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Review

k-mer-Based Genome-Wide Association Studies in Plants: Advances, Challenges, and Perspectives

by
Benjamin Karikari
1,2,3,†,
Marc-André Lemay
1,2,† and
François Belzile
1,2,*
1
Département de Phytologie, Université Laval, Quebec City, QC G1V 0A6, Canada
2
Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Quebec City, QC G1V 0A6, Canada
3
Department of Agricultural Biotechnology, Faculty of Agriculture, Food and Consumer Sciences, University for Development Studies, Tamale P.O. Box TL 1882, Ghana
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Genes 2023, 14(7), 1439; https://doi.org/10.3390/genes14071439
Submission received: 13 June 2023 / Revised: 4 July 2023 / Accepted: 7 July 2023 / Published: 13 July 2023
(This article belongs to the Special Issue Genetic Mapping in Plants)
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Abstract

:
Genome-wide association studies (GWAS) have allowed the discovery of marker–trait associations in crops over recent decades. However, their power is hampered by a number of limitations, with the key one among them being an overreliance on single-nucleotide polymorphisms (SNPs) as molecular markers. Indeed, SNPs represent only one type of genetic variation and are usually derived from alignment to a single genome assembly that may be poorly representative of the population under study. To overcome this, k-mer-based GWAS approaches have recently been developed. k-mer-based GWAS provide a universal way to assess variation due to SNPs, insertions/deletions, and structural variations without having to specifically detect and genotype these variants. In addition, k-mer-based analyses can be used in species that lack a reference genome. However, the use of k-mers for GWAS presents challenges such as data size and complexity, lack of standard tools, and potential detection of false associations. Nevertheless, efforts are being made to overcome these challenges and a general analysis workflow has started to emerge. We identify the priorities for k-mer-based GWAS in years to come, notably in the development of user-friendly programs for their analysis and approaches for linking significant k-mers to sequence variation.

1. Use of Genome-Wide Association Studies in Crops

Genome-wide association studies (GWAS) have routinely been used in plant science for the discovery of significant markers, candidate genes, and beneficial alleles, as well as understanding the genetic architecture of traits of economic importance [1,2,3]. Put simply, GWAS analyses involve determining the statistical relationship that may exist between phenotypes (traits) and molecular markers. Such analyses have become mainstream over the last decade as a result of the availability of crop reference genomes, drastic reductions in genotyping costs, the development of more powerful statistical models, and advancements in bioinformatics tools [4,5,6,7]. In addition, the advent and application of new phenotyping platforms have increased the throughput and improved data accuracy compared to conventional phenotyping [8].
The GWAS provides several advantages over its sister mapping strategy, linkage mapping. Notably, the GWAS requires less time to assemble the mapping population, provides higher resolution and more recombination events, facilitates the identification of candidate genes, and generates results that can be transferred more easily to marker-assisted breeding [4,9]. For example, Li, et al. [10] developed a derived cleaved amplified polymorphic sequence (dCAPS) marker for a SW9-1 (ss246792949T/C) locus associated with soybean seed size, and the dCAPS marker was able to discriminate between accessions with small and large seeds.
The most commonly used markers in GWAS are single-nucleotide polymorphisms (SNPs), which can be easily discovered and genotyped on the genome scale using various array- or sequencing-based genotyping platforms [11]. SNP arrays are affordable and convenient, as they can be designed once and manufactured in as many copies as needed [12]. However, they typically rely on a single reference genome and a few additional accessions, which constrains the set of SNPs that can be genotyped from them [13]. Sequencing-based approaches are more flexible and can provide a genome-wide assessment of variation if whole-genome sequencing (WGS) data are available [14]. However, many sequencing-based GWAS analyses rely on methods such as genotyping-by-sequencing (GBS) to genotype fewer markers on a larger number of samples (e.g., [15]).

2. Limitations of Current GWAS Methods

Although the GWAS has proved useful for quantitative trait loci (QTL) discovery in crop species, the task of identifying candidate genes and causal variants from the output of GWAS is still difficult [16,17]. As a result, GWAS analyses often fail to pinpoint the causal genes or variants responsible for variation in a particular trait. Although this failure to pinpoint causal variation could be addressed through various methodological improvements, the use of SNPs as a single type of variant in GWAS is an obvious limitation. Indeed, SNP datasets are typically based on mapping to a single reference genome, which may lead to inaccurate genotyping or the exclusion of genomic regions that are not represented in the reference genome, particularly in complex genomes like maize, soybean, and wheat [18,19,20,21,22].
Some limitations of SNP-based GWAS could be overcome using different types of genotypic data [23]. For example, many studies have revealed that large structural variations (SVs) are more likely to have phenotypic impacts than SNPs [24,25,26,27,28]. In contrast to SNPs, SVs are large variations involving a difference of at least 50 nucleotides between a reference sequence and an alternative sequence [29]. These variants can take various forms, such as insertions, deletions, duplications, copy number variants (CNVs), or translocations [30,31]. The pioneering report of genic SVs affecting a phenotype dates back almost a century, when it was discovered and documented that a duplication of the Bar gene is linked to small eyes in fruit flies [32]. Over the last decade, it has been demonstrated that SVs affect traits in plants such as shoot architecture, flowering time, fruit size, and stress tolerance [30,33,34,35,36,37,38,39]. SVs are reported to influence gene expression in various ways, such as disruption to gene structure, alteration of CNV, or the composition/positioning of cis-regulatory sequences [40]. The potential of a greater functional impact has motivated the use of SVs instead of SNPs for GWAS in crops such as cotton [26], soybean [41], cucumber [42], and tomato [43], an approach which we refer to here as an SV-based GWAS.
Despite the relevance of SVs, the identification of SVs from short-read sequencing is difficult and unreliable, leaving the majority of SVs poorly resolved and their molecular as well as phenotypic impacts largely overlooked [31,44,45]. As a consequence, population-scale assessments of variation in plants are disproportionally skewed toward SNPs and small insertions and/or deletions (indels) [30,46]. Despite increasing population-scale reports of SV in several crop species in the last few years [37,47,48,49,50,51], it is still difficult to obtain accurate SV genotype data on the scale required for GWAS.
In summary, SNP datasets are not sufficient to describe the whole spectrum of sequence variation and SVs are difficult to accurately discover and genotype on the population scale. Therefore, methods that enable researchers to encompass all types of variation without the pitfalls of genome-wide SV discovery are much needed. To overcome these limitations, k-mer-based GWAS approaches that are agnostic to variant types and reference-free have been developed [52,53,54]. The subsequent sections in this review will focus on k-mer-based GWAS approaches, examples of use in crops, and specific challenges, as well as future developments and perspectives.

3. The Concept of k-mer-Based GWAS

k-mers are subsequences of a fixed length k which can be obtained from sequencing data or genome assemblies by extracting all such subsequences found in the input dataset. For example, from the sequence ACCGTCG, the following k-mers of length four (4-mers) can be observed: ACCG, CCGT, CGTC, and GTCG. k-mers have been used for various applications in genomics, notably genome assembly [55], alignment-free sequence comparison [56], and variant genotyping [57,58]. In the context of GWAS, k-mers can be used to identify statistical associations between the occurrence of k-mers in a dataset and traits of interest. In its simplest implementation, the k-mer-based GWAS relies on a presence/absence table of k-mers found across various accessions (e.g., [54,59]). In this case, the output of the analysis indicates whether the presence of a given k-mer is associated with phenotypic variation. In more complex models, the number of times that a given k-mer is observed is used for modelling the statistical relationship between k-mers and phenotypic observations (e.g., [52]). In all cases, the output of the k-mer-based GWAS is a p-value indicating the probability that a given k-mer is associated with the trait being studied.
k-mer-based GWAS analyses work because variation in genomic sequences between individuals ultimately results in differences in the occurrence of k-mers in sequencing data (Figure 1). For example, if two individuals harbor different genotypes in an SNP locus, k-mers overlapping with the SNP will differ. These differences in k-mer occurrence patterns may theoretically occur due to any kind of sequence variation [53]. In practice, given that k-mers are typically quite short (25- to 51-mers are typical for use in GWAS), variation in repetitive regions may not result in k-mer patterns that can be observed in typical short-read WGS data. Also, although k-mer-based GWAS could theoretically be applied to reduced-representation sequencing methods such as GBS, their full potential to pinpoint causal variants will only be reaped using WGS data.
k-mer-based GWAS provide several advantages over conventional SNP-based GWAS. First, k-mers are agnostic to the nature of the sequence variation underlying differences in their occurrence [24,60,61]. This provides a universal way to assess variation due to SNPs, indels, and SVs in GWAS without having to specifically discover and genotype these variants. Second, k-mer-based analyses do not depend on a reference genome and can therefore be used in species that lack a reference genome (e.g., [22]). Even for species that do have a reference genome, k-mer-based analyses allow the detection of significant k-mers originating from regions that are absent from the reference, such as large insertions (e.g., [54]). Third, using k-mers derived directly from raw sequencing data avoids potentially error-prone variant discovery and genotyping steps. This facilitates the identification of causal variants with k-mers as compared to SNP- or SV-based GWAS (e.g., [62]). Finally, variants that occur at a distance of at most k nucleotides can be captured within the same k-mers and therefore provide a way to assess phenotypic differences due to haplotypes rather than single markers (see, e.g., [54]).

