What is the difference between linkage mapping and association mapping

Only one candidate gene was found in LD with the associated SNP on Chr1 for all three stages of female flowering date. The identified candidate gene of 1. This SNP overlaps with a candidate gene, coding for a chromosome transmission fidelity protein 8 homolog. These primers were used to genotype 96 unreleased breeding line accessions of the Walnut Improvement Program of the University of California, Davis.

Among them, 48 are early leafing accessions and 48 are late leafing accessions. Since we designed the KASP primers using the complementary strand, we expect to find the allele A versus C as involved in late leafing genotypes.

The three different genotypes were clearly separated for 95 accessions, whereas one of them could not be determined Table S4. Allelic effect of the KASP marker associated to the leafing date. For the entire set of studied traits, we observed no influence of population structure on the phenotypes. Most likely, the familial relatedness among accessions accounted for most of the genetic variation affecting the traits studied, leading to redundant PCA information [ 38 ]. Therefore, it is not surprising to observe accessions of the same geographical origin that are both early and late in phenology.

Results found using the F 1 progeny show that strong QTLs for all the stages of female flowering date segregating in both parents on LG 1, co-localize with strong QTLs for budbreak date. In walnut trees, budbreak phenotyping relies on buds that will produce both leaves and female flowers.

Those traits are, therefore, correlated and likely controlled by common genetic and metabolic pathways, as suggested by the identification of QTLs within the same genomic region on Chr1. Similar results were observed in walnut for leafing and harvest dates [ 35 ]. On the other hand, GWAS enabled further dissection of the genetic control of these traits by identifying a location for the most significant marker-trait associations for budbreak date at about 2.

This is one of the major advantages of GWAS, whose higher resolution defines more precisely the underlying regions of traits of interest compared to classical QTL mapping. While the major loci controlling the traits studied were identified with both GWAS and linkage mapping, this did not happen for the minor associations. This finding can be explained in two ways: i the loci identified in the GWAS likely are not segregating in the F 1 progeny, and ii the QTLs found in the linkage mapping analysis have rare alleles in the association panel.

For both GWAS results and QTLs mapping, the minor trait-loci associations were not stable across years, suggesting they are more affected by environmental conditions. Nevertheless, results based on the legacy data allowed confirmation of the major SNP associated with budbreak date. We observed a different scenario for all the stages of male flowering date: while the major QTL found on LG 1 co-localizes with those for female flowering dates and budbreak date, these results were not confirmed in the GWAS panel.

Here, instead, we identified the most significant associations on chromosomes 11 and 4. Then, since heterodichogamy is the measurement of how the male and female flowering dates overlap, it is not surprising to find a marker-trait association on Chr11 with more consistency across years. In addition, a few regions were not segregating in our F 1 progeny, due to monomorphism in the parents, likely preventing accurate QTL detection.

Combining multi-locus models for GWAS has become widespread [ 40 ], and the associations found by multiple methods are usually reliable [ 41 ]. These results indicate that most likely on Chr1 there is a genomic region controlling budbreak, leafing, and female flowering dates in walnut. Since strong positive correlations exist among these traits, with high allelic effects of the marker-trait associations, this would have an impact for selection.

For instance, breeding for budbreak and female flowering and more broadly for all phenological-related traits would be a difficult job, and would complicate the creation of shorter-cycle cultivars. In this work, classical QTL mapping mostly confirmed the major marker-trait associations identified. We found a major locus on Chr1 for budbreak date and female flowering dates, and a major locus on Chr11 for male flowering dates.

Also, the high complexity of phenology-related traits indicates that many loci may be involved in their expression [ 23 ], making the detection of minor loci difficult.

Lateral bearing is a crucial trait in walnut breeding since it contributes to increased yield [ 42 ]. In the present study, we reconfirmed that walnut bearing habit is controlled by a major QTL in the centromeric region of Chr We confirm previous results regarding genetic control of lateral bearing by performing GWAS in genetically different plant materials, further supporting the power and high resolution of this method in walnut.

