A Two-Stage Mutual Information Based Bayesian Lasso Algorithm for Multi-Locus Genome-Wide Association Studies

Guo, Hongping; Yu, Zu-Guo; An, Jiyuan; Han, Gangwen; Ma, Yuanlin; Tang, Runbin

doi:10.3390/e22030329

Cited by 7 publications

(3 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Today, it is well established that mutual information is an appropriate metric to measure the association between SNPs and qualitative (case–control) phenotypes [ 39 , 44 , 46 , 74 , 75 , 76 , 77 ]. However, we apply here for the first time this metric to quantitative traits.…”

Section: Resultsmentioning

confidence: 99%

MIDESP: Mutual Information-Based Detection of Epistatic SNP Pairs for Qualitative and Quantitative Phenotypes

Heinrich

Ramzan

Rajavel

et al. 2021

Biology

View full text Add to dashboard Cite

The interactions between SNPs result in a complex interplay with the phenotype, known as epistasis. The knowledge of epistasis is a crucial part of understanding genetic causes of complex traits. However, due to the enormous number of SNP pairs and their complex relationship to the phenotype, identification still remains a challenging problem. Many approaches for the detection of epistasis have been developed using mutual information (MI) as an association measure. However, these methods have mainly been restricted to case–control phenotypes and are therefore of limited applicability for quantitative traits. To overcome this limitation of MI-based methods, here, we present an MI-based novel algorithm, MIDESP, to detect epistasis between SNPs for qualitative as well as quantitative phenotypes. Moreover, by incorporating a dataset-dependent correction technique, we deal with the effect of background associations in a genotypic dataset to separate correct epistatic interaction signals from those of false positive interactions resulting from the effect of single SNP×phenotype associations. To demonstrate the effectiveness of MIDESP, we apply it on two real datasets with qualitative and quantitative phenotypes, respectively. Our results suggest that by eliminating the background associations, MIDESP can identify important genes, which play essential roles for bovine tuberculosis or the egg weight of chickens.

show abstract

Section: Resultsmentioning

confidence: 99%

MIDESP: Mutual Information-Based Detection of Epistatic SNP Pairs for Qualitative and Quantitative Phenotypes

Heinrich

Ramzan

Rajavel

et al. 2021

Biology

View full text Add to dashboard Cite

show abstract

“…The larger the mutual information value is, the stronger the correlation between the two random variables. In GWAS, the phenotypic vector is considered one random variable, and the genotype vector is another random variable [41].…”

Section: Mutual Informationmentioning

confidence: 99%

Enhancing genotype-phenotype association with optimized machine learning and biological enrichment methods

Jangale,

Sharma,

Shekhawat

et al. 2024

Preprint

View full text Add to dashboard Cite

Genome-wide association studies (GWAS) are surging again owing to newer high-quality T2T-CHM13 and human pangenome references. Conventional GWAS methods have several limitations, including high false negatives. Non-conventional machine learning-based methods are warranted for analyzing newly sequenced, albeit complex, genomic regions.We present a robust machine learning-based framework for feature selection and association analysis, incorporating functional enrichment analysis to avoid false negatives. We benchmarked four popular single nucleotide polymorphism (SNP) feature selection methods: least absolute shrinkage and selection operator, ridge regression, elastic-net, and mutual information. Furthermore, we evaluated four association methods: linear regression, random forest, support vector regression (SVR), and XGBoost. We assessed proposed framework on diverse datasets, including subsets of publicly available PennCATH datasets as well as imputed, rare-variants, and simulated datasets. Low-density lipoprotein (LDL) cholesterol level was used as a phenotype for illustration. Our analysis revealed elastic-net combined with SVR consistently outperformed other methods across various datasets. Functional annotation of top 100 SNPs from PennCATH-real dataset revealed their expression in LDL cholesterol-related tissues. Our analysis validated three previously known genes (APOB, TRAPPC9, and EEPD1) implicated in cholesterol-regulated pathways. Also, rare-variant dataset analysis confirmed 37 known genes associated with LDL cholesterol. We identified several important genes, including APOB (familial-hypercholesterolemia), PTK2B (Alzheimer’s disease), and PTPN12 (myocardial ischemia/reperfusion injuries) as potential drug targets for cholesterol-related diseases.Our comprehensive analyses highlight elastic-net combined with SVR for association analysis could overcome limitations of conventional GWAS approaches. Our framework effectively detects common and rare variants associated with complex traits, enhancing the understanding of complex diseases.

show abstract

“…Regression models provide useful frameworks for multivariate mutual information analysis that uses information concepts when choosing covariates (also called features) that are important for the analysis and prediction. A recent article that includes both the concept of mutual information and the Lasso is [ 1 ]. This paper develops properties of methods that use the information in a vector X to reduce prediction error, that is, to reduce entropy.…”

Section: Introductionmentioning

confidence: 99%

Ensemble Linear Subspace Analysis of High-Dimensional Data

Ahmed

Amiri

Doksum

2021

Entropy

View full text Add to dashboard Cite

Regression models provide prediction frameworks for multivariate mutual information analysis that uses information concepts when choosing covariates (also called features) that are important for analysis and prediction. We consider a high dimensional regression framework where the number of covariates (p) exceed the sample size (n). Recent work in high dimensional regression analysis has embraced an ensemble subspace approach that consists of selecting random subsets of covariates with fewer than p covariates, doing statistical analysis on each subset, and then merging the results from the subsets. We examine conditions under which penalty methods such as Lasso perform better when used in the ensemble approach by computing mean squared prediction errors for simulations and a real data example. Linear models with both random and fixed designs are considered. We examine two versions of penalty methods: one where the tuning parameter is selected by cross-validation; and one where the final predictor is a trimmed average of individual predictors corresponding to the members of a set of fixed tuning parameters. We find that the ensemble approach improves on penalty methods for several important real data and model scenarios. The improvement occurs when covariates are strongly associated with the response, when the complexity of the model is high. In such cases, the trimmed average version of ensemble Lasso is often the best predictor.

show abstract

A Two-Stage Mutual Information Based Bayesian Lasso Algorithm for Multi-Locus Genome-Wide Association Studies

Cited by 7 publications

References 32 publications

MIDESP: Mutual Information-Based Detection of Epistatic SNP Pairs for Qualitative and Quantitative Phenotypes

MIDESP: Mutual Information-Based Detection of Epistatic SNP Pairs for Qualitative and Quantitative Phenotypes

Enhancing genotype-phenotype association with optimized machine learning and biological enrichment methods

Ensemble Linear Subspace Analysis of High-Dimensional Data

Contact Info

Product

Resources

About