4. Methods Used in k-mer-Based GWAS

The main disadvantage of k-mer-based GWAS approaches is the lack of standardized methods for conducting such analyses. Indeed, these methods are relatively recent and therefore have not yet matured to the point where state-of-the-art analysis pipelines have emerged. As a result, user-friendly programs such as TASSEL [63], mrMLM.GUI [7], and GAPIT [64] have been developed for conventional GWAS analyses using SNPs. On the contrary, much work remains to be conducted in making k-mer-based GWAS programs user-friendly for wider application in the plant genomics community. Nevertheless, a general workflow for conducting k-mer-based GWAS has started to emerge (Figure 2). In this section, we will review this general workflow and discuss how it has been applied in previous studies.
The first step in conducting k-mer-based GWAS is to count the k-mers occurring in sequencing data (Figure 2a). This step is computationally straightforward, as several efficient bioinformatics tools have already been developed for this task (see [65,66,67,68,69]). Still, care must be taken in choosing parameters for counting and filtering k-mers [61]. Studies published to date have used various k-mer lengths (25-mers to 51-mers) for GWAS, but the thorough testing of a wide range of k-mer lengths has yet to be performed. Shorter k-mers reduce the probability of including sequencing errors and result in more k-mers being tallied for a given dataset, therefore providing higher counts for resolving copy number variants based on k-mer counts. On the other hand, longer k-mers would provide a larger number of unique k-mers and more power to resolve sequences stemming from repetitive regions. In addition to properly choosing the k-mer length, raw k-mer counts must be filtered prior to downstream analyses. Typically, k-mers that are only observed once in a sample are discarded as they might be due to sequencing errors (Figure 3a). Moreover, Voichek and Weigel [54] advocate for k-mers to be filtered based on their occurrence on both strands. Indeed, a k-mer that is only ever observed on one strand (i.e., it is not observed as its reverse complement) may indicate contamination, e.g., due to sequencing adapters. k-mers may be further filtered based on minor allele frequency, as in standard GWAS methods.
Once k-mers are counted and filtered, the k-mer-based GWAS analysis per se can be computed (Figure 2b). The most straightforward approach is to conduct GWAS based on the presence/absence of k-mers, as in Voichek and Weigel [54] or Gaurav et al. [59]. In such analyses, the presence of a k-mer is considered an allele, whereas its absence is considered another allele; standard GWAS approaches used for SNPs can therefore be applied to such datasets. Voichek and Weigel [54] used the GEMMA software [70] to compute a mixed linear model (MLM) using a kinship matrix directly computed from the k-mer presence/absence table. Given the large number of k-mers involved (can be tens of millions), their approach first computes an approximate score for each k-mer in the dataset. The most significant k-mers identified using the approximate scores are then used to compute the exact p-values in GEMMA. Gaurav et al. [59] used a different approach for reducing the number of analyzed k-mers by keeping only those whose presence/absence pattern was correlated to phenotypic data above some preset threshold; exact p-values for the filtered k-mers were then computed using linear regression models, accounting for population structure. Presence/absence-based approaches are convenient because the minimum average sequencing depth required to attain low error rates in determining presence/absence is rather low. Using empirical data [62], and assuming that sequencing depth follows a Poisson distribution, we estimate that a sequencing depth of 10 to 15X will be sufficient for the purposes of most k-mer-based GWAS relying on presence/absence (Figure 3b,c).
Genes 14 01439 g002 550
Figure 2. Summary of the analytical steps involved in k-mer-based GWAS with examples of how these steps were carried out in studies published to date. (a) Examples of bioinformatics software that can be used to count k-mers in sequencing data. (b) List of studies that have developed software for computing k-mer-based GWAS. (c) Finding the genomic location of significant k-mers is typically carried out by mapping k-mers or reads containing them to a reference genome (when available). (d) Clustering k-mers into signals has been conducted in various ways and can help in identifying the number of loci controlling a trait or filtering out spurious associations. (e) Linking significant k-mers to sequence variation is typically achieved through the assembly of significant k-mers or reads containing them, but much work remains to be performed in developing systematic approaches for doing so. [52,53,54,59,62,65,67,71].
Figure 2. Summary of the analytical steps involved in k-mer-based GWAS with examples of how these steps were carried out in studies published to date. (a) Examples of bioinformatics software that can be used to count k-mers in sequencing data. (b) List of studies that have developed software for computing k-mer-based GWAS. (c) Finding the genomic location of significant k-mers is typically carried out by mapping k-mers or reads containing them to a reference genome (when available). (d) Clustering k-mers into signals has been conducted in various ways and can help in identifying the number of loci controlling a trait or filtering out spurious associations. (e) Linking significant k-mers to sequence variation is typically achieved through the assembly of significant k-mers or reads containing them, but much work remains to be performed in developing systematic approaches for doing so. [52,53,54,59,62,65,67,71].
Genes 14 01439 g002
Genes 14 01439 g003 550
Figure 3. Implications of sample sequencing depth for k-mer-based GWAS. (a) Frequency distribution of k-mers of length 31 (31-mers) in a soybean sample analyzed by Lemay et al. [62], showing a clear peak for k-mers observed only once or twice due to sequencing errors. Note the logarithmic scale on the y-axis. The vertical dotted line indicates the average mapping depth of the sample shown (15X). (b) Distribution of the number of times a given genomic position is covered by sequencing data in that same sample. In the sample shown, 1.5% of positions are observed only once. The vertical line again indicates the average mapping depth. (c) Theoretical probability that a given position is observed less than two (blue circles) or four (green circles) times for a given average sequencing depth by assuming a Poisson distribution with lambda equal to the average sequencing depth. According to these distributions, sequencing depths of 7 and 11 ensure a probability < 1% (horizontal dotted line) to falsely declare a given position as “absent” based on a minimum of 2 or 4 reads, respectively. While sequencing data do not exactly obey a Poisson distribution, it is safe to assume that a minimum sequencing depth of 10 to 15X will be sufficient for most GWAS applications relying on the presence/absence of a k-mer.
Figure 3. Implications of sample sequencing depth for k-mer-based GWAS. (a) Frequency distribution of k-mers of length 31 (31-mers) in a soybean sample analyzed by Lemay et al. [62], showing a clear peak for k-mers observed only once or twice due to sequencing errors. Note the logarithmic scale on the y-axis. The vertical dotted line indicates the average mapping depth of the sample shown (15X). (b) Distribution of the number of times a given genomic position is covered by sequencing data in that same sample. In the sample shown, 1.5% of positions are observed only once. The vertical line again indicates the average mapping depth. (c) Theoretical probability that a given position is observed less than two (blue circles) or four (green circles) times for a given average sequencing depth by assuming a Poisson distribution with lambda equal to the average sequencing depth. According to these distributions, sequencing depths of 7 and 11 ensure a probability < 1% (horizontal dotted line) to falsely declare a given position as “absent” based on a minimum of 2 or 4 reads, respectively. While sequencing data do not exactly obey a Poisson distribution, it is safe to assume that a minimum sequencing depth of 10 to 15X will be sufficient for most GWAS applications relying on the presence/absence of a k-mer.
Genes 14 01439 g003
More elaborate k-mer-based GWAS approaches use k-mer counts instead of their mere presence/absence patterns for GWAS [52,53]. These models are potentially more powerful than the method developed by Voichek and Weigel [54], as they can distinguish between heterozygous genotypes and variants (such as copy number variants) that would not be detected through presence/absence alone. Rahman et al. [53] proposed a method that used k-mer counts but could only analyze qualitative traits based on the comparison of k-mer counts in case and control groups. On the other hand, He et al. [52] proposed the first method based on k-mer counts for analyzing quantitative traits using linear regression models controlling for population structure. They also tested various transformations of the k-mer counts and compared their impacts on the results. Despite their great potential, there are pitfalls associated with count-based methods, as k-mer counts will vary stochastically due to sequencing technology bias (e.g., the GC content of reads may affect sequencing depth). Count-based methods will therefore need to rely on deeper sequencing to accurately resolve the statistical relationship between k-mer copy number and phenotypic variation.
k-mer-based GWAS analyses truly differ from conventional GWAS when the time comes to analyze significant signals (Figure 2c). While the genomic position of molecular markers used in conventional GWAS is typically known, the output of k-mer-based GWAS consists solely of a list of k-mers and their associated p-values. Consequently, the burden of associating these k-mers with genomic positions must be handled in downstream analyses, for which no standard method exists yet. The most straightforward approach for linking significant k-mers to their genomic location is to map them to a reference genome, and it is indeed the approach used by most studies published so far (e.g., [53,54]). Once the genomic positions are known, results can be displayed using Manhattan plots as in standard GWAS analyses, except that results are typically only displayed for significant k-mers instead of genome-wide ones. A slightly different approach was used by Lemay et al. [62], who developed the katcher program (https://github.com/malemay/katcher) to extract all of the sequence reads containing significant k-mers. This approach is more exhaustive than simply mapping k-mers to a reference because it considers the entire read sequence for mapping purposes.
Another problem that is somewhat related to finding the position of significant k-mers lies in clustering them into coherent signals (Figure 2d). Indeed, the immediate output of k-mer-based GWAS analyses tells us nothing about the number of significant loci identified. An obvious approach, which is analogous to what is the case in conventional GWAS, consists of grouping k-mers that can be mapped in contiguous regions of a reference genome. This approach is implied by the peaks observed in Manhattan plots and was used by Voichek and Weigel [54] and by Lemay et al. [62] to identify the number of loci found by a given GWAS analysis. However, this approach alone is hardly sufficient because k-mers may not map onto the reference genome or may appear to be mapped in different regions and yet belong to the same locus. Published k-mer-based GWAS analyses have therefore also typically used some form of clustering to group co-segregating significant k-mers together. For example, Voichek and Weigel [54] and Lemay et al. [62] computed linkage disequilibrium between k-mers to identify the number of loci found by a given analysis. This method has also allowed Lemay et al. [62] to determine that apparently spurious significant hits found for some traits actually co-segregated with genuine loci. He et al. [52] instead used co-occurrence networks to identify co-segregating k-mers.
Identifying the variation underlying significant k-mers is likely the most difficult aspect of conducting k-mer-based GWAS (Figure 2e). Indeed, the fact that k-mers can act as a molecular signature for all kinds of sequence variation is a double-edged sword: while it makes the approach universal, it also makes it difficult to link significant k-mers to biologically meaningful variation. This problem has typically been tackled through the assembly of significant k-mers [53] or reads containing them [54,62]. These assemblies are then compared to a reference assembly to identify the sequence variation implied by significant k-mers (Figure 4a,b). However, this approach is time-consuming and difficult to generalize. Efficient, standardized methods for identifying sequence variation from k-mers are therefore much needed.
It is important to note that only steps 1 and 2 (counting k-mers and computing GWAS) of our summarized framework are truly mandatory; steps 3 to 5 (finding genomic location, clustering k-mers into signals, and linking k-mers to sequence variation) are optional to some extent. For example, in species that lack a reference genome, step 3 is not possible, but significant k-mers can nevertheless be clustered and/or assembled to gather meaningful information. For example, in a study of resistance to wheat stem rust in Aegilops tauschii, for which no reference genome was available, Arora et al. [22] focused on nucleotide-binding/leucine-rich repeat (NLR) genes through the use of sequencing data enriched in such sequences. Significant k-mers were subsequently mapped to local assemblies of NLR genes generated from the sequencing data themselves. For such analyses involving species without a reference genome, the local assembly of significant k-mers or reads containing them will likely be necessary to obtain meaningful results. Step 5 (linking k-mers to sequence variation) may however not be necessary at all to obtain meaningful results from k-mer-based GWAS. For example, in a set of studies involving Ae. tauschii [22,59] and wheat [73], the functional impact candidate genes could be demonstrated without the causal variant itself being identified at the sequence level.

