Imprinted genes, which are expressed in a parent of origin-specific manner, are thought to mediate the genetic priorities of each parent in pregnancy. Recently we reported that some fetal imprinted gene variants are associated with maternal glucose concentrations and blood pressures in pregnancy. We suggest that the conflict between the effects of paternal and maternal transmitted genes starts at conception and may already be evident in measures of maternal metabolism in early pregnancy, before gestational diabetes is manifest.n = 200) of women from the Cambridge Baby Growth Study using direct infusion mass spectrometry. Associations between 151 haplotype-tag fetal polymorphisms in 16 imprinted genes and lipids were determined using partial least squares discriminant analysis. Variable importance in projection scores were used to identify those lipid species that contribute most to the underlying variation in the lipid profile and the concentrations of these species tested for associations with fetal imprinted gene alleles using linear regression. In an internal Validation Cohort (n = 567 women from the same cohort) the lipid fraction was profiled using liquid chromatography-mass spectrometry and tested for associations with the same fetal imprinted gene variants as above, followed by meta-analysis of associations from the Discovery and Validation Cohorts.Lipid fractions in maternal non-fasting serum collected around week 15 of pregnancy were profiled using direct infusion mass spectrometry in a subset Discovery Cohort  and maternally-transmitted fetal FAM99A rs7131362 . This same triglyceride isoform was also associated with subsequent week 28 fasting glucose concentrations  and homeostasis model assessment of insulin resistance .The most significant associations were between a monounsaturated triglyceride (44:1) and both paternally-transmitted fetal Fetal imprinted genes may influence maternal circulating clinically relevant triglyceride concentrations early in pregnancy. H19 knockout mouse model [IGF2 gene were associated with maternal glucose concentrations in late pregnancy [The Kinship (or Conflict) Hypothesis , 2 suggese model , in humaregnancy  and GDM regnancy . None ofIn addition to the influence of the mother’s dietary intake, concentrations of triglycerides in the maternal circulation during pregnancy appear to be able to be influenced by polymorphic variation in the maternal genome (e.g. ). As welIGF2 and late pregnancy maternal glucose concentrations [Previously we have found associations between paternally-transmitted fetal trations , 9. Thispost-partum. In this cohort 96.9% of the offspring were Caucasian, 0.8% were mixed race, 0.6% were Black (African or Caribbean), 0.8% were Oriental and 0.9% were Indo-Asian.The prospective and longitudinal Cambridge Baby Growth Study recruited mothers (and their partners and offspring) attending ultrasound clinics during early pregnancy at the Rosie Maternity Hospital, Cambridge, U.K. between 2001 and 2009 –10, 16. IGF2 rs1077025 allele [H19 rs2071094 allele [n = 567), were used as an internal Validation Cohort. Clinical characteristics of participants in the Discovery and Validation Cohorts are shown in Table The current analysis was restricted to 767 women for whom we had both DNA family triad samples available and serum samples from around week 15 of pregnancy. To be of use in a range of studies, 200 of these samples (“the Discovery Cohort”) were specifically chosen to have broadly equal: (1) numbers drawn from tertiles of the venous glucose concentration 60 min. post glucose load at the week 28 OGTT, (2) proportions of pregnancies where the baby exhibited “normal”, “catch-up” or “catch-down” growth  and (3) numbers of the various combinations of compound fetal alleles formed from the paternally-transmitted 5 allele  and the 4 allele . The remDLK1, FAM99A, GNAS, GRB10, H19, IGF2, INS, KCNQ1OT1, MEST, NNAT, PEG3, PEG10, PLAGL1, SGCE, SNRPN, ZIM2) were chosen because they were all imprinted at some stage of development in certain tissues. They were also all placentally-expressed [2 > 0.8 and minor allele frequency > 0.2) from