H
Huihui Li
Researcher at International Maize and Wheat Improvement Center
Publications - 64
Citations - 7249
Huihui Li is an academic researcher from International Maize and Wheat Improvement Center. The author has contributed to research in topics: Population & Quantitative trait locus. The author has an hindex of 26, co-authored 56 publications receiving 5863 citations. Previous affiliations of Huihui Li include Cornell University & Beijing Normal University.
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Journal ArticleDOI
The Genetic Architecture of Maize Flowering Time
Edward S. Buckler,Edward S. Buckler,James B. Holland,James B. Holland,Peter J. Bradbury,Peter J. Bradbury,Charlotte B. Acharya,Patrick J. Brown,C. A. Browne,C. A. Browne,Elhan S. Ersoz,Sherry Flint-Garcia,Sherry Flint-Garcia,Arturo Garcia,Arturo Garcia,Jeffrey C. Glaubitz,Major M. Goodman,Carlos Harjes,Kate E. Guill,Kate E. Guill,Dallas E. Kroon,Sara Larsson,Nicholas Lepak,Nicholas Lepak,Huihui Li,Huihui Li,Sharon E. Mitchell,Gaël Pressoir,Jason A. Peiffer,Marco Oropeza Rosas,Torbert Rocheford,Torbert Rocheford,M. Cinta Romay,M. Cinta Romay,Susan Romero,Stella Salvo,Stella Salvo,Hector Sanchez Villeda,Hector Sanchez Villeda,H. Sofia da Silva,Qi Sun,Feng Tian,N. Upadyayula,Doreen Ware,Doreen Ware,Heather Yates,Jianming Yu,Zhiwu Zhang,Stephen Kresovich,Michael D. McMullen,Michael D. McMullen +50 more
TL;DR: A simple additive model accurately predicts flowering time for maize, in contrast to the genetic architecture observed in the selfing plant species rice and Arabidopsis.
Journal ArticleDOI
QTL IciMapping: Integrated software for genetic linkage map construction and quantitative trait locus mapping in biparental populations
TL;DR: QTL IciMapping is freely available public software capable of building high-density linkage maps and mapping quantitative trait loci (QTL) in biparental populations and to perform analysis of variance for multi-environmental trials.
Journal ArticleDOI
Genetic properties of the maize nested association mapping population.
Michael D. McMullen,Michael D. McMullen,Stephen Kresovich,Hector Sanchez Villeda,Peter J. Bradbury,Peter J. Bradbury,Huihui Li,Huihui Li,Qi Sun,Sherry Flint-Garcia,Sherry Flint-Garcia,Jeffry M. Thornsberry,Charlotte B. Acharya,Christopher A. Bottoms,Patrick J. Brown,C. A. Browne,Magen S. Eller,Kate E. Guill,Carlos Harjes,Dallas E. Kroon,Nick Lepak,Sharon E. Mitchell,Brooke Peterson,Gaël Pressoir,Susan Romero,Marco Oropeza Rosas,Stella Salvo,Heather Yates,Mark Hanson,Elizabeth S. Jones,Stephen Smith,Jeffrey C. Glaubitz,Major M. Goodman,Doreen Ware,Doreen Ware,James B. Holland,James B. Holland,Edward S. Buckler,Edward S. Buckler +38 more
TL;DR: Maize genetic diversity has been used to understand the molecular basis of phenotypic variation and to improve agricultural efficiency and sustainability and it is suggested that selection in inbred lines has been less efficient in these regions because of reduced recombination frequency.
Journal ArticleDOI
A modified algorithm for the improvement of composite interval mapping
TL;DR: A modified algorithm called inclusive composite interval mapping (ICIM) is proposed in this article, which retains all advantages of CIM over IM and avoids the possible increase of sampling variance and the complicated background marker selection process in CIM.
Journal ArticleDOI
Metabolome-based genome-wide association study of maize kernel leads to novel biochemical insights
Weiwei Wen,Dong Li,Xiang Li,Yanqiang Gao,Wenqiang Li,Huihui Li,Jie Liu,Haijun Liu,Wei Chen,Jie Luo,Jianbing Yan +10 more
TL;DR: A comprehensive study of maize metabolism, combining genetic, metabolite and expression profiling methodologies to dissect the genetic basis of metabolic diversity in maize kernels, finds metabolite features associated with kernel weight could be used as biomarkers to facilitate genetic improvement of maize.