Machine learning as an effective method for identifying true SNPs in polyploid plants

Korani, Walid; Clevenger, Josh; Chu, Ye; Ozias‐Akins, Peggy

doi:10.1101/274407

Cited by 3 publications

(4 citation statements)

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“…Zhou et al ( 2014 ) used SOAP software (Li et al, 2009 ) for SNP calling while Liang et al ( 2017 ) used the Bowtie2 method (Langmead and Salzberg, 2012 ) for alignment to reference genome. We used the SWEEP software (Clevenger and Ozias-Akins, 2015 ) along with SNP-ML (Korani et al, 2018 ) for SNP identification. All reads were aligned to the reference genomes ( http://peanutbase.org ) and SNPs were called using Samtools mpileup and SNP-ML filtering.…”

Section: Discussionmentioning

confidence: 99%

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A SNP-Based Linkage Map Revealed QTLs for Resistance to Early and Late Leaf Spot Diseases in Peanut (Arachis hypogaea L.)

et al. 2018

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Cultivated peanut (Arachis hypogaea L.) is an important oilseed crop that is grown extensively in Africa, Asia and America. The diseases early and late leaf spot severely constrains peanut production worldwide. Because multiple genes control resistance to leaf spot diseases, conventional breeding is a time-consuming approach for pyramiding resistance genes into a single genotype. Marker-assisted selection (MAS) would complement and accelerate conventional breeding once molecular markers tightly associated with the resistance genes are identified. In this study, we have generated a large number of SNPs through genotyping by sequencing (GBS) and constructed a high-resolution map with an average distance of 1.34 cM among 2,753 SNP markers distributed on 20 linkage groups. QTL mapping has revealed that major QTL within a confidence interval could provide an efficient way to detect putative resistance genes. Analysis of the interval sequences has indicated that a major QTL for resistance to late leaf spot anchored by two NBS-LRR resistance genes on chromosome B05. Two major QTLs located on chromosomes A03 and B04 were associated with resistance genes for early leaf spot. Sequences within the confidence interval would facilitate identifying resistance genes and applying marker-assisted selection for resistance.

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Section: Discussionmentioning

confidence: 99%

“…For SNP calling, two methods were used: First, SNPs were called between the parents of the RIL population using Samtools mpileup. Resulting SNPs were then filtered using SNP-ML (Korani et al, 2018 ). The following command for SNP-ML was used - SNP-ML -c 0.7 -i input.vcf -iM peanut_DNA -o outputs.…”

Section: Methodsmentioning

confidence: 99%

A SNP-Based Linkage Map Revealed QTLs for Resistance to Early and Late Leaf Spot Diseases in Peanut (Arachis hypogaea L.)

et al. 2018

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“…The genetic linkage map was constructed using Joinmap v4.1 [ 48 ] maximum likelihood (ML) algorithm with a minimum LOD of 3.0 and the Haldane mapping function. The graphical representation of the linkage maps was generated through Mapchart v2.3 [ 49 ]. Confirmation of the loci positions was done as previously described [ 47 ] with few modifications (BLASTN (e value < 1 × 10 − 18 ) and mismatch of less than 2).…”

Section: Methodsmentioning

confidence: 99%

Identification of consistent QTL for time to maturation in Virginia-type Peanut (Arachis hypogaea L.)

