콩은 단백질 함량이 높아 조사료 작물로 많이 이용되고 있다. 생체중은 조사료의 수량과 품질을 결정하는 중요한 기준이 된다. 그렇지만 콩에서 조사료의 수량이나 조사료 품질에 관여하는 형질에 대한 유전적 연구는 현재까지 미비한 상황이다.
본 연구에서는 야생콩인 PI483463과 재배콩인 Hutcheson을 교배하여 육성한 재조합자식계통을 이용하여 조사료의 생체중, 조단백질 함량, 조지방 함량, neutral detergent fiber (NDF)와 acid detergent fiber (...
콩은 단백질 함량이 높아 조사료 작물로 많이 이용되고 있다. 생체중은 조사료의 수량과 품질을 결정하는 중요한 기준이 된다. 그렇지만 콩에서 조사료의 수량이나 조사료 품질에 관여하는 형질에 대한 유전적 연구는 현재까지 미비한 상황이다.
본 연구에서는 야생콩인 PI483463과 재배콩인 Hutcheson을 교배하여 육성한 재조합자식계통을 이용하여 조사료의 생체중, 조단백질 함량, 조지방 함량, neutral detergent fiber (NDF)와 acid detergent fiber (ADF) 함량을 조절하는 양적유전자좌(QTL)에 대한 mapping 연구를 실시하였다. 188개의 F5:8 재조합 자식 계통을 2013, 2014, 2015년에 경북대학교 군위 실습농장에 심어 생체중을 조사하였다. 생체중은 콩이 R6 생육단계에 도도 했을 때 시험구당 무게로 측정하였으며, 또한 생체중 조사시 각 시험구에서 조사료 품질을 조사하기 위한 샘플을 채취하여 NIR을 이용하여 조단백질 함량, 조지방 함량, NDF, ADF 함량을 측정하였다.
Inclusive composite interval mapping으로 생체중에 대한 양적유전자좌를 분석한 결과 총 3개의 QTL(qSFW6_1, qSFW15_1 and qSFW19_1)을 검출하였다. 이 QTL은 각각 6번 염색체, 15번 염색체, 19번 염색체에 위치하고 있었다. 특히 3개 중 qSFW19_1은 3번의 재배환경에서 모두 검출된 QTL이었다. QTL로 설명할 수 있는 표현형 변이는 6.34-21.32% 이였고, 상가적 효과는 -0.54 to 0.33이었다. 조사료의 품질 관련 형질에 대한 QTL mapping 결과 조단백질은 6개의 QTL, 조지방은 2, NDF는 6개, 그리고 ADF의 경우 2개의 QTL이 확인이 되었다. QTL의 표현형 변이의 범위를 살펴보면 조단백질은 7.04- 26.46%, 조지방 6.62-29.33%, NDF 5.79-28.19%, 그리고 ADF 7.66-41.72% 이었다. 흥미롭게도 생체중과
콩은 단백질 함량이 높아 조사료 작물로 많이 이용되고 있다. 생체중은 조사료의 수량과 품질을 결정하는 중요한 기준이 된다. 그렇지만 콩에서 조사료의 수량이나 조사료 품질에 관여하는 형질에 대한 유전적 연구는 현재까지 미비한 상황이다.
본 연구에서는 야생콩인 PI483463과 재배콩인 Hutcheson을 교배하여 육성한 재조합자식계통을 이용하여 조사료의 생체중, 조단백질 함량, 조지방 함량, neutral detergent fiber (NDF)와 acid detergent fiber (ADF) 함량을 조절하는 양적유전자좌(QTL)에 대한 mapping 연구를 실시하였다. 188개의 F5:8 재조합 자식 계통을 2013, 2014, 2015년에 경북대학교 군위 실습농장에 심어 생체중을 조사하였다. 생체중은 콩이 R6 생육단계에 도도 했을 때 시험구당 무게로 측정하였으며, 또한 생체중 조사시 각 시험구에서 조사료 품질을 조사하기 위한 샘플을 채취하여 NIR을 이용하여 조단백질 함량, 조지방 함량, NDF, ADF 함량을 측정하였다.
