Prunus species) breeding is currently difficult to achieve due to the polygenic nature of the most relevant agronomic traits linked to fruit quality. Genotyping by sequencing (GBS), however, provides a large quantity of useful data suitable for fine mapping using Single Nucleotide Polymorphisms (SNPs) from a reference genome. In this study, GBS was used to genotype 272 seedlings of three F1 Japanese plum  progenies derived from crossing “98–99”  with “Angeleno,” “September King,” and “September Queen” as male parents. Raw sequences were aligned to the Peach genome v1, and 42,909 filtered SNPs were obtained after sequence alignment. In addition, 153 seedlings from the “98–99” × “Angeleno” cross were used to develop a genetic map for each parent. A total of 981 SNPs were mapped (479 for “98–99” and 502 for “Angeleno”), covering a genetic distance of 688.8 and 647.03 cM, respectively. Fifty five seedlings from this progeny were phenotyped for different fruit quality traits including ripening time, fruit weight, fruit shape, chlorophyll index, skin color, flesh color, over color, firmness, and soluble solids content in the years 2015 and 2016. Linkage-based QTL analysis allowed the identification of genomic regions significantly associated with ripening time (LG4 of both parents and both phenotyping years), fruit skin color (LG3 and LG4 of both parents and both years), chlorophyll degradation index (LG3 of both parents in 2015) and fruit weight (LG7 of both parents in 2016). These results represent a promising situation for GBS in the identification of SNP variants associated to fruit quality traits, potentially applicable in breeding programs through MAS, in a highly heterozygous crop species such as Japanese plum.Marker-assisted selection (MAS) in stone fruit ( Prunus salicina Lindl.) is a crop tree species native to China. It has a diploid genome (2n = 2x = 16), unlike the European plum (P. domestica L.), which has a hexaploid genome (6n = 2x = 48), although both species belong to the Rosaceae family, subfamily Prunoideae, genus Prunus, and subgenus Prunophora .Japanese plum  Batsch] (2n = 2x = 16); sweet cherry (P. avium L.) (2n = 2x = 16); and apricot (P. armeniaca L.) (2n = 2x = 16).Marker-assisted selection (MAS) is an important biotechnological tool used in Meloidogine incognita Kofoid et White and Meloidogine arenaria  Chitwood] resistance using plum progenies and interspecific plum × almond [P. amygdalus (Batsch) syn. P. dulcis (Miller) Webb] (2n = 2x = 16) × peach progenies  technology makes it possible to generate more saturated genetic maps, thus facilitating more accurate QTL identification. This next generation technology makes it possible to build libraries of thousands of DNA fragments (tags), as has been done in maize (IBM) and barley (Oregon Wolfe Barley), for which 200,000 and 25,000 sequence tags, respectively, have been mapped. A single GBS experiment can provide 25,000 SNPs, which can be used for germplasm characterization, plant breeding progeny studies and QTL mapping  were generated in the Rinconada de Maipu Experimental Station  in 2011 by bumble bee pollination under a fine plastic mesh cage using a common female parent genotype “98–99” and three different pollen donors, including “September King,” “September Queen,” and “Angeleno.” The “98–99” genotype is a mid-early maturing selection with red skin and yellow flesh and with high organoleptic quality. “Angeleno” is a mid-late maturing cultivar with purple skin color and excellent postharvest performance, while “September King” and “September Queen” are both very late maturing genotypes.AD), firmness (FIRM), and the soluble solids concentration (SSC). RT was calculated in Julian days and FW in grams. SHP and fruit color  were visually determined and classified in numerical categories. SHP was defined according to the following scale: elongated (1), hearted (2), spherical (3), or oblate (4), while SKC was classified as green (1), yellow (2), red (3), purple (4), or black (5). FLSC was classified as white (1), green (2), yellow (3), or red (4). In addition, OVC was determined on a scale of 1–4, determined as percentages of color covering the fruit: 25% (1), 50% (2), 75% (3), and 100% (4). On the other hand, FIRM was quantified in Newton (N) by a texture analyzer  using a 7.9 mm diameter plunger, and SSC was determined using an ATAGO® hand-held refractometer calibrated as the percentage of sucrose at 20°C. IAD chlorophyll content-related maturity index was measured by DA-meter  and 1 week after harvest .Fifty-five seedlings from the “98–99” × “Angeleno” progeny were phenotyped in the years 2015 and 2016. Nine agronomic traits linked to fruit quality were evaluated including ripening time (RT), fruit weight (FW), shape (SHP), skin color (SKC), flesh color (FLSC), over color (OVC), chlorophyll index  using Restricted Maximum Likehood (REML) was calculated for determining the genetic effect of each seedling by Best Linear Unbiased Predictors (BLUPs), considering the genotype factor as a random effect. All analyses and histograms were calculated and edited using INFOSTAT v16 software with the exception of the GLMM analysis, which was calculated using an interface between INFOSTAT and R.Young leaves for each individual were collected and lyophilized for DNA extraction, which was carried out according to the CTAB procedure described by Doyle and Doyle . A totalp = 0.05 using SNPs from each parent was applied in order to discern the parentage of the 272 seedlings. A total of 161 individuals were obtained from “Angeleno,” 62 from “September King” and 15 from “September Queen,” and the genotype “98–99” was the common female. One hundred fifty-three seedlings from the “98–99” × “Angeleno” progeny were used for genetic mapping. Genotyping errors, under-calling homozygotes and heterozygotes and SNPs with over 10% missing data were eliminated prior to genetic mapping. A total of 14,697 SNPs were obtained, including common SNPs  and uncommon SNPs for “98–99”  and “Angeleno” . Genetic linkage maps for each parent were constructed by JoinMap v.3.0 software  using numeric data joined to genotype data and principal component analysis (PCA) was used for generating Manhattan plots for each trait and year. In addition, a Mixed Lineal Model (MLM) was applied using kinship data to define the relationship between individuals, because MLM sometimes has higher statistical power than GLM and may detect more true associations. The width of the QTL intervals was calculated by interval-mapping (a parametric test) and the Kruskal–Wallis test (a non-parametric test) using MAPQTL v4. LOD significance thresholds were determined for each trait and year with a test of 1,000 permutations between phenotypic and genotypic data using the “Permutation Test” function. The strongest marker cofactors of each QTL were identified by “Automatic Cofactor Selection” using SNP markers that exceeded the LOD significance threshold as the most significant marker. Linkage maps and QTL intervals were drawn using MapChart 2.3 software  between years, suggesting that these traits might be mostly influenced by genotype, rather than the year effect , all of which were significant with 05 Table . Other p05 Table  and apri05 Table  show esp05 Table .Prunus species, with an average number of reads per genotype of 1,843,261 in cherry  were generated, and over 220 million reads were read per lane, resulting in over 16 million tags for each plate or lane. This made it possible to get 2,272,102 good barcoded reads per each individual Table . SimilarFigure 3). This implies a mean genetic distance between markers of below 2 cM. As expected, the LG1 was the longest chromosome with 156.3 cM in “98–99” and 153.1 cM in “Angeleno.” LG5 was the shortest chromosome, with 61.5 cM in “98–99” and 79.8 cM in “Angeleno.” This supposes close to 45 Mbp for LG1 and below 20 Mbp for LG5  were identified by IM and KW for 2015 and 2016. The majority of these QTLs were localized in LG3, including IG6 Table , S6. S4_Regarding the SKC QTL, LG3, and LG4 were the LGs most linked to this trait for both parents and years Tables , S6. TheAD at different maturity states (IAD_1-2). For this QTL, S3_4166803 and S3_8549572 were identified as the cofactors of “98–99” and “Angeleno,” respectively. The explained phenotypic variance was over 30% for both parents  as a candidate gene encoding transcription factors of the NAC family  as transcription factors (TFs) involved in the expression of both traits , cherry (PavMYB10) and apple (MdMYB1/MdMYBA/MdMYB10). In apple, several authors claim that anthocyanin biosynthesis is regulated by MYB transcription factors that are positively correlated with red skin color . This strong effect was confirmed by the finding QTLs segregating in both parents, on two harvest seasons and located in a region that spans between positions 10,446,158 and 14,698,248 of chromosome 3. Marker showing the most significant effect in trait variation was S3_13359114, segregating only in “Angeleno” (Expl% = 41.9–85.5). Other important component of this trait corresponded to the QTLs mostly detected in “98–99” parent, and co-localizing with ripening time QTLs. Several QTLs for fruit skin color have been reported for peach in different LGs: in LG2 ; INVERTASE INHIBITOR ; and SUSY (Prupe.1G131700).FW QTL located in the LG7 means a promising QTL because usually fruit weight or fruit size are strongly influenced by climatic conditions, with a very polygenic nature, as reported in t et al.  placed dt et al.  located t et al.  located t et al.  identifit et al.  located t et al.  identifiAD_1-2), skin color (SKC), over color (OVC), soluble solids content (SSC), weight (FW), and shape (SHP). QTLs with a significant and consistent effect through years were found for RT, IAD_1-2, and SKC. Due to the complexity of these polygenic traits, it will be necessary to carry out a more detailed study in order to validate the reported QTLs, considering the phenotyping of more seedlings, in order to include more recombinants, and thus obtain QTLs covering narrower genomic regions for fine mapping of the traits; moreover, more phenotyping years must be included to have a more precise estimation of genetic and seasonal effects on these traits. Moreover, new molecular markers specifically designed for each QTL region must be included in order to assist fine mapping of genes controlling these traits. Such further studies would make it possible to use MAS as a breeding tool for Japanese plum. Finally, from a postharvest point of view, non-destructive control of the firmness of the fruit during the critical maturation period , chlorophyll degradation index (IJS: Writer and analyst of results. IP: Analyst of results and reviewer. PS: Management and technical support. PZ and CS: Laboratory technician. MA and DV: Adviser and rewiewer. DR and PM: Writer and reviewer. RI: Corresponding author and reviewer.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.


