@article{fdi:010085381,
  title = {{E}valuation of nine statistics to identify {QTL}s in bulk segregant analysis using next generation sequencing approaches},
  author = {{D}e la {F}uente {C}anto, {C}arla and {V}igouroux, {Y}ves},
  editor = {},
  language = {{ENG}},
  abstract = {{B}ackground {B}ulk segregant analysis ({BSA}) combined with next generation sequencing is a powerful tool to identify quantitative trait loci ({QTL}). {T}he impact of the size of the study population and the percentage of extreme genotypes analysed have already been assessed. {B}ut a good comparison of statistical approaches designed to identify {QTL} regions using next generation sequencing ({NGS}) technologies for {BSA} is still lacking. {R}esults {W}e developed an {R} code to simulate {QTL}s in bulks of {F}2 contrasted lines. {W}e simulated a range of recombination rates based on estimations using different crop species. {T}he simulations were used to benchmark the ability of statistical methods identify the exact location of true {QTL}s. {A} single {QTL} led to a shift in allele frequency across a large fraction of the chromosome for plant species with low recombination rate. {T}he smoothed version of all statistics performed best notably the smoothed {E}uclidean distance-based statistics was always found to be more accurate in identifying the location of {QTL}s. {W}e propose a simulation approach to build confidence interval statistics for the detection of {QTL}s. {C}onclusion {W}e highlight the statistical methods best suited for {BSA} studies using {NGS} technologies in crops even when recombination rate is low. {W}e also provide simulation codes to build confidence intervals and to assess the impact of recombination for application to other studies. {T}his computational study will help select {NGS}-based {BSA} statistics that are useful to the broad scientific community.},
  keywords = {{BSA} ; {NGS} ; {S}tatistics ; {C}onfidence interval ; {QTL} ; {BSA}-{S}eq ; {S}imulation},
  booktitle = {},
  journal = {{BMC} {G}enomics},
  volume = {23},
  numero = {1},
  pages = {490 [12 ]},
  ISSN = {1471-2164},
  year = {2022},
  DOI = {10.1186/s12864-022-08718-y},
  URL = {https://www.documentation.ird.fr/hor/fdi:010085381},
}