In our F 1 progeny, this trait did not segregate and all hybrids are lateral bearing. Maybe because our progeny is too small or because of segregation distorsion, we did not observe any terminal bearing individuals.

Availability of the new chromosome-scale reference genome Chandler v2. For instance, we found the chromosome transmission fidelity ctf protein 8 homolog gene as candidate gene for all female flowering dates. Previous studies in Arabidopsis thaliana suggest the involvement of the ctf genes family in the cell division processes [ 44 ].

In particular, the inactivation of the ctf7—1 and ctf7—2 genes resulted in developmental defects, including aberrations in flower morphology and male and female gametophytes. Interestingly, a TPX2 encoding gene required for spindle assembly during cell division process was also found likely to be involved in harvest date determination in walnut [ 35 ]. These findings suggest that cell division is a crucial process for correct flower and fruit development in walnut, and suggest the ctf8 protein homolog locus is an excellent candidate gene for female flowering.

Trichomes consist of only one cell type, appearing as a long, slender appendage [ 45 ]. Since these tepals become hirsute, it is not surprising to find a trichome birefringence-like 13 protein encoding gene within the LD block surrounding the SNP on Chr4 which is associated with male flowering dates.

Also, the SNP on Chr11 associated with heterodichogamy and male flowering dates is in LD with a candidate gene encoding a probable trehalose-phosphate phosphatase D. This dephosphorylating enzyme and other members of the gene family are highly expressed in male flowers of Jatropha , a perennial woody plant, and in transgenic plants of A. Other authors reviewed the roles of sugars in the control of flowering time, and apart from its involvement in carbohydrate metabolism, trehalosephosphate seems to serve as a signal for inducing flowering transition in plants [ 48 , 49 ].

In this regard, we found a probable trehalose-phosphate phosphatase D encoding gene for heterodichogamy. Surprisingly, we did not find widely known genes to be involved in flowering process or dormancy in our candidate genes. We then compared them with our marker-trait associations, regardless of the LD blocks, and we found interesting results.

The first one co-localizes with the major QTL found for budbreak date using the F 1 progeny and the second is very near to the major marker-trait association found for female flowering dates 9. Then, our results showed several marker-trait associations for both female and male flowering dates on Chr7 between Interestingly, we found the following encoding genes within this window: a flowering time control protein FCA-like 33 Mbp , a flowering-promoting factor 1-like protein 3 Finally, we noticed that two genes are near to our marker-trait associations for the dichogamy For the first time in walnut, a KASP marker related to phenology is released.

Since this marker is dominant, it will greatly help breeders to accurately select individuals with delayed budbreak. However, due to the complex genetic basis of phenology-related traits in perennials [ 23 ], additional markers, especially for minor loci, will be needed to improve the selection. Finally, this SNP is located in a gene encoding an uncharacterized protein, and it would be interesting to know more about the functional role of this gene.

The marker can predict a significant portion of the phenotype but we still do not know the regulatory networks involved in this complex trait.

Due to the significant influence of environment on phenology-related traits in walnut, unravelling their genetic architecture is of upmost importance for the development of markers that could assist the selection of superior genotypes and, therefore, the release of new walnut cultivars adapted to different climatic conditions.

Using GWAS with two different multi-locus models, we identified significant associations for budbreak date, and male and female flowering dates, confirmed by classical QTL mapping. In addition, we provide a list of candidate genes for these traits, that will be fundamental in future studies of functional genomics and understanding the metabolic pathways underlying phenology in walnut.

We also developed and validated the first KASP marker for budbreak date in walnut, which will allow accurate selection of individuals with a delayed budbreak and, therefore, suitable for cultivation in France and other regions where late spring frosts are challenging.

In parallel, the genetic bases for the expression of lateral bearing were confirmed. Since the future French walnut breeding program needs cultivars with high quality kernels, efforts are underway phenotype the entire collection regarding chemical content e.

