Comparisons of improved genomic predictions generated by different imputation methods for genotyping by sequencing data in livestock populations

Background:Genotyping by sequencing(GBS)still has problems with missing genotypes.Imputation is important for using GBS for genomic predictions,especially for low depths,due to the large number of missing genotypes.Minor allele frequency(MAF)is widely used as a marker data editing criteria for genomic predictions.In this study,three imputation methods(Beagle,IMPUTE2 and FImpute software)based on four MAF editing criteria were investigated with regard to imputation accuracy of missing genotypes and accuracy of genomic predictions,based on simulated data of livestock population.Results:Four MAFs(no MAF limit,MAF≥0.001,MAF≥0.01 and MAF≥0.03)were used for editing marker data before imputation.Beagle,IMPUTE2 and FImpute software were applied to impute the original GBS.Additionally,IMPUTE2 also imputed the expected genotype dosage after genotype correction(GcIM).The reliability of genomic predictions was calculated using GBS and imputed GBS data.The results showed that imputation accuracies were the same for the three imputation methods,except for the data of sequencing read depth(depth)=2,where FImpute had a slightly lower imputation accuracy than Beagle and IMPUTE2.GcIM was observed to be the best for all of the imputations at depth=4,5 and 10,but the worst for depth=2.For genomic prediction,retaining more SNPs with no MAF limit resulted in higher reliability.As the depth increased to 10,the prediction reliabilities approached those using true genotypes in the GBS loci.Beagle and IMPUTE2 had the largest increases in prediction reliability of 5 percentage points,and FImpute gained 3 percentage points at depth=2.The best prediction was observed at depth=4,5 and 10 using GcIM,but the worst prediction was also observed using GcIM at depth=2.Conclusions:The current study showed that imputation accuracies were relatively low for GBS with low depths and high for GBS with high depths.Imputation resulted in larger gains in the reliability of genomic predictions for GBS with lower depths.These results suggest that the application of IMPUTE2,based on a corrected GBS(GcIM)to improve genomic predictions for higher depths,and FImpute software could be a good alternative for routine imputation.

Improving genomic predictions by correction of genotypes from genotyping by sequencing in livestock populations

Background: Genotyping by sequencing(GBS) is a robust method to genotype markers. Many factors can influence the genotyping quality. One is that heterozygous genotypes could be wrongly genotyped as homozygotes,dependent on the genotyping depths. In this study, a method correcting this type of genotyping error was demonstrated. The efficiency of this correction method and its effect on genomic prediction were assessed using simulated data of livestock populations.Results: Chip array(Chip) and four depths of GBS data was simulated. After quality control(call rate ≥ 0.8 and MAF ≥ 0.01), the remaining number of Chip and GBS SNPs were both approximately 7,000, averaged over 10 replicates. GBS genotypes were corrected with the proposed method. The reliability of genomic prediction was calculated using GBS, corrected GBS(GBSc), true genotypes for the GBS loci(GBSr) and Chip data. The results showed that GBSc had higher rates of correct genotype calls and higher correlations with true genotypes than GBS. For genomic prediction, using Chip data resulted in the highest reliability. As the depth increased to 10, the prediction reliabilities using GBS and GBSc data approached those using true GBS data. The reliabilities of genomic prediction using GBSc data were 0.604, 0.672, 0.684 and 0.704 after genomic correction, with the improved values of 0.013, 0.009, 0.006 and 0.001 at depth = 2, 4, 5 and 10, respectively.Conclusions: The current study showed that a correction method for GBS data increased the genotype accuracies and, consequently, improved genomic predictions. These results suggest that a correction of GBS genotype is necessary, especially for the GBS data with low depths.

