Association Between Copy Number Variation Losses and Alcohol Dependence Across African American and European American Ethnic Groups

Background: With up to 70% of adverse drug reactions (ADRs) having high genetic associations, the clinical utility of pharmacogenomics (PGx) has been gaining traction. Nala PGx Core is a multi-gene qPCR-based panel that comprises 18 variants and 2 CYP2D6 Copy Number markers across 4 pharmacogenes - CYP2C9, CYP2C19, CYP2D6 and SLCO1B1. Objectives: In this study, we validated the performance of Nala PGx Core against benchmark methods, on the Singaporean and Indonesian populations. Additionally, we examined the allele and diplotype frequencies across 5 major ethnic groups present in these populations namely, Indonesians, Chinese, Malays, Indians and Caucasians. Methods: Human gDNA samples, extracted from the buccal swabs of 246 participants, were tested on Nala PGx Core and two chosen benchmarks, Agena VeriDose Core and CYP2D6 Copy Number Variation (CNV) Panel, and TaqMan DME Genotyping Assays. Performance was evaluated based on assay robustness, precision and accuracy at the genotype- and diplotype-level. Results: Nala PGx Core demonstrated high genotype- and diplotype-level call rates of >97% and >95% respectively in CYP2D6, and 100% for CYP2C9, CYP2C19 and SLCO1B1. A precision rate of 100% was observed on both intra- and inter-precision studies. Variant-level concordance to the benchmark methods was >96.9% across all assays, which consequently resulted in a diplotype-level concordance of >94.7% across CYP2C9, CYP2C19 and CYP2D6. Overall, the allele frequencies of CYP2D6*10 and CYP2D6*36 were higher in our cohort as compared to previous records. Notably, CYP2D6 copy number variation (CNV) analysis demonstrated a CYP2D6 *10/*36 frequency of 26.5% amongst the Indonesian cohort. Conclusion: Nala PGx Core produced robust and accurate genotyping when compared to other established benchmarks. Furthermore, the panel successfully characterized alleles of clinical relevance in the Singaporean and Indonesian populations such as CYP2D6*10 and CYP2D6*36, suggesting its potential for adoption in clinical workflows regionally.

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Abstract PO-202: Copy number variation (CNV) analysis identifies variants in 1p36 in African American and Caucasian hereditary prostate cancer cases

10.1158/1538-7755.disp21-po-202 ◽

2022 ◽

Author(s):

Alan F. Williams ◽

Kirsten W. Termine ◽

John Waldron ◽

Oliver Sartor ◽

Joan Bailey-Wilson ◽

...

Keyword(s):

Prostate Cancer ◽

African American ◽

Copy Number Variation ◽

Copy Number ◽

Hereditary Prostate Cancer ◽

Number Variation

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Characterization of germline copy number variation in high-risk African American families with prostate cancer

The Prostate ◽

10.1002/pros.22602 ◽

2012 ◽

Vol 73 (6) ◽

pp. 614-623 ◽

Cited By ~ 16

Author(s):

Elisa M. Ledet ◽

Xiaofeng Hu ◽

Oliver Sartor ◽

Walter Rayford ◽

Marilyn Li ◽

...

Keyword(s):

Prostate Cancer ◽

African American ◽

High Risk ◽

Copy Number Variation ◽

Copy Number ◽

African American Families ◽

Number Variation ◽

American Families

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Deep learning prediction of attention-deficit hyperactivity disorder in African Americans by copy number variation

Experimental Biology and Medicine ◽

10.1177/15353702211018970 ◽

2021 ◽

pp. 153537022110189

Author(s):

Yichuan Liu ◽

Hui-Qi Qu ◽

Xiao Chang ◽

Kenny Nguyen ◽

Jingchun Qu ◽

...

Keyword(s):

Attention Deficit Hyperactivity Disorder ◽

African American ◽

Deep Learning ◽

Copy Number Variation ◽

Genome Sequencing ◽

Copy Number ◽

Whole Genome ◽

Structural Variations ◽

Hyperactivity Disorder ◽

Number Variation

Current understanding of the underlying molecular network and mechanism for attention-deficit hyperactivity disorder (ADHD) is lacking and incomplete. Previous studies suggest that genomic structural variations play an important role in the pathogenesis of ADHD. For effective modeling, deep learning approaches have become a method of choice, with ability to predict the impact of genetic variations involving complicated mechanisms. In this study, we examined copy number variation in whole genome sequencing from 116 African Americans ADHD children and 408 African American controls. We divided the human genome into 150 regions, and the variation intensity in each region was applied as feature vectors for deep learning modeling to classify ADHD patients. The accuracy of deep learning for predicting ADHD diagnosis is consistently around 78% in a two-fold shuffle test, compared with ∼50% by traditional k-mean clustering methods. Additional whole genome sequencing data from 351 European Americans children, including 89 ADHD cases and 262 controls, were applied as independent validation using feature vectors obtained from the African American ethnicity analysis. The accuracy of ADHD labeling was lower in this setting (∼70–75%) but still above the results from traditional methods. The regions with highest weight overlapped with the previously reported ADHD-associated copy number variation regions, including genes such as GRM1 and GRM8, key drivers of metabotropic glutamate receptor signaling. A notable discovery is that structural variations in non-coding genomic (intronic/intergenic) regions show prediction weights that can be as high as prediction weight from variations in coding regions, results that were unexpected.

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