the Centre d’Etude du Polymorphisme Human population (Utah residents with ancestry from Northern and Western Europe) of HapMap Project Build 36 using Haploview [IGF2 where 11 of the tagging SNPs were identified by Rodríguez et al. [p > 0.05 using the χ2 test) and had a repeat genotyping discordancy rate of < 1.0%. For each of the 151 SNPs that were successfully designated, genotypes from both parents and their child were used to infer parental allelic transmission to the fetus according to Table Genomic DNA was extracted from blood samples or mouth swabs using an Autopure LS Machine . The 16 genes that were studied , containing the following internal standards: 1,2-di-O-octadecyl-glycero-sn-3-phosphatidylcholine, 1,2-di-O-phytanyl-glycero-sn-3-phosphatidylethanolamine, C8-ceramide, N-heptadecanoyl-D-erythro-sphingosylphosporylcholine, undecanoic acid and trilaurin on glass coated 2.4 mL deep well plates  as described [4Ac IPA:MeOH (2:1)) using a Hydra Matrix, after which the plate was sealed using Corning aluminium microplate sealing tape  and stored at − 20 °C until analysis.Samples were subjected to a balanced randomisation to achieve an approximately equal number of genotypes in each batch (2 × 68 samples and 1 × 64 samples). A serum pool was created by using 5 μL of a random subset of samples, to create the first quality control (QC1). Part of the pooled sample was diluted with phosphate buffered saline (PBS), either mixing the pooled sample 1:1 with PBS (to create QC2) or 1:4 with PBS (to create QC3). From the QC material 15 μL was aliquoted into 1.2 mL Cryovials and stored at − 80 °C prior to use. Quality assurance (QA) samples used were based on commercially available human plasma. Each batch had 6 x blanks, 6 x QAs, 4 x QC1, 4 x QC2, 4 xQC3. The extraction method for the lipids used a Flexus automated liquid handler  for pipetting. 15 μL serum samples were extracted using escribed . A 96 heAt 15 °C throughout, 5 μL of sample was dispensed into the mass spectrometer  using an Advion Triversa Nanomate  electrospray ioniser under 0.2 psi nitrogen pressure. Ionisation was achieved with a potential of 1.2 kV. Data acquisition started 20 s. after sample aspiration began. After 72 s. of acquisition in positive mode, negative mode was adopted by changing the potential to − 1.5 kV. The spray was maintained for another 66 s.Lipids were identified using a selected subset of samples analysed using a LTQ Orbitrap Velos Elite coupled to a Surveyor  LC system as described . The OrbThe maternal serum lipid profiles of the Validation Cohort were subsequently analysed by LC–MS rather than by the direct infusion mass spectrometry that was used for the Discovery Cohort , using thttp://bioconductor.org/packages/release/bioc/html/xcms.html). Additional processing was then conducted using an in-house R script (Peakpicker version 2.0) to select peaks, deisotope and annotate direct-infusion mass spectrometry data sets. Only those lipid species where more than 70% (140) of the samples contained detectable concentrations and showed a linear correlations with diluted QC range were used. Accordingly 173 lipid/triglyceride species were considered for statistical analysis.The raw mass spectrometry data were converted to .mzXML format using MS Convert, parsed to R and 50 spectra averaged per mode using XCMS  run according to the manufacturer’s instructions. Homeostatic model assessment of insulin resistance (HOMA IR) was estimated using the HOMA calculator . Body maIn the Discovery Cohort there was 80% statistical power to detect a difference of 1.37 standard deviations in lipid species concentrations for a variant with a minor allele frequency of 0.4 (α = 0.05). The equivalent value for the Validation Cohort was 1.21. There was 80% statistical power in the random-effects meta-analysis with moderate heterogeneity for a small effect size (Cohen’s d) of 0.29 (α = 0.05).Q2  was positive [https://cran.r-project.org/packages/metacor/index.html). Additional analyses were then performed to assess potential imprinting effects on the associations, by testing associations between the lipid species and the maternal