Genetic mapping of quantitative trait loci in crops

Dissecting the genetic architecture of complex traits is an ongoing challenge for geneticists.Two complementary approaches for genetic mapping,linkage mapping and association mapping have led to successful dissection of complex traits in many crop species.Both of these methods detect quantitative trait loci(QTL) by identifying marker–trait associations,and the only fundamental difference between them is that between mapping populations,which directly determine mapping resolution and power.Based on this difference,we first summarize in this review the advances and limitations of family-based mapping and natural population-based mapping instead of linkage mapping and association mapping.We then describe statistical methods used for improving detection power and computational speed and outline emerging areas such as large-scale meta-analysis for genetic mapping in crops.In the era of next-generation sequencing,there has arisen an urgent need for proper population design,advanced statistical strategies,and precision phenotyping to fully exploit high-throughput genotyping.

Association Analysis and Identification of SNP Markers for Stemphylium Leaf Spot (Stemphylium botryosum f. sp. spinacia) Resistance in Spinach (Spinacia oleracea)

Stemphylium leaf spot, caused by Stemphylium botryosum f. sp. spinacia, is an important fungal disease of spinach (Spinacia oleracea L.). The aim of this study was to conduct association analysis to identify single nucleotide polymorphism (SNP) markers associated with Stemphylium leaf spot resistance in spinach. A total of 273 spinach genotypes, including 265 accessions from the USDA spinach germplasm collection and eight commercial cultivars, were used in this study. Phenotyping for Stemphylium leaf spot resistance was evaluated in greenhouse;genotyping was conducted using genotyping by sequencing (GBS) with 787 SNPs;and single marker regression, general linear model, and mixed linear model were used for association analysis of Stemphylium leaf spot. Spinach genotypes showed a skewed distribution for Stemphylium leaf spot resistance, with a range from 0.2% to 23.5% disease severity, suggesting that Stemphylium leaf spot resistance in spinach is a complex, quantitative trait. Association analysis indicated that eight SNP markers, AYZV02052595_115, AYZV02052595_122, AYZV02057770_10404, AYZV02129827_205, AYZV0-2152692_182, AYZV02180153_337, AYZV02225889_197, and AYZV02258563_213 were strongly associated with Stemphylium leaf spot resistance, with a Log of the Odds (LOD) of 2.5 or above. The SNP markers may provide a tool to select for Stemphylium leaf spot resistance in spinach breeding programs through marker-assisted selection (MAS).

Genetic dissection of ear-related traits using immortalized F2 population in maize

Ear-related traits are often selection targets for maize improvement. This study used an immortalized F(IF) population to elucidate the genetic basis of ear-related traits. Twelve ear-related traits(namely, row number(RN), kernel number per row(KNPR), ear length(EL), ear diameter(ED), ten-kernel thickness(TKT), ear weight(EW), cob diameter(CD),kernel length(KL), kernel width(KW), grain weight per ear(GW), 100-kernel weight(HKW), and grain yield per plot(GY)),were collected from the IFpopulation. The ear-related traits were comprised of 265 crosses derived from 516 individuals of the recombinant inbred lines(RILs) under two separated environments in 2017 and 2018, respectively. Quantitative trait loci(QTLs) analyses identified 165 ear traits related QTLs, which explained phenotypic variation ranging from 0.1 to 12.66%. Among the 165 QTLs, 19 underlying nine ear-related traits(CD, ED, GY, RN, TKT, HKW, KL, GW, and KNPR)were identified across multiple environments and recognized as reliable QTLs. Furthermore, 44.85% of the total QTLs showed an overdominance effect, and 12.72% showed a dominance effect. Additionally, we found 35 genomic regions exhibiting pleiotropic effects across the whole maize genome, and 17 heterotic loci(HLs) for RN, EL, ED and EW were identified. The results provide insights into genetic components of ear-related traits and enhance the understanding of the genetic basis of heterosis in maize.