The maximum entropy (ME) method is a recently-developed approach for estimating local false discovery rates (LFDR) that incorporates external information allowing assignment of a subset of tests to a category with a different prior probability of following the null hypothesis. Using this ME method, we have reanalyzed the findings from a recent large genome-wide association study of coronary artery disease (CAD), incorporating biologic annotations. Our revised LFDR estimates show many large reductions in LFDR, particularly among the genetic variants belonging to annotation categories that were known to be of particular interest for CAD. However, among SNPs with rare minor allele frequencies, the reductions in LFDR were modest in size. Current technologies for measuring genome-wide genetic variation easily capture millions of variants across the genome. High dimensional genotyping arrays already commonly include several million variants. Through direct sequencing or by imputation against large previously-sequenced reference panels where sequencing has been performed–3, the n-8, established to control the family-wise error rate (FWER) at approximately 5% for genome-wide testing, have been in standard usage in the field of human genetics for many years[Stringent genome-wide significance thresholds of 5x10any years. Certainany years–9.However, although few positive results are now published when associations meet genome-wide significance thresholds controlling FWER, power can be severely compromised by the use of these necessarily very small significance thresholds. Substantial missing heritability has been seen for many traits and diseases, yet many true loci of small effect may not be identified due to low power studies.In order to improve power, many research groups are increasing their sample sizes through collaborations and meta-analyses (e.g.). Other Despite the potential benefits of using FDR-defined significance thresholds instead of the FWER-defined thresholds, control of type 1 errors through the use of a chosen, fixed FDR threshold may be suboptimal. In fact, the use fixed FDR thresholds incurs a bias and tends to allow a higher proportion of false positives than indicated by the selected FDR. TherefoPower remains low even when FDR methods (or LFDR methods) are used, due not only to the chosen significance thresholds, but also because the effect sizes of most genetic variants tend to be small. Furthermore, power is particularly low for variants that have lower minor allele frequencies—i.e. variants that are uncommon in the population—since the standard errors associated with the estimated effects are large due to the small number of individuals carrying the uncommon variants. Additional strategies for increasing power are, therefore, of great interest. Approaches motivated along these lines include finding subsets of genetic variants that are of particular interest, and performing significance threshold adjustments that prioritize these subsets. It has been shown that judicious use of good external annotation about the genetic variants can increase the statistical power to identify associations with low prior power, and that the significance rankings can be improved, 14. ForIn this paper, we focus on obtaining improved false discovery rate estimates for coronary artery disease (CAD) through the use of an LFDR method that incorporates external information about the genetic variants, leading to a posterior probability of non-association that varies with the annotations. Similar approaches for the FDR, i.e. methods that stratify or modify the FDR as a function of external information, have been shown to be effective in reducing the overall type 1 error rates, 19, 20.We modelled the p-values arising from the CARDIOGRAMplusC4D genome-wide association (GWA) consortium to explohttp://www.cardiogramplusc4d.org/data-downloads/) meta-analysis GWA study of coronary artery disease[The p-values from the CARDIOGRAMplusC4D Consortium (y disease (CAD) wey disease. ImputatThere are several well-known approaches to estimate LFDR–28. In tTo decide whether a separate or a combined reference class should be used, the ME method bases the LFDR estimate mostly on the separate reference class if it has enough SNPs for reliable estimation and otherwise uses the combined reference class alone , 29. ME SNPs were categorized into 53 overlapping functional categories based on the annotation data from Finucane et al.  and thei-8  known to significantly contribute to CAD heritability . Changes−16) indicating differences between the distributions. In Finally, in -4. This SNP lies in the CS gene (citrate synthase), and the gene has been previously associated with psoriasis, height and celiac disease. Similarly, the SNP with the largest LFDR decrease among the 67 SNPs highlighted in DENND5A. The naïve p-value is 8.76x10-5; LFDR falls from 18.5% to 9.5% with the ME method. Although no previous GWAS associations have been reported with this SNP, the gene DENND5A has been associated with Beta2-glycoprotein plasma levels. Finally, the SNP whose LFDR was most influenced by use of the fetal DHS mark is rs75274818 on chromosome 12 , located in SLC39A5, a zinc transporter. Again no previous GWAS associations have been reported with this SNP, but this gene has been associated with inflammatory skin disease and height.Among the selected subset of 93 SNPs from the H3K9ac annotation, rs41423244 on chromosome 12 showed the greatest LFDR decrease of 14.7%, from 24.7% to 9.998%, with a raw p-value of 1.35x10These data showed many associations with CAD as has been previously reported. HoweverAlthough the LFDR estimates changed for many SNPs, the LFDR estimates that changed most due to the use of the ME method were not those that were particularly rare. When we restricted SNPs to those with MAF <0.1 and LFDR < 0.1, it was always the SNPs near the upper MAF bound that had the largest LFDR decreases . It seemreference class problem; see [Here, we explored the effect on LFDR estimates by partitioning SNPs into reference sets using functional annotation categories pointing towards excess risk for CAD that were obtained from LD-score regression. The applem; see  for refelem; see  discusselem; see ). The malem; see , 19, or lem; see , 35. Annlem; see . Here, tThe general concept of giving different priority to different subsets of hypotheses has been previously approached in several ways. Stratified FDR estimates can be obtained by separately calculating the FDR in different classes, and then combining the results, 19. TheSome substantial decreases in LFDR were seen in our work—as large as a drop of 0.4 in the LFDR estimate. Inevitably, these very large changes tended to occur for SNPs where the original LFDR estimate was large, and hence these SNPs may not be of great interest. Nevertheless, the ME LFDR method has the potential to increase the level of interest for pursuing a SNP for further investigations by using external annotation in a statistically principled way, and we saw larger reductions in the LFDR. Since all the LFDR estimates calculated here are relevant for functional annotations shown to be significantly associated with CAD, SNPs associated with substantially reduced LFDR estimates may be worth further investigation. Therefore, we have provided a spreadsheet  for all ti, follows a chi-square distribution. Under the null hypothesis that there is no association between SNP i and the disease, the test statistic ti follows the central chi-square distribution with one degree of freedom and the corresponding density is denoted by g0(.). Under the alternative hypothesis, the test statistic ti is assumed to follow a non-central chi-square distribution with one degree of freedom and non-centrality parameter δ. We refer to the corresponding density by gδ(.). Now, letti, where π0 is prior probability of SNPs not being associated with the disease. To estimate ψi, one would have to estimate the parameters π0 and δ. If SNP i belongs to both the separate and combined reference classes, the estimation procedure can be based on either a small or a combined reference class selection. For such a SNP, working only with those SNPs that belong to the separate reference class S, one can get π0 and δ. Replacing the estimated values C, one might get π0 and δ which results in Following  the ME mS, define the following likelihood setτ = , L(τ) = Πi(π0g0(ti) + (1 − π0)gδ(ti)) is the likelihood function based on SNPs falling into the separate reference class S, a is a pre-determined threshold, and d1 and d2 are pre-specified limits of the non-centrality parameter δ, and τ, i.e. a = 3, d1 = 0.1 and d2 = 50.Following , a likelrding to , we chosLs provides a set of pairs of  that satisfy the condition π0, δ), a value of ψi can be computed. Computing ψi values for all pairs of  of Ls would provide us a range of LFDR values, say ψi, consider the following relative entropy functionThe likelihood set ψi,ME, the ME estimate, is the value of ψi that minimizes the relative entropy function Then refer to .S1 Fig(TIF)Click here for additional data file.S2 Fig(TIF)Click here for additional data file.S1 TableColumns include the SNP id (legendrs), chromosome (chr), position (pos), minor allele frequency (maf), slope coefficient (beta) and p-value (p_dgc) for association with CAD from the consortium, z-squared (z_sq), and then various LFDR estimates. They are named for the set of SNPs used  as well as for which annotation category was used . Differences between overall LFDR and maximum entropy LFDR are also provided (Diff). The gene name is provided if the SNP is in a gene.(XLSX)Click here for additional data file.


