“Best Linear Unbiased Prediction for Phenotypic Selection in Plant Breeding”

UNIVERSITY OF AGRICULTURAL SCIENCES, RAICHUR

DEPARTMENT OF GENETICS AND PLANT BREEDING

COLLEGE OF AGRICULTURE, RAICHUR – 584101

Seminar on

“Best Linear Unbiased Prediction for Phenotypic Selection in Plant Breeding”

    Best Linear Unbiased Prediction (BLUP) is a widely accepted method for estimating random effects in mixed models. Originally developed in animal breeding for estimating breeding values, BLUP is now extensively used in plant breeding and other research fields. In breeding programs, where the development of new cultivars or recommendation of varieties requires selection among numerous candidate genotypes, accurate estimation of genotypic values is central to decision-making.

    One of the most desirable features of BLUP is its ability to borrow information from relatives by exploiting genetic correlations that arise from shared pedigree. The closer the genetic relationship among relatives, the more precise the predictions that can be obtained using phenotypic information from related individuals. Traditionally, pedigree-based kinship matrices (numerator relationship matrix) have been employed to capture this correlation (Mrode, 1996).

    With the advent of molecular markers, pedigree-based kinship was progressively replaced by marker-based kinship. In 2007, efficient computational methods to derive marker-based kinship were introduced, leading to the development of genomic BLUP (gBLUP) (Van Raden, 2008). The gBLUP method, with its computational efficiency and similarity to classical approaches, became the standard for genomic selection (Hayes et al., 2009).

    Oliveira et al. (2016) evaluated BLUP in hybrid maize using diallel analysis across three environments. Their study revealed that both additive and non-additive genetic effects contributed to trait expression. BLUP-based predictions showed moderate to high correlation with combining abilities, demonstrating its usefulness in selecting superior parents for traits like ear height and ear position.

    Similarly, Bauer et al. (2006) highlighted limitations of coancestry coefficients in self-pollinated crops where pedigree information may be incomplete. Their findings supported replacing pedigree-based kinship with marker-derived genetic similarities in BLUP estimations. This allowed greater accuracy in breeding value prediction under diverse conditions.

    Further advancements were proposed by Wang et al. (2018), who introduced SUPER BLUP (sBLUP) and compressed BLUP (cBLUP). The sBLUP method uses estimated quantitative trait nucleotides (QTNs) to improve kinship estimation, while cBLUP clusters individuals into groups to reduce computational demand. Both approaches enhanced prediction accuracy across traits with varied genetic architectures. Importantly, sBLUP performed better for traits governed by few major genes, while cBLUP excelled in low-heritability traits.

    Thus, BLUP and its genomic extensions (gBLUP, sBLUP, and cBLUP) have become indispensable tools in modern plant breeding. Their integration allows breeders to effectively exploit genetic variation, enhance prediction accuracy, and accelerate selection decisions, ultimately contributing to the development of improved and resilient crop varieties.

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References

• Bauer, A.M., Reetz, T.C. and Léon, J., 2006. Estimation of breeding values of inbred lines using best linear unbiased prediction (BLUP) and genetic similarities. Crop Sci., 46(6): 2685–2691.

• Hayes, B.J., Visscher, P.M. and Goddard, M.E., 2009. Increased accuracy of artificial selection by using the realized relationship matrix. Genetics Res., 91(1): 47–60.

• Mrode, R.A., 1996. Linear models for the prediction of animal breeding values. CAB International, Wallingford.

• Oliveira, G.H., Buzinaro, R., Revolti, L., Giorgenon, C.H., Charnai, K., Resende, D. and Moro, G.V., 2016. An accurate prediction of maize crosses using diallel analysis and best linear unbiased predictor (BLUP). Chilean J. Agric. Res., 76(3): 294–299.

• Van Raden, P.M., 2008. Efficient methods to compute genomic predictions. J. Dairy Sci., 91(11): 4414–4423.

• Wang, J., Zhou, Z., Zhang, Z., Li, H., Liu, D., Zhang, Q., Bradbury, P.J., Buckler, E.S. and Zhang, Z., 2018. Expanding the BLUP alphabet for genomic prediction adaptable to the genetic architectures of complex traits. Heredity, 121(6): 648–662.