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Unravelling consensus genomic regions associated with quality traits in wheat using meta-analysis of quantitative trait loci.
Gudi, Santosh; Saini, Dinesh Kumar; Singh, Gurjeet; Halladakeri, Priyanka; Kumar, Pradeep; Shamshad, Mohammad; Tanin, Mohammad Jafar; Singh, Satinder; Sharma, Achla.
Afiliação
  • Gudi S; Department of Plant Breeding and Genetics, Punjab Agricultural University, Ludhiana, Punjab, India. santosh-pbg@pau.edu.
  • Saini DK; Department of Plant Breeding and Genetics, Punjab Agricultural University, Ludhiana, Punjab, India.
  • Singh G; Department of Plant Breeding and Genetics, Punjab Agricultural University, Ludhiana, Punjab, India.
  • Halladakeri P; Department of Genetics and Plant Breeding, Anand Agricultural University, Gujarat, India.
  • Kumar P; Department of Plant Breeding and Genetics, Punjab Agricultural University, Ludhiana, Punjab, India.
  • Shamshad M; Department of Plant Breeding and Genetics, Punjab Agricultural University, Ludhiana, Punjab, India.
  • Tanin MJ; Department of Plant Breeding and Genetics, Punjab Agricultural University, Ludhiana, Punjab, India.
  • Singh S; Department of Plant Breeding and Genetics, Punjab Agricultural University, Ludhiana, Punjab, India.
  • Sharma A; Department of Plant Breeding and Genetics, Punjab Agricultural University, Ludhiana, Punjab, India.
Planta ; 255(6): 115, 2022 May 05.
Article em En | MEDLINE | ID: mdl-35508739
MAIN CONCLUSION: Meta-analysis in wheat for three major quality traits identified 110 meta-QTL (MQTL) with reduced confidence interval (CI). Five GWAS validated MQTL (viz., 1A.1, 1B.2, 3B.4, 5B.2, and 6B.2), each involving more than 20 initial QTL and reduced CI (95%) (< 2 cM), were selected for quality breeding programmes. Functional characterization including candidate gene mining and expression analysis discovered 44 high confidence candidate genes associated with quality traits. A meta-analysis of quantitative trait loci (QTL) associated with dough rheology properties, nutritional traits, and processing quality traits was conducted in wheat. For this purpose, as many as 2458 QTL were collected from 50 interval mapping studies published during 2013-2020. Of the total QTL, 1126 QTL were projected onto the consensus map saturated with 249,603 markers which led to the identification of 110 meta-QTL (MQTL). These MQTL exhibited an 18.84-fold reduction in the average CI compared to the average CI of the initial QTL (ranging from 14.87 to 95.55 cM with an average of 40.35 cM). Of the 110, 108 MQTL were physically anchored to the wheat reference genome, including 51 MQTL verified with marker-trait associations (MTAs) reported from earlier genome-wide association studies. Candidate gene (CG) mining allowed the identification of 2533 unique gene models from the MQTL regions. In-silico expression analysis discovered 439 differentially expressed gene models with > 2 transcripts per million expressions in grains and related tissues, which also included 44 high-confidence CGs involved in the various cellular and biochemical processes related to quality traits. Nine functionally characterized wheat genes associated with grain protein content, high-molecular-weight glutenin, and starch synthase enzymes were also found to be co-localized with some of the MQTL. Synteny analysis between wheat and rice MQTL regions identified 23 wheat MQTL syntenic to 16 rice MQTL associated with quality traits. Furthermore, 64 wheat orthologues of 30 known rice genes were detected in 44 MQTL regions. Markers flanking the MQTL identified in the present study can be used for marker-assisted breeding and as fixed effects in the genomic selection models for improving the prediction accuracy during quality breeding. Wheat orthologues of rice genes and other CGs available from MQTLs can be promising targets for further functional validation and to better understand the molecular mechanism underlying the quality traits in wheat.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Oryza / Locos de Características Quantitativas Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Oryza / Locos de Características Quantitativas Idioma: En Ano de publicação: 2022 Tipo de documento: Article