Simulation studies show that compared with existing summary statistics, our measure has improved power to detect balancing selection, and is reasonably powered in non-equilibrium demographic models and under a range of recombination and mutation rates. We compute β on 1000 Genomes Project data to identify loci potentially subjected to long-term balancing selection in humans. We report two balanced haplotypes—localized to the genes WFS1 and CADM2—that are strongly linked to association signals for complex traits. Our approach is computationally efficient and applicable to species that lack appropriate outgroup sequences, allowing for well-powered analysis of selection in the wide variety of species for which population data are rapidly being generated.Balancing selection occurs when multiple alleles are maintained in a population, which can result in their preservation over long evolutionary time periods. A characteristic signature of this long-term balancing selection is an excess number of intermediate frequency polymorphisms near the balanced variant. However, the expected distribution of allele frequencies at these loci has not been extensively detailed, and therefore existing summary statistic methods do not explicitly take it into account. Using simulations, we show that new mutations which arise in close proximity to a site targeted by balancing selection accumulate at frequencies nearly identical to that of the balanced allele. In order to scan the genome for balancing selection, we propose a new summary statistic,  Homozygotes for one allele have sickle-cell anemia, and homozygotes for the other allele have an increased risk of malaria. In contrast, heterozygotes are protected from malaria, and at most have a mild case of sickle-cell anemia   will be substantially increased if selection is maintained long enough . This elspecies) .This deviation in the site frequency spectrum has been harnessed to identify signals of balancing selection in population data, genome-wide. These methods include Tajima’s D , which dIf the selection began prior to the divergence of two species, then both species can share the balanced haplotype or variant . These sMore powerful methods to detect balancing selection have been developed, though challenges have limited their broader application. Despite these methodological advances, the exact frequencies of the excess intermediate frequency alleles seen under balancing selection have not been precisely quantified. The key insight motivating our work was the observation that the frequencies of these excess variants closely match the balanced allele’s frequency. We confirm this signature using simulations.Motivated by this observation, and inspired by the structure of summary-spectrum based statistics , we deveWFS1 and CAMD2) with functional evidence supporting these as the target genes, as well as signals at several previously known loci.We report a genome-wide scan applying our statistic to humans using 1000 Genomes Project data , focusini.e., become a substitution). However, if the locus is under balancing selection, then the allele’s frequency can reach no higher than the frequency of the balanced allele it arose in linkage with, assuming no recombination  returns the folded allele frequency and m is the maximum possible folded allele frequency difference between the core SNP and SNP i, We then measure the similarity in frequency, id, by:To capture this signature, we derive a measurement of frequency similarity between a core variant and a second variant of interest. Let tant see  online. m, the maximum folded frequency difference possible with the core SNP, and then divide by m. This gives the fraction of the maximum folded frequency difference of the SNP under consideration compared with the core SNP. We then raise it to the power p so that we can weight variants in a nonlinear fashion with respect to this fraction. Therefore, id can range from 0 if a SNP has the maximum frequency difference with the core SNP, to 1 if SNP i is at the same frequency as the core SNP. We give guidance on the choice of p in the β is fairly insensitive to its value  or those with only neutral mutations . As a reference, we also measured the likelihood-based statistic, T2.We used forward simulations  to calcuβ, Tajima’s D and HKA use a sliding window approach, in units of base pairs, when scanning the genome, whereas T1 and T2 use the number of informative sites (polymorphisms plus substitutions). In order to make a fair comparison between these methods, we first determined the most powerful window size for each method using simulations . For the summary statistics, a 1-kb window size did well across a range of selection timings and equilibrium frequencies. This 1-kb region matches the approximate size of the ancestral region, in which there have been no expected recombination events between allelic classes (see ).ulations  online. sses see  online.. Furthermore, this roughly matches the expected number of informative sites in a 1-kb region under selection  compatible with recent human history  to detect signatures of balancing selection .We applied the unfolded version of election . We calcIn addition, we filtered out variants which did not have a folded frequency of at least 15% in a minimum of one population. The purpose of the frequency filter is to prevent false positives: we were unable to simulate balancing selection with a folded equilibrium frequency of <15%, due to the high frequency of one allele drifting out of the population. Although this phenomenon has not been described for population sizes near that of humans to our knowledge, it has been detailed for lower effective population sizes . Therefoβ scores as those in the top 1% in the population under consideration . We analyzed the autosomes and X-chromosome separately. Because our method is substantially better powered to detect older selection, we focus on signals of selection that predate the split of modern populations. For this reason, we further filtered for loci that were top-scoring in at least half of the populations tested . We focus on results of our unfolded β scan, however, we also scanned using the folded β statistic to test for robustness of our top scoring sites.We defined extreme (see ). Together, these variants comprise 2,453 distinct autosomal and 86 X-chromosomal loci, and these signatures overlapped 692 autosomal and 29 X-chromosomal genes.We identified 8,702 autosomal, and 317 X-chromosomal, top-scoring variants that were shared among at least half . These haplotypes are highly unlikely to occur by chance, unlike trans-species SNPs, which are expected to be observed in the genome due to recurrent mutations . These h(see ). Several major signals occurred on chromosome 6 near the HLA, a region long presumed to be subjected to balancing selection .Our scan identified several loci that have been previously implicated as putative targets of balancing selection see  online. election . In partelection . Severalelection , such asenotypes , and BICigmatism . We focuCADM2. This locus contains a haplotype with β scores falling in the top 0.25 percentile in 17 of the 1000 Genomes populations, and scoring in the top 0.75 percentile across all 26 populations . This site was also a top scoring SNP in the CEU population based on the T2 statistic  with eQTLs of CADM2 in numerous tissues , though notably not in brain (r2 between 0.9 and 1.0 with rs17518584), which include regions of open chromatin in Cerebellum and other cell types . WFS1 functions in protein assembly . Finally, several SNPs with regulatory potential (RegulomeDB scores of 2 b or higher) are also strongly tagged by our high-scoring haplotype (EUR r2 between 0.9 and 1.0 with rs4458523). Taken collectively, these data suggest that our haplotype tags a region of strong regulatory potential that is likely to influence the expression of WFS1.Our identified high-scoring haplotype tags several functional and phenotypic variant associations. First, one variant that perfectly tags our signature  and one with a balanced variant . In the second set, a single balanced variant was introduced at the center of the simulated region in the human population, either at the time of speciation , or 150,000 generations after speciation . The simulations then continued as normal, conditional on maintenance of the balanced SNP in the population. If this balanced variant was lost, the simulation restarted at the generation in which the balanced variant was introduced. In the second  set, no balanced variant was introduced, so all variants are selectively neutral.h and selection coefficient s. The fitness of the heterozygote is then s values: h values were paired with negative s values.Each balanced SNP had an overdominance coefficient After simulation completion, the frequency of each variant in the sampled individuals was calculated. Substitutions were defined as any variant in which the allele from the chimpanzee chromosome was not found in the sampled human individuals. For each set of balanced simulations, we define the core SNP as the variant under balancing selection. For each set of balanced simulations, we then found a corresponding set of core SNPs in our neutral simulations which were within 10% of the equilibrium frequency of the balanced variants. We then calculated the score for each statistic on these core variants. In this way, we have statistic scores for the balanced variant from each balanced simulation replicate, and a score for a neutral variant matched for similar frequency. For more details on how each statistic was calculated, see To increase simulation speed, we rescaled our simulations by a factor of 10 for specified power analyses in the supporting information ; resultsβ in 1-kb-sized windows centered around each SNP for each population.To apply our method to 1000 Genomes data, we first downloaded data for each of the 26 populations in phase 3 of the project . We then calculated allele frequencies separately for each population, and calculated β scan.Because poorly sequenced regions can artificially inflate the number of SNPs in a region, we then filtered out regions that contained one or more base pairs that were ruled as poor quality in the 1000 Genomes phase 3 strict mask file. For further confirmation that the signal was not a result of poor mapping quality, we overlapped SNPs of interest with hg19 human RepeatMasker regions, downloaded from the UCSC Table Browser on February 9, 2017. We then removed all core SNPs from consideration that were found within a repeat, similar to WFS1 and CADM2 were not the result of obvious technical artifacts. In order to do this, we used the –hardy flag in vcftools . All variants on these haplotypes had P values above Unknown paralogs or other technical artifacts could inflate the number of intermediate frequency alleles. Although the 1000 Genomes data provides strict quality filter masks, we wanted to further verify that our haplotypes of interest in vcftools , and invβ score of 47.49. This score is in the top 0.05 percentile of core SNPs in neutral simulations corresponding to an equilibrium frequency of 0.5 (The lowest autosomal significance cut-off of any population, ASW, corresponds to a y of 0.5  online.r2 of 0.9, a maximum distance of 50 kb and a minimum minor allele frequency of 5%. We then overlapped these LD proxies with GWAS hits obtained from the GWAS Catalog to get our final list of putatively balanced GWAS hits (To find top-scoring sites that are also GWAS hits, we obtained LD proxies in European populations for our top-scoring SNPs, using a cut-off of WAS hits . Gene naβ Score and distance to nearest gene.For our trSNP comparison, we used the Human/Chimp shared haplotypes from Molecular Biology and Evolution online.Supplementary DataClick here for additional data file.


