Prioritizing single-nucleotide polymorphisms and variants associated with clinical mastitis

Abstract

Next-generation sequencing technology has provided resources to easily explore and identify candidate single-nucleotide polymorphisms (SNPs) and variants. However, there remains a challenge in identifying and inferring the causal SNPs from sequence data. A problem with different methods that predict the effect of mutations is that they produce false positives. In this hypothesis, we provide an overview of methods known for identifying causal variants and discuss the challenges, fallacies, and prospects in discerning candidate SNPs. We then propose a three-point classification strategy, which could be an additional annotation method in identifying causalities.

Genome-Wide DNA Methylation Analysis of Mammary Gland Tissues From Chinese Holstein Cows With Staphylococcus aureus Induced Mastitis

“…Genomic selection has recently been identified as a complementary or alternative tool for selection to reduce mastitis (Martin et al, 2018;Weigel and Shook, 2018). To support genomic selection, several genome wide association studies (GWAS) have identified genes, markers and quantitative trait loci (QTL) significantly associated with mastitis Suravajhala and Benso, 2017;Cai et al, 2018;Welderufael et al, 2018;Kurz et al, 2019).…”

SNP prioritization in targeted sequencing data associated with humoral immune responses in chicken

“…Such statistical solution could improve the accuracy of detection association between phenotype and causal variant. Also, other authors suggested that prioritization methods are effective in such approaches ( Hou and Zhao, 2013 ; Suravajhala and Benso, 2017 ).…”

Adjustment of p-value expression to ontology using machine learning for genetic prediction, prioritization, interaction, and its validation in glomerular disease

“…Many computational approaches have been developed to support the identification of the most promising candidates (Zolotareva and Kleine, 2019). oPOSSUM (Ho Sui et al, 2007) web applications containing a great variety of the conserved non-coding regions of the promoters/enhancers were used to select the interaction between our candidate's genes, whose interactions between genes and transcription factors (TFs) were a major to understand gene regulation and the origin of complex protein components (Suravajhala and Benso, 2017).…”