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Replication study of AD‐associated rare variants

In the study of Alzheimer’s disease (AD)–associated rare variants, Prokopenko et al.1 presented a highly detailed analysis highlighting the utility of whole-genome sequencing (WGS) to identify AD-associated rare variants, including those outside the exonic regions. They used deep (> 40x) WGS data from 2247 individuals (family-based discovery dataset from National Institute of Mental Health [n = 1393] and National Institute on Aging Alzheimer’s Disease Sequencing Project [ADSP; n = 854] cohorts) for rare variants (minor allele frequency [MAF] ≤ 1%) analysis, and validated their findings in independent datasets (n = 1650) of AD cases and controls from publicly available WGS (ADSP case-control population) datasets. Their singlevariant association analysis identified novel intronic variants associated with four AD candidate genes, SEL1L, FNBP1L, LINC00298, and C15orf41. Through spatial clustering/region-based analysis, they identified nine newADcandidate gene regions (PRKCH,C2CD3,KIF2A,APC, LHX9,NALCN, CTNNA2, SYTL3, and CLSTN2). Nonetheless, they recommended that these results should be confirmed in additional datasets because of the possibility for the spurious association of some loci in their study.We also think it is in the best interest of the scientific community to confirm whether the association of these genes and regions only affects non-coding variants. In direct extension to their work, we used independent datasets at Washington University (WashU) to confirm the associations described by Prokopenko et al. Specifically, we wanted to confirm whether their association is mostly driven by variants in non-coding regions or also conveys to exonic variants. We used our familial late-onset AD (fLOAD) dataset from the Familial Alzheimer Sequencing (FASe) project2 (N= 1803; 356 families; 1291 cases and 330 controls), as well as an independent unrelated dataset (N = 1590; 667 cases and 651 controls) which includes non-overlapping Alzheimer’s Disease Neuroimaging Initiative3 andWashU’s MAP4 samples recruited by Joanne Knight Alzheimer’s Disease Research Center (Knight ADRC) at the WashU School ofMedicine. The approval number for the Knight ADRC Genetics Core family studies is 201104178.We have processed all the data using the same bioinformatics pipeline as described previously2 with the following changes: we used GRCh38 for sequence alignment and GATKv4.1.2.5 Our entire dataset was processed at the McDonnell Genome Institute center using docker images freely available at: https://github.com/NeuroGenomicsAndInformatics/dockerNGS. We performed the same statistical analysis with the same statistical packages as described in Prokopenko et al.1 Briefly, we performed an association analysis of rare variants (MAF≤ 1%) on our familial dataset using family-based association tests,6 logistic regression on the unrelated dataset (MAF ≤1%), followed by a fixed-effects meta-analysis of the former two datasets. Our single-variant association analysis of familial datasets detected onenovel intronic variant inNALCN (P< .05). Our analysis on the unrelated dataset revealed one novel rare variant in C2CD3 (Table 1). Both NALCN and C2CD3 were detected by Prokopenko et al. in the region-based analysis. We did not identify any significant variant in the genes that Prokopenko et al. identified via single-variant association analysis, not even when we analyzed these regions via meta-analysis of the two cohorts. Both of the variants detected in our study were intronic or non-coding variants with a frequency of <0.5%. As a note, we are capable of capturing variants ≈100bpup/downstreamof the coding regions, despite the fact thatour dataset is restricted to exonic regions. Because we did not detect major associations in the single-variant analysis, we conducted gene-based analysis for all the reported genes and regions byProkopenkoet al. on our familial andunrelateddatasets. Two sets of single-nucleotide variants (SNVs) were tested: first, using SNVs with a MAF ≤1%, then using SNVs with Combined Annotation Dependent Depletion scores ≥20, as registered in ExAC.7 We did not detect any genes associatedwith ADat P< .05 in either of our datasets (Table 1). Consistent with Prokopenko et al., we also used the spatial clustering approach to systematically group our familial data into non-overlapping regions, albeit these cluster regions were remarkably different from the clusters obtained for WGS. Our multi-marker testing on both familial and unrelated datasets on these same regions using FBAT for rare variants8 also did not detect the candidate genes reported in Prokopenko et al. (P< .05). To summarize, we have found two novel nominally significant variants in 2 of the 13 genes reported by Prokopenko et al.: NALCN and C2CD3. Our familial dataset is slightly underpowered compared to that of Prokopenko et al., but not the unrelated dataset. We understand that some variants in intronic regions might trigger abnormal splicing or enhance the expression of genes, but there is no direct way to understand their downstream biological consequences in the absence of exonic variants. Therefore, we extended this study to look at variants in exonic regions that potentially correlate with intronic variants in these reported genes.