A comparison of BeadChip and WGS genotyping outputs using partial validation by sanger sequencing

Danilov, Kirill A.; Nikogosov, Dimitri A.; Musienko, Sergey; Baranova, Ancha

doi:10.1186/s12864-020-06919-x

Cited by 7 publications

(6 citation statements)

References 10 publications

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“…In addition, we prioritized brain eQTLs in regulatory regions unlikely to be covered in array data. Indeed, previous studies have reported that the WGS genotype calling results in higher overall precision for genetic variant analysis [ 73 ].…”

Section: Discussionmentioning

confidence: 99%

Whole-genome sequencing analysis of suicide deaths integrating brain-regulatory eQTLs data to identify risk loci and genes

Han,

DiBlasi,

Monson

et al. 2023

Mol Psychiatry

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Recent large-scale genome-wide association studies (GWAS) have started to identify potential genetic risk loci associated with risk of suicide; however, a large portion of suicide-associated genetic factors affecting gene expression remain elusive. Dysregulated gene expression, not assessed by GWAS, may play a significant role in increasing the risk of suicide death. We performed the first comprehensive genomic association analysis prioritizing brain expression quantitative trait loci (eQTLs) within regulatory regions in suicide deaths from the Utah Suicide Genetic Risk Study (USGRS). 440,324 brain-regulatory eQTLs were obtained by integrating brain eQTLs, histone modification ChIP-seq, ATAC-seq, DNase-seq, and Hi-C results from publicly available data. Subsequent genomic analyses were conducted in whole-genome sequencing (WGS) data from 986 suicide deaths of non-Finnish European (NFE) ancestry and 415 ancestrally matched controls. Additional independent USGRS suicide deaths with genotyping array data (n = 4657) and controls from the Genome Aggregation Database were explored for WGS result replication. One significant eQTL locus, rs926308 (p = 3.24e−06), was identified. The rs926308-T is associated with lower expression of RFPL3S, a gene important for neocortex development and implicated in arousal. Gene-based analyses performed using Sherlock Bayesian statistical integrative analysis also detected 20 genes with expression changes that may contribute to suicide risk. From analyzing publicly available transcriptomic data, ten of these genes have previous evidence of differential expression in suicide death or in psychiatric disorders that may be associated with suicide, including schizophrenia and autism (ZNF501, ZNF502, CNN3, IGF1R, KLHL36, NBL1, PDCD6IP, SNX19, BCAP29, and ARSA). Electronic health records (EHR) data was further merged to evaluate if there were clinically relevant subsets of suicide deaths associated with genetic variants. In summary, our study identified one risk locus and ten genes associated with suicide risk via gene expression, providing new insight into possible genetic and molecular mechanisms leading to suicide.

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Section: Discussionmentioning

confidence: 99%

Whole-genome sequencing analysis of suicide deaths integrating brain-regulatory eQTLs data to identify risk loci and genes

Han,

DiBlasi,

Monson

et al. 2023

Mol Psychiatry

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“…The whole protocol was performed in Código 46’s laboratory of human genetics in Mexico. The Infinium HTS protocol has also been used and described in previous works [ 16 ]. All the participants signed an informed consent for the present study and their personal data was anonymized.…”

Section: Methodsmentioning

confidence: 99%

Frequency of allele variations in the CFTR gene in a Mexican population

et al. 2021

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Background Cystic fibrosis (CF) is an autosomal recessive disorder caused by pathogenic variants in the cystic fibrosis transmembrane conductance regulator (CFTR) gene. The CF variants incidence is highly variable and even undetermined in some countries like Mexico. Methods In this study, the allele frequencies of 361 variants in the CFTR gene were investigated in 1455 Mexicans without a CF or CFTR-related disorders (CFTR-RD) diagnosis. We also performed a statistical comparative analysis against allele frequencies of different populations to measure genetic differences in the prevalence of CFTR variants. Results In the vast majority of cases, the allele frequencies of this cohort were comparable to those found in other populations. However, some variants displayed significant differences in their allele frequencies when compared with European and African populations. Conclusions This study provides information about CFTR variants to predict the prevalence of CF in Mexico and uncover other unknown but frequent pathogenic variants in the country. Additionally, other CFTR-RD variants have also been studied using population data of the same CFTR variants. Studies like this could help develop a regional molecular diagnostic screen to optimize the medical care of CF patients.

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“…Genotypes derived from array and whole genome sequencing data for the same samples should be consistent but are often not. 20,21 To evaluate the consistency of genotyping, we performed a technical comparison of array and WGS data using the same set of samples from COPDGene (n=3,235 with African-American ancestry). The array data were phased independently and integrated with the WGS phased data available in TOPMed, followed by imputation and association testing using genotyping platform as the outcome.…”

Section: Reproducibility Across Genotyping Technologies Copdgene Has Both Array and Wgsmentioning

confidence: 99%

Expanding the pool of public controls for GWAS via a method for combining genotypes from arrays and sequencing

Mathur

Fang

Gaddis

et al. 2021

Preprint

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Genome-wide association studies (GWAS) have made impactful discoveries for complex diseases, often by amassing very large sample sizes. Yet, GWAS of many diseases remain underpowered, especially for non-European ancestries. One cost-effective approach to increase sample size is to combine existing case-only cohorts with public controls, but this approach is limited by the need for a large overlap in variants across genotyping arrays and the scarcity of non-European controls. We developed and validated a protocol, Genotyping Array-WGS Merge (GAWMerge), for combining genotypes from arrays and whole genome sequencing, ensuring complete variant overlap, and allowing for diverse samples like Trans-Omics for Precision Medicine to be used. Our protocol involves phasing, imputation, and filtering. We illustrated its ability to control type I error and recover known disease-associated signals across technologies, independent datasets, and ancestries in smoking-related cohorts. GAWMerge enables genetic studies to leverage existing cohorts to validly increase sample size and enhance discovery.

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A comparison of BeadChip and WGS genotyping outputs using partial validation by sanger sequencing

Cited by 7 publications

References 10 publications

Whole-genome sequencing analysis of suicide deaths integrating brain-regulatory eQTLs data to identify risk loci and genes

Whole-genome sequencing analysis of suicide deaths integrating brain-regulatory eQTLs data to identify risk loci and genes

Frequency of allele variations in the CFTR gene in a Mexican population

Expanding the pool of public controls for GWAS via a method for combining genotypes from arrays and sequencing

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