**3.2. Rampant divergent selection: interpreting genomic signatures of adaptation in common bean beyond genomic constrains**

As a follow-up of the previous example, associated genomic windows were enriched for SNP density and positive Tajima's D scores. This conclusion was achieved after implementing a sliding window analysis to explore the patterns of genome-wide diversity (**Figure 3**). Marker density decayed drastically toward the centromeres. This decay in diversity proportional to the decay in the rate of recombination was first described in *D. melanogaster* and has been confirmed in many organisms since then. The correlation was initially understood as an effect of genetic hitchhiking, but background selection has been increasingly appreciated as a contributing factor [28], perhaps in many cases the dominating one.

(79

± 6 vs. 39

**4. Conclusions**

extreme ecologies [

and more positive Tajima's D scores (0.71

The average marker density was 44 SNPs per million base pairs (95% CI, 4–143). The average nucleotide diversity as measured by π was 0.3 per million base pairs (95% CI, 0.2–0.4). The average Watterson's theta (θ) was 0.20 per million base pairs (95% CI, 0.19–0.21). The average Tajima's D was 0.68 per million base pairs (95% CI, 0.05–1.22). These very same statistics were compared between 1 Mb sliding windows that contained (associated) or did not contain (no associated) at least one marker that was associated with the bioclimatic-based drought index. Genomic windows containing at least one associated SNP had an overall higher SNP density

Lessons from Common Bean on How Wild Relatives and Landraces Can Make Tropical Crops...

± 0.02 vs. 0.678

ciated markers. Nucleotide diversity, as measured by π, was slightly elevated in associated

Selective process, such as purifying selection and local adaptation (divergent selection), dif

ferent populations, and eliminate low-frequency polymorphism. Consequently, few haplo

types with high frequency are retained, corresponding to high values of nucleotide diversity and Tajima's D and low scores of the Watterson's theta (θ) estimator [33]. We have identified these signatures in the various genomic regions associated with a bioclimatic-based drought index. Therefore, it is unlikely that independent domestication events, extensive population structure, and population expansions after bottlenecks are responsible for these patterns because the mixed linear model that we used to identify the genome-environment associa

tions accounted for population structure, while demographic processes would leave genome-

Wild accessions and landraces of common bean occupy more geographical regions with

regions include the arid areas of Peru, Bolivia and Argentina, and the valleys of northwest Mexico. Hence, a broad habitat distribution for wild common bean has exposed these gen

otypes to both dry and wetter conditions, while cultivated common bean has a narrower distribution and is traditionally considered susceptible to drought. These differences in the ecologies of wild and cultivated common bean have been associated with higher genetic diversity in the former group when surveying candidate genes for drought tolerance such as the ASR [47], DREB [48], and ERECTA [49] gene families, once the population structure [41]

Also, as identified through the genome-environment association approach that was illus

trated in this chapter, there are notorious differences between the adaptations of wild acces

sions and landraces found in arid and more humid environments, in congruence with natural divergent selection acting for thousands of years. Several of these differences might be valu

able for plant breeding. Therefore, we reinforce, as was envisioned by Acosta [50], that wild

and the background distribution of genetic diversity have been accounted for.

2] and extensive drought stress [12] than cultivated accessions. Those

ferentially imprint regions within the same genome, causing a heterogeneous departure of genetic variation from the neutral expectations and from the background trend [28]. Divergent selection tends to homogenize haplotypes within the same niche, fix polymorphisms in dif

± 0.0001 vs. 0.2026

± 0.003).

± 0.009) than windows without asso

http://dx.doi.org/10.5772/intechopen.71669

± 0.006 vs. 0.317

± 0001),


123









± 2), lower values for Watterson's theta (θ) scores (0.2016

windows when compared with no associated windows (0.322

wide signatures in both, associated and no-associated windows.

The average marker density was 44 SNPs per million base pairs (95% CI, 4–143). The average nucleotide diversity as measured by π was 0.3 per million base pairs (95% CI, 0.2–0.4). The average Watterson's theta (θ) was 0.20 per million base pairs (95% CI, 0.19–0.21). The average Tajima's D was 0.68 per million base pairs (95% CI, 0.05–1.22). These very same statistics were compared between 1 Mb sliding windows that contained (associated) or did not contain (no associated) at least one marker that was associated with the bioclimatic-based drought index. Genomic windows containing at least one associated SNP had an overall higher SNP density (79 ± 6 vs. 39 ± 2), lower values for Watterson's theta (θ) scores (0.2016 ± 0.0001 vs. 0.2026 ± 0001), and more positive Tajima's D scores (0.71 ± 0.02 vs. 0.678 ± 0.009) than windows without associated markers. Nucleotide diversity, as measured by π, was slightly elevated in associated windows when compared with no associated windows (0.322 ± 0.006 vs. 0.317 ± 0.003).

Selective process, such as purifying selection and local adaptation (divergent selection), differentially imprint regions within the same genome, causing a heterogeneous departure of genetic variation from the neutral expectations and from the background trend [28]. Divergent selection tends to homogenize haplotypes within the same niche, fix polymorphisms in different populations, and eliminate low-frequency polymorphism. Consequently, few haplotypes with high frequency are retained, corresponding to high values of nucleotide diversity and Tajima's D and low scores of the Watterson's theta (θ) estimator [33]. We have identified these signatures in the various genomic regions associated with a bioclimatic-based drought index. Therefore, it is unlikely that independent domestication events, extensive population structure, and population expansions after bottlenecks are responsible for these patterns because the mixed linear model that we used to identify the genome-environment associations accounted for population structure, while demographic processes would leave genomewide signatures in both, associated and no-associated windows.
