Wang, Xue

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.

AbstractIntroductionWe hypothesized that common Alzheimer's disease (AD)‐associated variants within the triggering receptor expressed on myeloid (TREM) gene cluster influence disease through gene expression.MethodsExpression microarrays on temporal cortex and cerebellum from ∼400 neuropathologically diagnosed subjects and two independent RNAseq replication cohorts were used for expression quantitative trait locus analysis.ResultsA variant within a DNase hypersensitive site 5′ of TREM2, rs9357347‐C, associates with reduced AD risk and increased TREML1 and TREM2 levels (uncorrected P = 6.3 × 10−3 and 4.6 × 10−2, respectively). Meta‐analysis on expression quantitative trait locus results from three independent data sets (n = 1006) confirmed these associations (uncorrected P = 3.4 × 10−2 and 3.5 × 10−3, Bonferroni‐corrected P = 6.7 × 10−2 and 7.1 × 10−3, respectively).DiscussionOur findings point to rs9357347 as a functional regulatory variant that contributes to a protective effect observed at the TREM locus in the International Genomics of Alzheimer's Project genome‐wide association study meta‐analysis and suggest concomitant increase in TREML1 and TREM2 brain levels as a potential mechanism for protection from AD.

Abstract Motivation: RNA-seq has become the method of choice to quantify genes and exons, discover novel transcripts and detect fusion genes. However, reliable variant identification from RNA-seq data remains challenging because of the complexities of the transcriptome, the challenges of accurately mapping exon boundary spanning reads and the bias introduced during the sequencing library preparation. Method: We developed RVboost, a novel method specific for RNA variant prioritization. RVboost uses several attributes unique in the process of RNA library preparation, sequencing and RNA-seq data analyses. It uses a boosting method to train a model of ‘good quality’ variants using common variants from HapMap, and prioritizes and calls the RNA variants based on the trained model. We packaged RVboost in a comprehensive workflow, which integrates tools of variant calling, annotation and filtering. Results: RVboost consistently outperforms the variant quality score recalibration from the Genome Analysis Tool Kit and the RNA-seq variant-calling pipeline SNPiR in 12 RNA-seq samples using ground-truth variants from paired exome sequencing data. Several RNA-seq–specific attributes were identified as critical to differentiate true and false variants, including the distance of the variant positions to exon boundaries, and the percent of the reads supporting the variant in the first six base pairs. The latter identifies false variants introduced by the random hexamer priming during the library construction. Availability and implementation: The RVboost package is implemented to readily run in Mac or Linux environments. The software and user manual are available at http://bioinformaticstools.mayo.edu/research/rvboost/. Supplementary information: Supplementary data are available at Bioinformatics online.