Genetic diversity analysis of elite European maize (Zea mays L.) inbred lines using AFLP, SSR, and SNP markers reveals ascertainment bias for a subset of SNPs

Abstract

Recent advances in high-throughput sequencing technologies have triggered a shift toward single-nucleotide polymorphism (SNP) markers. A systematic bias can be introduced if SNPs are ascertained in a small panel of genotypes and then used for characterizing a larger population (ascertainment bias). With the objective of evaluating a potential ascertainment bias of the Illumina MaizeSNP50 array with respect to elite European maize dent and flint inbred lines, we compared the genetic diversity among these materials based on 731 amplified fragment length polymorphisms (AFLPs), 186 simple sequence repeats (SSRs), 41,434 SNPs of the MaizeSNP50 array (SNP-A), and two subsets of it, i.e., 30,068 Panzea (SNP-P) and 11,366 Syngenta markers (SNP-S). We evaluated the bias effects on major allele frequency, allele number, gene diversity, modified Roger's distance (MRD), and on molecular variance (AMOVA). We revealed ascertainment bias in SNP-A, compared to AFLPs and SSRs. It affected especially European flint lines analyzed with markers (SNP-S) specifically developed to maximize differences among North American dent germplasm. The bias affected all genetic parameters, but did not substantially alter the relative distances between inbred lines within groups. For these reasons, we conclude that the SNP markers of the MaizeSNP50 array can be employed for breeding purposes in the investigated material. However, attention should be paid in case of comparisons between genotypes belonging to different heterotic groups. In this case, it is advisable to prefer a marker subset with potentially low ascertainment bias, like in our case the SNP-P marker set.

Usefulness of Multiparental Populations of Maize (Zea mays L.) for Genome-Based Prediction

“…Principal component analysis (PCA) was performed separately on dent and flint DH lines. For the analyses of genotypic relatedness a subset of SNP markers known to generate ascertainment bias (Frascaroli et al 2013) was excluded.The similarity of linkage phases across two populations was calculated according to Technow et al (2012) for SNP pairs polymorphic in both populations under consideration. We calculated the fraction of SNP pairs with equal sign of the linkage disequilibrium D AB ¼ p AB 2 p A p B in both populations, where p AB denotes the haplotype frequency of AB, p A the frequency of allele A at one marker locus, and p B the frequency of allele B at the other locus.…”

Determination of the optimal number of markers and individuals in a training population necessary for maximum prediction accuracy in F₂ populations by using genomic selection models

“…The segregation test was carried out in order to verify whether the simulation process established a population with genetic characteristics of an F 2 population, as proposed by Falconer and Mackay (1996). It was observed that the allelic and genotypic frequencies were close to the expected value for an F 2 population, for all the markers that control the trait (QTL -Table 1) and the other simulated markers ( Table S1).…”

“…The great importance of recovering all the information from an F 2 population using the simulation process is that the genetic variance, environmental variance, and heritability are easy to estimate in this type of population. According to Falconer and Mackay (1996), genetic variance in F 2 population is estimated as follows: in which d value was simulated as 0 for all loci, and thus the genetic variance is equal to the additive variance. This is easily calculated, since 2 α is the variance of the markers calculated using the RR-BLUP method.…”

“…This is easily calculated, since 2 α is the variance of the markers calculated using the RR-BLUP method. Thus, heritability can be estimated from the equation proposed by Falconer and Mackay (1996) for an F 2 population: and 2 σ is the residual variance of the markers estimated by the RR-BLUP method. Consequently, all the genetic and environmental parameters were accurately calculated, and hence, these parameters were the criteria for choosing the best genomic selection model, i.e., the model composed of the ideal number of individuals in the training population, and the number of markers required to accurately train the model.…”

“…Moreover, in the F 2 generation, it is possible to estimate the allelic frequency for each gene by the Mendelian segregation, to evaluate possible deviations from the Hardy-Weinberg equilibrium (Falconer and Mackay, 1996), to estimate genotypic and environmental variance, and consequently, to estimate heritability (Tang et al, 1996). The use of genomic selection in F 2 populations is still restricted to a few studies (Ren et al, 2015) and little is known about how the factors affecting the prediction accuracy can affect the estimate of genetic parameters in F 2 populations.…”