Inclusive composite interval mapping으로 생체중에 대한 양적유전자좌를 분석한 결과 총 3개의 QTL(qSFW6_1, qSFW15_1 and qSFW19_1)을 검출하였다. 이 QTL은 각각 6번 염색체, 15번 염색체, 19번 염색체에 위치하고 있었다. 특히 3개 중 qSFW19_1은 3번의 재배환경에서 모두 검출된 QTL이었다. QTL로 설명할 수 있는 표현형 변이는 6.34-21.32% 이였고, 상가적 효과는 -0.54 to 0.33이었다. 조사료의 품질 관련 형질에 대한 QTL mapping 결과 조단백질은 6개의 QTL, 조지방은 2, NDF는 6개, 그리고 ADF의 경우 2개의 QTL이 확인이 되었다. QTL의 표현형 변이의 범위를 살펴보면 조단백질은 7.04- 26.46%, 조지방 6.62-29.33%, NDF 5.79-28.19%, 그리고 ADF 7.66-41.72% 이었다. 흥미롭게도 생체중과
Soybeans [Glycine max (L.) Merr.] provide a high protein and high energy as a forage crop for livestock. Shoot fresh weight (SFW) is one of the parameters, used to estimate the total plant biomass yield in soybean. Dissecting the genetic control of SFW would provide important information to improve ...
Soybeans [Glycine max (L.) Merr.] provide a high protein and high energy as a forage crop for livestock. Shoot fresh weight (SFW) is one of the parameters, used to estimate the total plant biomass yield in soybean. Dissecting the genetic control of SFW would provide important information to improve soybean forage quality and yield. A total of 188 F5:8 Recombinant Inbred Line (RILs) derived from a cross of PI483463 (G. soja) and Hutcheson (G. max) were investigated to map the quantitative trait loci (QTL) for forage quality parameters, crude protein (CP), crude oil (CF), neutral detergent fiber (NDF) and acid detergent fiber (ADF) and yield (SFW) in soybean. Inclusive composite interval mapping (CIM) implemented in QTL IciMapping was used to detect putative QTLs in each environment.
The parental lines and RILs were phenotyped in the field at R6 stage, by measuring SFW in kg per plot. A total of 3 QTLs, namely qSFW6_1, qSFW15_1 and qSFW19_1 were identified on chromosomes (linkage groups) 6(C2), 15(E) and 19(L), respectively. The QTL qSFW19_1 was detected in all the environments. The phenotypic variation explained by the QTLs across all environments studied ranged from 6.34 to 21.32%, while additive effects ranged from -0.54 to 0.33. The additive and additive x environment interaction effects indicated alleles from both the parents and the environment affected the expression of SFW QTL.
To do mapping QTLs for forage quality parameters, CP, CF, NDF and ADF were estimated by developed Near-Infrared Reflectance Spectroscopy (NIRS) equations. The variance components of genotype, environment and genotype x environment interactions found to be highly significant for all the traits in RIL population. Significant positive and negative correlations were observed among forage quality traits. CIM analysis identified 6 QTLs for CP, 2 QTLs for CF, 6 QTLs for NDF and 2 QTLs for ADF. The individual QTLs explained phenotypic variation in the range of 7.04- 26.46 for CP, 6.62-29.33 for CF, 5.79-28.19 for NDF, and 7.66-41.72 for ADF respectively. Interestingly, favorable alleles of majority of the QTLs identified for CF, ADF and NDF were contributed by wild parent PI483463.
The QTL qSFW19_1 identified on chromosome 19 for SFW, CP, CF, NDF and ADF, therefore considered as a major QTL and was selected for further characterization and candidate gene analysis. QTL mapping using binmap (Bin_3704-Bin_3705) pinpointed the candidate gene Glyma.19g159600, contained three exons and encoded a serine-threonine protein kinase. The single nucleotide polymorphisms (SNP) and insertions/deletions (indel) were identified, using high quality NGS sequence data of parental samples.
The findings in this study can provide useful information for understanding the genetic of forage quality and yield.
Soybeans have been a favored livestock forage for centuries. However, only a few studies have been conducted to estimate the forage quality of soybean by NIRS. Forage soybean consisting of 353 samples was used to develop near-infrared reflectance (NIR) equations to estimate four forage quality parameters: CP, CF, NDF, and ADF. Samples included 181 RILs derived from PI 483463 (G. soja) × Hutcheson (G. max), 104 cultivated soybeans (G. max), and 68 wild soybeans (G. soja). Two NIR equations developed for CP and CF (2,5,5,1; multiple scatter correction [MSC]) and for NDF and ADF (1,4,4,1; MSC) were the best prediction equations for estimating these parameters. The coefficients of determination in the external validation set (r2) were 0.934 for CF, 0.909 for CP, 0.767 for NDF, and 0.748 for ADF. The relative predictive determinant ratios for MSC (2,5,5,1) calibration indicate that the CP (3.25) and CF (3.85) equations were acceptable for quantitative prediction of soybean forage quality, whereas the NDF (2.07) and ADF (1.97) equations for MSC (1,4,4,1) were useful for screening purposes. The NIR calibration equations developed in this study will be useful in predicting soybean forage quality for these four quality parameters.
