Research Article

Genomic selection of seed weight based on low-density SCAR markers in soybean

Published: July 03, 2013
Genet. Mol. Res. 12 (3) : 2178-2188 DOI: 10.4238/2013.July.3.2

Abstract

With the development of molecular marker technology, crop breeding has been accelerated by marker-assisted selection for the improvement of quantitative traits. However, due to the traits' polygenic nature, traditional marker-assisted selection methods are ill-suited for identification of quantitative trait loci. Genomic selection (GS) was introduced into crop breeding to achieve more accurate predictions by considering all genes or markers simultaneously. We used dozens of sequence-characterized amplified region (SCAR) markers for genotyping soybean varieties, and we identified markers associated with hundred-seed weight. The best linear unbiased predictor and Bayesian liner regression methods were used to construct GS models to predict the hundred-seed weight trait based upon genotype information for trait selection. Both GS models showed good prediction performance in soybean, as the correlation coefficient between genomic estimated breeding values and true breeding values was as high as 0.904. This indicated that GS was performed effectively based on dozens of SCAR markers in soybean; these markers were of low density but easily detectable. Therefore, the combination of GS modeling and highly effective molecular marker technology involving SCAR markers can facilitate genetic breeding in soybean. This approach may also be suitable for genetic selection in other crops, such as wheat, maize, and rice.

With the development of molecular marker technology, crop breeding has been accelerated by marker-assisted selection for the improvement of quantitative traits. However, due to the traits' polygenic nature, traditional marker-assisted selection methods are ill-suited for identification of quantitative trait loci. Genomic selection (GS) was introduced into crop breeding to achieve more accurate predictions by considering all genes or markers simultaneously. We used dozens of sequence-characterized amplified region (SCAR) markers for genotyping soybean varieties, and we identified markers associated with hundred-seed weight. The best linear unbiased predictor and Bayesian liner regression methods were used to construct GS models to predict the hundred-seed weight trait based upon genotype information for trait selection. Both GS models showed good prediction performance in soybean, as the correlation coefficient between genomic estimated breeding values and true breeding values was as high as 0.904. This indicated that GS was performed effectively based on dozens of SCAR markers in soybean; these markers were of low density but easily detectable. Therefore, the combination of GS modeling and highly effective molecular marker technology involving SCAR markers can facilitate genetic breeding in soybean. This approach may also be suitable for genetic selection in other crops, such as wheat, maize, and rice.