Our F 1 progeny is too small to pursue investigations but this study confirms that the INRAE walnut germplasm repository contains an array of plant material suitable for this type of work. New genome-wide analyses now are being initiated to further increase our knowledge concerning the genetic architecture of the main traits of interest in walnut.

This INRAE walnut germplasm collection is publicly available and is a result of important collecting work performed between and by Eric Germain retired and former head of the INRAE walnut breeding program in 23 countries including the European, American, and Asian continents. Eric Germain initiated an international cooperation during his activity concerning the exchange of walnut plant materials especially in Europe and obtained all permissions to bring them.

The panel choice was based on previous genetic diversity work using 13 SSR markers and evaluation of phenotypic variability [ 37 ]. The intraspecific F 1 mapping progeny results from bi-parental controlled crosses made from to by Eric Germain between two cultivars with contrasting phenology-related traits, for his needs under his breeding program.

Phenotypic evaluation for the following 10 traits was conducted in 2 years and for both the GWAS panel and the F 1 progeny, at a rate of two to three visits in orchards per week during the months of March, April and May: budbreak date, beginning, peak, end, and duration of female flowering, heterodichogamy, and beginning, peak, end, and duration of male flowering Table S5.

Heterodichogamy, degree of male and female flowering overlap, was computed by subtracting peak female flowering date from peak male flowering date.

For the GWAS panel, we also used phenotypical data based on the same scoring scales used for many years previously mainly between and on three sites. Moreover, as bearing habit is not affected by environment conditions, this trait was recorded only in For both the GWAS panel and the F 1 progeny, the means of genotypic effects were obtained for each accession by adjusting for known environmental factors using the BLUPs.

When using two-year data, the means were predicted using a mixed linear model considering the year effect a. When using phenological data from many years and three sites only available for the GWAS panel [ 21 ], the means were predicted using a mixed linear model considering the effects of year, site and combined of year and site b :.

Based on the previous mixed linear models, broad-sense heritability of each trait was estimated using the variance components. We have always only one replication by genotype. Nevertheless, the legacy data are very unbalanced for the years and sites available considering each genotype. These SNPs were then filtered through several criteria Table 2. First, the filtering metrics were performed by ThermoFisher considering default thresholds: dish quality control greater than or equal to 0.

SNPs with Mendelian errors in the F 1 progeny were removed. Finally, , robust SNPs In addition, individuals were checked for outlying heterozygosity rate. A minimum LOD value of Single trait analysis was performed using a Multiple Interval Mapping MIM method [ 62 ], in which the highest effect QTL is taken as a cofactor to control the genetic background, whereas another QTL is searched in a different position.

The threshold for MIM was 0. For the PCA, the ten largest eigenvalues were obtained to check for structure. LD levels around the most associated loci were estimated using HaploView v4. Each physical position of these trait-SNPs associations was investigated to explore the extension of the surrounding LD blocks and determine the genomic regions where to search for candidate genes. The KASP reaction was prepared as follows: 1. The phenotypic raw datasets generated and analyzed during the current study for the GWAS panel and for the F 1 progeny are available respectively in Table S6 , and Table S7.

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SNP markers are the richest and ultimate point mutations in genomes, and are representative of ancient and stable variation. Genotyping using SNPs can be standardized, and the differences can be as simple as a single base pair. Moreover, SNP chips are highly-integrated, and can be combined with analysis software and relevant breeding information Wang et al. Thus, SNP markers can be used in a molecular breeding platform.

For instance, Thompson et al. Wang et al. Jin et al. The total chromosome length was 4, cM with an average marker distance of 0. The 90K wheat iSelect SNP chip used in this study consisted of 81, SNP markers and scanned 34, polymorphic markers in the cultivar population, with a polymorphism frequency of Finally, 7, polymorphic markers were scanned in the RIL population, with a polymorphism frequency of 8. Since the marker sequences of this SNP chip were known, sequence alignment can be used in evaluating marker effectiveness.