5. Case Studies of k-mer-Based GWASs in Plants

Applications of k-mer-based GWAS are limited in plants compared with prokaryotic organisms [53,74,75,76,77]. This section highlights empirical results obtained via k-mer-based GWAS conducted in plants, with an emphasis on their findings and key features (Table 1).
In the first study reporting k-mer-based association in plants, Arora et al. [22] phenotyped 151 Aegilops tauschii accessions from diverse sources against six different races of Puccinia granminis f. sp. tritici (PGT). Given the expectation that genes associated with resistance to this pathogen would be NLR genes, they performed their analysis on sequencing data enriched for NLR genes, an approach that they named AgRenSeq. In a presence/absence-based strategy that involved pre-filtering based on a correlation with the trait and linear regression models accounting for population structure, 51-mers were used to derive p-values. Candidate genes were identified by mapping significant k-mers to locally assembled NLR gene sequences. This analysis readily identified four NLRs that were consistent with the results published in previous studies [78,79,80,81], and two of them were validated using transformation. While the study published by Arora et al. [22] was not truly genome-wide because sequence enrichment was used, it still demonstrated the use of k-mers for association mapping in plants.
Table
Table 1. k-mer-based GWAS papers in plant science.
Table 1. k-mer-based GWAS papers in plant science.
CropNumber of GenotypesTraitLength of k-merKey FeatureReference
Wild diploid wheat (Aegilops tauschii)195 (151 were used for phenotyping)Stem rust (caused by Puccinia graminis f. sp. tritici)51Used sequencing data enriched for NLR genes instead of a genome-wide approach.[22]
Arabidopsis1135Germination, seedling growth, flowering time, etc.25, 31Discovered new associations with structural variants and with regions missing from reference genomes.[54]
Tomato246Days to tassel, ear weight, etc.
Maize28296 metabolites, including guaiacol
Soybean438 Gylcine accessionsSeed pigmentation31k-mer-based approach mapped genomic region for recombinant event at I locus.[27]
Maize282Upper leaf angle, flowering time, cob and kernel color, and seed oil content25, 31Used whole-genome sequencing data from the Maize 282 Association Panel (maize282) [82] to conduct both k-mer- and SNP-based GWAS.[52]
Wild diploid wheat (Aegilops tauschii)242Stem rust (caused by P. graminis f. sp. tritici), powdery mildew (caused by Blumeria graminis f. sp. tritici), resistance to the wheat curl mite Aceria tosichella (vector of wheat streak mosaic virus), leaf trichomes, flowering time, and spikelet number per spike51Genome-wide extension of the method developed by Arora et al. [22].[59]
Wheat320, including 300 landracesBlast fungus (caused by Pyricularia oryzae)51Functional validation of a candidate gene via virus-induced gene silencing and development of functional markers.[73]
Soybean363 G. max13 traits including pod color, pubescence form, and resistance to the oomycete Phytophthora sojae31Detected several well-known loci/genes for each of the traits.[62]
Voichek and Weigel [54] used 1135, 246, and 282 accessions of Arabidopsis, tomato, and maize, respectively, to conduct k-mer- and SNP-based GWAS with several hundreds of traits (Table 1). In their study, the associations detected via the k-mers largely overlapped with the results from the SNP-based approach. The former approach had stronger statistical support, with p-values being generally more significant for k-mers than for SNPs at a locus. In addition to the ability of k-mers to detect genomic regions found via SNP-based GWAS, they detected short indels and SVs, as well as signals in regions outside of the reference genomes of Arabidopsis and tomato. Nevertheless, some significant associations were only detected through SNP-based analysis, suggesting that SNP-based analysis may be complementary to k-mers.
Kim et al. [27] mapped the genetic basis of seed pigmentation traits in soybean with SNP- and k-mer-based GWAS. Their k-mer-based analysis was focused on the I locus as the SNP-based approach failed to dissect the detailed genomic structure of the I locus due to incomplete assembly around the corresponding region. The k-mer approach highlighted a recombination event at the I locus that explained the occurrence of yellow seed coats. Although limited in scope in this study, the k-mer-based approach yielded a comparative advantage over the SNP-based approach by allowing the detection of this complex variant.
He et al. [52] used the Maize 282 Association Panel (maize282) from Flint-Garcia, et al. [82] to conduct k-mer- and SNP-based GWAS on upper leaf angle, flowering time, cob and kernel color, and seed oil content (Table 1). A number of known loci/candidate genes were identified via k-mer-based GWAS, whereby most of which were detected using SNP-based GWAS. Nevertheless, an additional Pericarp color 1 homologous gene, Pericarp color 2, was found via associated k-mers for cob color, but not detected via SNP-based GWAS. Several other known genes such as DGAT1-2 (for kernel oil), yellow endosperm 1, carotenoid cleavage dioxygenase 1, and zeaxanthin epoxidase 1 (kernel color) [52] were detected. They also provided an integrative analysis by integrating their k-mer analysis with gene expression data and used k-mers to predict phenotypes.
Gaurav et al. [59] extended the method developed by Arora et al. (2019) to the whole genome in their analysis of 242 Ae. Tauschii accessions. In their study, the significantly associated k-mers for stem rust (caused by P. graminis f. sp. tritici) were mapped to both the Ae. tauschii AL8/78 reference genome and a de novo assembly of a relevant accession (with two cloned stem rust resistance genes), which was anchored to the reference genome. In addition, they identified a number of candidate genes which had previously been demonstrated to influence the studied traits. These include stem rust (SrTA1662) [22,79], flowering time (FLOWERING LOCUS T1) [83,84,85], leaf trichomes (RAMOSA3 and TPP4) [86], spikelet number per spike (SISTER OF RAMOSA3) [87], and wheat curl mite (Cmc3 and Cmc4) [88,89]. Interestingly, they detected no NLR immune-receptor-encoding gene for powdery mildew resistance, but identified an insertion containing a wheat-tandem kinase (WTK) belonging to a gene family known to confer resistance to wheat stripe rust (Yr15) [90], stem rust (Rpg1 and Sr60) [91,92], and powdery mildew [93].
Arora et al. [73] used 320 wheat lines (including 300 landraces) to screen with Br48 a strain of Pyricularia oryzae (alternatively called Magnaporthe oryzae), transformed with either PWT3 or PWT4 from a previous study [94]. They conducted k-mer-based association mapping based on NLR-enriched sequencing data, from which they identified two candidate genes, TraesCS1D02G029900 and TraesJAG1D03G00423690, for PWT3 and PWT4, respectively. These genes, respectively, overlapped with Rwt3 [95] and Rwt4 [96] from a biparental mapping study. To validate these candidate genes, further screenings were conducted and different types of mutations were identified in TraesCS1D02G029900 (Rwt3) in a TILLING population of Jagger [97]. A virus-induced gene silencing experiment was further conducted to functionally validate TraesCS1D02G029900 (Rwt3), which showed that this gene is required for resistance to P. oryzae, expressing the PWT3 effector. They subsequently developed kompetitive allele-specific PCR markers (for both Rwt3 and Rwt4), which they tested in the landrace subpopulation, and obtained a validation rate of 97 and 99%, respectively. The functional validation results from this study provide compelling evidence for the value of k-mer application in GWAS mapping.
A recent study by Lemay et al. [62] from our research group conducted SV-, SNP-, and k-mer-based GWAS with 363 Glycine max accessions for 13 traits including flower, pubescence, seed coat, and hilum color, as well as stem termination type, pubescence density, and seed coat luster (Table 1). A number of well-known validated loci/genes were detected, including the W1 locus/Glyma.13g72100 for flower color [98], the T/Glyma.06g202300 [99] and Td/Glyma.03g258700 [100] loci for pubescence color, and the G locus/Glyma.01g198500 for seed coat color [72], among others. Of the three GWAS approaches, the k-mer-based strategy proved to be the best at pinpointing causal variants based on the most significant k-mers. Moreover, using the combined results of the SNP- and k-mer-based approaches, they were able to suggest novel candidate genes at some loci. This study therefore advocates for the complementarity of SNP- and k-mer-based approaches in identifying candidate genes.
So far, these seven k-mer-based GWAS papers highlight the power of this strategy compared to other known strategies (SV- and SNP-based), and warrant application in plant science to discover novel loci/candidate genes for traits of economic importance.