et al. 2021

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Background Time-to-maturation (TTM) is an important trait contributing to adaptability, yield and quality in peanut (Arachis hypogaea L). Virginia market-type peanut belongs to the late-maturing A. hypogaea subspecies with considerable variation in TTM within this market type. Consequently, planting and harvesting schedule of peanut cultivars, including Virginia market-type, need to be optimized to maximize yield and grade. Little is known regarding the genetic control of TTM in peanut due to the challenge of phenotyping and limited DNA polymorphism. Here, we investigated the genetic control of TTM within the Virginia market-type peanut using a SNP-based high-density genetic map. A recombinant inbred line (RIL) population, derived from a cross between two Virginia-type cultivars ‘Hanoch’ and ‘Harari’ with contrasting TTM (12–15 days on multi-years observations), was phenotyped in the field for 2 years following a randomized complete block design. TTM was estimated by maturity index (MI). Other agronomic traits like harvest index (HI), branching habit (BH) and shelling percentage (SP) were recorded as well. Results MI was highly segregated in the population, with 13.3–70.9% and 28.4–80.2% in years 2018 and 2019. The constructed genetic map included 1833 SNP markers distributed on 24 linkage groups, covering a total map distance of 1773.5 cM corresponding to 20 chromosomes on the tetraploid peanut genome with 1.6 cM mean distance between the adjacent markers. Thirty QTL were identified for all measured traits. Among the four QTL regions for MI, two consistent QTL regions (qMIA04a,b and qMIB03a,b) were identified on chromosomes A04 (118680323–125,599,371; 6.9Mbp) and B03 (2839591–4,674,238; 1.8Mbp), with LOD values of 5.33–6.45 and 5–5.35 which explained phenotypic variation of 9.9–11.9% and 9.3–9.9%, respectively. QTL for HI were found to share the same loci as MI on chromosomes B03, B05, and B06, demonstrating the possible pleiotropic effect of HI on TTM. Significant but smaller effects on MI were detected for BH, pod yield and SP. Conclusions This study identified consistent QTL regions conditioning TTM for Virginia market-type peanut. The information and materials generated here can be used to further develop molecular markers to select peanut idiotypes suitable for diverse growth environments.

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“…However, for allopolyploid and segmental allopolyploid lineages, divergence between the parental subgenomes means that identifying homoeologous positions for genotyping is more difficult, and it is further complicated by having to distinguish between SNPs within a subgenome and fixed differences between subgenomes. Models to separately estimate SNPs within allopolyploid subgenomes have been proposed and used in a variety of crop species (Blischak et al 2018a;Clevenger and Ozias-Akins 2015;Clevenger et al 2018;Korani et al 2019;Clark et al 2019), but they typically require knowledge about all parental subgenomes. If the nature of a polyploid's formation mode is unknown or if parental information is not available for an allopolyploid, then these approaches are not able to be used.…”

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confidence: 99%

Demographic History Inference and the Polyploid Continuum

Blischak

Sajan

Barker

et al. 2022

Preprint

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Polyploidy is an important generator of evolutionary novelty across diverse groups in the Tree of Life, including many crops. However, the impact of whole-genome duplication (WGD) depends on the mode of formation: doubling within a single lineage (autopolyploidy) versus doubling after hybridization between two different lineages (allopolyploidy). Researchers have historically treated these two scenarios as completely separate cases based on patterns of chromosome pairing, but these cases represent ideals on a continuum of chromosomal interactions among duplicated genomes. Understanding the history of polyploid species thus demands quantitative inferences of demographic history and rates of exchange between subgenomes. To meet this need, we developed diffusion models for genetic variation in polyploids with subgenomes that cannot be bioinformatically separated and with potentially variable inheritance patterns, implementing them in the dadi software. We validated our models using forward SLiM simulations and found that our inference approach is able to accurately infer evolutionary parameters (timing, bottleneck size) involved with the formation of auto- and allotetraploids, as well as exchange rates in segmental allotetraploids. We then applied our models to empirical data for allotetraploid shepherd's purse (Capsella bursa-pastoris), finding evidence for allelic exchange between the subgenomes. Taken together, our model provides a foundation for demographic modeling in polyploids using diffusion equations, which will help increase our understanding of the impact of demography and selection in polyploid lineages.

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Machine learning as an effective method for identifying true SNPs in polyploid plants

Cited by 3 publications

References 31 publications

A SNP-Based Linkage Map Revealed QTLs for Resistance to Early and Late Leaf Spot Diseases in Peanut (Arachis hypogaea L.)

A SNP-Based Linkage Map Revealed QTLs for Resistance to Early and Late Leaf Spot Diseases in Peanut (Arachis hypogaea L.)

Identification of consistent QTL for time to maturation in Virginia-type Peanut (Arachis hypogaea L.)

Demographic History Inference and the Polyploid Continuum

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