Description Usage Arguments Value Author(s) References See Also Examples

Searches for QTL and adds them one at a time to a multiple random-effect QTL model based on score statistics.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 |

`data` |
an object of class |

`offset.data` |
a data frame with the same dimensions of |

`model` |
an object of class |

`w.size` |
the window size (in cM) to avoid on either side of QTL already in the model when looking for a new QTL. |

`sig.fwd` |
the desired score-based |

`score.null` |
an object of class |

`polygenes` |
if |

`n.rounds` |
number of search rounds; if |

`n.clusters` |
number of parallel processes to spawn. |

`plot` |
a suffix for the file's name containing plots of every QTL search round, e.g. "search" (default); if |

`verbose` |
if |

`x` |
an object of class |

`pheno.col` |
a numeric vector with the phenotype column numbers to be printed; if |

`...` |
currently ignored |

An object of class `qtlpoly.search`

which contains a list of `results`

for each trait with the following components:

`pheno.col` |
a phenotype column number. |

`stat` |
a vector containing values from score statistics. |

`pval` |
a vector containing |

`qtls` |
a data frame with information from the mapped QTL. |

Guilherme da Silva Pereira, gdasilv@ncsu.edu

Pereira GS, Gemenet DC, Mollinari M, Olukolu BA, Wood JC, Mosquera V, Gruneberg WJ, Khan A, Buell CR, Yencho GC, Zeng ZB (2020) Multiple QTL mapping in autopolyploids: a random-effect model approach with application in a hexaploid sweetpotato full-sib population, *Genetics* 215 (3): 579-595. doi: 10.1534/genetics.120.303080.

Qu L, Guennel T, Marshall SL (2013) Linear score tests for variance components in linear mixed models and applications to genetic association studies. *Biometrics* 69 (4): 883–92.

Zou F, Fine JP, Hu J, Lin DY (2004) An efficient resampling method for assessing genome-wide statistical significance in mapping quantitative trait loci. *Genetics* 168 (4): 2307-16. doi: 10.1534/genetics.104.031427

1 2 3 4 5 6 7 8 9 10 11 12 13 | ```
# Estimate conditional probabilities using mappoly package
library(mappoly)
library(qtlpoly)
genoprob4x = lapply(maps4x[c(5)], calc_genoprob)
data = read_data(ploidy = 4, geno.prob = genoprob4x, pheno = pheno4x, step = 1)
# Build null model
null.mod = null_model(data, pheno.col = 1, n.clusters = 1)
# Perform forward search
search.mod = search_qtl(data, model = null.mod, w.size = 15, sig.fwd = 0.01, n.clusters = 1)
``` |

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