6. Challenges and Perspectives

A number of challenges have been reported in the k-mer-based GWAS papers published to date [24,52,54,62]. Some prominent challenges are data size and complexity, which not all research teams may be prepared to deal with. For example, He et al. [52] detected a total of 1.1 billion non-redundant k-mers from 261 inbred lines in their study, with the majority of lines having 0.35–0.55 billion non-redundant k-mers. This can lead to challenges in data storage, processing, and analysis. Therefore, large k-mer datasets can be computationally intensive and require significant computational resources, such as dedicated high-performance computing servers. Efficient algorithms and programs designed specifically for the analysis of k-mer-based GWAS have therefore been developed (e.g., [52,54,62]) and will still need to be developed in years to come.
A related problem is the development of user-friendly programs for conducting k-mer-based GWAS and associated analyses. To date, the programs available for k-mer-based GWAS have largely targeted an audience who is already proficient with programming and bioinformatics. Efforts to bring these programs to a wider audience have been made by the likes of kGWASflow [101], kmdiff [71], and gwask [62]. Still, much work remains to be conducted to make these methods available to the community. In particular, packages written in popular scripting languages (e.g., R, Python) should be developed to help with the analysis and visualization of the results of k-mer-based GWAS.
Another challenge faced by k-mer-based GWAS analyses is the appropriate control of false positive associations. While a k-mer-based kinship matrix was successfully used by Voichek and Weigel [54], this approach may not be sufficient to prevent the spurious associations that could arise from using k-mers instead of conventional molecular markers. For example, Lemay et al. [62] found spurious associations scattered throughout the genome for some of the traits that were analyzed. The fact that these spurious associations had less significant p-values than bona fide loci suggests that work remains to be conducted in appropriately choosing significance thresholds for k-mer-based GWAS. These spurious associations may also have been due to the presence of repetitive sequences, which resulted in some significant k-mer mapping to apparently random locations throughout the genome.
As discussed above, the biggest improvement that has yet to be made in k-mer-based GWAS lies in the identification of sequence variation underlying significant k-mers. Assembly-based approaches used to date have been useful for some purposes but have not provided a universal method to link significant k-mers to biologically meaningful variation. Going forward, we believe that approaches based on pangenome graphs [57,102,103,104] hold great promise. Such approaches have been identified as a priority for further research in plant genomics [23,40] but have yet to be applied to the analysis of k-mer-based GWAS. Pangenome graphs could facilitate the analysis of significant k-mers by allowing their alignment to the graph and the identification of haplotypes associated with phenotypic variation.
We anticipate that future developments in sequencing technology should enhance the use of k-mers in GWAS. So far, sufficient sequencing depth and sequence quality for k-mer-based analyses have only been provided by Illumina short-read sequencing technology. As a result, k-mer length for use in GWAS is limited to a few dozen nucleotides, whereas k-mer lengths in excess of a hundred nucleotides would provide a much greater resolution of variants located in repetitive regions. The relatively high error rates of current long-read technologies (PacBio and Oxford Nanopore technologies, [105]) prohibit the extraction of long k-mers from their sequences, as the probability of including errors in such k-mers would be extremely high. One exciting development is PacBio HiFi data [106], which can deliver highly accurate long reads; however, this method is still too costly to apply on the population scale required for GWAS. In the meantime, the use of long-read correction methods [107] may be an interesting avenue for the use of long-read sequencing data in k-mer. Overall, given the pace of recent developments in long-read technologies, the moment when long-read sequencing will fuel k-mer-based GWAS efforts is probably not too far away.
Beyond methodological improvements in GWAS analysis, we believe that the use of k-mer-based association methods in years to come will prove to be transformative for the discovery of new gene–trait associations. As discussed in this review, k-mer-based methods have already provided novel insights into crops such as soybean, maize, and wheat. Going forward, we believe that such approaches will greatly enhance our understanding of crop genetics and ultimately our ability to develop crop varieties that will tackle the challenges associated with population growth and climate change.

Author Contributions

Conceptualization, F.B.; writing—original draft preparation, B.K. and M.-A.L.; writing—review and editing, B.K., M.-A.L. and F.B.; visualization, B.K. and M.-A.L.; supervision, F.B.; project administration, F.B.; funding acquisition, F.B. All authors have read and agreed to the published version of the manuscript.

Funding

This work was performed as part of the SoyaGen project (www.soyagen.ca) thanks to funds (Genome Canada grant #5801) awarded to F. Belzile (Principal Investigator). The SoyaGen project was also supported by funds from Génome Québec, the government of Canada, the Ministère de l’Économie, Science et Innovation du Québec, Semences Prograin Inc., Syngenta Canada Inc., Sevita Genetics, Coop Fédérée, Grain Farmers of Ontario, Saskatchewan Pulse Growers, Manitoba Pulse & Soybean Growers, the Canadian Field Crop Research Alliance, and Producteurs de grains du Québec. The APC was funded by Genome Canada, grant #6548A.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

No data were generated in the compilation of this review.

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish this review.