Future work will enable construction of a high-density genetic linkage map for the RIL population. Association analysis and traditional linkage mapping can be used in a complementary manner for gene identification and validation Nordborg and Weigel, Using germplasm and RIL populations of soybean, Korir et al.

The combination of the two methods improved the efficiency of screening for aluminum resistance candidate genes in soybean. Li et al. Loci uq. A and uq. A were significantly associated in a germplasm population grown in multiple environments. Maccaferri et al. The sequences surrounding these QTL will permit functional marker development and gene cloning. Among the 47 associated loci, 12 Among the 47 associated loci 35 The main reason for this was the restricted bi-allelic polymorphism in a single segregating population.

New strategies for combining linkage mapping and association analysis have been reported. For example, nested association mapping NAM is considered the most effective method to explain the genetic basis of quantitative traits for low-level LD species. NAM more effectively and economically scans at the genome-wide level, and helps to integrate molecular variation at the molecular level with that of complex phenotypic traits Maurer et al.

Our findings theoretically permit cloning of candidate genes for KNPS. Moreover, the more important loci discovered here can be preferentially targeted in marker-assisted selection for high yield.

Thus, the combination of association analysis and linkage mapping, development and application of powerful statistical models, and application of high-density SNP markers will promote research on the genetics of complex quantitative traits in crop species.

In this study, 12 of the 47 loci were validated to be correlated with KNPS in both populations. Besides, Gao et al. Breeding is a process of combining favorable alleles Ge et al. Gao et al. Three loci, QKNS. The skewed values imply that these alleles might have undergone selection during breeding. Analyzed the data: JG and WS. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

We gratefully acknowledge help from Professor Robert A. McIntosh, University of Sydney, with English editing. Ain, Q. Genome-wide association for grain yield under rainfed conditions in historical wheat cultivars from Pakistan. Plant Sci. Akhunov, E. Single nucleotide polymorphism genotyping in polyploid wheat with the Illumina goldengate assay. Allen, A. Transcript-specific, single-nucleotide polymorphism discovery and linkage analysis in hexaploid bread wheat Triticum aestivum L.

Plant Biotechnol. Atwell, S. Genome-wide association study of phenotypes in Arabidopsis thaliana inbred lines. Nature , — Beales, J. A pseudo-response regulator is misexpressed in the photoperiod insensitive Ppd-D1a mutant of wheat Triticum aestivum L. Bernardo, R. Test cross additive and dominance effects in best linear unbiased prediction of maize single-cross performance.

Best linear unbiased prediction of maize single-cross performance. Crop Sci. Marker-based estimates of identity by descent and alikeness in state among maize inbreds. Bradbury, P. Bioinformatics 23, — Breseghello, F. Association analysis as a strategy for improvement of quantitative traits in plants. Cadic, E. Combined linkage and association mapping of flowering time in sunflower Helianthus annuus L. Cavanagh, C. Genome-wide comparative diversity uncovers multiple targets of selection for improvement in hexaploid wheat landraces and cultivars.

Cui, F. QTL detection of seven spike-related traits and their genetic correlations in wheat using two related RIL populations. Euphytica , — Construction of an integrative linkage map and QTL mapping of grain yield-related traits using three related wheat RIL populations. Deng, S. Characterization and precise mapping of a QTL increasing spike number with pleiotropic effects in wheat.

Devos, K. Comparative RFLP maps of the homoeologous group-2 chromosomes of wheat, rye and barley. Dobrovolskaya, O. Plant Physiol. Evanno, G. This reference map was produced with the R package LPmerge [ 49 ], by de Miguel et al. For the G2 population, the AFLP markers used to construct the female and male parental maps for the G2 population were combined with SNP markers [ 33 ] that were also included in the composite map.

Marker order was highly conserved between the reference map and the parental maps produced from data for the mapping populations studied. Only 1. Marker inversions occurred only with tightly linked loci separated by less than 2 cM.

