A method for the identification of variants in Alzheimer’s disease candidate genes and transcripts using hybridization capture combined with long-read sequencing
Alzheimer’s disease (AD) is a devastating neurodegenerative disease that is genetically complex. Although great progress has been made in identifying fully penetrant mutations in genes such as APP, PSEN1 and PSEN2 that cause early-onset AD, these still represent a very small percentage of AD cases. Large-scale, genome-wide association studies (GWAS) have identified at least 20 additional genetic risk loci for the more common form of late-onset AD. However, the identified SNPs are typically not the actual risk variants, but are in linkage disequilibrium with the presumed causative variant (Van Cauwenberghe C, et al., The genetic landscape of Alzheimer disease: clinical implications and perspectives. Genet Med 2015;18:421-430). Long-read sequencing together with hybrid-capture targeting technologies provides a powerful combination to target candidate genes/transcripts of interest. Shearing the genomic DNA to ~5 kb fragments and then capturing with probes that span the whole gene(s) of interest can provide uniform coverage across the entire region, identifying variants and allowing for phasing into two haplotypes. Furthermore, capturing full-length cDNA from the same sample using the same capture probes can also provide an understanding of isoforms that are generated and allow them to be assigned to their corresponding haplotype. Here we present a method for capturing genomic DNA and cDNA from an AD sample using a panel of probes targeting approximately 20 late-onset AD candidate genes which includes CLU, ABCA7, CD33, TREM2, TOMM40, PSEN2, APH1 and BIN1. By combining xGen® Lockdown® probes with SMRT Sequencing, we provide completely sequenced candidate genes as well as their corresponding transcripts. In addition, we are also able to evaluate structural variants that due to their size, repetitive nature, or low sequence complexity have been un-sequenceable using short-read technologies.