References

  1. Aranzana, M.J.; Kim, S.; Zhao, K.; Bakker, E.; Horton, M.; Jakob, K.; Lister, C.; Molitor, J.; Shindo, C.; Tang, C.; et al. Genome-Wide Association Mapping in Arabidopsis Identifies Previously Known Flowering Time and Pathogen Resistance Genes. PLoS Genet. 2005, 1, e60. [Google Scholar] [CrossRef] [Green Version]
  2. Ozaki, K.; Ohnishi, Y.; Iida, A.; Sekine, A.; Yamada, R.; Tsunoda, T.; Sato, H.; Sato, H.; Hori, M.; Nakamura, Y.; et al. Functional SNPs in the lymphotoxin-α gene that are associated with susceptibility to myocardial infarction. Nat. Genet. 2002, 32, 650–654. [Google Scholar] [CrossRef] [PubMed]
  3. Paterson, A.H.; Lander, E.S.; Hewitt, J.D.; Peterson, S.; Lincoln, S.E.; Tanksley, S.D. Resolution of quantitative traits into Mendelian factors by using a complete linkage map of restriction fragment length polymorphisms. Nature 1988, 335, 721–726. [Google Scholar] [CrossRef] [PubMed]
  4. Ibrahim, A.K.; Zhang, L.; Niyitanga, S.; Afzal, M.Z.; Xu, Y.; Zhang, L.; Zhang, L.; Qi, J. Principles and approaches of association mapping in plant breeding. Trop. Plant Biol. 2020, 13, 212–224. [Google Scholar] [CrossRef]
  5. Korte, A.; Farlow, A. The advantages and limitations of trait analysis with GWAS: A review. Plant Methods 2013, 9, 29. [Google Scholar] [CrossRef] [Green Version]
  6. Varshney, R.K.; Sinha, P.; Singh, V.K.; Kumar, A.; Zhang, Q.; Bennetzen, J.L. 5Gs for crop genetic improvement. Curr. Opin. Plant Biol. 2020, 56, 190–196. [Google Scholar] [CrossRef] [PubMed]
  7. Zhang, Y.; Tamba, C.L.; Wen, Y.-J.; Li, P.; Ren, W.-L.; Ni, Y.-L.; Gao, J.; Zhang, Y.-M. mrMLM v4.0.2: An R Platform for Multi-locus Genome-wide Association Studies. Genom. Proteom. Bioinform. 2020, 18, 481–487. [Google Scholar] [CrossRef]
  8. Li, W.; Liu, J.; Zhang, H.; Liu, Z.; Wang, Y.; Xing, L.; He, Q.; Du, H. Plant pan-genomics: Recent advances, new challenges, and roads ahead. J. Genet. Genom. 2022, 49, 833–846. [Google Scholar] [CrossRef]
  9. Sinha, P.; Singh, V.K.; Saxena, R.K.; Khan, A.W.; Abbai, R.; Chitikineni, A.; Desai, A.; Molla, J.; Upadhyaya, H.D.; Kumar, A.; et al. Superior haplotypes for haplotype-based breeding for drought tolerance in pigeonpea (Cajanus cajan L.). Plant Biotechnol. J. 2020, 18, 2482–2490. [Google Scholar] [CrossRef]
  10. Li, J.; Zhao, J.; Li, Y.; Gao, Y.; Hua, S.; Nadeem, M.; Sun, G.; Zhang, W.; Hou, J.; Wang, X.; et al. Identification of a novel seed size associated locus SW9-1 in soybean. Crop J. 2019, 7, 548–559. [Google Scholar] [CrossRef]
  11. Gupta, P.K.; Kulwal, P.L.; Jaiswal, V. Association Mapping in Crop Plants: Opportunities and Challenges. In Advances in Genetics; Friedmann, T., Dunlap, J.C., Goodwin, S.F., Eds.; Academic Press: Cambridge, MA, USA, 2014; Volume 85, pp. 109–147. [Google Scholar]
  12. Gresham, D.; Dunham, M.J.; Botstein, D. Comparing whole genomes using DNA microarrays. Nat. Rev. Genet. 2008, 9, 291–302. [Google Scholar] [CrossRef] [PubMed]
  13. Shendure, J. The beginning of the end for microarrays? Nat. Methods 2008, 5, 585–587. [Google Scholar] [CrossRef] [PubMed]
  14. Nielsen, R.; Paul, J.S.; Albrechtsen, A.; Song, Y.S. Genotype and SNP calling from next-generation sequencing data. Nat. Rev. Genet. 2011, 12, 443–451. [Google Scholar] [CrossRef] [Green Version]
  15. Sonah, H.; O’Donoughue, L.; Cober, E.; Rajcan, I.; Belzile, F. Identification of loci governing eight agronomic traits using a GBS-GWAS approach and validation by QTL mapping in soya bean. Plant Biotechnol. J. 2015, 13, 211–221. [Google Scholar] [CrossRef]
  16. Tam, V.; Patel, N.; Turcotte, M.; Bossé, Y.; Paré, G.; Meyre, D. Benefits and limitations of genome-wide association studies. Nat. Rev. Genet. 2019, 20, 467–484. [Google Scholar] [CrossRef] [PubMed]
  17. Tibbs Cortes, L.; Zhang, Z.; Yu, J. Status and prospects of genome-wide association studies in plants. Plant Genome 2021, 14, e20077. [Google Scholar] [CrossRef]
  18. Abbott, J.C.; Butcher, S.A. Strategies towards sequencing complex crop genomes. Genome Biol. 2012, 13, 322. [Google Scholar] [CrossRef]
  19. Lu, F.; Romay, M.C.; Glaubitz, J.C.; Bradbury, P.J.; Elshire, R.J.; Wang, T.; Li, Y.; Li, Y.; Semagn, K.; Zhang, X.; et al. High-resolution genetic mapping of maize pan-genome sequence anchors. Nat. Commun. 2015, 6, 6914. [Google Scholar] [CrossRef] [Green Version]
  20. Shi, J.; Tian, Z.; Lai, J.; Huang, X. Plant pan-genomics and its applications. Mol. Plant 2023, 16, 168–186. [Google Scholar] [CrossRef]
  21. Walkowiak, S.; Gao, L.; Monat, C.; Haberer, G.; Kassa, M.T.; Brinton, J.; Ramirez-Gonzalez, R.H.; Kolodziej, M.C.; Delorean, E.; Thambugala, D.; et al. Multiple wheat genomes reveal global variation in modern breeding. Nature 2020, 588, 277–283. [Google Scholar] [CrossRef]
  22. Arora, S.; Steuernagel, B.; Gaurav, K.; Chandramohan, S.; Long, Y.; Matny, O.; Johnson, R.; Enk, J.; Periyannan, S.; Singh, N.; et al. Resistance gene cloning from a wild crop relative by sequence capture and association genetics. Nat. Biotechnol. 2019, 37, 139–143. [Google Scholar] [CrossRef] [PubMed]
  23. Zanini, S.F.; Bayer, P.E.; Wells, R.; Snowdon, R.J.; Batley, J.; Varshney, R.K.; Nguyen, H.T.; Edwards, D.; Golicz, A.A. Pangenomics in crop improvement—From coding structural variations to finding regulatory variants with pangenome graphs. Plant Genome 2022, 15, e20177. [Google Scholar] [CrossRef] [PubMed]
  24. Gupta, P.K. GWAS for genetics of complex quantitative traits: Genome to pangenome and SNPs to SVs and k-mers. BioEssays 2021, 43, 2100109. [Google Scholar] [CrossRef] [PubMed]
  25. Gupta, P.K. Quantitative genetics: Pan-genomes, SVs, and k-mers for GWAS. Trends Genet. 2021, 37, 868–871. [Google Scholar] [CrossRef]
  26. Jin, S.; Han, Z.; Hu, Y.; Si, Z.; Dai, F.; He, L.; Cheng, Y.; Li, Y.; Zhao, T.; Fang, L.; et al. Structural variation (SV)-based pan-genome and GWAS reveal the impacts of SVs on the speciation and diversification of allotetraploid cottons. Mol. Plant 2023, 16, 678–693. [Google Scholar] [CrossRef]
  27. Kim, J.-H.; Park, J.-S.; Lee, C.-Y.; Jeong, M.-G.; Xu, J.L.; Choi, Y.; Jung, H.-W.; Choi, H.-K. Dissecting seed pigmentation-associated genomic loci and genes by employing dual approaches of reference-based and k-mer-based GWAS with 438 Glycine accessions. PLoS ONE 2020, 15, e0243085. [Google Scholar] [CrossRef]
  28. Alonge, M.; Wang, X.; Benoit, M.; Soyk, S.; Pereira, L.; Zhang, L.; Suresh, H.; Ramakrishnan, S.; Maumus, F.; Ciren, D.; et al. Major Impacts of Widespread Structural Variation on Gene Expression and Crop Improvement in Tomato. Cell 2020, 182, 145–161.e123. [Google Scholar] [CrossRef]
  29. Sudmant, P.H.; Rausch, T.; Gardner, E.J.; Handsaker, R.E.; Abyzov, A.; Huddleston, J.; Zhang, Y.; Ye, K.; Jun, G.; Hsi-Yang Fritz, M.; et al. An integrated map of structural variation in 2504 human genomes. Nature 2015, 526, 75–81. [Google Scholar] [CrossRef] [Green Version]
  30. De Coster, W.; Van Broeckhoven, C. Newest Methods for Detecting Structural Variations. Trends Biotechnol. 2019, 37, 973–982. [Google Scholar] [CrossRef] [Green Version]
  31. Ho, S.S.; Urban, A.E.; Mills, R.E. Structural variation in the sequencing era. Nat. Rev. Genet. 2020, 21, 171–189. [Google Scholar] [CrossRef]
  32. Bridges, C.B. The Bar “gene” a duplication. Science 1936, 83, 210–211. [Google Scholar] [CrossRef] [PubMed]
  33. Cook, D.E.; Lee, T.G.; Guo, X.; Melito, S.; Wang, K.; Bayless, A.M.; Wang, J.; Hughes, T.J.; Willis, D.K.; Clemente, T.E.; et al. Copy Number Variation of Multiple Genes at Rhg1 Mediates Nematode Resistance in Soybean. Science 2012, 338, 1206–1209. [Google Scholar] [CrossRef] [Green Version]
  34. Gabur, I.; Chawla, H.S.; Snowdon, R.J.; Parkin, I.A.P. Connecting genome structural variation with complex traits in crop plants. Theor. Appl. Genet. 2019, 132, 733–750. [Google Scholar] [CrossRef] [PubMed]