genotypes, the maternal untransmitted allele and the fetal imprinted allele transmitted from the parent whose allele did not produce significant fitted models in Metaboanalyst.Lipid profiles in the Discovery Cohort were analysed by Partial Least Squares – Discriminant Analysis (PLS-DA), a multivariate dimensionality-reduction tool with awareness of class labels, using Metaboanalyst 3.0 . The peapositive . The imppositive  for rand2 or Fisher’s exact tests, or logistic regression. Associations with p < 0.05 were considered significant throughout. Apart from the use of Metaboanalyst, and the meta-analyses which were performed in R , all other statistical analyses were performed in Stata .Categorical and ordinal data were analysed using the χH19 rs7950932 and maternally-transmitted FAM99A rs7131362. Of the associations in this list, not all of which reached statistical significance, there was a predominance of associations of species with maternally-expressed fetal alleles in the Discovery Cohort analyses (p = 0.01). Due to this we also tested associations between the same species and both the maternal genotype  peak intensity , p = 3.6 × 10− 3).The associations between the peak intensities of individual lipid species  and fetal imprinted gene alleles are shown in Table ternal TG4:1 peak 10 , p = 5.7 × 10− 33, n = 1083) and HOMA IR , p = 2.7 × 10− 18, n = 1059) were also both positively associated with GDM in the Cambridge Baby Growth Study as a whole. In addition they were both positively associated with baby’s birth weight .The species most heavily represented in the list of associations with fetal imprinted gene variant alleles by the VIP scores was TG44:1), a monounsaturated triglyceride. It is also the triglyceride involved in the two most significant associations between fetal imprinted gene alleles and triglycerides in analyses of lipid species where PLS-DA analysis of the Discovery Cohort data led to significant fitted models , which may include configurations 15:0/15:0/14:1 and/or 15:1/14:0/15:0 . This li4:1, whicd allele . Howeverpression , making trations , 9. TogeFAM99A gene, and the abundance of maternal week 15 TG(44:1) again. The association was with the maternally-transmitted fetal allele, and with the associations having larger regression coefficients  than those of the equivalent associations with the maternal genotype and the untransmitted maternal alleles, it appears to be directly related to the transmitted fetal allele itself rather than the maternal gene per se. The lack of association with the paternally-transmitted allele is consistent with the fetal effect being modified by imprinting. This variant is in a chromosomal region populated by reciprocally imprinted genes a number of which are maternally-expressed [The second strongest association in our population was between fetal rs7131362, a downstream variant of the xpressed . Althougxpressed  shows a xpressed . As descEven the most significant associations found in this study should be considered preliminary in nature. The main limitation of the study is that it was only powered to detect associations with large effect sizes. Nevertheless the main associations in our population appear biologically plausible based on current knowledge in the published literature. The associations need external validation in other cohorts, rather than the internal validation that was used in this study, but they will be useful for meta-analyses once studies in other populations have been completed. Our results suggest that associations with TG(44:1) in pregnancy may be particularly important.The results of this study suggest that the interaction between the fetal imprinted genotype and maternal metabolism may be detected early in pregnancy through alterations in serum levels of clinically relevant triglycerides in the maternal circulation. This result is remarkable because it suggests that triglycerides, traditionally regarded as merely energy storage molecules, may be involved in the metabolic signalling that controls fetal development. However further validation in more highly powered studies is required to establish this.


