Results 221 to 230 of about 40,628 (317)

Extensive phenotyping and genome‐wide analysis of grain number‐related traits in a diverse wheat core collection

The Plant Genome, Volume 19, Issue 1, March 2026.
Abstract Bread wheat (Triticum aestivum L.) is one of the most important staple crops globally. Grain number per spikelet (GPS) is an important yield component in wheat. It is influenced by floret number per spikelet (FPS) and floret fertility. Through three consecutive years of observation of FPS, GPS, and floret fertility per spikelet (FFPS) in a ...
Zhuo Su, Shun Sakuma, Miyuki Nitta, Yasir S. A. Gorafi, Shuhei Nasuda   +4 more
wiley   +1 more source

Identification of candidate genes for drought tolerance in soybean through QTL mapping and gene expression analysis. [PDF]

Front Genet
Park GR, Bae SH, Kang BK, Seo JH, Oh JH.
europepmc   +1 more source

Fine mapping and candidate gene analysis of a major QTL for grain length on chromosome 5BS in bread wheat

Jianqi Zeng, Dehui Zhao, Yang Li, Yu-feng YANG, Dan Liu, Yubing Tian, F. Wang, Shuanghe Cao, Xian-chun XIA, Zhonghu He, Yong Zhang   +10 more
openalex   +1 more source

Integrated GWAS and Fst analysis identify flowering‐time regulatory genes in maize

The Plant Genome, Volume 19, Issue 1, March 2026.
Abstract Flowering time in maize is a complex quantitative trait regulated by multiple genes, and its genetic variation mechanisms remain to be fully elucidated. In this study, we phenotypically evaluated flowering‐related traits (days to tasseling, days to pollen shedding, days to silking) across six different environments using an association ...
Dong Wang, Zhuoya Gao, Mengyue Dong, Tuan Li, Wenjing Zhou, Jialei Ou, Shiya Zhang, Shuaishuai Zhang, Xian Dong, Yalan Li, Caihui Zhang, Jiahao Tie, Yinuo Wang, Gaoyang Sun, Huiling Xie, Weihua Li, Jihua Tang, Pengshuai Yan   +17 more
wiley   +1 more source

Construction of a genetic linkage map and QTL mapping of the agronomic traits in Foxtail millet (Setaria italica). [PDF]

BMC Genomics
Gao L, Zhu Q, Li H, Wang S, Fan J, Wang T, Yang L, Zhao Y, Ma Y, Chen L, Li X, Dong S, Chu X, Diao X, Yuan X, Wang J, Yang G.   +16 more
europepmc   +1 more source

Genome‐wide association and haplotype analyses reveal genetic architecture of seed dormancy in grain sorghum with implications for preharvest sprouting tolerance

The Plant Genome, Volume 19, Issue 1, March 2026.
Abstract Preharvest sprouting (PHS), triggered primarily by wet or humid conditions at maturity and further influenced by temperature, causes premature grain germination in sorghum (Sorghum bicolor (L.) Moench), reducing yield and grain quality. To elucidate the genetic architecture of seed dormancy and germination responses related to PHS tolerance ...
Yusa Ichinose, Dinesh Kumar Saini, Juliana M. Espíndola Lima, Gunvant B. Patil, Impa Somayanda, S. V. Krishna Jagadish   +5 more
wiley   +1 more source

Integrating QTL mapping and GWAS to decipher the genetic mechanisms behind the calcium contents of <i>Brassica napus</i> shoots. [PDF]

Front Plant Sci
Xiang Y, Chen F, Shi R, Yang T, Zhang W, Zhou X, Wang C, Sun C, Fu S, Wang X, Zhang J, Shen Y.   +11 more
europepmc   +1 more source

Do maize chromosomes contribute to genetic variance in proportion to their size in elite populations?

The Plant Genome, Volume 19, Issue 1, March 2026.
Abstract Quantitative trait loci (QTLs) are often assumed to be uniformly distributed across the genome in modeling and simulation studies. Our aim was to assess whether the contribution of each chromosome to genetic variance (VG) is proportional to its length in maize (Zea mays L.).
Inés Rebollo, Rex Bernardo
wiley   +1 more source

Integrative QTL Mapping and Transcriptomic Profiling to Identify Growth-Associated QTL and Candidate Genes in Hong Kong Catfish (<i>Clarias fuscus</i>). [PDF]

Animals (Basel)
Zhu Y, Zhou D, Shen Y, Pan C, Chen Y, Zhang Y, Yang B, Li G, Chen H, Tian C.   +9 more
europepmc   +1 more source

Impact of environmental covariates summarization on predictive ability in genomic selection

The Plant Genome, Volume 19, Issue 1, March 2026.
Abstract Integrating genomic and environmental information holds the potential for enhancing the predictive power of genomic prediction models when accounting for the genotype‐by‐environment interactions. Hence, incorporating environmental covariates (EC) into these models can significantly influence their predictive accuracy.
Vitor Seiti Sagae, Moysés Nascimento, Ana Carolina Campana Nascimento, Felipe Lopes da Silva, Diego Jarquin   +4 more
wiley   +1 more source