Characterization of Rsg2.a3:A new greenbug resistance allele at the Rsg2 locus from wild barley(Hordeum vulgare ssp.spontaneum)

Greenbug(Schizaphis graminum Rondani)is a destructive insect pest that not only damages plants,but also serves as a vector for many viruses.Host plant resistance is the preferred strategy for managing greenbug.Two greenbug resistance genes,Rsg1 and Rsg2,have been reported in barley.To breed cultivars with effective resistance against various greenbug biotypes,additional resistance genes are urgently needed to sustain barley production.Wild barley accession WBDC053(PI 681777)was previously found to be resistant to several greenbug biotypes.In this study,a recombinant inbred line(RIL)population derived from Weskan×WBDC053 was evaluated for response to two greenbug biotypes(E and TX1)and genotyped using genotyping by sequencing(GBS).A set of 3347 high quality GBS-derived single nucleotide polymorphisms(SNPs)were then used to map the greenbug resistance gene in this wild barley accession.Linkage analysis placed the greenbug resistance gene in a 2.35 Mb interval(0-2,354,645 bp)in the terminal region of the short arm of chromosome 2H.This interval harbors 15 genes with leucine-rich-repeat(LRR)protein domains.An allelism test indicated that the greenbug resistance gene in WBDC053,designated Rsg2.a3,is likely allelic or closely linked to Rsg2.GBS-SNPs 2H_1318811and 2H_1839499 co-segregating with Rsg2.a3 in the RIL population were converted to Kompetitive allele specific PCR(KASP)markers KASP-Rsg2.a3-1 and KASP-Rsg2.a3-2,respectively.The two KASP markers can be used to select Rsg2.a3 and have the potential to tag Rsg2 in barley improvement programs.

Genetic dissection of maize seedling root system architecture traits using an ultra-high density bin-map and a recombinant inbred line population

Maize（Zea mays） root system architecture（RSA）mediates the key functions of plant anchorage and acquisition of nutrients and water. In this study,a set of 204 recombinant inbred lines（RILs） was derived from the widely adapted Chinese hybrid ZD958（Zheng58 Chang7-2）,genotyped by sequencing（GBS） and evaluated as seedlings for 24 RSA related traits divided into primary,seminal and total root classes. Signi ficant differences between the means of the parental phenotypes were detected for 18 traits,and extensive transgressive segregation in the RIL population was observed for all traits. Moderate to strong relationships among the traits were discovered. A total of 62 quantitative trait loci（QTL） were identi fied that individually explained from1.6% to 11.6%（total root dry weight/total seedling shoot dry weight） of the phenotypic variation. Eighteen,24 and 20 QTL were identi fied for primary,seminal and total root classes of traits,respectively. We found hotspots of 5,3,4 and 12 QTL in maize chromosome bins 2.06,3.02-03,9.02-04,and 9.05-06,respectively,implicating the presence of root gene clusters or pleiotropic effects. These results characterized the phenotypic variation and genetic architecture of seedling RSA in a population derived from a successful maize hybrid.

Population isolation shapes plant genetics, phenotype and germination in naturally patchy ecosystems

Aims Habitat connectivity is important in conservation since isolation can diminish the potential of a population for adaptation and increase its risk of extinction.However,conservation of naturally patchy ecosystems such as peatlands has mainly focused on preserving specific sites with exceptional characteristics,neglecting the poten-tial interconnectivity between patches.In order to better under-stand plant dynamics within a peatland network,we assessed the effect of population isolation on genetic distinctiveness,phenotypic variations and germination rates using the peatland-obligate white-fringed orchid(Platanthera blephariglottis).Methods Fifteen phenotypic traits were measured for 24 individuals per pop-ulation(20 distinct populations,Quebec,Canada)and germination rates of nearly 20000 seeds were assessed.Genetic distinctiveness was quantified for 26 populations using single nucleotide polymor-phism markers obtained via a pooled genotyping-by-sequencing approach.Geographic isolation was measured as the distance to the nearest population and as the number of populations occurring in concentric buffer zones(within a radius of 2,5 and 10 km)around the studied populations.Important Findings All phenotypic traits showed significant differences among popu-lations.Genetic results also indicated a pattern of isolation-by-distance,which suggests that seed and/or pollen exchange is restricted geographically.Finally,all phenotypic traits,as well as a reduced germination rate,were correlated with either geographic isolation or genetic distance.We conclude that geographic iso-lation likely restricts gene flow,which in turn may affect germi-nation.Consequently,it is imperative that conservation programs take into account the patchy nature of such ecosystems,rather than targeting a few specific sites with exceptional character for preservation.