The GAW20 group formed on the theme of methods for association analyses of repeated measures comprised 4sets of investigators. The provided “real” data set included genotypes obtained from a human whole-genome association study based on longitudinal measurements of triglycerides (TGs) and high-density lipoprotein in addition to methylation levels before and after administration of fenofibrate. The simulated data set contained 200 replications of methylation levels and posttreatment TGs, mimicking the real data set.The different investigators in the group focused on the statistical challenges unique to family-based association analyses of phenotypes measured longitudinally and applied a wide spectrum of statistical methods such as linear mixed models, generalized estimating equations, and quasi-likelihood–based regression models. This article discusses the varying strategies explored by the group’s investigators with the common goal of improving the power to detect association with repeated measures of a phenotype.Although it is difficult to identify a common message emanating from the different contributions because of the diversity in the issues addressed, the unifying theme of the contributions lie in the search for novel analytic strategies to circumvent the limitations of existing methodologies to detect genetic association. Even though genome-wide association studies (GWAS) have successfully identified novel genetic variants that confer risk to various complex disorders, the proportion of trait variance that can be explained by the identified variants remains abysmally low compared to the estimates of heritability of these traits obtained from twin studies. It has been argued that analyzing quantitative precursors of a clinical end-point trait, which carry more information on interindividual phenotypic variability compared to an often binary end-point trait, may be a more powerful strategy to unravel the genetic architecture of the underlying complex disorder . HoweverThe major statistical challenge encountered in the genetic association analyses of repeated phenotype measurements is the modeling of the phenotype values across the different time points. Cross-sectional analyses of phenotype values at different time points may not only provide inconsistent inferences but also can exacerbate the problem of multiple testing inherent in GWAS. On the other hand, a naïve multivariate analysis ignoring the correlation structure of phenotype values across the different time points may result in power loss . It is aThe GAW20 provided an excellent opportunity to explore the various statistical issues pertaining to association analyses of longitudinal phenotypes, whole-genome single-nucleotide polymorphism (SNP) data, and methylation (cytosine-phosphate-guanine [CpG]) data. The real data provided in GAW20 includes pedigrees from the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) study comprising 1105 participants and including genome-wide information on 597,145 variant sites and methylation levels at 463,995 CpG sites. Repeated measurements are available on 2 phenotypes: triglyceride (TG) levels and high-density lipoprotein (HDL) levels, measured twice each before and after the intervention of fenofibrate, a blood lipid-lowering drug. Information was also available on a few additional variables, such as age, sex, smoking status, and study center. The simulated data provided in GAW20 was created to mimic the real data and assumed 5 “major gene” causal SNP effects. Each of these effects on TG response was modeled to be proportional to the degree that the physically closest CpG site is unmethylated. Two hundred simulation replicates of posttreatment TG and methylation levels were generated under this growth-curve-based pharmacoepigenetic response model .There were 4 contributions from our group, of which 3 considered the real data while 1  analyzedA primary challenge in analyzing a phenotype in a longitudinal framework lies in the integration of the phenotype values across different time points in a multivariate phenotype vector or a reduced univariate phenotype. All the contributions used logarithmic transformations on phenotypic values to induce normality. Das et al.  considerEnvironmental confounders as well as clinically correlated endophenotypes are known to adversely affect inferences on genetic association , especiaDas et al.  and KulkThe different contributions explored contrasting statistical approaches to analyze TG levels before and after the administration of fenofibrate. The most popular approach to model the longitudinal data in the family-based framework is to use linear mixed effects models, primarily because such models can both account for relatedness within families and correct for population stratification between families via incorporation of principal components . MoreoveStrickland et al.  considerBecause contrasting statistical methodologies were applied to analyze different subsets of the GAW20 data on repeated measurements of TG and HDL levels, it was not feasible to explore the extent of similarity in the results obtained by the different investigators in the group. The phenotype definitions as well as the objectives with respect to these definitions were also not strictly comparable. Thus, it is not unexpected that the significantly associated SNPs obtained by Das et al.  and KulkA majority of the contributions , 10, 12 Although it is difficult to determine whether novel association findings obtained from real data sets are indeed true positives, the probability of the finding to be a false positive is reduced if contrasting statistical methods provide significant evidence of association in similar genomic regions in the absence of confounding effects. Because the phenotype definitions were not identical in the different contributions, it was difficult to evaluate the consistency of the different analytical strategies in yielding similar association findings. However, all contributions that analyzed the GOLDN data –12 obtaiFinally, we wish to highlight that a major issue corresponding to any novel methodology is the computational burden involved in the analysis. Even though the transmission-based test using the quasi-likelihood approach requires partitioning of large pedigrees into nuclear families, it is not computationally expensive. On the other hand, large pedigrees can be directly incorporated in linear mixed effects models that assume the genetic and phenotypic correlation structures between individuals within a pedigree, but, being likelihood-based in nature, such methods are often susceptible to inflated false positives when underlying distributional assumptions on cross-sectional as well as time-dependent correlation structures on phenotype values are violated. It may be more appropriate to use empirical thresholds  rather than asymptotic thresholds to determine the significance of a test statistic value to increase the robustness of the test. However, such a strategy may prohibitively increase the computational complexity of the methods.The common aim of the group was to explore powerful statistical methodologies for identifying genetic variants modulating triglyceride levels in a longitudinal framework. Despite the varied statistical approaches to model repeated phenotype measurements, the crucial paradigm unifying the contributions was that analyzing repeated phenotype measurements may be a more powerful strategy compared to cross-sectional analyses for identifying genetic and epigenetic factors underlying a complex trait.


