A machine learning-based SNP-set analysis approach for identifying disease-associated susceptibility loci

Abstract

Identifying disease-associated susceptibility loci is one of the most pressing and crucial challenges in modeling complex diseases. Existing approaches to biomarker discovery are subject to several limitations including underpowered detection, neglect for variant interactions, and restrictive dependence on prior biological knowledge. Addressing these challenges necessitates more ingenious ways of approaching the “missing heritability” problem. This study aims to discover disease-associated susceptibility loci by augmenting previous genome-wide association study (GWAS) using the integration of random forest and cluster analysis. The proposed integrated framework is applied to a hepatitis B virus surface antigen (HBsAg) seroclearance GWAS data. Multiple cluster analyses were performed on (1) single nucleotide polymorphisms (SNPs) considered significant by GWAS and (2) SNPs with the highest feature importance scores obtained using random forest. The resulting SNP-sets from the cluster analyses were subsequently tested for trait-association. Three susceptibility loci possibly associated with HBsAg seroclearance were identified: (1) SNP rs2399971, (2) gene LINC00578, and (3) locus 11p15. SNP rs2399971 is a biomarker reported in the literature to be significantly associated with HBsAg seroclearance in patients who had received antiviral treatment. The latter two loci are linked with diseases influenced by the presence of hepatitis B virus infection. These findings demonstrate the potential of the proposed integrated framework in identifying disease-associated susceptibility loci. With further validation, results herein could aid in better understanding complex disease etiologies and provide inputs for a more advanced disease risk assessment for patients.

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“…In the era of precision medicine, disease-associated genomic factors can be considered as biomarkers for disease. Machine learning models facilitate the identification of disease using biomarkers [ 40 , 41 ]. As a result, the future prospect is to develop machine learning methods to identify the incidence or progression of OP.…”

Genetic risk assessment based on association andprediction studies

“…More so, using the entire feature space without considering the relevance of the individual features hinders achieving an optimal model performance. Given that genomic datasets suffer from the curse of dimensionality 6,8,[29][30][31][32] , it is crucial to eliminate irrelevant features and retain only the most informative variants related to the phenotype under investigation. Removing noise from the data improves models' accuracy and reliability, thereby gaining a deeper understanding of the genetic mechanisms underlying risk susceptibility.…”

“…Since the genetic architecture of complex diseases follows a polygenic rather than a Mendelian model [1][2][3][4] , understanding disease emergence and progression through gaining insights into genomic instability continues to challenge researchers. While genomic instability reveals only a portion of the biological underpinnings of complex diseases [5][6][7][8][9] , identifying genetic biomarkers can facilitate targeted and personalized treatments for individuals with increased genetic susceptibility to specific diseases.…”

“…Machine learning (ML) is a widely accepted methodical framework in analyzing high-dimensional and complex data 6,8,[29][30][31][32] , owing to its unparalleled ability to handle high-volume data and uncover implicit and nonlinear patterns that are pertinent for predictive modeling. By selecting a minimum subset of individually relevant and neighboring features while minimizing information loss 33 , ML captures complex interactions, leading to the identification of highly-predictive features.…”

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“…Zhou et al (2019) combined GWAS and ML feature selection, revealing significant identification power for mining minor QTL to help understand biological activities between genotypes and phenotypes related to the causal networks of interacting genes. Other studies have combined ML‐based algorithms with GWAS to identify genetic variants associated with traits of interest, such as disease susceptibility (Silva et al, 2022), root‐knot nematode resistance (Vieira et al, 2022), and yield‐related traits (Yoosefzadeh‐Najafabadi et al, 2022).…”