Phenotype-based selection in crop breeding has been less effective due to significant crossover genotype by environmental interactions which results in less than satisfactory genetic gain. Hence, DNA markers are being used as powerful surrogates in selection for complexly inherited traits. The use of DNA markers as surrogates demands establishment of marker-trait association based on exploiting linkage disequilibrium (LD) between loci controlling traits and DNA markers. LD is non-random association between the alleles at traits loci and those at DNA marker loci. To establish such marker-trait association, two approaches are available. One is family-based mapping approach where transient LD between marker and trait loci are exploited. While another approach known as association mapping (AM) where historical LD in the genome is exploited.
Marker-Trait association uses random sample from natural population as panel. Mapping resolution in AM is relatively high (up to 1cM or even less) and can detect multiple alleles. However, only major alleles can be detected in AM with considerable power. For minor alleles, family-based mapping approaches are suitable. Two approaches are possible in AM, they are Genome-Wide Association Studies (GWAS) and candidate gene-based approach. If specific genomic regions are selected for mapping, the approach is referred to as candidate gene-based approach. In the absence of prior knowledge on the position of the gene/QTL(s), GWAS is used. Both of these mapping approaches use statistical tools such as regression to detect either whole or target genomic regions co-segregating with markers. Generalized linear models (GLM) and mixed linear models (MLM) are used in the absence and presence of population structure and kinship, respectively (Aminu et al., 2020).
Cotton fiber yield is a complex trait, which can be influenced by multiple traits. Suresh et al. (2018) performed mapping in cotton by using 63K infinium beadchip with 201 germplasm lines. Through fastSTRUCTURE analysis, lines were grouped into 12 subgroups and revealed genome sharing among the groups. The critical value (R²) was set to 0.243 as threshold to claim LD between two loci. About 3.13 % marker pairs recorded significantly high LD (R² = 1), and 8.19 % of marker pairs were in the range of 0.3 to 0.99 R². In MLM, 349 significant marker-trait associations were detected as against 642 in GLM because of effectiveness in eliminating false associations in MLM.
A total of 68 markers explained >10% phenotypic variation for yield related traits.
Unravelling the genetic basis of cotton fiber yield-related traits contributes to genetic improvement of cotton. Guo et al. (2021) used 503 upland cotton varieties covering the four breeding stages (BS1–BS4) in China for association mapping and domestication analysis. One hundred and forty SSR markers significantly associated with ten fiber yield-related traits were identified, among which, 29 markers showed an increasing trend contribution to cotton yield-related traits from BS1 to BS4 and 26 markers showed decreased trend effect.
Association mapping offers great potential to enhance crop genetic improvement. This is strengthened by the use of high throughput and cost-effective next generation sequencing techniques which enabled GWAS to become a popular and routine approach. However, association mapping remains complementary as replacement for linkage mapping and other gene identification and validation techniques. Moreover, the contrast between the large numbers of variants with small effects identified by GWAS versus the small number of genomic regions with large effects identified by linkage mapping remains a challenge to our current understanding of the genetic architecture of complex traits.
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