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The Haplotype-Based Analysis of Aegilops Tauschii Introgression Into Hard Red Winter Wheat and Its Impact on Productivity Traits

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The Haplotype-Based Analysis of Aegilops Tauschii Introgression Into Hard Red Winter Wheat and Its Impact on Productivity Traits bioRxiv preprint doi: https://doi.org/10.1101/2021.05.29.446303; this version posted May 30, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. The haplotype-based analysis of Aegilops tauschii introgression into hard red winter wheat and its impact on productivity traits 1 Moses Nyine1, Elina Adhikari1, Marshall Clinesmith2, Robert Aiken2, Bliss Betzen1, Wei 2 Wang1, Dwight Davidson1, Zitong Yu1, Yuanwen Guo1, Fei He1, Alina Akhunova3, Katherine 3 W Jordan1,4, Allan K Fritz2, Eduard Akhunov1*. 4 5 1 Department of Plant Pathology, Kansas State University, Manhattan, USA 6 2 Department of Agronomy, Kansas State University, Manhattan, USA 7 3 Integrated Genomics Facility, Kansas State University, Manhattan, USA 8 4 USDA-ARS Hard Winter Wheat Genetics Research Unit, Manhattan, USA 9 *Corresponding author: 10 Eduard Akhunov 11 Email: [email protected] 12 13 Keywords: wheat, Aegilops tauschii, wild relative introgression, whole genome sequencing, 14 haplotypes, genotype imputation, grain yield, yield component traits. 15 16 Abstract 17 Introgression from wild relatives have a great potential to broaden beneficial allelic diversity 18 available for crop improvement in breeding programs. Here, we assessed the impact of introgression 19 from 21 diverse accessions of Aegilops tauschii, the diploid ancestor of the wheat D genome, into six 20 hard red winter wheat cultivars on yield and yield component traits. We used 5.2 million imputed D 21 genome SNPs identified by whole-genome sequencing of parental lines and the sequence-based 22 genotyping of introgression population including 351 BC1F3:5 lines. Phenotyping data collected from 23 the irrigated and non-irrigated field trials revealed that up to 23% of the introgression lines produce 24 more grain than the parents and check cultivars. Based on sixteen yield stability statistics, the yield of 25 twelve introgression lines (3.4%) was stable across treatments, years and locations; five of these lines 26 were also high yielding, producing 9.8% more grain than the average yield of check cultivars. The 27 most significant SNP-trait and haplotype-trait associations were identified on chromosome arms 2DS 28 and 6DL for spikelet number per spike (SNS), on chromosome arms 2DS, 3DS, 5DS and 7DS for 29 grain length and on chromosome arms 1DL, 2DS, 6DL and 7DS for grain width. Introgression of 30 haplotypes from Ae. tauschii parents was associated with increase in SNS, which positively 31 correlated with heading date, whereas haplotypes from hexaploid wheat parents were associated with 32 increased grain width. While some introgressed haplotypes exhibited significant pleiotropic effects 33 with the direction of effects on the yield component traits being largely consistent with the previously 34 reported trade-offs, there were haplotype combinations associated with the positive trends in yield. 35 The characterized repertoire of the introgressed haplotypes derived from Ae. tauschii accessions with 36 the combined positive effects on yield and yield components traits in elite germplasm provides a bioRxiv preprint doi: https://doi.org/10.1101/2021.05.29.446303; this version posted May 30, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International Genetic effects license. of Ae. tauschii-wheat introgression 37 valuable source of alleles for improving the productivity of winter wheat by optimizing the 38 contribution of component traits to yield. 39 Number of words: 7389, Number of figures: 5 40 1 Introduction 41 The gap between population expansion and food production is increasing due to marginal 42 improvements in crop yield, which is attributed to declining soil fertility, pests and diseases, and 43 climate change (Bailey-Serres et al., 2019). Wild relatives of wheat are a rich source of novel 44 underutilized allelic diversity with great potential to improve cultivated wheat through introgression 45 (Placido et al., 2013; Zhang et al., 2017; Hao et al., 2020). Introgression from wild relatives into elite 46 wheat cultivars was reported to increase pest and disease resistance (Periyannan et al., 2013; 47 Saintenic et al., 2013), improve resilience towards environmental stress (Peleg et al., 2005; Placido et 48 al., 2013) and increase yield (Pasquariello et al., 2019). The success of introgression breeding, 49 however, could be affected by the negative epistasis between the multiple alleles of wild and 50 cultivated wheat (Nyine et al., 2020), especially in the low recombing regions of chromosomes, 51 where linkage with the negatively selected alleles could reduce the efficiency of selection for 52 beneficial variants (Hill and Robertson 1966). 