Younkin, Steven G.

Abstract Background: Genetic studies have shifted to sequencing-based rare variants discovery after decades of success in identifying common disease variants by Genome-Wide Association Studies using Single Nucleotide Polymorphism chips. Sequencing-based studies require large sample sizes for statistical power but often inadvertently introduce batch effects because samples are typically collected, processed, and sequenced at multiple centers. Conventionally, batch effects are first detected and visualized using Principal Components Analysis and then controlled by including batch covariates in the disease association models. For sequencing-based genetic studies, because all variants included in the association analyses have passed quality control measures, this conventional approach treats every variant as equal and ignores the substantial differences still remaining in variant qualities and characteristics such as genotype quality scores, alternative allele fractions (fraction of reads supporting alternative allele at a variant position) and sequencing depths. Results: In the Alzheimer’s Disease Sequencing Project (ADSP) exome dataset of 9,904 cases and controls, we discovered hidden variant-level differences between sample batches of three sequencing centers and two exome capture kits. Although sequencing centers were included as a covariate in our association models, we observed differences at the variant level in genotype quality and alternative allele fraction between samples processed by different exome capture kits that significantly impacted both the confidence of variant detection and the identification of disease-associated variants. Furthermore, we found that the association signals of a subset of top disease risk variants came exclusively from samples processed by one exome capture kit that was more effective at capturing the alternative alleles compared to the other kit. Conclusions: Our findings highlight the importance of additional variant-level quality control for large sequencing-based genetic studies. More importantly, we demonstrate that automatically filtering out variants with batch differences may lead to false negatives if the batch discordance came largely from quality differences and if the variants from one batch had better quality scores.

The most recent late-onset Alzheimer's disease (LOAD) genome-wide association study revealed genome-wide significant association of two new loci: rs744373 near BIN1 (p = 1.6 × 10 −11 ) and rs597668 near EXOC3L2/BLOC1S3/MARK4 (p = 6.5 × 10 −9 ). We have genotyped these variants in a large (3,287 LOAD, 4,396 controls), independent dataset comprising eleven case-control series from the USA and Europe. We performed meta-analyses of the association of these variants with LOAD and also tested for association using logistic regression adjusted by age-at-diagnosis, gender, and APOE ε4 status. Meta-analysis results showed no evidence of series heterogeneity and logistic regression analysis successfully replicated the association of BIN1 (rs744373) with LOAD with an odds ratio (OR = 1.17, p = 1.1 × 10 −4 ) comparable to that previously reported (OR = 1.15). The variant near EXOC3L2 (rs597668) showed only suggestive association with LOAD (p = 0.09) after correcting for the presence of the APOE ε4 allele. Addition of our follow-up data to the results previously reported increased the strength of evidence for association with BIN1 (11,825 LOAD, 32,570 controls, rs744373 Fisher combined p = 3.8 × 10 −20 ). We also tested for epistatic interaction between these variants and APOE ε4 as well as with the previously replicated LOAD GWAS genes (CLU: rs11136000, CR1: rs3818361, and PICALM: rs3851179). No significant interactions between these genes were detected. In summary, we provide additional evidence for the variant near BIN1 (rs744373) as a LOAD risk modifier, but our results indicate that the effect of EXOC3L2 independent of APOE ε4 should be studied further.

We conducted a comprehensive screening of rare coding variants in an African American cohort to identify novel pathogenic mutations within the early-onset Alzheimer’s disease (EOAD) genes ( APP, PSEN1 , and PSEN2) in this understudied population. Whole-exome sequencing of 238 African American subjects identified 6 rare missense variants within the EOAD genes, which were observed in AD cases but never among controls. These variants were analyzed in an independent cohort of 300 African American subjects in which PSEN2 :NM_000447:exon5:c.T331C:p.Phe111Leu and PSEN1-minilin rs777923890 variants were again not observed, indicating that these novel rare variants, may contribute to AD risk in this population.