Genome-Wide Analyses Reveal Footprints of Divergent Selection and Drought Adaptive Traits in Synthetic-Derived Wheats

Abstract

Crop-wild introgressions have long been exploited without knowing the favorable recombination points. Synthetic hexaploid wheats are one of the most exploited genetic resources for bread wheat improvement. However, despite some QTL with major effects, much less is known about genome-wide patterns of introgressions and their effects on phenotypes. We used two genome-wide association approaches: SNP-GWAS and haplotype-GWAS to identify SNPs and haplotypes associated with productivity under water-limited conditions in a synthetic-derived wheat (SYN-DER) population. Haplotype-GWAS further enriched and identified 20 more genomic regions associated with drought adaptability that did not overlap with SNP-GWAS. Since GWAS is biased to the phenotypes in the study and may fail to detect important genetic diversity during breeding, we used five complementary analytical approaches (t-test, Tajima's D, nucleotide diversity (π), Fst, and EigenGWAS) to identify divergent selections in SYN-DER compared to modern bread wheat. These approaches consistently pinpointed 89 'selective sweeps', out of which 30 selection loci were identified on D-genome. These key selections co-localized with important functional genes of adaptive traits such as TaElf3-D1 (1D) for earliness per se (Eps), TaCKX-D1 (3D), TaGS1a (6D) and TaGS-D1 (7D) for grain size, weight and morphology, TaCwi-D1 (5D) influencing drought tolerance, and Vrn-D3 (7D) for vernalization. Furthermore, 55 SNPs and 23 haplotypes of agronomic and physiological importance such as grain yield, relative water content and thousand grain weight in SYN-DER, were among the top 5% of divergent selections contributed by synthetic hexaploid wheats. These divergent selections associated with improved agronomic performance carry new alleles that have been introduced to wheat. Our results demonstrated that GWAS and selection sweep analyses are powerful approaches for investigating favorable introgressions under strong selection pressure and the use of crop-wild hybridization to assist the improvement of wheat yield and productivity under moisture limiting environments.

Keywords: genome-wide association studies (GWAS); haplotype analysis; selective sweeps; single nucleotide polymorphisms (SNPs); synthetic-derived wheats (SYN-DER).

Figure 1

a) Genome-wide SNP marker density on each bread wheat chromosome, and b) phylogenetic analysis of diversity panel including bread wheat (red) and SYN-DER (blue) genotypes, and c) genetic diversity visualization in diversity panel using 90K SNP array by principal component analysis.

Figure 2

a) Haplotypes density along each of the wheat chromosomes. b) Distribution of haplotypes along wheat genome, where y-axis represent size of the haplotype block (kb) and size of the bubble represent number of SNPs/block. Green haplotype blocks are specific to the SYN-DER panel.

Figure 3

Linkage disequilibrium (LD) decay in the SYN-DER panel based SNP markers. The locally weighted polynomial regression-based (LOESS) representing decay of r² along physical distance (kb) is illustrated for a) A-genome chromosomes, b) B-genome chromosomes, and c) D-genome chromosomes. Stacked bar plots of the LD statistic r² as a function of physical distance (kb) between fraction of SNP pairs in SYN-DER for d) A-genome chromosomes, e) B-genome chromosomes, and f) D-genome chromosomes.

Figure 4

Histogram for grain yield and boxplot for SNPs and haplotypes associated with grain yield in different water regimes. a) Histogram for grain yield under well-watered (WW) and water-limited (WL) conditions. Allelic effects of Hap1A-GY1 b), Hap1B-GY1 (c), Hap5B-GY1 (d) and SNP IWB72112 (e) on GY across three water regimes (all information is annotated).

Figure 5

a) Manhattan plots for SNP-GWAS for GY in WL (inner-most), WW (middle) and DIFF (outer-most) conditions, b) Biplot showing co-localization of GY-GWAS and EigenGWAS highlighting the loci under divergent selections, c) Allelic effects of SNP (IWB1876) on GY across three water regimes, d) Manhattan plots for haplotype-GWAS for GY in WL (inner-most), WW (middle) and DIFF (outer-most) conditions, e) Allelic effects of haplotype (Hap7B-GY1) on GY across three water regimes, f) Allelic effects of SNP (IWB61578) on GY across three water regimes.

Figure 6

Histogram for relative water contents (RWC %) and boxplot for SNPs and haplotypes associated with RWC in different water regimes. a) Histogram for RWC under well-watered (WW) and water-limited (WL) conditions. Allelic effects of Hap3B-RWC1 b), Hap7A-RWC1 c), SNP (IWB50770) d), SNP (IWB53305) e) on RWC across three water regimes (all information is annotated).

Figure 7

a) Manhattan plots for SNP-GWAS for relative water contents (%) in WL (inner-most), WW (middle) and DIFF (outer-most) conditions, b) Biplot showing co-localization of RWC-GWAS and EigenGWAS highlighting the loci under divergent selections, c) Allelic effects of SNP (IWB22779) on RWC across three water regimes, d) Manhattan plots for haplotype-GWAS for RWC in WL (inner-most), WW (middle) and DIFF (outer-most) conditions, e) Allelic effects of haplotype (Hap2A-RWC1) on RWC across three water regimes, f) Allelic effects of SNP (IWB2303) on RWC across three water regimes.

Figure 8

Circos plot of five analytical procedures (legends are given above) used to identify selection loci in SYN-DER and modern bread wheat panels and then highlighting ‘divergent selections’ in SYN-DER.