This high degree of collinearity between maps made it possible to project the QTLs detected on the F2 and G2 maps onto a single reference map with Biomercator V4. For the association mapping population, 2, of the 2, SNPs available for marker-trait association were assigned to a genetic map position on the reference map S4 Fig. Information for each marker and its location on the genetic maps is provided in S1 Table.

The map covered a total of 1,cM, spread over 12 linkage groups LGs , corresponding to the haploid number chromosomes of the pine genome. Mean LG length was The highest levels of distortion were found on LG8 and LG2. The region of LG2 was already highlighted in a previous study on the same cross [ 23 ].

The percentage of phenotypic variance explained by each of these QTLs was relatively small and ranged from 3. The LOD score patterns for total height blue and stem straightness orange over the 12 linkage groups of maritime pine are represented.

For the additive and dominance effects, the proportions in standard deviation is indicated in parenthesis. G2 population —The coefficient of phenotypic variation for height increments ranged from 0. The coefficients of correlation between height increments ranged from 0. For the male parent, the percentage of the phenotypic variance explained by a QTL was 3.

For the female parent, the QTL identified for height increments accounted for up to 4. For the allelic substitution effect, the proportion of standard deviation is indicated in parenthesis. The comparison between the expected kinship coefficients obtained with pedigree information matrix A and the realized kinship coefficients obtained with genomic information matrix G highlighted differences between the two estimates S7 Fig. Indeed, marker-based analysis revealed inconsistencies in the pedigree for 39 G1 and nine G0 trees.

Kinship coefficients between G0 and G1 and within G1 based on pedigree data were therefore considered erroneous for these individuals e. The 39 G1 and nine G0 individuals presenting inconsistencies between pedigree-based and marker-based kinship findings were removed for subsequent analyses.

Marker data also identified the male parents of eight G1 individuals. As reported by Plomion et al. Relatedness between individuals is known to bias p -values in association analysis.

We therefore compared different models, using representations of the observed and expected—log 10 p-value values on Q-Q plots Fig 2.

Given the low level of bias observed with the FASTA method and matrix G , we can conclude that population structure was effectively controlled by the family relatedness captured in matrix G. We therefore used this model for the detection of marker-trait associations.

The p -value profiles in terms of—log 10 p for all tested SNPs for both traits were plotted on Manhattan plots Fig 3A. HT was found to be significantly associated with three SNPs located on three different contigs. All the significant SNPs were represented on the reference map of P.

The putative protein associated with this SNP accounting for 4. Overall, no coincidence between marker-trait association and QTL positions was found Fig 4. Distribution of p- values on a negative log 10 scale over the 12 linkage groups from the genome-wide association analysis panel A and the absolute effect of markers in the Bayesian LASSO model panel B for height and stem straightness. Only mapped markers are displayed.

The locations of markers significantly associated with height HT, purple and stem straightness STR, red are also indicated. On linkage group 2, the two significant associations for HT are co-located cM and Most traits of interest in forest tree breeding, including height growth and wood properties, are quantitative traits with complex genetic architectures and low to medium heritabilities [ 52 ].

The identification of relevant markers from LA and LD studies would therefore improve the prediction of breeding values for individuals from genotypic data alone, thereby increasing the efficiency of selection strategies [ 53 — 55 ]. In this study, we investigated the genetic architecture of height growth and stem straightness, two major traits in the maritime pine breeding program, through a combination of linkage and genome-wide association mapping. LA identified five regions of the P. Interestingly, the favorable allele of one of the three detected QTLs for stem straightness comes from the Landes grand-parent.

Consistent with this observation made at the molecular level, it should be mentioned that stem straightness is genetically variable within the Landes provenance and heritable, resulting in positive genetic gains for the first breeding generations [ 20 ]. The percentage of the variance explained by individual QTLs was small up to 5. For example, Devey et al. Conversely, in Pseudotsuga menziesii, two QTLs were detected for height growth, explaining Various factors, including population size, can lead to an overestimation of the effect of QTLs [ 61 , 62 ].