FGF5 and RSPO2 genes, respectively, with only a few samples per breed. In conclusion, we demonstrated that EG-GWAS can identify loci associated with Mendelian phenotypes both within and across breeds.Genome-wide association studies (GWAS) are widely used to identify loci associated with phenotypic traits in the domestic dog that has emerged as a model for Mendelian and complex traits. However, a disadvantage of GWAS is that it always requires subsequent fine-mapping or sequencing to pinpoint causal mutations. Here, we performed whole exome sequencing (WES) and canine high-density (cHD) SNP genotyping of 28 dogs from 3 breeds to compare the SNP and linkage disequilibrium characteristics together with the power and mapping precision of exome-guided GWAS (EG-GWAS) versus cHD-based GWAS. Using simulated phenotypes, we showed that EG-GWAS has a higher power than cHD to detect associations within target regions and less power outside target regions, with power being influenced further by sample size and SNP density. We analyzed two real phenotypes (hair length and furnishing), that are fixed in certain breeds to characterize mapping precision of the known causal mutations. EG-GWAS identified the associated exonic and 3′UTR variants within the  Canis lupus familiaris is also genetically exceptional. While population bottlenecks, the popular sire effect and stringent breeding programs have led to an extreme phenotypic diversity, they resulted at the same time in a high number of genetic disorders2. Several disorders are breed-specific, others are more widely dispersed through the population. A large number of these disorders share clinical and laboratory abnormalities with human diseases and over 50% of the genetic diseases in the dog are considered to be potential models for human diseases according to the Online Mendelian Inheritance in Animals database3.Aside from its special place as a companion animal, the dog or 4. Aside from the method used, they also differ in price and initial hypothesis: while GWAS and WGS are supposed to give a general overview of the genome, WES focuses on pre-specified target regions, generally the exonic part of protein-coding regions 5.To identify the causal mutations responsible for these traits, two different approaches are often used. In genome-wide association studies (GWAS), it is generally not the causal mutation that is directly genotyped. Instead, tag Single Nucleotide Polymorphisms (tagSNPs) are genotyped. Definition wise, any variant that is in linkage disequilibrium (LD) with other variants in the neighborhood and that as such can be used to tag the mutational variability in that region, can be seen as a tagSNP. Because of the LD between a tagSNP and the causal SNP, associations between a phenotype and the causal SNP can be found, even though the causal variant itself is not genotyped. When GWAS is used that way, it is an indirect method and requires further steps downstream  to detect the causal mutation. This is in contrast to whole exome sequencing (WES) and whole genome sequencing (WGS) where one tries to identify the disease-causing mutation directly, often by the sequential use of a set of heuristic filters7. Taking advantage of this long-range LD, mapping of disease loci with GWAS becomes highly efficient, even with a relatively sparse number of tagSNPs8. As the LD is far less between breeds, fine mapping can next be achieved by combining different breeds8. Also for WES, the high degree of relatedness of dogs within breeds is beneficiary: with a limited sample size, variant reduction is substantial and should allow pinpointing the causal allele quickly9.Together with huge phenotypical consequences, the population history of dogs has also left its marks at the level of the genome: within dog breeds, LD is often extensive up to 2 Mb and far higher compared to the human genome10. Whereas WES is generally considered a direct strategy to identify causal variants, recently, an LD-based WES approach has been proposed11. In this approach, associations are detected between phenotypes and causal variants by exploiting the LD between this causal variant and WES variants discovered during sequencing. Here, WES variants are also used to tag surrounding causal variants, i.e. they can be seen as tagSNPs. In this paper, we investigate in detail the mapping power and precision of this LD-based WES approach within the Poodle breed and for fixed traits across multiple dog breeds.With the recent introduction of WES in the dog, several modern tools are currently available to identify disease-causing mutations12. The tagSNP characteristics were assessed and the LD was compared. Next, power analyses based on simulated phenotypes were conducted to analyze the influence of sample size, SNP density and causal variant location within the Poodle breed. The power of the association was also investigated for long intergenic non-coding RNAs (lincRNAs), which are regulatory regions residing outside the exome that have for example been implicated in cancer14. Finally, using real phenotypes, the mapping precision of EG-GWAS to identify variants inside the RSPO2 and FGF5 genes, associated with two coat types in dogs was investigated16. As these coat phenotypes are fixed within breeds, they demonstrate the power and precision of WES for mapping across multiple breeds.To enable a direct comparison, 28 samples from three different breeds  underwent genotyping (Illumina canine high-density (cHD) 170k Whole-Genome Genotyping BeadChip) and were also sequenced with the exome-1.0, the first canine WES design publishedA direct comparison of the physical distance of cHD tagSNPs and WES informative SNPs that passed the filters for LD calculation, shows that whereas the mean distance between WES SNPs (1 SNP every 40 kb) is larger relative to cHD SNPs (1 SNP every 17 kb), the median distance is smaller , indicating a highly skewed distance distribution Fig. , at cert17. The former seemed to be largely related to the higher number of SNPs available for cHD because the median r² became identical when the number of SNPs was stepwise subsampled until they were similar for both techniques (Suppl. Table 18.A direct comparison of the amount of LD between subsequent SNPs (expressed as r²) for cHD and WES revealed that, for the majority of regions, the r² was higher for cHD Fig. , while t2. For the Poodle, the r² was more than 0.6 at a distance of up to 5 kb, which is in the same range as the r² of 0.59, 0.62 and 0.52 at a distance of 0 to 10 kb that was found for the Golden retriever, Rottweiler and Newfoundland, respectively19. At a distance of 100 kb, the r² decreased quickly to values of around 0.2 while over a longer distance , the LD remains above background  are provided in Suppl. Fig. Power analyses, i.e. analyses that determine the probability to correctly reject the null hypothesis, were conducted to assess how power depends on the position of the causal SNP (within or outside target regions), the SNP density and sample size. Causal mutation signals were simulated according to a monogenic recessive phenotype (see methods). Based on an extensive preliminary analysis, detailed in Suppl. Data S2 and Suppl. Fig. 21.A direct comparison for a sample size of n = 16, reveals that WES and cHD differ with respect to power to detect a signal inside and outside target regions Fig. . For a sth percentile or higher i.e. ≥48 informative SNPs per 1 Mb bin) and a low-density region (15th percentile or lower i.e. ≤4 informative SNPs per 1 Mb bin). In high-density regions, WES outperformed cHD (median power of 25% relative to 13%), while the opposite happened in low-density regions , sequencing settings , and even varies widely between countries and laboratories. In more detail, at our laboratory, GWAS prices range from €200 and €500 (depending on the choice of the array), while the price range for WES goes from a little under €500 up to €1000 per sample (depending on the WES design used). As such, the relative number of samples, expressed as GWAS:WES, that can be analyzed for the same price ranges from 1:1 to 5:1. To assess both the direct effect of sample size and indirect effect of price on power, the sample size was gradually lowered for each technique while simulating autosomal recessive non-exonic phenotypes. By comparing the power difference between 16 samples and lower sample sizes within each technique separately, the effect of sample size can be assessed. By comparing the power for two different sample sizes between the two techniques, corresponding to a GWAS:WES sample ratio of interest, the indirect effect of price on power can be estimated.Firstly, when the effect of sample size is assessed for EG-GWAS, the median power remained relatively constant for 14 and 12 samples relative to the power with 16 samples Fig. . After tcis-acting regulatory elements, which typically regulate gene transcription by functioning as binding sites for transcription factors.We estimated the fraction of the genome that is in high LD with WES variants. Earlier analyses demonstrated that WES outperforms GWAS within targeted regions. For the exome-1.0, this means 53 Mb is highly accessible. As we determined that r² > 0.6 at a distance up to 5 kb, we hypothesized that within 5 kb of the target regions, the power likely remains high(er) for WES. By combining the target region size and adding 5 kb on both side of target regions with variants, we estimated that WES has a high LD for 12.6% of the genome which is a more than 6 times extension of the exome part (≈2%) directly targeted by WES. The portion under high LD varies from 6% to 22% per chromosome. As a consequence of the target definition, the regions that are likely under high LD contain all exons and their regulatory sequences including promoter regions of genes or 22. Phenotypes were simulated within lincRNAs on chromosome 1 that are by definition located outside the WES target regions. In WES, the median power was 11% with a median distance of 165 kb between the signal and the tagSNP. For cHD, the power was 48% with a median distance of only 23 kb  as described in the recent updated dog annotation kb Fig. . The dif25. Here, we used the coat patterns within 3 breeds to compare EG-GWAS and cHD for their capability and precision to detect loci associated with hair length (long hair is fixed in Golden retriever and Poodles) and furnishings (fixed in the Poodle breed)16. Given the fixation status of these traits, these well-defined phenotypes are relevant to investigate the mapping precision of an EG-GWAS approach across breeds. Furnishings is a characteristic pattern of a moustache and eyebrows and is determined by the presence of a 167 bp insertion in the 3′UTR of the RSPO2 gene, which is located at chromosome 13 and is inherited in an autosomal dominant manner16. Hair length is associated with an autosomal recessively inherited G > T variant in the FGF5 gene15. For each phenotype, two analyses were performed. Firstly, using the entire dataset of 28 dogs, the p-value and the distance between the closest significant SNP were compared for EG-GWAS and cHD. Next, the sample size was stepwise reduced to find the minimal sample size necessary to identify a significant association.Many dog breeds share mutations causing common traits and several phenotypes have been mapped by using an across breed mapping approach15. The variant was also ranked number 1 in the list of most significant SNPs. The causative variant identified by EG-GWAS is within exon 1 of FGF5, in which a highly conserved Cys is changed to Phe (Cys95Phe)16. In comparison, for cHD-based GWAS, the best SNP is significantly associated  but ranked 32th and is located 