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All the technologies have been used in one or the other study of SNP based mapping in plants.
Many areas critical to agricultural production and research, such as the breeding and trait mapping in plants and livestock, require robust and scalable genotyping platforms.
The emergence of more cost-effective, high-throughput genotyping platforms have rendered AM an increasingly attractive approach for QTL mapping in plants [ 11].
Nevertheless, in-depth studies determining the impact of imputing missing values on the power of genome-wide association mapping in plants are still lacking.
Following the success of mQTL mapping in plants [ 72, 73] and then in mammalian models [ 75], this approach was quickly followed by the development of mGWAS in humans cohorts ([ 77- 83], see also the review by J Adamski [ 84]).
Overgos have been used successfully for physical mapping in animals, but their utility for physical and comparative mapping in plants has not been studied as extensively as in animals.
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There were two main reasons to use a larger RH panel when developing high density RH maps in plants.
In addition, ESTs are useful sources of simple sequence repeats (SSRs) and single-nucleotide polymorphisms (SNPs), both of which are useful markers for creating genetic maps in plants [ 13- 19].
A large number of quantitative trait loci (QTL) for disease resistance have been mapped in plants [ 6, 9], but little is known about the underlying genetic basis or defense mechanisms involved.
Most QTL mapping studies in plants have been based on biparental populations; comparisons of QTL detected in mapping populations often reveal distinct sets of QTL (Blanc et al., 2006; Holland, 2007; Sneller et al., 2009).
Construction of a representative and genetically diverse panel of fixed lines is the first step for any successful association mapping study in plants.
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