Given the experimental design used here sample size of less than genotypes and no clonal replicates only strong QTL effects would be detectable. In maritime pine, QTL mapping, based on two- or three-generation pedigrees, has been carried out for height and radial growth [ 63 ], water-use efficiency [ 64 , 65 ], wood properties [ 35 ], and traits relating to photosynthesis [ 66 ], but no previous study has addressed the genetic architecture of stem straightness.

To the best of our knowledge, only one study in a P. However, this study identified no QTLs for this trait. Confirmation in other genetic backgrounds is required, but this result is encouraging, as it suggests that it may be possible to overcome the small, but significant negative genetic correlation between these two traits [ 20 ].

In addition to QTL mapping in dedicated full-sib families, we also performed genome-wide association with related genotypes from the first two generations of the maritime pine breeding population. Population stratification or relatedness can result in the detection of spurious marker-trait associations [ 68 , 69 ]. However, no structure was detected in the founder population G0 trees [ 26 ], so only relatedness between individuals was taken into account, by integrating the realized genomic relationship matrix into the marker-trait association model.

This consideration of relatedness considerably reduced p -value inflation, for both traits. Similar results were reported for wood quality and growth traits in Eucalyptus globulus [ 70 , 71 ] and for wood property traits in Cryptomeria japonica [ 72 ]. In previous studies of growth traits in conifer, a small number of associations were highlighted through the use of candidate genes [ 14 , 16 , 73 ] or a larger set of markers [ 13 , 74 ].

In maritime pine, Lepoittevin et al. They identified only one association. In the same species, Cabezas et al. LD decays rapidly in maritime pine [ 27 , 75 , 76 ], as in other conifers [ 77 — 79 ].

It has been observed that not only LD decay over a distance less than the size of a single gene, but even two SNPs that are immediately adjacent might be in complete linkage equilibrium, which may reflect that the respective mutations occurred at different places in the coalescent history of the sampled sequences. The few association mapping studies carried out to date have thus considered polymorphisms within carefully selected candidate genes [ 14 , 73 , 80 ].

These studies have yielded promising results with hundreds as opposed to thousands of SNP markers. However, none of the genes associated with growth traits in these studies were identified here.

A higher proportion of significant associations was reported by Prunier et al. In general, the proportion of marker-trait associations detected for other quantitative traits, such as wood properties [ 72 , 81 , 82 ], adaptive traits [ 83 — 85 ], and metabolite content [ 86 ], was slightly higher than that for growth traits. The strategy used candidate gene-based vs. As pointed out by Grattapaglia et al. Indeed, marker-trait associations have been difficult to validate. In a study on P.

Moreover, the authors found discrepancies in allele effects between the discovery and validation populations for one SNP, which they suggested might be due to genotype-by-environment interactions. This low repeatability, together with the small proportion of the gene space explored here in term of both the number of genes sampled and SNPs per gene as well as the partially different genetic background used Landes and Corsican ecotypes , might account for the discrepancies between the LA and LD mapping results.

Two previous studies that also combined LA and LD mapping approaches to decipher the genetic architecture of growth in black spruce [ 16 ] and in poplar [ 15 ] reported better consistency between the locations of the QTLs identified by the two approaches. However, unlike this study, they used a two-step strategy in which the QTL regions detected in the first step were used to target specific genomic regions and to select SNPs.

Marker-trait association mapping was then performed with the selected SNPs in a population with a broader genetic background. Two different strategies have been used for LD mapping in conifers: i early studies focused on a selected set of candidate genes with the depth of SNP coverage clearly favored, resulting in the discovery of significant associations [ 16 , 73 , 82 , 88 ], ii later studies, including this one, have made use of higher-throughput genotyping platforms, resulting in a greater emphasis on the breadth of SNP coverage, i.