Soybeans [Glycine max (L.) Merr.] provide a high protein and high energy as a forage crop for livestock. Shoot fresh weight (SFW) is one of the parameters, used to estimate the total plant biomass yield in soybean. Dissecting the genetic control of SFW would provide important information to improve soybean forage quality and yield. A total of 188 F5:8 Recombinant Inbred Line (RILs) derived from a cross of PI483463 (G. soja) and Hutcheson (G. max) were investigated to map the quantitative trait loci (QTL) for forage quality parameters, crude protein (CP), crude oil (CF), neutral detergent fiber (NDF) and acid detergent fiber (ADF) and yield (SFW) in soybean. Inclusive composite interval mapping (CIM) implemented in QTL IciMapping was used to detect putative QTLs in each environment.
The parental lines and RILs were phenotyped in the field at R6 stage, by measuring SFW in kg per plot. A total of 3 QTLs, namely qSFW6_1, qSFW15_1 and qSFW19_1 were identified on chromosomes (linkage groups) 6(C2), 15(E) and 19(L), respectively. The QTL qSFW19_1 was detected in all the environments. The phenotypic variation explained by the QTLs across all environments studied ranged from 6.34 to 21.32%, while additive effects ranged from -0.54 to 0.33. The additive and additive x environment interaction effects indicated alleles from both the parents and the environment affected the expression of SFW QTL.
To do mapping QTLs for forage quality parameters, CP, CF, NDF and ADF were estimated by developed Near-Infrared Reflectance Spectroscopy (NIRS) equations. The variance components of genotype, environment and genotype x environment interactions found to be highly significant for all the traits in RIL population. Significant positive and negative correlations were observed among forage quality traits. CIM analysis identified 6 QTLs for CP, 2 QTLs for CF, 6 QTLs for NDF and 2 QTLs for ADF. The individual QTLs explained phenotypic variation in the range of 7.04- 26.46 for CP, 6.62-29.33 for CF, 5.79-28.19 for NDF, and 7.66-41.72 for ADF respectively. Interestingly, favorable alleles of majority of the QTLs identified for CF, ADF and NDF were contributed by wild parent PI483463.
The QTL qSFW19_1 identified on chromosome 19 for SFW, CP, CF, NDF and ADF, therefore considered as a major QTL and was selected for further characterization and candidate gene analysis. QTL mapping using binmap (Bin_3704-Bin_3705) pinpointed the candidate gene Glyma.19g159600, contained three exons and encoded a serine-threonine protein kinase. The single nucleotide polymorphisms (SNP) and insertions/deletions (indel) were identified, using high quality NGS sequence data of parental samples.
The findings in this study can provide useful information for understanding the genetic of forage quality and yield.
Soybeans have been a favored livestock forage for centuries. However, only a few studies have been conducted to estimate the forage quality of soybean by NIRS. Forage soybean consisting of 353 samples was used to develop near-infrared reflectance (NIR) equations to estimate four forage quality parameters: CP, CF, NDF, and ADF. Samples included 181 RILs derived from PI 483463 (G. soja) × Hutcheson (G. max), 104 cultivated soybeans (G. max), and 68 wild soybeans (G. soja). Two NIR equations developed for CP and CF (2,5,5,1; multiple scatter correction [MSC]) and for NDF and ADF (1,4,4,1; MSC) were the best prediction equations for estimating these parameters. The coefficients of determination in the external validation set (r2) were 0.934 for CF, 0.909 for CP, 0.767 for NDF, and 0.748 for ADF. The relative predictive determinant ratios for MSC (2,5,5,1) calibration indicate that the CP (3.25) and CF (3.85) equations were acceptable for quantitative prediction of soybean forage quality, whereas the NDF (2.07) and ADF (1.97) equations for MSC (1,4,4,1) were useful for screening purposes. The NIR calibration equations developed in this study will be useful in predicting soybean forage quality for these four quality parameters.
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