Macrobrachium rosenbergii was initiated in China in 2015. In this program, the M. rosenbergii resources were widely collected from four countries, the origin of the founders was verified with 16 microsatellites and the pedigree was reconstructed, and the optimum contribution selection was used to make the mating design. In this study, we evaluated the genetic parameters and selection response for the harvest body weight (HBW) of M. rosenbergii after being communally reared for 95–109 days. The data were collected from two generations that comprised 25,212 progenies from 150 sires and 198 dams. The residual maximum-likelihood methodology was employed to evaluate the variance components, by fitting an animal model. The accuracy of estimated breeding values increased by 0.38% after pedigree reconstruction using microsatellite markers. The estimated heritability (h2) for HBW was moderate (0.212 ± 0.049) and the common environmental coefficient (c2) was low (0.063 ± 0.017) when all the data were used for the analysis. Within generations, h2 was moderate to high (0.198 ± 0.080 to 0.338 ± 0.049). c2 could only be estimated in G1, which was 0.055 ± 0.030. The average HBW of males was significantly larger than that of females (P < 0.01). h2 estimated for female HBWs were higher than that for males within generations, while h2 estimated for female HBWs were lower than that for males across generations. But they were not significantly different (P > 0.05). The genetic correlations between sexes were moderate to high within each generation (0.529 to 0.763). Two methods were used to estimate the realized response. One method was calculated from the differences between the least squares means of the selected population HBW and that of control population HBW, which was 14.01%. The other method was calculated from the differences between the EBVs of the selected population HBW and that of control population HBW, which was 11.52%. The predicted responses derived from two sets of genetic parameters acquired from within- and across- generation datasets were 11.68% and 10.67%, respectively. The present study provides valuable information for breeding programs of M. rosenbergii.A multi-trait selective breeding program of  Macrobrachium rosenbergii, is a species with great economic value in the world. In 2016, its production reached 0.23 million tons globally, with a value of more than 1.90 billion US dollars. China is an important producer of M. rosenbergii. China’s production was over 0.13 million tons in 2016, accounting for more than half of the global production [M. rosenbergii is not a native species in China, most cultured populations are wild broodstocks introduced from other Southeast Asia countries, or selected populations with narrow genetic variation. There would be a risk of introducing pathogens and inbreeding, if using such populations over the long term.The giant freshwater prawn, Salmo salar), rohu carp (Labeo rohita), Nile tilapia (Oreochromis niloticus), and pacific white shrimp (Litopenaeus vannamei), with great success [M. rosenbergii have been initiated in several countries, such as China, India, and Vietnam [M. rosenbergii was initiated in 2006. A newly selected breed named “Nantaihu No. 2” was approved by the National Certification Committee for Aquatic Varieties of China in 2009 (registration no. GS01-001-2009). The new breed brought remarkable economic and social benefits. However, for the previous breeding program of M. rosenbergii, the founder population was generally considered to have no genetic linkage, which was often not the case. Also, assortative mating design , was a common mating method to produce the next generation; however, it may not be the best method. Truncation selection based on breeding values may result in inbreeding accumulation in progeny [Selective breeding could effectively improve the production traits of farmed animals and help the animals adapt to local conditions. In aquaculture, selective breeding has been used to improve growth, disease resistance, or quality traits in a lot of species, like Atlantic salmon . PCR amplification was performed in a BIO-RAD T100™ thermal cycler with the following cyclic conditions: initial denaturation at 94°C for 5 min, followed by 30 cycles at 94°C for 30 s, 30 s for annealing at a specific temperature (O) varied from 0.120 to 0.806 while the expected heterozygosity (HE) varied from 0.681 to 0.893. The polymorphic information content (PIC) varied from 0.630 to 0.879. The PIC measures were all larger than 0.5 in this study.Pleopods were collected from 134 founder broodstocks and stored