  35. Liao, Z.; Zhang, X.; Zhang, S.; Lin, Z.; Zhang, X.; Ming, R. Structural variations in papaya genomes. BMC Genom. 2021, 22, 335. [Google Scholar] [CrossRef]
  36. Maron, L.G.; Guimarães, C.T.; Kirst, M.; Albert, P.S.; Birchler, J.A.; Bradbury, P.J.; Buckler, E.S.; Coluccio, A.E.; Danilova, T.V.; Kudrna, D.; et al. Aluminum tolerance in maize is associated with higher MATE1 gene copy number. Proc. Natl. Acad. Sci. USA 2013, 110, 5241–5246. [Google Scholar] [CrossRef]
  37. Song, J.-M.; Guan, Z.; Hu, J.; Guo, C.; Yang, Z.; Wang, S.; Liu, D.; Wang, B.; Lu, S.; Zhou, R.; et al. Eight high-quality genomes reveal pan-genome architecture and ecotype differentiation of Brassica napus. Nat. Plants 2020, 6, 34–45. [Google Scholar] [CrossRef] [Green Version]
  38. Yan, H.; Sun, M.; Zhang, Z.; Jin, Y.; Zhang, A.; Lin, C.; Wu, B.; He, M.; Xu, B.; Wang, J.; et al. Pangenomic analysis identifies structural variation associated with heat tolerance in pearl millet. Nat. Genet. 2023, 55, 507–518. [Google Scholar] [CrossRef]
  39. Studer, A.; Zhao, Q.; Ross-Ibarra, J.; Doebley, J. Identification of a functional transposon insertion in the maize domestication gene tb1. Nat. Genet. 2011, 43, 1160–1163. [Google Scholar] [CrossRef] [Green Version]
  40. Jayakodi, M.; Schreiber, M.; Stein, N.; Mascher, M. Building pan-genome infrastructures for crop plants and their use in association genetics. DNA Res. 2021, 28, dsaa030. [Google Scholar] [CrossRef]
  41. Liu, Y.; Du, H.; Li, P.; Shen, Y.; Peng, H.; Liu, S.; Zhou, G.-A.; Zhang, H.; Liu, Z.; Shi, M.; et al. Pan-Genome of Wild and Cultivated Soybeans. Cell 2020, 182, 162–176.e113. [Google Scholar] [CrossRef]
  42. Li, H.; Wang, S.; Chai, S.; Yang, Z.; Zhang, Q.; Xin, H.; Xu, Y.; Lin, S.; Chen, X.; Yao, Z.; et al. Graph-based pan-genome reveals structural and sequence variations related to agronomic traits and domestication in cucumber. Nat. Commun. 2022, 13, 682. [Google Scholar] [CrossRef] [PubMed]
  43. Domínguez, M.; Dugas, E.; Benchouaia, M.; Leduque, B.; Jiménez-Gómez, J.M.; Colot, V.; Quadrana, L. The impact of transposable elements on tomato diversity. Nat. Commun. 2020, 11, 4058. [Google Scholar] [CrossRef]
  44. Sedlazeck, F.J.; Rescheneder, P.; Smolka, M.; Fang, H.; Nattestad, M.; von Haeseler, A.; Schatz, M.C. Accurate detection of complex structural variations using single-molecule sequencing. Nat. Methods 2018, 15, 461–468. [Google Scholar] [CrossRef] [Green Version]
  45. Willson, J. Resolving the roles of structural variants. Nat. Rev. Genet. 2020, 21, 507. [Google Scholar] [CrossRef] [PubMed]
  46. Della Coletta, R.; Qiu, Y.; Ou, S.; Hufford, M.B.; Hirsch, C.N. How the pan-genome is changing crop genomics and improvement. Genome Biol. 2021, 22, 3. [Google Scholar] [CrossRef] [PubMed]
  47. Gao, L.; Gonda, I.; Sun, H.; Ma, Q.; Bao, K.; Tieman, D.M.; Burzynski-Chang, E.A.; Fish, T.L.; Stromberg, K.A.; Sacks, G.L.; et al. The tomato pan-genome uncovers new genes and a rare allele regulating fruit flavor. Nat. Genet. 2019, 51, 1044–1051. [Google Scholar] [CrossRef]
  48. Li, C.; Xiang, X.; Huang, Y.; Zhou, Y.; An, D.; Dong, J.; Zhao, C.; Liu, H.; Li, Y.; Wang, Q.; et al. Long-read sequencing reveals genomic structural variations that underlie creation of quality protein maize. Nat. Commun. 2020, 11, 17. [Google Scholar] [CrossRef] [Green Version]
  49. Pucker, B.; Irisarri, I.; de Vries, J.; Xu, B. Plant genome sequence assembly in the era of long reads: Progress, challenges and future directions. Quant. Plant Biol. 2022, 3, e5. [Google Scholar] [CrossRef]
  50. Zhang, F.; Xue, H.; Dong, X.; Li, M.; Zheng, X.; Li, Z.; Xu, J.; Wang, W.; Wei, C. Long-read sequencing of 111 rice genomes reveals significantly larger pan-genomes. Genome Res. 2022, 32, 853–863. [Google Scholar] [CrossRef]
  51. Torkamaneh, D.; Laroche, J.; Tardivel, A.; O’Donoughue, L.; Cober, E.; Rajcan, I.; Belzile, F. Comprehensive description of genomewide nucleotide and structural variation in short-season soybean. Plant Biotechnol. J. 2017, 16, 749–759. [Google Scholar] [CrossRef] [Green Version]
  52. He, C.; Washburn, J.D.; Hao, Y.; Zhang, Z.; Yang, J.; Liu, S. Trait Association and Prediction Through Integrative K-mer Analysis. bioRxiv 2021, 2021-11. [Google Scholar] [CrossRef]
  53. Rahman, A.; Hallgrímsdóttir, I.; Eisen, M.; Pachter, L. Association mapping from sequencing reads using k-mers. eLife 2018, 7, e32920. [Google Scholar] [CrossRef] [PubMed]
  54. Voichek, Y.; Weigel, D. Identifying genetic variants underlying phenotypic variation in plants without complete genomes. Nat. Genet. 2020, 52, 534–540. [Google Scholar] [CrossRef] [PubMed]
  55. Luo, R.; Liu, B.; Xie, Y.; Li, Z.; Huang, W.; Yuan, J.; He, G.; Chen, Y.; Pan, Q.; Liu, Y.; et al. SOAPdenovo2: An empirically improved memory-efficient short-read de novo assembler. GigaScience 2012, 1, 2047-217X. [Google Scholar] [CrossRef] [PubMed]
  56. Huang, G.-D.; Liu, X.-M.; Huang, T.-L.; Xia, L.-C. The statistical power of k-mer based aggregative statistics for alignment-free detection of horizontal gene transfer. Synth. Syst. Biotechnol. 2019, 4, 150–156. [Google Scholar] [CrossRef] [PubMed]
  57. Ebler, J.; Ebert, P.; Clarke, W.E.; Rausch, T.; Audano, P.A.; Houwaart, T.; Mao, Y.; Korbel, J.O.; Eichler, E.E.; Zody, M.C.; et al. Pangenome-based genome inference allows efficient and accurate genotyping across a wide spectrum of variant classes. Nat. Genet. 2022, 54, 518–525. [Google Scholar] [CrossRef]
  58. Sibbesen, J.A.; Maretty, L.; Krogh, A.; The Danish Pan-Genome, C. Accurate genotyping across variant classes and lengths using variant graphs. Nat. Genet. 2018, 50, 1054–1059. [Google Scholar] [CrossRef] [PubMed]
  59. Gaurav, K.; Arora, S.; Silva, P.; Sánchez-Martín, J.; Horsnell, R.; Gao, L.; Brar, G.S.; Widrig, V.; John Raupp, W.; Singh, N.; et al. Population genomic analysis of Aegilops tauschii identifies targets for bread wheat improvement. Nat. Biotechnol. 2022, 40, 422–431. [Google Scholar] [CrossRef]
  60. Wang, Y.; Chen, Q.; Deng, C.; Zheng, Y.; Sun, F. KmerGO: A tool to identify group-specific sequences with k-mers. Front. Microbiol. 2020, 11, 2067. [Google Scholar] [CrossRef]
  61. Koren, S.; Rhie, A.; Walenz, B.P.; Dilthey, A.T.; Bickhart, D.M.; Kingan, S.B.; Hiendleder, S.; Williams, J.L.; Smith, T.P.L.; Phillippy, A.M. De novo assembly of haplotype-resolved genomes with trio binning. Nat. Biotechnol. 2018, 36, 1174–1182. [Google Scholar] [CrossRef]
  62. Lemay, M.-A.; de Ronne, M.; Bélanger, R.; Belzile, F. k-mer-based GWAS enhances the discovery of causal variants and candidate genes in soybean. bioRxiv 2023, 2023-03. [Google Scholar] [CrossRef]
  63. Bradbury, P.J.; Zhang, Z.; Kroon, D.E.; Casstevens, T.M.; Ramdoss, Y.; Buckler, E.S. TASSEL: Software for association mapping of complex traits in diverse samples. Bioinformatics 2007, 23, 2633–2635. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  64. Wang, J.; Zhang, Z. GAPIT Version 3: Boosting Power and Accuracy for Genomic Association and Prediction. Genom. Proteom. Bioinform. 2021, 19, 629–640. [Google Scholar] [CrossRef]
  65. Kokot, M.; Długosz, M.; Deorowicz, S. KMC 3: Counting and manipulating k-mer statistics. Bioinformatics 2017, 33, 2759–2761. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  66. Manekar, S.C.; Sathe, S.R. A benchmark study of k-mer counting methods for high-throughput sequencing. GigaScience 2018, 7, giy125. [Google Scholar] [CrossRef] [Green Version]
  67. Marçais, G.; Kingsford, C. A fast, lock-free approach for efficient parallel counting of occurrences of k-mers. Bioinformatics 2011, 27, 764–770. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  68. Rhie, A.; Walenz, B.P.; Koren, S.; Phillippy, A.M. Merqury: Reference-free quality, completeness, and phasing assessment for genome assemblies. Genome Biol. 2020, 21, 245. [Google Scholar] [CrossRef]
