Fast and accurate imputation of genotypes from noisy low-coverage sequencing data in bi-parental populations
Genotyping of bi-parental populations can be performed with low-coverage next-generation sequencing (LC-NGS). This allows the creation of highly saturated genetic maps at reasonable cost, precisely localized recombination breakpoints (i.e., the crossovers), and minimized mapping intervals for quanti...
| Autores principales: | , , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/176829 |
| _version_ | 1855520634059096064 |
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| author | Triay, Cecile Boizet, Alice Fragoso, Christopher Gkanogiannis, Anestis Rami, Jean Francois Lorieux, Mathias |
| author_browse | Boizet, Alice Fragoso, Christopher Gkanogiannis, Anestis Lorieux, Mathias Rami, Jean Francois Triay, Cecile |
| author_facet | Triay, Cecile Boizet, Alice Fragoso, Christopher Gkanogiannis, Anestis Rami, Jean Francois Lorieux, Mathias |
| author_sort | Triay, Cecile |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Genotyping of bi-parental populations can be performed with low-coverage next-generation sequencing (LC-NGS). This allows the creation of highly saturated genetic maps at reasonable cost, precisely localized recombination breakpoints (i.e., the crossovers), and minimized mapping intervals for quantitative-trait locus analysis. The main issues with these low-coverage genotyping methods are (1) poor performance at heterozygous loci, (2) high percentage of missing data, (3) local errors due to erroneous mapping of sequencing reads and reference genome mistakes, and (4) global, technical errors inherent to NGS itself. Recent methods like Tassel-FSFHap or LB-Impute are excellent at addressing issues 1 and 2, but nonetheless perform poorly when issues 3 and 4 are persistent in a dataset (i.e., “noisy” data). Here, we present a new algorithm for imputation of LC-NGS data that eliminates the need of complex pre-filtering of noisy data, accurately types heterozygous chromosomal regions, precisely estimates crossover positions, corrects erroneous data, and imputes missing data. The imputation of genotypes and recombination breakpoints is based on maximum-likelihood estimation. We compare its performance with Tassel-FSFHap and LB-Impute using simulated data and two real datasets. NOISYmputer is consistently more efficient than the two other software tested and reaches average breakpoint precision of 99.9% and average recall of 99.6% on illumina simulated dataset. NOISYmputer consistently provides precise map size estimations when applied to real datasets while alternative tools may exhibit errors ranging from 3 to 1845 times the real size of the chromosomes in centimorgans. Furthermore, the algorithm is not only highly effective in terms of precision and recall but is also particularly economical in its use of RAM and computation time, being much faster than Hidden Markov Model methods. Availability NOISYmputer and its source code are available as a multiplatform (Linux, macOS, Windows) Java executable at the URL https://gitlab.cirad.fr/noisymputer/noisymputerstandalone/-/tree/1.0.0-RELEASE?reftype=tags . |
| format | Journal Article |
| id | CGSpace176829 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| record_format | dspace |
| spelling | CGSpace1768292025-12-08T09:54:28Z Fast and accurate imputation of genotypes from noisy low-coverage sequencing data in bi-parental populations Triay, Cecile Boizet, Alice Fragoso, Christopher Gkanogiannis, Anestis Rami, Jean Francois Lorieux, Mathias genotypes data computer applications single nucleotide polymorphisms chromosome mapping Genotyping of bi-parental populations can be performed with low-coverage next-generation sequencing (LC-NGS). This allows the creation of highly saturated genetic maps at reasonable cost, precisely localized recombination breakpoints (i.e., the crossovers), and minimized mapping intervals for quantitative-trait locus analysis. The main issues with these low-coverage genotyping methods are (1) poor performance at heterozygous loci, (2) high percentage of missing data, (3) local errors due to erroneous mapping of sequencing reads and reference genome mistakes, and (4) global, technical errors inherent to NGS itself. Recent methods like Tassel-FSFHap or LB-Impute are excellent at addressing issues 1 and 2, but nonetheless perform poorly when issues 3 and 4 are persistent in a dataset (i.e., “noisy” data). Here, we present a new algorithm for imputation of LC-NGS data that eliminates the need of complex pre-filtering of noisy data, accurately types heterozygous chromosomal regions, precisely estimates crossover positions, corrects erroneous data, and imputes missing data. The imputation of genotypes and recombination breakpoints is based on maximum-likelihood estimation. We compare its performance with Tassel-FSFHap and LB-Impute using simulated data and two real datasets. NOISYmputer is consistently more efficient than the two other software tested and reaches average breakpoint precision of 99.9% and average recall of 99.6% on illumina simulated dataset. NOISYmputer consistently provides precise map size estimations when applied to real datasets while alternative tools may exhibit errors ranging from 3 to 1845 times the real size of the chromosomes in centimorgans. Furthermore, the algorithm is not only highly effective in terms of precision and recall but is also particularly economical in its use of RAM and computation time, being much faster than Hidden Markov Model methods. Availability NOISYmputer and its source code are available as a multiplatform (Linux, macOS, Windows) Java executable at the URL https://gitlab.cirad.fr/noisymputer/noisymputerstandalone/-/tree/1.0.0-RELEASE?reftype=tags . 2025-01-30 2025-10-06T08:09:31Z 2025-10-06T08:09:31Z Journal Article https://hdl.handle.net/10568/176829 en Open Access application/pdf Triay, C.; Boizet, A.; Fragoso, C.; Gkanogiannis, A.; Rami, J.F.; Lorieux, M. (2025) Fast and accurate imputation of genotypes from noisy low-coverage sequencing data in bi-parental populations. PLoS ONE 20(1): e0314759. ISSN: 1932-6203 |
| spellingShingle | genotypes data computer applications single nucleotide polymorphisms chromosome mapping Triay, Cecile Boizet, Alice Fragoso, Christopher Gkanogiannis, Anestis Rami, Jean Francois Lorieux, Mathias Fast and accurate imputation of genotypes from noisy low-coverage sequencing data in bi-parental populations |
| title | Fast and accurate imputation of genotypes from noisy low-coverage sequencing data in bi-parental populations |
| title_full | Fast and accurate imputation of genotypes from noisy low-coverage sequencing data in bi-parental populations |
| title_fullStr | Fast and accurate imputation of genotypes from noisy low-coverage sequencing data in bi-parental populations |
| title_full_unstemmed | Fast and accurate imputation of genotypes from noisy low-coverage sequencing data in bi-parental populations |
| title_short | Fast and accurate imputation of genotypes from noisy low-coverage sequencing data in bi-parental populations |
| title_sort | fast and accurate imputation of genotypes from noisy low coverage sequencing data in bi parental populations |
| topic | genotypes data computer applications single nucleotide polymorphisms chromosome mapping |
| url | https://hdl.handle.net/10568/176829 |
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