000			02974nab a22003497a 4500
001			G94654
003			MX-TxCIM
005			20230714230751.0
008			210805t2010 ii |||p|op||| 00| 0 eng d
022	0		_a0019-6363
040			_aMX-TxCIM
041			_aeng
090			_aCIS-6191
100	1		_aPrasanna, B.M. _gGlobal Maize Program _8INT3057 _9887
245	1	0	_aStatistical genomics for crop improvement : _bopportunities and challenges
260			_aIndia : _bIndian Society of Agricultural Statistics, _c2010.
500			_aPeer review
500			_aPeer-review: No - Open Access: Yes|http://www.isas.org.in/html/journal.html
520			_aEffective analysis of molecular data in combination with rigorous phenotypic data using appropriate statistical methods can provide enhanced understanding of the genetic and molecular bases of complex phenotypic traits. Coupled with the rapid development related to genome sequencing of crop plants, advances in statistical methods have aided in detecting Quantitative Trait Loci (QTL) influencing an array of traits, including epistatic QTLs, besides analysis of genotype x environment interactions, discovery of 'consensus QTL' through meta-analysis of data, expression-QTL (eQTL) through genetical genomics, and even epigenomic QTL. The profusion of powerful DNA-based markers, particularly single nucleotide polymorphisms (SNPs) and the evolution of statistical algorithms and experimental strategies, including the extension of the concept of linkage disequilibrium (LD)-based association mapping in crop plants, further promises to revolutionize the discovery of marker-trait associations for several important traits. While these exciting advances have brought closer the statisticians, bioinformatics experts, geneticists and molecular biologists, the new focus on genomiscs has also highlighted a significant challenge; how to integrate the different views of the genome given by various types of experimental data and provide a proper biological perspective that can lead to crop improvement. In the article, from the user's perspective, I shall review some of the ongoing work on the above-mentioned areas in crop plants, especially using maize as a model system, and the opportunities and challenges for application of statistical genomics in molecular plant breeding.
536			_aGlobal Maize Program
546			_aText in English
594			_aINT3057
650		7	_aGenetic markers _2AGROVOC _91848
650		7	_aQuantitative Trait Loci _2AGROVOC _91853
650		7	_aChromosome mapping _2AGROVOC _92084
650		7	_aGenomics _2AGROVOC _91132
650		7	_aStatistical methods _2AGROVOC _92624
650		7	_aCrops _2AGROVOC _91069
773	0		_tJournal of the Indian Society of Agricultural Statistics _gv. 64, no. 1, p. 77-87 _dIndia : Indian Society of Agricultural Statistics, 2010. _x0019-6363
856	4		_yAccess only for CIMMYT Staff _uhttps://hdl.handle.net/20.500.12665/379
942			_cJA _2ddc _n0
999			_c28326 _d28326