  69. Melsted, P.; Pritchard, J.K. Efficient counting of k-mers in DNA sequences using a bloom filter. BMC Bioinform. 2011, 12, 333. [Google Scholar] [CrossRef] [Green Version]
  70. Zhou, X.; Stephens, M. Genome-wide efficient mixed-model analysis for association studies. Nat. Genet. 2012, 44, 821–824. [Google Scholar] [CrossRef] [Green Version]
  71. Lemane, T.; Chikhi, R.; Peterlongo, P. kmdiff, large-scale and user-friendly differential k-mer analyses. Bioinformatics 2022, 38, 5443–5445. [Google Scholar] [CrossRef]
  72. Wang, M.; Li, W.; Fang, C.; Xu, F.; Liu, Y.; Wang, Z.; Yang, R.; Zhang, M.; Liu, S.; Lu, S.; et al. Parallel selection on a dormancy gene during domestication of crops from multiple families. Nat. Genet. 2018, 50, 1435–1441. [Google Scholar] [CrossRef]
  73. Arora, S.; Steed, A.; Goddard, R.; Gaurav, K.; O’Hara, T.; Schoen, A.; Rawat, N.; Elkot, A.F.; Korolev, A.V.; Chinoy, C.; et al. A wheat kinase and immune receptor form host-specificity barriers against the blast fungus. Nat. Plants 2023, 9, 385–392. [Google Scholar] [CrossRef]
  74. Allen, J.P.; Snitkin, E.; Pincus, N.B.; Hauser, A.R. Forest and Trees: Exploring Bacterial Virulence with Genome-wide Association Studies and Machine Learning. Trends Microbiol. 2021, 29, 621–633. [Google Scholar] [CrossRef]
  75. Aun, E.; Brauer, A.; Kisand, V.; Tenson, T.; Remm, M. A k-mer-based method for the identification of phenotype-associated genomic biomarkers and predicting phenotypes of sequenced bacteria. PLoS Comput. Biol. 2018, 14, e1006434. [Google Scholar] [CrossRef] [Green Version]
  76. Jaillard, M.; Lima, L.; Tournoud, M.; Mahé, P.; van Belkum, A.; Lacroix, V.; Jacob, L. A fast and agnostic method for bacterial genome-wide association studies: Bridging the gap between k-mers and genetic events. PLoS Genet. 2018, 14, e1007758. [Google Scholar] [CrossRef] [Green Version]
  77. Jaillard, M.; Palmieri, M.; van Belkum, A.; Mahé, P. Interpreting k-mer–based signatures for antibiotic resistance prediction. GigaScience 2020, 9, giaa110. [Google Scholar] [CrossRef]
  78. Yu, G.; Zhang, Q.; Friesen, T.L.; Rouse, M.N.; Jin, Y.; Zhong, S.; Rasmussen, J.B.; Lagudah, E.S.; Xu, S.S. Identification and mapping of Sr46 from Aegilops tauschii accession CIae 25 conferring resistance to race TTKSK (Ug99) of wheat stem rust pathogen. Theor. Appl. Genet. 2015, 128, 431–443. [Google Scholar] [CrossRef]
  79. Olson, E.L.; Rouse, M.N.; Pumphrey, M.O.; Bowden, R.L.; Gill, B.S.; Poland, J.A. Simultaneous transfer, introgression, and genomic localization of genes for resistance to stem rust race TTKSK (Ug99) from Aegilops tauschii to wheat. Theor. Appl. Genet. 2013, 126, 1179–1188. [Google Scholar] [CrossRef]
  80. Rouse, M.N.; Olson, E.L.; Gill, B.S.; Pumphrey, M.O.; Jin, Y. Stem Rust Resistance in Aegilops tauschii Germplasm. Crop Sci. 2011, 51, 2074–2078. [Google Scholar] [CrossRef]
  81. Steuernagel, B.; Periyannan, S.K.; Hernández-Pinzón, I.; Witek, K.; Rouse, M.N.; Yu, G.; Hatta, A.; Ayliffe, M.; Bariana, H.; Jones, J.D.G.; et al. Rapid cloning of disease-resistance genes in plants using mutagenesis and sequence capture. Nat. Biotechnol. 2016, 34, 652–655. [Google Scholar] [CrossRef]
  82. Flint-Garcia, S.A.; Thuillet, A.-C.; Yu, J.; Pressoir, G.; Romero, S.M.; Mitchell, S.E.; Doebley, J.; Kresovich, S.; Goodman, M.M.; Buckler, E.S. Maize association population: A high-resolution platform for quantitative trait locus dissection. Plant J. 2005, 44, 1054–1064. [Google Scholar] [CrossRef]
  83. Yan, L.; Fu, D.; Li, C.; Blechl, A.; Tranquilli, G.; Bonafede, M.; Sanchez, A.; Valarik, M.; Yasuda, S.; Dubcovsky, J. The wheat and barley vernalization gene VRN3 is an orthologue of FT. Proc. Natl. Acad. Sci. USA 2006, 103, 19581–19586. [Google Scholar] [CrossRef]
  84. Bonnin, I.; Rousset, M.; Madur, D.; Sourdille, P.; Dupuits, C.; Brunel, D.; Goldringer, I. FT genome A and D polymorphisms are associated with the variation of earliness components in hexaploid wheat. Theor. Appl. Genet. 2008, 116, 383–394. [Google Scholar] [CrossRef]
  85. Dixon, L.E.; Farré, A.; Finnegan, E.J.; Orford, S.; Griffiths, S.; Boden, S.A. Developmental responses of bread wheat to changes in ambient temperature following deletion of a locus that includes FLOWERING LOCUS T1. Plant Cell Environ. 2018, 41, 1715–1725. [Google Scholar] [CrossRef] [Green Version]
  86. Claeys, H.; Vi, S.L.; Xu, X.; Satoh-Nagasawa, N.; Eveland, A.L.; Goldshmidt, A.; Feil, R.; Beggs, G.A.; Sakai, H.; Brennan, R.G.; et al. Control of meristem determinacy by trehalose 6-phosphate phosphatases is uncoupled from enzymatic activity. Nat. Plants 2019, 5, 352–357. [Google Scholar] [CrossRef]
  87. Koppolu, R.; Anwar, N.; Sakuma, S.; Tagiri, A.; Lundqvist, U.; Pourkheirandish, M.; Rutten, T.; Seiler, C.; Himmelbach, A.; Ariyadasa, R.; et al. Six-rowed spike4 (Vrs4) controls spikelet determinacy and row-type in barley. Proc. Natl. Acad. Sci. USA 2013, 110, 13198–13203. [Google Scholar] [CrossRef]
  88. Malik, R.; Brown-Guedira, G.L.; Smith, C.M.; Harvey, T.L.; Gill, B.S. Genetic Mapping of Wheat Curl Mite Resistance Genes Cmc3 and Cmc4 in Common Wheat. Crop Sci. 2003, 43, 644–650. [Google Scholar] [CrossRef]
  89. Zhao, J.; Abdelsalam, N.R.; Khalaf, L.; Chuang, W.-P.; Zhao, L.; Smith, C.M.; Carver, B.; Bai, G. Development of Single Nucleotide Polymorphism Markers for the Wheat Curl Mite Resistance Gene Cmc4. Crop Sci. 2019, 59, 1567–1575. [Google Scholar] [CrossRef]
  90. Klymiuk, V.; Yaniv, E.; Huang, L.; Raats, D.; Fatiukha, A.; Chen, S.; Feng, L.; Frenkel, Z.; Krugman, T.; Lidzbarsky, G.; et al. Cloning of the wheat Yr15 resistance gene sheds light on the plant tandem kinase-pseudokinase family. Nat. Commun. 2018, 9, 3735. [Google Scholar] [CrossRef] [Green Version]
  91. Brueggeman, R.; Rostoks, N.; Kudrna, D.; Kilian, A.; Han, F.; Chen, J.; Druka, A.; Steffenson, B.; Kleinhofs, A. The barley stem rust-resistance gene Rpg1 is a novel disease-resistance gene with homology to receptor kinases. Proc. Natl. Acad. Sci. USA 2002, 99, 9328–9333. [Google Scholar] [CrossRef]
  92. Chen, S.; Rouse, M.N.; Zhang, W.; Zhang, X.; Guo, Y.; Briggs, J.; Dubcovsky, J. Wheat gene Sr60 encodes a protein with two putative kinase domains that confers resistance to stem rust. New Phytol. 2020, 225, 948–959. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  93. Lu, P.; Guo, L.; Wang, Z.; Li, B.; Li, J.; Li, Y.; Qiu, D.; Shi, W.; Yang, L.; Wang, N.; et al. A rare gain of function mutation in a wheat tandem kinase confers resistance to powdery mildew. Nat. Commun. 2020, 11, 680. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  94. Inoue, Y.; Vy, T.T.P.; Yoshida, K.; Asano, H.; Mitsuoka, C.; Asuke, S.; Anh, V.L.; Cumagun, C.J.R.; Chuma, I.; Terauchi, R.; et al. Evolution of the wheat blast fungus through functional losses in a host specificity determinant. Science 2017, 357, 80–83. [Google Scholar] [CrossRef] [Green Version]
  95. Vy, T.T.P.; Hyon, G.-S.; Nga, N.T.T.; Inoue, Y.; Chuma, I.; Tosa, Y. Genetic analysis of host–pathogen incompatibility between Lolium isolates of Pyricularia oryzae and wheat. J. Gen. Plant Pathol. 2014, 80, 59–65. [Google Scholar] [CrossRef]
  96. Hirata, K.; Tosa, Y.; Nakayashiki, H.; Mayama, S. Significance of PWT4–Rwt4 interaction in the species specificity of Avena isolates of Magnaporthe oryzae on wheat. J. Gen. Plant Pathol. 2005, 71, 340–344. [Google Scholar] [CrossRef]
  97. Rawat, N.; Joshi, A.; Pumphrey, M.; Singh, L.; Mahlandt, A.; Chhabra, B.; Wilson, D.; Gill, B.; Poland, J.; Tiwari, V. A TILLING Resource for Hard Red Winter Wheat Variety Jagger. Crop Sci. 2019, 59, 1666–1671. [Google Scholar] [CrossRef]
  98. Zabala, G.; Vodkin, L.O. A Rearrangement Resulting in Small Tandem Repeats in the F3′5′H Gene of White Flower Genotypes Is Associated with the Soybean W1 Locus. Crop Sci. 2007, 47, S-113–S-124. [Google Scholar] [CrossRef]