in 95% alcohol. Genomic DNA was extracted by the standard phenol and chloroform method . Primer perature , 45 s foperature . The infCervus 3.0 software was used to carry out the identity analysis for the 134 founder individuals with the likelihood-based method and the information of the 16 loci . COLONY 0 generation was established in 2016 by using an incomplete diallel crossing. The breeding design and the results of the family number for each breeding unit is shown in G0 to G1. The relative weights were 70% for individual breeding values for harvest body weight (HBW), and 30% for family breeding values for pond survival. Herein, we focus on the genetic evaluation for the harvest body weights from G0 to G1. Pond survival was estimated using a standard threshold (probit) and sire-dam model, which will be described in detail in next paper for publication.A selection index was used to rank all harvest shrimps from G1 generation was established by using an optimal contribution selection based on a selection index, with a rate of inbreeding of 0.5% [1. Within each mating group, one male was mated to 3–5 females from different families. The control population was constructed using 15 full-sib families, whose parents had the mean selection index in the G0 generation and were from different full-sib families. Not all groups can be mated successfully according to the mating design. Sometimes only one pair could succeed in a group, which lead to the number of half-sib families being less than the number of candidate sires used in two generations.G of 0.5% . 72 mati2 tank. These candidates were identified by unique color combinations of “Visible Implant Fluorescent Elastomers” (VIE) for each family. Each female prawn carrying fertilized eggs was put into a separate 170 L tank until hatching. Two thousand larvae were randomly selected from each tank and put into a 50 L culture tank. The family establishment periods ranged from 32 to 35 days in G0 and G1 . Families with pathogens were eliminated.Pathogens, including 2) and P14 (1933 m2) were utilized in the G0 generation, and P10 (1933 m2) and P11 (2000 m2) were utilized in the G1 generation. The temperature was 25 ± 6°C during the grow-out stage. Juveniles in grow-out ponds were fed with a commercial pellet containing 37% crude protein, 6% crude fat, 2% crude fiber, 2% calcium, 1% total phosphorus, 1.8% lysine and 0.6% methionine. Feeding volume accounted for 2–3% of body weight, which were fed twice a day. After 95–109 days communally rearing the two generations , except for interaction between generation and pond (P > 0.05), which was statistically significant for each sex (P < 0.001). Within each sex, different effects showed different degrees of influence, which was mostly because the classification criterions of the two sexes were different.The results of the significance test of fixed effects and covariate (age) are shown in When estimating variance components and genetic correlations between sexes, the reconstructed and unreconstructed pedigrees were used at the same time for comparisons. When calculating selective responses and making the mating design, only reconstructed pedigree analysis results were used.yijklmn is the observed HBW of the mth individual of the breeding population; μ is the overall mean; Geni (the ith generation), Sexj (the jth gender), their interactions with Speck , and Ponl (the lth pond)  are fitted as fixed effects among generations; Agem(Geni * Sexj * Speck * Ponl) is a linear covariate nested within the interaction among generation, sex and pond; am is the additive genetic effect of the mth prawn, a~ approach in ASReml 4 . The anir) of estimated breeding values before and after pedigree reconstruction was calculated using the following formula [sm is the standard error of the mth individual, fm is the inbreeding coefficient of the mth individual, and The genetic parameters before and after the pedigree reconstruction were estimated. The accuracy  was obtained from the c2) was obtained from the The phenotypic variance , Sexj(the jth gender), Ponl (the lth pond) and their interactions with Speck (the kth sex specific phenotype)  are fitted as fixed effects in G1, Agem(Sexj * Speck * Ponl) is a linear covariate, which is nested in the interaction among sex, sex specific phenotype and pond; Popj*Famn is the random effect of the nth family which is nested in the jth population; eijklmn is the random residual error of the mth individual.Two methods were used to estimate the realized response. One method was calculated from the differences between the least squares means of the selected