Fuzzy Logic as a Strategy for Combining Marker Statistics to Optimize Preselection of High-Density and Sequence Genotype Data
The high dimensionality of genotype data available for genomic evaluations has presented a motivation for developing strategies to identify subsets of markers capable of increasing the accuracy of predictions compared to the current commercial single nucleotide polymorphism (SNP) chips. In this simu...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-11-01
|
| Series: | Genes |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4425/13/11/2100 |
| _version_ | 1797465213034823680 |
|---|---|
| author | Ashley Ling El Hamidi Hay Samuel E. Aggrey Romdhane Rekaya |
| author_facet | Ashley Ling El Hamidi Hay Samuel E. Aggrey Romdhane Rekaya |
| author_sort | Ashley Ling |
| collection | DOAJ |
| description | The high dimensionality of genotype data available for genomic evaluations has presented a motivation for developing strategies to identify subsets of markers capable of increasing the accuracy of predictions compared to the current commercial single nucleotide polymorphism (SNP) chips. In this simulation study, an algorithm for combining statistics used in the preselection and prioritization of SNP markers from a high-density panel (1.3 million SNPs) into a composite “fuzzy” ranking score based on a Sugeno-type fuzzy inference system (FIS) was developed and evaluated for performance in preselection for genomic predictions. F<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mrow></mrow><mi>ST</mi></msub></semantics></math></inline-formula> scores, and <i>p</i>-values were evaluated as inputs for the FIS. The accuracy of genomic predictions for fuzzy-score-preselected panel sizes of 1–50 k SNPs ranged from −0.4–11.7 and −0.3–3.8% higher than F<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mrow></mrow><mi>ST</mi></msub></semantics></math></inline-formula> and <i>p</i>-value preselection, respectively. Though gains in prediction accuracies using only two inputs to the FIS were modest, preselection based on fuzzy scores yielded more accurate predictions than both F<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mrow></mrow><mi>ST</mi></msub></semantics></math></inline-formula> scores and <i>p</i>-values for the majority of evaluated panel sizes under all genetic architectures. FIS have the potential to aggregate information from multiple criteria that reflect SNP-trait associations and biological relevance in a flexible and efficient way to yield higher quality genomic predictions. |
| first_indexed | 2024-03-09T18:19:18Z |
| format | Article |
| id | doaj.art-18eeb7e03c9b48e18f23db72c41a0903 |
| institution | Directory Open Access Journal |
| issn | 2073-4425 |
| language | English |
| last_indexed | 2024-03-09T18:19:18Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Genes |
| spelling | doaj.art-18eeb7e03c9b48e18f23db72c41a09032023-11-24T08:26:18ZengMDPI AGGenes2073-44252022-11-011311210010.3390/genes13112100Fuzzy Logic as a Strategy for Combining Marker Statistics to Optimize Preselection of High-Density and Sequence Genotype DataAshley Ling0El Hamidi Hay1Samuel E. Aggrey2Romdhane Rekaya3USDA Agricultural Research Service, Fort Keogh Livestock and Range Research Laboratory, Miles City, MT 59301, USAUSDA Agricultural Research Service, Fort Keogh Livestock and Range Research Laboratory, Miles City, MT 59301, USADepartment of Poultry Science, The University of Georgia, Athens, GA 30602, USAInstitute of Bioinformatics, The University of Georgia, Athens, GA 30602, USAThe high dimensionality of genotype data available for genomic evaluations has presented a motivation for developing strategies to identify subsets of markers capable of increasing the accuracy of predictions compared to the current commercial single nucleotide polymorphism (SNP) chips. In this simulation study, an algorithm for combining statistics used in the preselection and prioritization of SNP markers from a high-density panel (1.3 million SNPs) into a composite “fuzzy” ranking score based on a Sugeno-type fuzzy inference system (FIS) was developed and evaluated for performance in preselection for genomic predictions. F<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mrow></mrow><mi>ST</mi></msub></semantics></math></inline-formula> scores, and <i>p</i>-values were evaluated as inputs for the FIS. The accuracy of genomic predictions for fuzzy-score-preselected panel sizes of 1–50 k SNPs ranged from −0.4–11.7 and −0.3–3.8% higher than F<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mrow></mrow><mi>ST</mi></msub></semantics></math></inline-formula> and <i>p</i>-value preselection, respectively. Though gains in prediction accuracies using only two inputs to the FIS were modest, preselection based on fuzzy scores yielded more accurate predictions than both F<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mrow></mrow><mi>ST</mi></msub></semantics></math></inline-formula> scores and <i>p</i>-values for the majority of evaluated panel sizes under all genetic architectures. FIS have the potential to aggregate information from multiple criteria that reflect SNP-trait associations and biological relevance in a flexible and efficient way to yield higher quality genomic predictions.https://www.mdpi.com/2073-4425/13/11/2100SNP preselectiongenomic predictionhigh-density genotypessequence genotypesfuzzy logicfuzzy inference |
| spellingShingle | Ashley Ling El Hamidi Hay Samuel E. Aggrey Romdhane Rekaya Fuzzy Logic as a Strategy for Combining Marker Statistics to Optimize Preselection of High-Density and Sequence Genotype Data Genes SNP preselection genomic prediction high-density genotypes sequence genotypes fuzzy logic fuzzy inference |
| title | Fuzzy Logic as a Strategy for Combining Marker Statistics to Optimize Preselection of High-Density and Sequence Genotype Data |
| title_full | Fuzzy Logic as a Strategy for Combining Marker Statistics to Optimize Preselection of High-Density and Sequence Genotype Data |
| title_fullStr | Fuzzy Logic as a Strategy for Combining Marker Statistics to Optimize Preselection of High-Density and Sequence Genotype Data |
| title_full_unstemmed | Fuzzy Logic as a Strategy for Combining Marker Statistics to Optimize Preselection of High-Density and Sequence Genotype Data |
| title_short | Fuzzy Logic as a Strategy for Combining Marker Statistics to Optimize Preselection of High-Density and Sequence Genotype Data |
| title_sort | fuzzy logic as a strategy for combining marker statistics to optimize preselection of high density and sequence genotype data |
| topic | SNP preselection genomic prediction high-density genotypes sequence genotypes fuzzy logic fuzzy inference |
| url | https://www.mdpi.com/2073-4425/13/11/2100 |
| work_keys_str_mv | AT ashleyling fuzzylogicasastrategyforcombiningmarkerstatisticstooptimizepreselectionofhighdensityandsequencegenotypedata AT elhamidihay fuzzylogicasastrategyforcombiningmarkerstatisticstooptimizepreselectionofhighdensityandsequencegenotypedata AT samueleaggrey fuzzylogicasastrategyforcombiningmarkerstatisticstooptimizepreselectionofhighdensityandsequencegenotypedata AT romdhanerekaya fuzzylogicasastrategyforcombiningmarkerstatisticstooptimizepreselectionofhighdensityandsequencegenotypedata |