Region-based analysis of rare genomic variants in whole-genome sequencing datasets reveal two novel Alzheimer's disease-associated genes: DTNB and DLG2

Alzheimer's disease (AD) is a genetically complex disease for which roughly 30 genes have been identified via genome-wide association studies. We attempted to identify rare variants (minor allele frequency <0.01) associated with AD in a region-based, whole genome sequencing (WGS) association study (GSAS) of two independent AD family datasets (NIMH/NIA; 2247 individuals; 605 families). Employing a sliding window approach across the genome, we identified several regions that achieved p-values < 10-6, using the burden test or the SKAT statistic. The genomic region around the dystobrevin beta (DTNB) gene was identified with the burden test and replicated in case/control samples from the ADSP study (pmeta= 4.74*10-8). SKAT analysis revealed region-based association around the discs large homolog 2 (DLG2) gene and replicated in case/control samples from the ADSP study (pmeta=1*10-6). Here, in a region-based GSAS of AD we identified two novel AD genes, DLG2 and DTNB, based on association with rare variants.

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Whole-genome sequencing reveals new Alzheimer’s disease-associated rare variants in loci related to synaptic function and neuronal development

10.1101/2020.11.03.20225540 ◽

2020 ◽

Author(s):

Dmitry Prokopenko ◽

Sarah L. Morgan ◽

Kristina Mullin ◽

Oliver Hofmann ◽

Brad Chapman ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Whole Genome Sequencing ◽

Genome Sequencing ◽

Rare Variants ◽

Spatial Clustering ◽

Synaptic Function ◽

Genome Wide Association Studies ◽

Whole Genome ◽

Genome Wide

AbstractINTRODUCTIONGenome-wide association studies have led to numerous genetic loci associated with Alzheimer’s disease (AD). Whole-genome sequencing (WGS) now permit genome-wide analyses to identify rare variants contributing to AD risk.METHODSWe performed single-variant and spatial clustering-based testing on rare variants (minor allele frequency ≤1%) in a family-based WGS-based association study of 2,247 subjects from 605 multiplex AD families, followed by replication in 1,669 unrelated individuals.RESULTSWe identified 13 new AD candidate loci that yielded consistent rare-variant signals in discovery and replication cohorts (4 from single-variant, 9 from spatial-clustering), implicating these genes: FNBP1L, SEL1L, LINC00298, PRKCH, C15ORF41, C2CD3, KIF2A, APC, LHX9, NALCN, CTNNA2, SYTL3, CLSTN2.DISCUSSIONDownstream analyses of these novel loci highlight synaptic function, in contrast to common AD-associated variants, which implicate innate immunity. These loci have not been previously associated with AD, emphasizing the ability of WGS to identify AD-associated rare variants, particularly outside of coding regions.

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P4-097: RARE VARIANTS IN FAMILIAL LATE-ONSET ALZHEIMER'S DISEASE IDENTIFIED FROM LARGE SCALE WHOLE GENOME SEQUENCING

Alzheimer s & Dementia ◽

10.1016/j.jalz.2019.06.3757 ◽

2019 ◽

Vol 15 ◽

pp. P1312-P1312

Author(s):

Badri N. Vardarajan ◽

James Jaworski ◽

Gary W. Beecham ◽

Sandra Barral ◽

Dolly Reyes-Dumeyer ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Whole Genome Sequencing ◽

Genome Sequencing ◽

Large Scale ◽

Rare Variants ◽

Late Onset ◽

Whole Genome

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Novel Alzheimer’s disease risk variants identified based on whole-genome sequencing of APOE ε4 carriers

Translational Psychiatry ◽

10.1038/s41398-021-01412-9 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Jong-Ho Park ◽

Inho Park ◽

Emilia Moonkyung Youm ◽

Sejoon Lee ◽

June-Hee Park ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Whole Genome Sequencing ◽

Genome Sequencing ◽

Disease Risk ◽

Association Studies ◽

European Ancestry ◽

Genome Wide Association Studies ◽

Whole Genome ◽

Ε4 Allele

AbstractAlzheimer’s disease (AD) is a progressive neurodegenerative disease associated with a complex genetic etiology. Besides the apolipoprotein E ε4 (APOE ε4) allele, a few dozen other genetic loci associated with AD have been identified through genome-wide association studies (GWAS) conducted mainly in individuals of European ancestry. Recently, several GWAS performed in other ethnic groups have shown the importance of replicating studies that identify previously established risk loci and searching for novel risk loci. APOE-stratified GWAS have yielded novel AD risk loci that might be masked by, or be dependent on, APOE alleles. We performed whole-genome sequencing (WGS) on DNA from blood samples of 331 AD patients and 169 elderly controls of Korean ethnicity who were APOE ε4 carriers. Based on WGS data, we designed a customized AD chip (cAD chip) for further analysis on an independent set of 543 AD patients and 894 elderly controls of the same ethnicity, regardless of their APOE ε4 allele status. Combined analysis of WGS and cAD chip data revealed that SNPs rs1890078 (P = 6.64E−07) and rs12594991 (P = 2.03E−07) in SORCS1 and CHD2 genes, respectively, are novel genetic variants among APOE ε4 carriers in the Korean population. In addition, nine possible novel variants that were rare in individuals of European ancestry but common in East Asia were identified. This study demonstrates that APOE-stratified analysis is important for understanding the genetic background of AD in different populations.

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Careful feature selection is key in classification of Alzheimer’s disease patients based on whole-genome sequencing data

NAR Genomics and Bioinformatics ◽

10.1093/nargab/lqab069 ◽

2021 ◽

Vol 3 (3) ◽

Author(s):

Marlena Osipowicz ◽

Bartek Wilczynski ◽

Magdalena A Machnicka ◽

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Feature Selection ◽

Whole Genome Sequencing ◽

Genome Sequencing ◽

Gwas Data ◽

Genome Wide Association Studies ◽

Whole Genome ◽

Testing Set

Abstract Despite great increase of the amount of data from genome-wide association studies (GWAS) and whole-genome sequencing (WGS), the genetic background of a partially heritable Alzheimer’s disease (AD) is not fully understood yet. Machine learning methods are expected to help researchers in the analysis of the large number of SNPs possibly associated with the disease onset. To date, a number of such approaches were applied to genotype-based classification of AD patients and healthy controls using GWAS data and reported accuracy of 0.65–0.975. However, since the estimated influence of genotype on sporadic AD occurrence is lower than that, these very high classification accuracies may potentially be a result of overfitting. We have explored the possibilities of applying feature selection and classification using random forests to WGS and GWAS data from two datasets. Our results suggest that this approach is prone to overfitting if feature selection is performed before division of data into the training and testing set. Therefore, we recommend avoiding selection of features used to build the model based on data included in the testing set. We suggest that for currently available dataset sizes the expected classifier performance is between 0.55 and 0.7 (AUC) and higher accuracies reported in literature are likely a result of overfitting.

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