200 kb away from the causative variant in exon 1 of FGF5. For furnishing, EG-GWAS identifies associated variants  close to the causative variant. The closest associated variant (ranked 3rd) is within the 3′UTR of RSPO2 at 1.3 kb away from the causative indel16. For GWAS with cHD, the best significantly associated SNP  is ranked first and resides 25 kb away from the causative indel. For EG-GWAS, 6 dogs per breeds  was the minimum to identify significant associations for both traits8. For cHD, the minimum total sample size was also found to equal 18 for furnishing, but for hair length, the sample size of 28 was necessary. Although these results are expected as both causal variants are located in exonic regions, they clearly illustrate the high power of EG-GWAS to discover associated genes and also evidence the practical usability of EG-GWAS for traits shared across breeds without having to predetermine linked loci first and, given the low sample size, at a reasonable cost.A detailed overview of the results and Manhattan plots are provided in Table 26. In the dog, as in other species, exome sequencing has been very successful in the identification of disease causing mutations for a range of dominant and recessive Mendelian disorders31. While WES is an efficient method to analyze directly the nucleotides of a patient’s DNA to discover the genetic cause of Mendelian phenotypes, we investigated the possibility to extend the paradigm of WES in the dog by evaluating the potential of WES to detect LD-based associations.WES allows a direct assessment of the protein-coding regions inside the genome8. For WES, the number of variants is unknown a priori and less limited because, theoretically, every targeted base pair can vary. Important is also that these variants are not selected to fulfill prespecified criteria, except for a bias towards the target regions. Based on this description, the more unequal distribution of SNPs and the observed association with gene density can be expected for WES. This also explains why from all SNPs detected, only a small fraction was in common (2.1% for EG-GWAS and 1.4% for cHD).Based on the definition of association studies, the two prerequisites to conduct efficient analysis are the presence of tagSNPs and LD between the tagSNP and the causal variant. Fundamentally, there is a clear difference between GWAS and WES with respect to these tagSNPs. Typically, in GWAS, a predefined number of more or less equally spaced, informative tagSNPs are interrogated32. As an example, in this study, from the 170k SNPs, subsets of only 59%, 66% and 71% of SNPs were polymorphic (MAF > 0.05) for the population of Golden retrievers, Labrador retrievers and Poodles, respectively. For WES, genotypes can be determined at every position that is sequenced at a sufficient depth and with sufficient quality and that for each individual. This enables the use of all SNPs, even those that occur at low frequencies. Especially when rare variants for common disorders are investigated, these SNPs are highly informative33. In that aspect, it should also be kept in mind that the detection of rare variants responsible for common disorders is typically one of the weaknesses of GWAS, as rare variants are less likely to be well-associated with GWAS tagSNPs. This is a consequence of the direct relation between r² and allele frequency: by imposing MAF restrictions on the tagSNP, the allele frequency of the causal SNP also has to be within the same range to have a high r² 34. This problem increases even more when de novo variants are investigated as these will always remain undetected with GWAS35. So, while traditional GWAS generally will be less expensive, allowing more samples to be analyzed for the same cost, these limitations of traditional GWAS have to be taken into account.Although the design of the canine 170 K SNP array ensures the presence of equally spaced SNPs, it does not guarantee informativeness of all SNPs i.e. predefined SNPs may not be polymorphic  > 0.05) in the breed and/or population being investigated. In addition, rare SNPs (MAF < 0.01 or < 0.05) are sometimes removed as part of the quality control36.Overall, we have demonstrated in this study that WES is partly capable of detecting associations with causal SNPs for monogenic traits outside target regions. The power to detect these associations depends on the combined effect of several factors. Whereas an increased SNP density generally results in both a higher power and a higher r², the power also depends on the region the causal SNP is located in. For some regions, a high r² can be attained, even with a limited number of SNPs, while for others, a relatively high density and/or a close distance between causal and tagSNPs does not necessarily result in the highest power. These unexplained differences between the different signal categories  might be  related to underlying differences in gene count, GC content and several other sequence features that have been shown to be associated with r² 37, cHD is then the method of choice for complex trait mapping. However, cancer-driving mutations in promoter regions, which regulate gene expression, are located nearby exons and might be identified through LD by EG-GWAS38. In addition, newer designs such as the exome-plus have left the path of a pure protein-coding focus, resulting in exome designs that combine protein-coding regions (including UTRs) and non-protein coding regions 5. For example, 82% of the 295 lincRNAs tested in this study have an at least partial overlap with the target regions of the exome-plus, hence, the power is expected to be far higher in that case5.This study is unique and highly informative by its design as experimental data from both WES and GWAS SNP chip genotyping was available for 28 dog samples from 3 different breeds. As a consequence, GWAS and WES could each time be directly compared. Our results revealed that generally, WES outperforms GWAS inside target regions, while the opposite happens for non-target regions for monogenic traits. As complex disease loci often fall outside these protein-coding regions36. Overall power estimates either way suggest the dog should generally be well-suited to detect associations2. While power calculations for a wide range of inheritance models are beyond the scope of this comparative paper, we do provide the necessary r² estimates in supplement , it is clear that a similar analysis for the exome-plus (target size of 152 Mb) would result in far higher estimates. The same applies for novel SNP array designs with higher density that will be developed. However, increasing SNP density does not necessarily always result in an increased power: as demonstrated for several regions (e.g. chromosome 1: position 50–60 Mb), a similar or even higher r² can sometimes be reached with a smaller number of SNPs. Secondly, we deliberately focused in this study on the Poodle breed for the power analysis. This breed had the lowest r², indicating that the association between causal and tagSNPs will generally be lower compared to other canine breeds. For the majority of other breeds, the LD is more extensive (as shown for the Golden and Labrador retriever), indicating that the power will in general also be higher7. Additionally, computational methods like genotype imputation have recently been shown to be both feasible and accurate in the dog and will likely improve mapping efforts in the near future39. Genotype imputation is a method that requires a reference panel to predict additional genotypes, based on a far smaller number of actually genotyped variants39. It thus has the potential to tremendously increase the number of variants. It should however be stressed that the quality of the imputation depends strongly on the reference panel and that especially rare variants can be difficult to impute39. As such, imputation should always be performed taking these limitations into account.Whereas we analyzed the power for monogenic traits in three different categories , it is difficult to predict in which category a genetic study will fall and thus what the exact power of a study will be because generally there is no prior knowledge on the causal variant25. The benefits of such an approach were also evidenced in this study. For two phenotypes, fixed within breeds, a mapping approach across breeds was adopted. Firstly, it allowed us to restate that this approach is highly powerful, as previously described8. Secondly, it also demonstrated practically what can be expected for an EG-GWAS or GWAS approach for an exonic causal variant: for both phenotypes investigated, EG-GWAS had a high mapping precision with significant SNPs corresponding directly to or very close to the causal variant and the minimum sample size might be slightly lower. It has to be emphasized nevertheless that this does not necessarily implies subsequent steps will never be necessary: the first time EG-GWAS was used, the correct locus was significantly associated, but downstream WGS was necessary to identify the causal inversion due to its non-exonic location11. As a note of warning, especially for across breed association studies, precautionary measures to avoid spurious associations due to population stratification have to be taken40.Given the population history of the dog, a multi-breed approach has been shown to be highly amenable, if used properlyBos taurus) and the domestic pig (Sus scrofa) WES designs have been developed42. However, in both species, the LD does tend to be lower44. These results suggest that it is likely that EG-GWAS will still be possible, however, sample size estimates will probably have to be increased to get significant results. This does seem to stress again that the dog is rather unique and highly amenable for these approaches.Whereas the focus lies here on the dog, we hypothesize the use of EG-GWAS in other species as well because the prerequisites are fairly limited. As WES designs are increasingly available for a wide range of species, the main limitation is the amount of LD in the genome. For both the cow . The power to detect associations varies with SNP density and the region the causal SNP is located in, as demonstrated for autosomal recessive phenotypes. In general, WES outperforms GWAS inside target regions, while the opposite happens for non-target regions. Specifically for the exome-1.0, given its focus on protein-coding regions, this implies that cHD is the method of choice for complex disorders. Overall, we introduce here the concept of EG-GWAS based on real datasets, and provide hands on instructions and examples on how to carry out EG-GWAS-based genotype to phenotype analyses.http://dog-genetics.genouest.org), which is part of the CRB-Anim infrastructure [ANR-11-INBS-0003]. The work with dog samples was approved by the CNRS ethical board, France (35-238-13) for UMR6290 and dog owners consented to the use of data for research purposes anonymously. All methods were carried out in accordance with relevant guidelines and regulations. A paired study design was adopted in which the same 28 samples  underwent both WES and GWAS genotyping.Blood and tissue biopsy samples from dogs were collected by a network of veterinarians through the French Cani-DNA biobank Dog exome libraries were generated from standard indexed Illumina libraries using a custom Roche/Nimblegen solution-based capture library  following the protocol provided. Index-specific hybridization enhancing oligos were used.45 using BWA 0.5.9. Duplicate reads were marked with Picard (http://broadinstitute.github.io/picard/), and the data were sorted. Using the GATK 3.5-0, local realignments were generated and base quality scores were recalibrated per GATK