A Genome-Wide Scan for Evidence of Selection in a Maize Population Under Long-Term Artificial Selection for Ear Number

Abstract

A genome-wide scan to detect evidence of selection was conducted in the Golden Glow maize long-term selection population. The population had been subjected to selection for increased number of ears per plant for 30 generations, with an empirically estimated effective population size ranging from 384 to 667 individuals and an increase of more than threefold in the number of ears per plant. Allele frequencies at .1.2 million single-nucleotide polymorphism loci were estimated from pooled wholegenome resequencing data, and F ST values across sliding windows were employed to assess divergence between the population preselection and the population postselection. Twenty-eight highly divergent regions were identified, with half of these regions providing gene-level resolution on potentially selected variants. Approximately 93% of the divergent regions do not demonstrate a significant decrease in heterozygosity, which suggests that they are not approaching fixation. Also, most regions display a pattern consistent with a soft-sweep model as opposed to a hard-sweep model, suggesting that selection mostly operated on standing genetic variation. For at least 25% of the regions, results suggest that selection operated on variants located outside of currently annotated coding regions. These results provide insights into the underlying genetic effects of long-term artificial selection and identification of putative genetic elements underlying number of ears per plant in maize.

Insights into the Effects of Long-Term Artificial Selection on Seed Size in Maize

“…For each population, valid alignments were processed using SAMtools v. 0.1.12a (Li et al 2009) as previously described (Beissinger et al 2014) to identify polymorphic positions and determine frequencies of each nucleotide at each position.…”

“…The most divergent sites represent candidates for selection. This approach has been implemented in other studies that have documented strong selection and dramatic phenotypic changes (Beissinger et al 2014) as is the case in this study. The high confidence set of SNPs described above was further filtered to include only biallelic SNPs (2,944,220 SNPs included).…”

“…For example, in natural populations, statistics that measure population divergence such as F ST (Wright 1951) can be calculated and loci displaying extreme values above an empirically determined genome-wide threshold are implicated as potentially associated with selection (Akey et al 2002;Oleksyk et al 2008). Identification of selection signatures has successfully been used to reveal the genetic basis of several traits across numerous species, including heat tolerance in yeast (Parts et al 2011), body-size variation in Drosophila melanogaster (Turner et al 2011) and chickens (Johansson et al 2010), milk production in Holstein cattle (Pan et al 2013), and prolificacy (Beissinger et al 2014) and northern leaf blight resistance (Wisser et al 2008) in maize.…”

A genome scan for selection signatures comparing farmed Atlantic salmon with two wild populations: Testing colocalization among outlier markers, candidate genes, and quantitative trait loci for production traits

“…Genome scans support the existence of long haplotype blocks in domestic chickens where regions of low heterozygosity containing previously described candidate genes for laying traits that can span several Megabases (Mb) suggesting recent hard sweeps (Qanbari et al., 2012). Soft sweeps characterized by narrow divergent haplotype blocks were more common than hard sweeps characterized by wide divergent haplotype blocks in a population of maize that had undergone strong positive directional selection for ear number for 30 generations (Beissinger et al., 2014). In practice, these two scenarios represent a continuum with the long haplotype blocks of the first becoming shorter and harder to detect over millennia unless they are in a nonrecombining region of the genome (Ai et al., 2015).…”

Using the variability of linkage disequilibrium between subpopulations to infer sweeps and epistatic selection in a diverse panel of chickens

“…For instance, in cases of directed evolution with experimental populations, especially for those with biological replication, changes in allele frequency may be directly measured to identify single-locus selection (Wisser et al, 2008;Turner et al, 2011;Parts et al, 2011;Hirsch et al, 2014). In a related test, which is particularly useful if samples from pre-selection populations are not available, patterns of nucleotide variability between vs within populations may be leveraged to identify selection at a single locus (Lewontin and Krakauer, 1973;Akey et al, 2002;Beissinger et al, 2013). This type of test may theoretically be able to distinguish between directional and balancing selection, since the former is expected to drive alleles toward fixation while the latter will maintain an elevated level of variability (Akey et al, 2002).…”