population HBW and that of control population HBW. The other method was calculated from the differences between the EBVs of the selected population HBW and that of control population HBW Formula . The lea0 and G1 based on the best linear unbiased prediction (BLUP) by ASReml 4 software. The predicted genetic gain of HBW after one generation selection was achieved by calculating the difference of the average EBVs between G1 and G0 (including the control families in G1). The selection response was achieved by calculating the difference between the least squares means of the selected population HBW and that of the control population HBW in G1.Formula 1 was smaller than that of the base population, G0. The relatively lower stocking and harvest density and longer days for grow-out test in G0 may be responsible for this trend .The observation numbers, simple means, minimum and maximum, standard deviation, and coefficients of variation (CVs) values for HBW in each generation are shown in is trend . In G1, 0, the percentages of male specific characteristics were 2.62% for M0C , 10.24% for M1C , and 87.11% for M2C . In G1, the percentages of male specific characteristics were 6.69–7.80% for M0C, 21.99–23.69% for M1C, and 68.03–71.09% for M2C. A total of 22 males and 6 females had no record. For males, M2C had the highest mean body weight, followed by M1C and M0C.In G0 could be estimated with reconstructed pedigree but not by unconstructed pedigree. This was mostly because the pedigree reconstruction increased genetic linkages among some founders. The accuracy of estimated breeding values after and before pedigree reconstruction was 0.633 and 0.631, respectively. After pedigree reconstruction, the accuracy improved by 0.38%.The results based on reconstructed pedigrees were very close to the results of the unconstructed pedigree, and there was no significant difference . The mosP < 0.05). The common environmental coefficient of G1 was 0.055 ± 0.030. The common environmental variance in G0 was negligible. The estimated heritability for the cross-generation dataset was moderate (0.212 ± 0.049). The common environmental coefficient was a little larger (0.063 ± 0.017) than within generations.Variance components, heritability, and the common environmental effect for HBW were estimated within- and across- generations. As shown in P > 0.05). The common environmental coefficients were larger in females than in males, from both within- and across- generations.The HBW heritabilities of male and female prawns were moderate to high, ranging from 0.199 ± 0.089 to 0.491 ± 0.170. For within generation datasets, the estimated heritabilities for female HBWs were higher than that for males. For across generation dataset, the estimated heritability for female HBWs were lower than that for males. No significant differences were obtained in the heritability between male and female prawns within- and across- generations . In G1, it was not significantly different from 1 .The genetic correlations between sexes were moderate to high within each generation (0.529 to 0.763). In GThe least squares means and EBVs of HBW in selected and control populations were shown in 1 were significantly increased compared to that in G0. After performing the selection for one generation, the genetic gains of HBW were 3.17 g and 3.47 g with parameters from Set 1 and Set 2, respectively. The percentage increases were 10.67% and 11.68%, respectively. The predicted genetic gains using Set 1 were a little lower than that predicted using Set 2.The mean breeding values in GAfter pedigree reconstruction, the estimated variance components were very close to the results of the unconstructed pedigree and the accuracy of estimating breeding value has been improved 3.8%, probably because there was not enough genetic relationship among founders themselves. The meaning for pedigree reconstruction is it would play an important role in avoiding mating among full-sib individuals.M. rosenbergii across generations was 0.212 ± 0.049, which was similar to the estimate of 0.22 ± 0.056 in India [M. rosenbergii in China only achieved an HBW heritability estimation of 0.056, which was considered to be caused by low genetic variations of the foundations [M. rosenbergii [The heritability for HBW of in India  and 0.11in India . A previndations . In thisenbergii . Besidesenbergii .0, the common environmental variances could not be successfully estimated. It was most probably because there were not enough genetic ties among families. Lack of the common environmental coefficient (c2) in the linear mixed model led to a higher heritability