Frequency of frontotemporal dementia gene variants in C9ORF72, MAPT, and GRN in academic versus commercial laboratory cohorts.

Background: Frontotemporal lobar degeneration (FTLD) is a leading cause of dementia, and elucidating its genetic underpinnings is critical. FTLD research centers typically recruit patient cohorts that are limited by the center's specialty and the ways in which its geographic location affects the ethnic makeup of research participants. Novel sources of data are needed to get population estimates of the contribution of variants in known FTLD-associated genes.

Methods: We compared FLTD-associated genetic variants in microtubule-associated protein tau (MAPT), progranulin (GRN), and chromosome nine open reading frame 72 (C9ORF72) from an academic research cohort and a commercial clinical genetics laboratory. Pathogenicity was assessed using guidelines of the American College of Medical Genetics and Genomics and a rule-based DNA variant assessment system. We conducted chart reviews on patients with novel or rare disease-associated variants.

Results: A total of 387 cases with FTLD-associated variants from the commercial (n=2,082) and 78 cases from the academic cohort (n=2,089) were included for analysis. In the academic cohort, the most frequent pathogenic variants were C9ORF72 expansions (63%, n=49), followed by GRN (26%, n=20) and MAPT (11%, n=9). Each gene's contribution to disease was similarly ranked in the commercial laboratory but differed in magnitude: C9ORF72 (89%, n=345), GRN (6%, n=24), and MAPT (5%, n=19). Of the 37 unique GRN/MAPT variants identified, only six were found in both cohorts. Clinicopathological data from patients in the academic cohort strengthened classification of two novel GRN variant as pathogenic (p.Pro166Leufs*2, p.Gln406*) and one GRN variant of unknown significance as a possible rare risk variant (p.Cys139Arg).

Conclusion: Differences in gene frequencies and identification of unique pathogenic alleles in each cohort demonstrate the importance of data sharing between academia and community laboratories. Using shared data sources with well-characterized clinical phenotypes for individual variants can enhance interpretation of variant pathogenicity and inform clinical management of at-risk patients and families.