Best Practices46.After capture, indexed libraries were pooled separately into groups of eight. Pools were run on an Illumina HiSeq2000 platform, with a target coverage of 60× depth to ensure maximal coverage of the 53 Mb targeted by the exome-1.0 WES design and high quality variant discovery. The reads were aligned to the CanFam3.1 reference genomeGATK 3.5-0 was used to realign the reads around indels and HaplotypeCaller was used to call SNPs on the resulting BAM files. The resulting VCF files were converted to PLINK (ped and map) format file using vcftools_0.1.13 and bi-allelic SNPs were considered (indels were not retained). In total, 83,869 unique SNPs were further used for Poodles, 66,809 for Labrador retrievers and 66,000 for Golden retrievers.47. The following filters were used: MAF of 0.05, Hardy-weinberg p = 0, exclude markers with >0.5 missing genotypes and less than 0.5 nonzero genotypes. The missing genotypes cut-off reflects the 5th percentile of the % genotyped distribution of the 16 Poodles to account for the higher performance variability associated with sequencing relative to GWAS assays. This cut-off accurately removed outliers as all other tagSNPs had a success rate of >90%. An in-depth discussion on the rationale of the setting applied for the Hardy-Weinberg equilibrium test is provided in Suppl. Data S352. The r² values between subsequent markers were retained and used in the downstream analysis to investigate the potential association with distance and chromosomal position. Minimum, Q1, mean, median, Q3 and maximum values were calculated for each chromosome per breed for cHD and WES separately. To allow an unbiased comparison of the r² between the three breeds, both raw r² values and r² values based on random subsampling (100 random combinations of each time 6 samples) were calculated for the Poodle. To evaluate the effect of the number of SNPs, random subsampling of the original 9541 cHD SNPs (on chromosome 1) was performed, reducing the number of SNPs from 9000 to 1500 in 6 steps of 1500. For each number of SNPs, the random subsampling and subsequent LD analysis was performed ten times. To determine the background LD, one SNP was randomly sampled from each chromosome, followed by calculating the r² between all pairwise combinations of these 38 unlinked SNPs. This random sampling was repeated 100 times and the median value of the obtained distribution was considered to represent the background r².Both the LD and power analyses were run from within R (version 3.3.1), using R-studio (version 0.99.902). The LD analysis was performed with Haploview 4.253.H1: simulation of a signal, followed by simulation under H0. To assess the power, two approaches are generally used: starting from phenotypes, genotypes can be simulated, followed by association analyses. This is however computationally demanding and requires a correct creation of the LD structure of the genome. A second option that starts from the genotype to simulate phenotypes has the advantage that it immediately can be used from existing (GWAS or WES) genotype data and that it uses as such the real LD pattern of the dog breed54. After each association test, the case-control labels were randomly permutated and the association test was performed again to evaluate the number of significant results under H0. To control the family-wise error rate (FWER), p-values were calculated with permutation . P-values ≤ 0.05 were considered significant. This principle is used in all power analyses with slight adaptations.The second procedure was used: for each SNP, the genotype distribution was determined. Under the assumption of a fully penetrant autosomal recessive disease, one homozygous genotype is considered “affected”, while the two remaining ones are considered “healthy”. To mimic a balanced case-control design of 8 cases and 8 controls, only those SNPs were selected that followed this distribution (n = 91). At that moment, a perfect association between the genotypes for that specific SNP and the phenotype has been created. This causal SNP is next omitted from the association analysis itself. The reason is that a tagSNP is typically considered not to be the causal SNP itself: leaving it in would make it a direct method instead of an indirect method that has to rely on LD to detect the association. This would bias the power comparison as one method would have the causal SNP directly interrogated, while the other method would have to rely on LD to detect the association. This is repeated for all SNPs by walking over the chromosome and results in various but at the same time balanced combinations of the original samples. For each SNP that fulfilled the balanced criterion, the association analysis was conducted with PLINK (version 1.90b.45) by applying the Cochrane Armitage trend test to detect the association between phenotype and tagSNPsth percentile or higher, threshold: ≥48 SNPs/Mb) were compared to regions with a low density of informative SNPs . The signal was each time simulated by cHD SNPs outside the regions targeted by WES, but inside the high density/low density regions, respectively.To enable an assessment of the effect of SNP density on power, regions with a high density of informative SNPs  at a physical distance between 1 bp and 20 kb, 50 and 300 kb, 500 and 800 kb and 1 to 2 Mb. Each time, the phenotype was simulated by the causal SNP and the association was tested using the tagSNP.22. To retain a sufficient number of SNPs, a wider range of cases was allowed (i.e 4 to 12 cases), which resulted in a total of 61 unique SNPs that were used for the simulation of phenotypes.To determine the power to detect signals in non-protein coding regulatory regions, cHD SNPs were identified that resided inside lincRNAs that did not overlap with any of the target regions on CFA116. The chi-squared allelic association analysis was performed with PLINK (version 1.90b.45) and Bonferroni corrected p-values were used54. For the first analysis, the entire dataset (n = 28) was used. As prior knowledge exists on the location of the causal variant, the closest significant SNP was selected and both the p-value and the distance to the causal variant were retained. The aim of the second analysis was to discover the minimum sample size. Stepwise subsampling was performed. For each sample size, 10 random combinations were selected and the corresponding GWAS and EG-GWAS datasets were used in an association study. The last sample size with at least 1 significant result was considered the minimum sample size. Across breed power studies were not possible due to the low number of SNPs that followed the expected fixed trait distribution.Two phenotypes were mapped as a proof of concept: furnishing and hair lengthThe datasets analyzed during the current study are available in the Sequence Read Archive repository, under accession number SRP106684.Supplementary Information


GL7/GW7 a cloned gene for grain dimension was found. Novel regions in chromosomes 10 and 11 were also found to be associated with cooked grain shape and raw grain width, respectively. The indel-based GWAS identified fine-mapped genetic regions GL3.1 and GWi5.1 that matched synteny breakpoints between indica and japonica. GL3.1 was positioned a few kilobases away from GS3, a cloned gene for cooked and raw grain lengths in indica. GWi5.1 found to be significantly associated with cooked and raw grain width. It anchors upstream of cloned gene GW5, which varied between indica and japonica accessions. GWi11.1 is present inside the 3′-UTR of a functional gene in indica that corresponds to a syntenic break in chromosome 11 of japonica. Our results identified novel allelic structural variants and haplotypes confirmed using single locus and multilocus SNP and indel-based GWAS.In this study, we used 2.9 million single nucleotide polymorphisms (SNP) and 393,429 indels derived from whole genome sequences of 591 rice landraces to determine the genetic basis of cooked and raw grain length, width and shape using genome-wide association study (GWAS). We identified a unique fine-mapped genetic region GWi7.1 significantly associated with cooked and raw grain width. Additionally, GWi7.2 that harbors  Oryza sativa L.) being the most important food crop for more than half of the world’s population fulfills 45–70% of the daily caloric requirement of rice consumers in Asia (http://ricepedia.org/rice-around-the-world/asia). Rice accumulated substantial genetic variation during domestication leading to differences in seed morphology. This rice domestication process that occurred in various rice-eating cultures in Asian societies has led to create substantial genetic variability between indica and japonica subspecies2. Specific allelic combinations of agronomically important genes have been selected over others that led to huge phenotypic variation for traits including grain dimensions and shape at the subspecies level8. The key grain quality traits that factor into different consumer preferences are grain length, width, and grain shape (ratio between grain length and width). Variation in indica and japonica subspecies is also evident through the specific conserved allelic variants of major genes such as grain size 3 (GS3) and grain width 5 (GW5)11. Based on observed inferences of grain dimensional characteristics, indica rice must have undergone positive selection for long grains while japonica for relatively shorter and bold grains12. In the case of tropical japonica , they have larger grains than that of temperate japonica varieties14. The genetic basis of huge variations for grain dimensions and shape at the subspecies level arose from chromosomal rearrangements and duplications resulting in altering syntenic relationship between the subspecies15.Rice  has emerged as a complementary strategy to harvest other allelic variations within the candidate genes underlying genomic regions that were implicated in preceding GWAS analyses. Hence, combining GWAS with an integrated approach of targeted haplotyping on candidate gene from TGAS is considered an effective strategy for the genetic dissection of complex quantitative agronomically important traits in rice18. Published GWAS on rice traits had always been on SNP-based genotyping data that practically missed out on the importance of capturing contributions of structural variations across sub-species in rice, which may be able to explain a fraction of missing heritability computed from mixed linear model SNP-based GWAS.The evolutionary and domestication histories of rice made it best suited for GWAS21. Hence, this study was focused on providing novel genetic information that could help explain further the variation in cooked and raw grain length, width and shape. Complementing GWAS with TGAS using high-resolution SNP- and indel-based genotype data on 591 diverse germplasm from the 3,000 Rice Genomes22 was seen as a robust and reliable analytical framework for the genetic dissection of these traits. Novel allelic variants identified from fine-mapped genetic regions on chromosomes 5 and 7 formed haplotypes that explained specific phenotypic ranges for cooked and raw grain width, while that in chromosome 11 specifically for raw grain width and chromosome 10 for cooked grain shape. Synteny between indica and japonica subspecies analyzed on the genic regions implicated through GWAS and TGAS revealed rich structural variations that deepen the genetic understanding of grain shape related traits in these pervasively cultivated rice subspecies. These haplotypes and structural variants will be useful targets for breeding programs to address the shape and dimensions of cooked rice.The grain dimensions and shape after cooking are