Transition-transversion encoding and genetic relationship metric in ReliefF feature selection improves pathway enrichment in GWAS.BACKGROUND
 ReliefF is a nearest-neighbor based feature selection algorithm that efficiently detects variants that are important due to statistical interactions or epistasis.  For categorical predictors, like genotypes, the standard metric used in ReliefF has been a simple (binary) mismatch difference.  We introduce a new two-dimensional transition/transversion genotype encoding for ReliefF, and we implement three ReliefF attribute metrics:  1.) genotype mismatch (GM), which is the ReliefF standard,  2.) allele mismatch (AM), which accounts for heterozygous differences and has not been used previously in ReliefF, and  We incorporate these attribute metrics into the ReliefF nearest neighbor calculation with a Manhattan metric, and we introduce GRM as a new ReliefF nearest-neighbor metric to adjust for allele frequency heterogeneity.


 We apply ReliefF with each metric to a GWAS of major depressive disorder and compare the detection of genes in pathways implicated in depression, including Axon Guidance, Neuronal System, and G Protein-Coupled Receptor Signaling.  Our results suggest that using more genetically motivated encodings, such as transition/transversion, and metrics that adjust for allele frequency heterogeneity, such as GRM, lead to ReliefF attribute scores with improved pathway enrichment.
ReliefF
Transition-transversion encoding and genetic relationship metric in ReliefF feature selection improves pathway enrichment in GWAS.BACKGROUND
 ReliefF is a nearest-neighbor based feature selection algorithm that efficiently detects variants that are important due to statistical interactions or epistasis.  For categorical predictors, like genotypes, the standard metric used in ReliefF has been a simple (binary) mismatch difference.  We introduce a new two-dimensional transition/transversion genotype encoding for ReliefF, and we implement three ReliefF attribute metrics:  1.) genotype mismatch (GM), which is the ReliefF standard,  2.) allele mismatch (AM), which accounts for heterozygous differences and has not been used previously in ReliefF, and  We incorporate these attribute metrics into the ReliefF nearest neighbor calculation with a Manhattan metric, and we introduce GRM as a new ReliefF nearest-neighbor metric to adjust for allele frequency heterogeneity.


 We apply ReliefF with each metric to a GWAS of major depressive disorder and compare the detection of genes in pathways implicated in depression, including Axon Guidance, Neuronal System, and G Protein-Coupled Receptor Signaling.  Our results suggest that using more genetically motivated encodings, such as transition/transversion, and metrics that adjust for allele frequency heterogeneity, such as GRM, lead to ReliefF attribute scores with improved pathway enrichment.
ReliefF
Transition-transversion encoding and genetic relationship metric in ReliefF feature selection improves pathway enrichment in GWAS.BACKGROUND
 ReliefF is a nearest-neighbor based feature selection algorithm that efficiently detects variants that are important due to statistical interactions or epistasis.  For categorical predictors, like genotypes, the standard metric used in ReliefF has been a simple (binary) mismatch difference.  We introduce a new two-dimensional transition/transversion genotype encoding for ReliefF, and we implement three ReliefF attribute metrics:  1.) genotype mismatch (GM), which is the ReliefF standard,  2.) allele mismatch (AM), which accounts for heterozygous differences and has not been used previously in ReliefF, and  We incorporate these attribute metrics into the ReliefF nearest neighbor calculation with a Manhattan metric, and we introduce GRM as a new ReliefF nearest-neighbor metric to adjust for allele frequency heterogeneity.


 We apply ReliefF with each metric to a GWAS of major depressive disorder and compare the detection of genes in pathways implicated in depression, including Axon Guidance, Neuronal System, and G Protein-Coupled Receptor Signaling.  Our results suggest that using more genetically motivated encodings, such as transition/transversion, and metrics that adjust for allele frequency heterogeneity, such as GRM, lead to ReliefF attribute scores with improved pathway enrichment.
ReliefF
Transition-transversion encoding and genetic relationship metric in ReliefF feature selection improves pathway enrichment in GWAS.BACKGROUND
 ReliefF is a nearest-neighbor based feature selection algorithm that efficiently detects variants that are important due to statistical interactions or epistasis.  For categorical predictors, like genotypes, the standard metric used in ReliefF has been a simple (binary) mismatch difference.  We introduce a new two-dimensional transition/transversion genotype encoding for ReliefF, and we implement three ReliefF attribute metrics:  1.) genotype mismatch (GM), which is the ReliefF standard,  2.) allele mismatch (AM), which accounts for heterozygous differences and has not been used previously in ReliefF, and  We incorporate these attribute metrics into the ReliefF nearest neighbor calculation with a Manhattan metric, and we introduce GRM as a new ReliefF nearest-neighbor metric to adjust for allele frequency heterogeneity.


 We apply ReliefF with each metric to a GWAS of major depressive disorder and compare the detection of genes in pathways implicated in depression, including Axon Guidance, Neuronal System, and G Protein-Coupled Receptor Signaling.  Our results suggest that using more genetically motivated encodings, such as transition/transversion, and metrics that adjust for allele frequency heterogeneity, such as GRM, lead to ReliefF attribute scores with improved pathway enrichment.
ReliefF