Rare coding mutations identified by sequencing of Alzheimer disease genome-wide association studies loci.

Annals of Neurology

PubMedID: 26101835

Vardarajan BN, Ghani M, Kahn A, Sheikh S, Sato C, Barral S, Lee JH, Cheng R, Reitz C, Lantigua R, Reyes-Dumeyer D, Medrano M, Jimenez-Velazquez IZ, Rogaeva E, St George-Hyslop P, Mayeux R. Rare coding mutations identified by sequencing of Alzheimer disease genome-wide association studies loci. Ann Neurol. 2015;78(3):487-98.
OBJECTIVE
To detect rare coding variants underlying loci detected by genome-wide association studies (GWAS) of late onset Alzheimer disease (LOAD).

METHODS
We conducted targeted sequencing of ABCA7, BIN1, CD2AP, CLU, CR1, EPHA1, MS4A4A/MS4A6A, and PICALM in 3 independent LOAD cohorts: 176 patients from 124 Caribbean Hispanics families, 120 patients and 33 unaffected individuals from the 129 National Institute on Aging LOAD Family Study; and 263 unrelated Canadian individuals of European ancestry (210 sporadic patients and 53 controls). Rare coding variants found in at least 2 data sets were genotyped in independent groups of ancestry-matched controls. Additionally, the Exome Aggregation Consortium was used as a reference data set for population-based allele frequencies.

RESULTS
Overall we detected a statistically significant 3.1-fold enrichment of the nonsynonymous mutations in the Caucasian LOAD cases compared with controls (p?=?0.002) and no difference in synonymous variants. A stop-gain mutation in ABCA7 (E1679X) and missense mutation in CD2AP (K633R) were highly significant in Caucasian LOAD cases, and mutations in EPHA1 (P460L) and BIN1 (K358R) were significant in Caribbean Hispanic families with LOAD. The EPHA1 variant segregated completely in an extended Caribbean Hispanic family and was also nominally significant in the Caucasians. Additionally, BIN1 (K358R) segregated in 2 of the 6 Caribbean Hispanic families where the mutations were discovered.

INTERPRETATION
Targeted sequencing of confirmed GWAS loci revealed an excess burden of deleterious coding mutations in LOAD, with the greatest burden observed in ABCA7 and BIN1. Identifying coding variants in LOAD will facilitate the creation of tractable models for investigation of disease-related mechanisms and potential therapies. Ann Neurol 2015;78:487-498.