among the major determinants of consumer preference, especially for rice-eating cultures that prefer non-bold varieties. Very limited studies have been conducted to identify the genetic causation of grain dimensions and shape in cooked rice12, the frequency distributions of the grain dimensions and shape of indica landraces was not only enriched primarily for grain length but depicted variability for favored bold grains as well. In indica accessions, cooked grain length (GLc) ranged from 7.0 to 14.0 mm and raw grain length (GL) ranged between 4.31 to 7.56 mm with the average value of 5.96. Cooked grain width (GWic) was narrower in indica ranging from 2.5 to 4.25 mm, and raw grain width (GWi) was between 1.79 to 3.09 mm with the mean value of 2.43 mm , the means across indica and japonica accessions were 3.0 and 2.85 mm, respectively  reference genome. Genetic structure and linkage disequilibrium estimation in rice germplasm panel was assessed. The mean SNP density was approximately one SNP at every 127 base pairs (or 8.053 SNPs/kb) across the rice genome. A total number of 2,260,030 SNPs were identified within indica, 1,562,078 from tropical and temperate japonica sub-groups and 2,933,037 from the population formed by merging indica and japonica genomic data (Supplementary note). All these quality-assured SNPs sets were used to calculate GWAS against cooked grain length, grain width, grain shape  and raw grain length, grain width, grain shape  within each subspecies and across the whole germplasm panel combining both subspecies. Both kinship and inferred population structure were used as covariates in a mixed linear model-based GWAS analysis. We used single-locus (SL)-GWAS approach using EMMAX for association analysis where Bonferroni corrected P-values with −log10P > 5 were used as a threshold criterion to fetch moderate to highly significant loci. Furthermore, we adopted the multi-locus (ML)-GWAS strategy by following three independent methods25. We detected the common highly significant hotspots validated using single and multi-locus methods on the characteristic genetic regions on chromosomes 3, 5, 7 and 11  associated with GL and GLc  for both GWi and GWic; GWi11.1 genetic region associated with only GWi. Detailed inferences on chromosome 3, 5 and 7 were presented together with indel results (see next sections).The high-quality re-sequencing data generated from 591 gene bank accessions composed of 11 Table . Signifis or 8.05 SNPs/kb s or 8.05 SNPs/kb GLc Figs  and S4. japonica but not for GWic  = 9.3) and snp_11_28989516 (−log10(p) = 11.36), had effect of 0.94 and 1.02, respectively    on chromosome 11 identified two novel SNPs that have highly significant associations with GWi in y Tables  and S2. y Tables  formed b8%) Fig. . Haploty0%) Fig. . Interesica Fig. .Figure 4c10.1 from chromosome 10 associated with cooked grain shape detected only in SL-GWAS method with higher significance, where a single prominent significant SNP  = 9.83) was detected in the intronic region of gene E3 ubiquitin ligase (LOC_Os10g35920). Mining the potentially causal variant in 3 K RGP data suggest a limited presence of the T allele within tropical japonica and Japx (higher ratio of GLc/GWic) in cooked rice grains (mean 3.48) than rest of the germplasm with C allele (mean GS 2.89)  Fig. .A complementary GWAS analysis that used 393,429 indels highlighted associations on chromosome 3 for GL, and chromosomes 5 and 7 for GWi Figs –S7. ThesGS3 that associated with GL in indica  ≥ 17) for GLc and GL were observed in a 0.42 Mb region (16.66 Mb – 17.11 Mb) on indica chromosome 3  that was previously reported26. Interestingly, this causal SNP was detected as the one most highly associating with both GL  = 26.23) and GLc  = 10.41), explaining mean GL of 6.5 mm and mean GLc of 11.5 mm, respectively  with high linkage disequilibrium (LD) decay in chromosome 3 downstream from ica Fig. . Furtherica Fig. . Interese 3 Fig. , mapped ly Table .Figure 5indica, a highly significant association signal (−log10(p) ≥ 18) was detected for GWi on a 0.11 Mb window (5.36 to 5.47 Mb) on chromosome 5, which was in the neighborhood region of qSW5/GW5  that causes wide and short heavy grains27. Other major effect SNPs at the promoter region of gene LOC_Os05g09520 were also found to significantly explain variation in GWi and GWic  were combined. This reflected the contribution of large effect allelic combination contributed by both subspecies  ≥ 5) were detected on chromosome 7 within-subspecies GWAS  and ], were identified to influence GWi and GWic  with indels and also confirmed to possess SNP-based haplotypes that can be used to discriminate GWi. Two additional non-synonymous SNPs and an indel in 3′-UTR detected in LOC_Os07g37920 (NAC transcription factor) were found to associate with GWi  = 4.15)  = 6.65) and cooked grain width. In addition, 10 other SNPs in untranslated and intronic regions were found in LOC_Os07g37820  Fig. . This SN15) Fig. . TGAS on820 Fig. . A totalely Fig. .Figure 6c7.1 region identified six additional loci  = 8.72)  present on intronic region of LOC_Os07g37790 (transcription regulator), positively associated with GWi  = 9.37)  a total of seven SNPs including three non-synonymous SNPs  were identified  Fig. . Low GWi37) Fig. . Similaried Fig. . These aely Fig. . The ideely Fig. . Additioely Fig. .c7.2 region had three LD blocks and matched a known gene GL7/GW7 (LOC_Os07g41200)  Fig. . By empl00) Fig. . Within nes Fig. . Upon TGnes Fig. . Throughnes Fig. . In addines Fig. .japonica and indica, respectively. We found that synteny was high for chromosomes 3, 5 and 7 with apparently several random break points occurring throughout these chromosomes  and 93-11, reference genomes of mes Fig. . Chromosmes Fig. . GL3.1, ape Fig. . Signifiion Fig. . These sGif1, GL3.1, big grain2, and short grain1, genes that were known to regulate grain size and shape in both indica and japonica28, could likely be attributed to preferential selection of alleles in both subspecies during domestication. The allelic variations in An-1, GS5, small grain11 were preferentially enriched in japonica, while GL7/GW7, small grain1, srs5 allelic variation were enriched in indica subspecies  compared to tropical and temperate japonica32. This study enabled the discovery of large numbers of SNPs and indels that differentiate the important traits especially cooked rice grain dimensions and shape that are being selected during domestication. For cooked grain, a distinct allelic variant was revealed in the intron region of gene encoding E3 ubiquitin ligase on chromosome 10, exerting substantial effect on cooked grain shape. Other E3-ubiquitin ligases were earlier revealed to regulate the grain width in rice33 and grain size in other crops34.Previously, moderate to high coverage SNP genotype data generated through genotyping by sequencing and Affymetrix array-based genotyping platforms were used to perform GWAS analysis and to decipher the genetics of rice grain lengthjaponica revealed the presence of two adjacent SNPs (7-bp apart) in the 3′-UTR region of LOC_Os11g48090, a gene that encodes helicase conserved C-terminal domain containing protein in rice , were detected in temperate japonica, and heterotic alleles T/C-C/T explaining lesser grain width (mean = 2.5 mm) abundantly represented in tropical japonica and aus  led to verify the significance of underlying target regions, GWi7.1 and GWi7.2 and simultaneously identify novel candidate genes. Furthermore, TGAS of the GWi7.1 region identified four putative candidate genes that are significantly associated with grain width. Among these, one encodes for NAC-transcription factor and three are unknown protein encoding genes that possess indel haplotypes and non-synonymous SNPs differentiating cooked and raw grain width phenotypes  influencing grain width. Employed multi-locus GWAS25 was helpful to validate novel loci identified from the genomic regions using single-locus GWAS method with a less stringent significance criterion. The present study demonstrated the power and resolution of whole genome re-sequencing to identify novel genomic variants and their haplotypes that contribute significantly in providing and fixing narrower grain width for both raw and cooked rice grains (~2.0 mm/~3.0 mm respectively).Genomic analysis on global MAGIC populations, developed specifically to explore effects of allelic recombination between dth Fig. . This repes Fig. . IdentifGW8/OsSPL16, SRS5 and TGW6 were mapped in conserved regions45. From high-resolution SNP- and indel-based GWAS, several significant associations that mapped to synteny breaks were revealed in this study. These findings show how structural variation influences grain size traits. In the indica population, the GL3.1 association signals for grain length on chromosome 3 (16.12–16.40 Mb) and GW5.1 detected on chromosome 5 (5.36–5.47 Mb) grain width were mapped to the break points. Comparative genomics to study synteny in this region unveiled extensive sequence collinearity with intermittent gaps within gene contents, intergenic regions and gene orders across indica and japonica reference genomes. This was consistent with the previous reports on rice genome diversity49. The breakage in synteny was confirmed due to several significant SNP variation and deletion detected upstream of cloned GW5 region influencing grain shape/size. The rearrangements leading to insertions and deletions caused larger synteny breakage relative to others, for instance, in case of upstream of GW5, a 1212 bp region was deleted in Nipponbare genome (japonica) corresponding to ~2300 bp fragment of Kasalath genome (indica)50. Although several important alleles explaining major QTLs for grain dimensions and shape in raw rice grains were confirmed in GS3 and GW5 genes51, this study has identified additional causal indels particularly in GW3.1. Additionally, the GWAS peak identified for GWi11.1 found only in japonica overlapped a break region.Many of the cloned size and shape related genes comprising japonica reference genome in this study to account for indel variation across japonica and indica sub-species was informative and effective in identifying novel genes located at regions where the synteny between these two subspecies breaks. For future studies, taking the subspecies level structural variations into account by mapping the reference genomes and calling SNPs using a much improved indica reference genome53 could further enhance the precision and resolution of the genome-wide mapping studies at the subspecies level. In this regard, utilization of the recently released map-based high-quality genome sequence and annotations of major varieties (Zhenshan 97 and Minghui 63) of indica as a reference54 could potentially lead to even more profound understanding of the genetics of certain traits.Using structural variants as genotype data in GWAS offered new insights to map novel alleles located in the break regions that influence grain size and shape. The use of Oryza sativa L. sub-species used in conjunction with GWAS using whole genome re-sequencing resources to identify structural variation in genes influencing cooked and raw grain size and shape in rice. Through this pipeline: (1) Significant association signals that implicated novel genes associated with cooked and raw grain width, length and shape for each subspecies were identified through SNP- and indel-based GWAS analysis; (2) Diagnostic haplotypes were defined through LD analysis and tag SNP approaches; (3) The syntenic maps were derived between the subspecies and mapped the GWAS peaks to identify genetic regions which falls in collinear break points, and conducted targeted associations