may be clarified using family history as an indicator of genetic architecture, illustrating the importance of covariates as markers of heterogeneity in genetic analyses.