  99. Zabala, G.; Vodkin, L. Cloning of the pleiotropic T locus in soybean and two recessive alleles that differentially affect structure and expression of the encoded flavonoid 3’ hydroxylase. Genetics 2003, 163, 295–309. [Google Scholar] [CrossRef]
  100. Yan, F.; Githiri, S.M.; Liu, Y.; Sang, Y.; Wang, Q.; Takahashi, R. Loss-of-Function Mutation of Soybean R2R3 MYB Transcription Factor Dilutes Tawny Pubescence Color. Front. Plant Sci. 2020, 10, 1809. [Google Scholar] [CrossRef] [Green Version]
  101. Corut, K. akcorut/kGWASflow: V1.2.0 (v1.2.0). Zenodo 2023. [Google Scholar] [CrossRef]
  102. Eizenga, J.M.; Novak, A.M.; Sibbesen, J.A.; Heumos, S.; Ghaffaari, A.; Hickey, G.; Chang, X.; Seaman, J.D.; Rounthwaite, R.; Ebler, J.; et al. Pangenome Graphs. Annu. Rev. Genom. Hum. Genet. 2020, 21, 139–162. [Google Scholar] [CrossRef] [PubMed]
  103. Garrison, E.; Sirén, J.; Novak, A.M.; Hickey, G.; Eizenga, J.M.; Dawson, E.T.; Jones, W.; Garg, S.; Markello, C.; Lin, M.F.; et al. Variation graph toolkit improves read mapping by representing genetic variation in the reference. Nat. Biotechnol. 2018, 36, 875–879. [Google Scholar] [CrossRef]
  104. Hickey, G.; Monlong, J.; Ebler, J.; Novak, A.M.; Eizenga, J.M.; Gao, Y.; Abel, H.J.; Antonacci-Fulton, L.L.; Asri, M.; Baid, G.; et al. Pangenome graph construction from genome alignments with Minigraph-Cactus. Nat. Biotechnol. 2023, 1–11. [Google Scholar] [CrossRef] [PubMed]
  105. Amarasinghe, S.L.; Su, S.; Dong, X.; Zappia, L.; Ritchie, M.E.; Gouil, Q. Opportunities and challenges in long-read sequencing data analysis. Genome Biol. 2020, 21, 30. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  106. Wenger, A.M.; Peluso, P.; Rowell, W.J.; Chang, P.-C.; Hall, R.J.; Concepcion, G.T.; Ebler, J.; Fungtammasan, A.; Kolesnikov, A.; Olson, N.D.; et al. Accurate circular consensus long-read sequencing improves variant detection and assembly of a human genome. Nat. Biotechnol. 2019, 37, 1155–1162. [Google Scholar] [CrossRef]
  107. Zhang, H.; Jain, C.; Aluru, S. A comprehensive evaluation of long read error correction methods. BMC Genom. 2020, 21, 889. [Google Scholar] [CrossRef]
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Figure 1. Illustration of the location of unique k-mers (red lines) originating from a reference genome (solid black lines) and an alternate genome (solid blue lines) depending on the underlying variant type. Dashed lines indicate genomic locations (vertical lines) or ranges of genomic positions (horizontal lines) that will induce unique k-mer patterns in the respective sample. (a) A single-nucleotide polymorphism (SNP, indicated by ticks on the genomes) will result in k-mers specific to each genome when k-mers overlap with the SNP. (b) For insertions/deletions, unique k-mers will originate from the breakpoints induced by the variation. In addition, if the sequence insertion is novel (i.e., not found elsewhere in the genome), unique k-mers will also originate from within the inserted sequence. (c) For inversions, unique k-mers arise at the inversion breakpoints. k-mers originating from within the inverted sequence will not differentiate between the two genomes because only the orientation of the sequence will differ. (d) For tandem duplications, novel adjacencies will only occur in the genome bearing the additional copies. However, k-mer counts may be able to differentiate between varying copy numbers at the locus.
Figure 1. Illustration of the location of unique k-mers (red lines) originating from a reference genome (solid black lines) and an alternate genome (solid blue lines) depending on the underlying variant type. Dashed lines indicate genomic locations (vertical lines) or ranges of genomic positions (horizontal lines) that will induce unique k-mer patterns in the respective sample. (a) A single-nucleotide polymorphism (SNP, indicated by ticks on the genomes) will result in k-mers specific to each genome when k-mers overlap with the SNP. (b) For insertions/deletions, unique k-mers will originate from the breakpoints induced by the variation. In addition, if the sequence insertion is novel (i.e., not found elsewhere in the genome), unique k-mers will also originate from within the inserted sequence. (c) For inversions, unique k-mers arise at the inversion breakpoints. k-mers originating from within the inverted sequence will not differentiate between the two genomes because only the orientation of the sequence will differ. (d) For tandem duplications, novel adjacencies will only occur in the genome bearing the additional copies. However, k-mer counts may be able to differentiate between varying copy numbers at the locus.
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Figure 4. Example of identification of sequence variants underlying significant k-mersat soybean loci. (a) Pairwise alignment between two haplotypes generated from the local assembly of reads containing significant k-mers at the G locus controlling seed coat color. The causal variant at this locus is A > G SNP [72]. (b) Table showing the most significant k-mers identified by Lemay et al. [58] at this locus and their associated p-values, sorted from the most to least significant. The colors of the two most significant k-mers match their location in the haplotypes in panel (a). (c) Pairwise alignment between two haplotypes generated from local assembly of reads containing significant k-mers at the T locus controlling pubescence color. The causal variant at this locus is 1-bp indel. (d) Table showing some significant k-mers identified by Lemay et al. [58] at this locus and their associated p-values, sorted from the most to least significant. The red and blue color of two significant k-mers match their location in the haplotypes in panel (c).
Figure 4. Example of identification of sequence variants underlying significant k-mersat soybean loci. (a) Pairwise alignment between two haplotypes generated from the local assembly of reads containing significant k-mers at the G locus controlling seed coat color. The causal variant at this locus is A > G SNP [72]. (b) Table showing the most significant k-mers identified by Lemay et al. [58] at this locus and their associated p-values, sorted from the most to least significant. The colors of the two most significant k-mers match their location in the haplotypes in panel (a). (c) Pairwise alignment between two haplotypes generated from local assembly of reads containing significant k-mers at the T locus controlling pubescence color. The causal variant at this locus is 1-bp indel. (d) Table showing some significant k-mers identified by Lemay et al. [58] at this locus and their associated p-values, sorted from the most to least significant. The red and blue color of two significant k-mers match their location in the haplotypes in panel (c).
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Karikari, B.; Lemay, M.-A.; Belzile, F. k-mer-Based Genome-Wide Association Studies in Plants: Advances, Challenges, and Perspectives. Genes 2023, 14, 1439. https://doi.org/10.3390/genes14071439

AMA Style

Karikari B, Lemay M-A, Belzile F. k-mer-Based Genome-Wide Association Studies in Plants: Advances, Challenges, and Perspectives. Genes. 2023; 14(7):1439. https://doi.org/10.3390/genes14071439

Chicago/Turabian Style

Karikari, Benjamin, Marc-André Lemay, and François Belzile. 2023. "k-mer-Based Genome-Wide Association Studies in Plants: Advances, Challenges, and Perspectives" Genes 14, no. 7: 1439. https://doi.org/10.3390/genes14071439

APA Style

Karikari, B., Lemay, M.-A., & Belzile, F. (2023). k-mer-Based Genome-Wide Association Studies in Plants: Advances, Challenges, and Perspectives. Genes, 14(7), 1439. https://doi.org/10.3390/genes14071439

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