53 Introgression could exhibit pleotropic effects, affecting multiple, often unrelated traits not 54 directly targeted by selection. For example, introgression from Ae. ventricosa into chromosome 7D 55 of wheat was associated with increase in grain protein content and resistance to eyespot at the 56 expense of reduced yield (Pasquariello et al., 2019). In durum wheat, introgression of the GNI-A1 57 gene from wild emmer increased grain weight by suppressing the fertility of distal florets, resulting in 58 a negative correlation between grain number and grain weight (Golan et al., 2019). Introgression 59 from Agropyron elongatum into the 7DL chromosome arm of wheat that is known to confer leaf rust 60 resistance (Lr19) (Wang and Zhang 1996) also influences root development, resulting in improved 61 adaption to water stress (Placido et al., 2013) and salinity (Dvorak et al., 1988), and increased 62 biomass and yield (Reynold et al., 2001). 63 Crop yield is a complex trait determined by many component traits, such as thousand gain 64 weight, grain number per spike, spikes per unit area, grain width, area and length among others (Del 65 Moral et al., 2003; Du et al., 2020; Zhang et al., 2018). Previous studies have shown that 66 introgression from wild and close relatives improve yield of hexaploid wheat by changing yield 67 component traits through the pleotropic interaction between introgressed and background alleles of 68 the hexaploid wheat (Jones et al., 2020). Significant trade-offs between yield, yield components and 69 yield stability have been reported in wheat. A study by Pennacchi et al. (2019) showed that yield and 70 yield stability have a negative linear relationship in most cases. Other factors such as heading date, 71 plant height and biomass influence the source-sink ratio, which in turn affects the harvest index 72 leading to variation in yield and yield stability (Reynold et al., 2001; Reynold et al., 2020). Balancing 73 the trade-off between yield components is therefore necessary to improve yield, maximize the yield 74 potential and improve yield stability in wheat. 75 Sequence-based genotyping generates high-density SNP marker data that could be used to 76 accurately detect the boundaries of genomic segments introgressed from wild relatives (Kuzay et al., 77 2019; Nyine et al., 2020), providing a unique opportunity to investigate the effect of introgression on 78 trade-off between traits affecting total yield. Even though whole genome sequencing became less 79 expensive, it is still not within the cost range that would allow wheat breeding programs to sequence 2 This is a provisional file, not the final typeset article bioRxiv preprint doi: https://doi.org/10.1101/2021.05.29.446303; this version posted May 30, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BYGenetic 4.0 International effects license of .Ae. tauschii-wheat introgression 80 large populations. Sequencing of founder lines at a high coverage depth and using these genotypes as 81 a reference panel to impute missing and ungenotyped markers in the progeny characterized by low- 82 coverage SKIM sequencing is a cost-effective alternative. The existing imputation algorithms 83 (Browning and Browning 2013; Swarts et al., 2014; Davies et al., 2016) provide highly accurate 84 whole-genome prediction of missing genotypes, and were shown to increase the power of genome- 85 wide association scans, thus enabling the identification of SNPs or haplotypes associated with the 86 traits of interest (Li et al., 2010; Nyine et al., 2019). One of the advantages of the increased marker 87 density provided by whole-genome sequencing is the ability to perform association mapping using 88 haplotype information, which improves the detection of quantitative trait loci that would otherwise be 89 missed when using single SNPs (Zhang et al., 2002; Lorenz et al., 2010). 90 Here we investigated the impact of introgression from Ae. tauschii into hard red winter wheat 91 lines on yield and yield component traits and how haplotypes from Ae. tauschii at different genomic 92 loci affect the component traits and total yield. For this purpose, we assessed the phenotypic 93 variability of yield and the component traits, biomass and tenacious glume traits in an introgression 94 population derived from Ae. tauschii and hexaploid winter wheat phenotyped under irrigated and 95 rainfed conditions. We applied SNP diversity data generated by the whole genome shotgun 96 sequencing at 10x coverage level of the parental lines to impute genotypes in this population (Nyine 97 et al., 2020). This strategy resulted in 5.2 million SNPs that enabled us to identify the introgressed 98 Ae. tauschii haplotypes and assess their effects on trait variation through genome-wide association 99 mapping and haplotype block effect analysis. This introgression population along with high-density 100 SNP genotype data provides a valuable resource for effective deployment of Ae.
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