This approach has generally identified smaller numbers of associations, due to the low physical LD between the causal polymorphisms of markers in these outbreeding species with large effective population sizes. Current technologies have made it possible to capture and sequence the coding fraction of any conifer genome [ 89 ]. Such approaches should facilitate the discovery of causal variants within the coding sequences of genes, but it remains unclear whether increasing both marker density and the sample size of the discovery population will make it possible to account for a large proportion of the phenotypic variance of targeted traits, as shown for height in humans [ 90 ].

Sequence capture should also allow targeting the regulatory fraction of the genome, but this exploration will require a better contiguity at least within the gene space that is currently available S6 Table.

Besides, the next decade will probably see a shift from gene-to-gene to gene network approaches, with the accumulation of functional information, as well as the consideration of epigenetic mechanisms [ 91 ]. For instance, a loss of stem straightness is associated with hormone regulation in the vascular cambium and secondary wood-forming tissue [ 92 ]. A deviation from verticality results in the formation of compression wood on the lower side of the leaning stem, which tends to restore the vertical position of the stem.

The advances in high-throughput molecular technologies made over the last 15 years have led to improvements in our understanding of the interactions between hormones, transcription factors and other regulatory molecules, such as microRNAs, in secondary growth and wood formation reviewed in [ 93 ]. Integrating knowledge about the regulatory network of interacting genes into genome-wide association studies should improve our understanding of genotype-phenotype maps [ 94 ]. Comparison between the composite map of P.

The composite map is represented in blue and the parental maps of the F2 and G2 populations in green. The numbers at the top of each linkage group indicate the number of markers common to different maps for each linkage group LG. The number of markers per linkage group is indicated beneath each linkage group. The genotype of the grandparents Landes or Corsican is indicated below the corresponding class. The absolute values of markers are plotted on the 12 linkage groups of the Pinus pinaster composite map.

The following information is presented: the contig and SNP IDs, the associated dbSNP ss accession number, the genotyping platform used, the alleles considered, the designability score from Illumina ADT software , the associated linkage group, and the position on the F2 map and on the Pinus pinaster composite map when available.

Formal analysis: JB CP. Funding acquisition: CP. Project administration: CP. Resources: CP. Software: JB. Supervision: CP. Visualization: JB. Writing — original draft: JB CP. Browse Subject Areas? Click through the PLOS taxonomy to find articles in your field.

Conclusions This study provides the first comparison of LA and LD mapping approaches in maritime pine, highlighting the complementary nature of these two approaches for deciphering the genetic architecture of two mandatory traits of the breeding program. Materials and Methods Plant material, phenotyping and genotyping LA mapping populations.

LD mapping population. Association mapping strategy Population structure and kinship coefficients. Marker-trait association analysis and estimation of marker effects. Projection of LD and LA results onto the reference map of maritime pine A composite linkage map of maritime pine was established by merging 14 component maps obtained by genotyping seven mapping progenies, including the three-generation inbred F2 and outbred G2 mapping populations studied here.

Download: PPT. Table 1. Descriptive statistics for the genetic linkage map of the parental genotype H12 hybrid of the F2 mapping population. Fig 1.

Results from QTL analysis of the F2 mapping population. Table 2. Table 3. QTL results for the analysis of the G2 mapping population for height increment. Association mapping Relatedness between genotypes. Marker-trait association. Fig 4. Table 4. Discussion Most traits of interest in forest tree breeding, including height growth and wood properties, are quantitative traits with complex genetic architectures and low to medium heritabilities [ 52 ]. Conclusion and Prospects Two different strategies have been used for LD mapping in conifers: i early studies focused on a selected set of candidate genes with the depth of SNP coverage clearly favored, resulting in the discovery of significant associations [ 16 , 73 , 82 , 88 ], ii later studies, including this one, have made use of higher-throughput genotyping platforms, resulting in a greater emphasis on the breadth of SNP coverage, i.

Supporting Information.