to reveal novel gene based haplotypes; (4) Mining the 3000 Rice Genomes for the rare haplotypes found in this study revealed rare germplasm that fall into specific grain width and length ranges in each Oryza sativa L. sub-species. This study has built a series of genomic pipelines that expedited the identification of novel grain dimensions and shape genes and also provided a comprehensive understanding of the distribution of alleles and haplotypes that are deemed preferentially selected for grain dimensions and shape for both raw and cooked grains though the process of domestication.A novel approach implemented in the present study, where the result of comparative genomics between reference genomes of two major 22 , Laguna, Philippines  during the 2015 dry season. Complete block design was used to group lines whose days to maturity were as close as possible to prevent confounding effects. Blocks were designed to accommodate 80 accessions. These accessions were randomly positioned within their respective blocks, and the blocks randomly positioned in each of the four replicates in the case of indica, and two replicates in the case of japonica. The dimension of each rectangular block was 12.5 m × 50 m. Uniform field and crop management procedures based on IRRI standard practices were followed across all replicates. Manual harvesting was done when seeds reached the optimum moisture content (MC) (22–24%), and subsequently hand-threshed to avoid physical trauma to the seeds. Standard IRRI drying method was followed until seeds attained 12–14% MC. Seeds were then stored in brown double-layer seed paper bags at seed storage room where temperature was maintained at 18 °C.A total of 591 diverse accessions whose days to maturity do not exceed 140 were selected from the 3000 resequenced rice genomes22 Table . This pahttp://www.knowledgebank.irri.org/ricebreedingcourse/bodydefault.htm#Grain_quality.htm).Quantities of 50 g seed material from every line with independent replicates were obtained and subsequently equilibrated at room temperature before any physical analysis was done. For cooking, rice grains were cooked at 100 °C using the standard operating procedure of IRRI Grain Quality and Nutrition Service Laboratory (GQNSL). Grain size and shape were measured using the ISO 17025 certified protocols of the IRRI GQNSL that used a SeedCount SC5000 Image Analyser  files from the published 3,000 rice genomes22 were used to compile the SNPs. These VCF files contained the complete base calls across the entire genome from which SNPs and INDELS were identified. Each VCF file was filtered using VCFtools55 to keep only those base calls with a Phred score of 30 or better. After filtering, the quality assured VCF files were then merged, all SNP and INDELs were then extracted separately, saved into two different VCF files for SNPs and INDELS, and then subsequently converted into PLINK56 format .A total of 591 diverse landrace accessions composed of 324 57. An LD cut-off of 0.99 was used that resulted in the selection of 673,846 SNPs in PCA calculation. The first two principal components accounted for 42.57% of the total genetic variation. For linkage disequilibrium (LD) decay prediction, we calculated the pairwise LD of all SNPs present in the three different populations using PLINK v1.90 beta (PLINK2). Then, bins representing multiples of 50-kb distances between SNPs were formed where the mean r2 for each bin calculated using a custom PERL script and plotted. This was done for each sub-population.Principal components analysis (PCA) was performed in diversity lines using the SNPRelate package in R to detect the population structureindica and japonica subspecies. Japonica accessions accounted for 1,562,079 total numbers of SNPs from 267 individuals, while 324 indica lines accounted for 2,260,030 SNPs. A total of 393,429 indels were considered after filtering with missing rate of not more than 5% and then filtered for a minor allele frequency of at least 5%. Phenotype data was then transformed using WarpedLMM58 to satisfy the data distribution requirement of mixed linear model separately for SNPs and INDELs. WarpedLMM uses a monotonic warping function to transform the phenotype data where instead of using a static function it searches for a most suitable transformation function for the given phenotype data. EMMAX59 was used for computing the single-locus association statistics where the kinship and population structure were added as covariates into the mixed linear model for both SNPs and INDELs. The statistical model underneath the EMMAX uses variance component model as described in the Kang et al.59 (See Online Methods of the paper for the complete details) that belongs to the family of mixed linear models. This approach to GWAS effectively corrects the confounding factors such as relatedness between samples and population structure that if uncorrected would lead to spurious associations. In this model, the predictors were composed of 1.5, 2.2 and 2.9 million SNPs for japonica, indica and combined population, respectively along with the identity-by-state kinship matrix and the population structure computed using principal components analysis. The markers were considered as fixed effects, while the kinship matrix and principal components were treated as random effects. We assumed that each term is normally distributed, although EMMAX did not explicitly mention the requirement. Beta-coefficient, indicating the effect size of the marker on respective phenotype, was mentioned as ‘effect’ in the outputs of GWAS.The genotype file was filtered prior to running GWAS analysis. Using plink2, we retained individuals and SNPs that had a missing rate of not more than 5% and then filtered for a minor allele frequency of at least 5%. This filtering step resulted to a final set of 2,933,037 SNPs and 585 distinct varieties from both indica and japonica set, as well as the indica-only set, while three principal components were required for the japonica set. These decisions were based on the scree-plot derived from the PCA results. The statistical model used for GWAS was a variance component model reported in the Kang et al.59. The threshold value was set at 1.70e-08 using Bonferroni correction (or −log10(0.05/2933037) = 7.77 as shown in the Manhattan plot) for identifying the peak association signals, however, a LD-based tagged SNP criteria was followed on SNPs with −log10P > 5 for detailed analysis on raw grain phenotype. The complete parameters when clumping for SNPs and INDELs (in plink2) were –clump-p1 1e-7, –clump-p2 1e-5, –clump-kb 200, and –clump-r2 0.5. These parameters ensures that the “index” SNPs around which the “clumps” were formed must have a p-value of at most 1e-7, while those SNPs forming the clump around the index SNP must have a p-value of at most 1e-5. Due to less significant SNPs–clump-p2 parameter was considered as 0.01  in case of cooked grain width by following the clumping method outlined in our previous study60. The farthest SNP that may be clumped to the index SNP was 200 kb with an LD r2 = 0.5. All genomic positions and gene annotations were based on the Nipponbare reference genome (MSUv7). Tag SNPs were identified using Haploview for groups having an LD coefficient D’ ≥ 0.8, while haplotype blocks were also formed from the same result61. In-house R-scripts were used for creating −log10 (p) value plots and box plots for depicting phenotype distribution within the designated haplotypes. Targeted-association study was performed where only genic SNPs and those falling within 2 kb upstream and 1 kb downstream of the genic region. After TGAS we plotted the significant SNPs showing causal association with phenotype (log10(p) value). Non-synonymous SNPs were determined upon completing SNP annotation using annovar.Kinship was calculated using emmax-kin while the population structure was represented using the first two principal components. IBS matrix was used as kinship matrix. Two principal components were sufficient covariates for the combined 25, mrMLM24 and ISIS EM-BLASSO23. SNP pruning was performed on the entire SNP set (mentioned above) since memory constraint was observed with entire SNP set to run MLA tools. Therefore, a window size of 5 Kb , a step size of 5 SNPs in each step and r2 = 0.5 were used as the pruning parameters in PLINK. Finally, with the 1393842 SNPs (combined indica and japonica), 1079207 unique SNPs for indica and 586697 unique SNPs for japonica were extracted and were directly used for conducting ML-GWAS using all of the three aforementioned methods. Default critical p-value criterion was adopted as per the details mentioned in respective method. Two principal components were used for both indica specific GWAS and for the combined set of indica and japonica, whereas three principal components were used for japonica population. Manhattan plots were created using the first step result of the multi-locus association. For tabulation of the SNP loci from all of the three methods, LOD ≥ 3 were considered as threshold parameter. The genomic regions surpassing the threshold significance criteria of LOD ≥ 3 (in case of ML-GWAS) and −log10P ≥ 5 were considered as common regions between ML-and SL-GWAS methods. The genetic regions simultaneously and individually identified in ML-and SL-GWAS were further categorized in respective Tables We reanalyzed the GWAS on a same set of population earlier conducted through single-locus (SL)-GWAS, using three different multi-locus (ML) GWAS tests–FASTmrEMMA62. The log2 transform values were utilized to construct a plot. For differential expression profiling, total RNA from selected lines was extracted from developing  grains for transcriptome analyses utilizing a genome-wide microarray platform (Agilent Technologies) (methods adapted from60). The gene expression profiling was conducted by hybridizing onto a genome wide microarray slide for rice based on the manufacturer’s protocols .The data was normalized using GeneSpring GX  following quantile normalization algorithm. Lines with contrasting phenotypes were selected for expression analysis and the log2 transform value has been shown as heat map using Genesis tool63 with variance of ±3.The expression profiling across different stages of plant growth and development was determined by using Affymetrix rice genome array at public database Genevestigator64 was used to determine the enrichment of the phenotype discriminating haplotype blocks in each of the different subspecies represented in the complete 3,000 rice genomes panel. The distribution representation of the haplotype blocks in each subspecies was calculated and visualized as percentages.SNP-seek databasejaponica and indica, adapted from reference genomes of MSU version 7 and gramene database, respectively. We followed the all-to-all blastP of respective protein sequence using NCBI-BLAST-2.2.28+ tool65. A stringent criterion of e-value of e-30 was used in blast alignment of protein sequence within japonica and indica considering their genetic similarity at sub-species level. Subsequently, collinearity was established using MCScanX66 with the threshold of 10 genes constructing each collinear block. On the basis of these outcomes, colinearity was identified and represented in the form of circos67 that further overlaid with genomic region corresponding to cloned and characterized genes regulating grain size and shape and significant genomic region regulating grain width detected as an outcome of GWAS.We used reference protein sequences of Supplementary note, Supplementary Figures S1-S15, Supplementary Table S4Supplementary Table S1, Supplementary Table S2, Supplementary Table S3 and Supplementary Table S5