Heat stress is common in most cereal-growing areas of the world. Maize (Zea mays L.) is the world’s most extensively grown cereal and is the principal staple food in many developing countries (Cassman, 1999; Morris et al., 1999;Vasal, 2000). Maize production and productivity are prone to rapid and constant changes due to global warming related environmental changes (Porter 2005; Wahid et al., 2007) Varieties with increased resilience to abiotic and biotic stresses will play an important role in autonomous adaptation to climate change (Easterling et al., 2007; Fedoroff et al., 2010). These efforts are particularly important in regions like South Asia, where current production systems are not sustainable and could be adversely impacted by climate change in the near future (IPCC, 2007; Niyogi et al., 2010; Rodell et al., 2009; ADB, 2009). Several climate modeling studies have suggested future increases in both day- and night-time temperatures, which could adversely impact maize production in the tropical regions (Lobell et al., 2011a; Cairns et al., 2012).
Maize genetics and breeding have undergone tremendous changes in the last few years. Molecular markers and doubled haploids (DH) have emerged as two most powerful technologies that are revolutionizing the way homozygous lines are developed in maize breeding programs (Mayor and Bernardo, 2009; Babu et al. 2013). Molecular marker-assisted breeding (referred commonly as molecular breeding), which seeks to accelerate the pace of phenotype-based breeding in resource-efficient manner, is gaining significance as more and more marker-trait associations are discovered, validated and becoming available for integration into product-oriented breeding pipelines. Besides marker-assisted selection (MAS) for simply inherited traits, whole genome-based ‘genomic selection’ (GS) has emerged as a powerful strategy for improving polygenic/complex traits such as abiotic stress tolerance or micronutrient concentrations. DH technology significantly reduces the time required to obtain genetically homozygous and pure lines compared to conventional inbreeding. Besides maximum genetic variance and increased precision in estimating the genotypic value of DH lines, this approach permits early selection of prospective hybrids, simplifies the logistics of inbred seed increase and maintenance and allows quick fixation of favorable alleles at quantitative trait loci (QTL) (Mayor and Bernardo, 2009; Lubberstedt and Ursula , 2012).
Though QTL mapping experiments successfully identified a number of small effect genomic regions, did not translate into tangible germplasm products especially for complex traits such as abiotic
stress tolerance or polygenic biotic stress resistance (Bernardo, 2010). A more recent breakthrough is “genomic selection” (Meeuwissesn, 2001) which takes into account genome-wide marker polymorphisms rather than specific genomic regions (or a priori QTL information), and promises to overcome many constraints associated with conventional QTL mapping based MAS. GS approach met with significant success in a number of animal breeding programs, and is currently being actively researched in the plant breeding realm especially for complex/polygenic trait improvement. This approach doesn’t require prior QTL information and focuses entirely on prediction of performance. Selections are typically performed based on GEBVs (Genomic estimated breeding values), which are calculated for each individual in the population by fitting all the polymorphic markers as random effects in a linear model. Genome-wide selection is likely to be useful for complex traits, governed by numerous minor QTLs with a low heritability.
Thus, advances in molecular technologies and strategies, coupled with new tools for abiotic stress phenotyping will provide a powerful combination for developing climate change resilient maize, for stress-prone agro-ecologies of India.
a. To evaluate test cross hybrids of Double Haploids [DH] lines that are derived from interconnected bi-parental populations under heat stress and optimal conditions at multiple locations for grain yield and other associated secondary traits.
b. To investigate the relationship between per se versus test cross performance of DH lines evaluated under heat stress conditions
c. To develop and validate a genomic selection model for resource-efficient identification of heat tolerant and high yielding DH lines derived from full- and half-sib populations
d. To systematically investigate how the composition of training population (TP) affects prediction accuracies for grain yield and other secondary traits associated with heat tolerance of DH lines obtained from individual crosses with different levels of relatedness.