estimation in G0. c2 was small but significantly different from zero across generations, which showed that the period of family construction between generations had a certain effect on HBW (0 was very low (3.19–3.29 ind/m2), which was far below the production density. So we increased the density in G1 (9.75–14 ind/m2). This would affect the social structure to some extent which could also be seen from the proportion of different male morphology in the two generations, but the impact on the analysis results was very limited. We analyzed the correlation among the initial stocking body weight, harvested body weight and survival rate, and found the results were very close in the two generations. It showed that the differences of density had little effect on growth, survival and other traits (In Gt on HBW . Notablyr traits .M. rosenbergii were significantly different at each generation (0 and 19.39% heavier in G1 (an average result from both selected and control populations). Previous studies also reported that male M. rosenbergii were much heavier, that is 62% [Penaeus monodon, Fenneropenaeus chinensis, and Litopenaeus vannamei [M. rosenbergii, the average HBW of males is higher than that of females, which might be because females would allocate lots of energy to ovarian development and incubation [M. rosenbergii used in the present study.The mean HBWs of male and female neration . Males wt is 62%  or 93% [vannamei –33. In Mcubation , 35. Thecubation . The relM. rosenbergii (0.278 ± 0.067) was lower than that of males (0.309 ± 0.067) although there were no significant difference. This is different from previous studies in M. rosenbergii [The HBW heritability of female ergii 0.28 ± 0.067enbergii , which wenbergii .M. rosenbergii has been reported in many studies. For males, the most common classification is divided into five distinct morphotypes: small claw, orange claw, blue claw, old blue claw, and no claw males, according to the claw color and the body size [M. rosenbergii breeding populations were often based on the above classification [M. rosenbergii was very high, and one claw accounted for about 10% of HBW. Males often face intensive social interactions in the process of communally rearing, which would lead to the loss of one or two claws. This will greatly affect the final harvest weights. In this study, one claw and no claw males accounted for 23.53% of the total number Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.


Oncorhynchus mykiss) farming is the facultative intracellular bacteria Piscirickettsia salmonis. Current treatments, such as antibiotics and vaccines, have not had the expected effectiveness in field conditions. Genetic improvement by means of selection for resistance is proposed as a viable alternative for control. Genomic information can be used to identify the genomic regions associated with resistance and enhance the genetic evaluation methods to speed up the genetic improvement for the trait. The objectives of this study were to i) identify the genomic regions associated with resistance to P. salmonis; and ii) identify candidate genes associated with the trait in rainbow trout. We experimentally challenged 2,130 rainbow trout with P. salmonis and genotyped them with a 57 K single nucleotide polymorphism (SNP) array. Resistance to P. salmonis was defined as time to death (TD) and as binary survival (BS). Significant heritabilities were estimated for TD and BS . A total of 2,047 fish and 26,068 SNPs passed quality control for samples and genotypes. Using a single-step genome wide association analysis (ssGWAS) we identified four genomic regions explaining over 1% of the genetic variance for TD and three for BS. Interestingly, the same genomic region located on Omy27 was found to explain the highest proportion of genetic variance for both traits . The identified SNP in this region is located within an exon of a gene related with actin cytoskeletal organization, a protein exploited by P. salmonis during infection. Other important candidate genes identified are related with innate immune response and oxidative stress. The moderate heritability values estimated in the present study show it is possible to improve resistance to P. salmonis through artificial selection in the rainbow trout population studied here. Furthermore, our results suggest a polygenic genetic architecture for the trait and provide novel insights into the candidate genes underpinning resistance to P. salmonis in O. mykiss.One of the main pathogens affecting rainbow trout ( Pisciricketssia salmonis, etiological agent of salmonid rickettsial syndrome (SRS). This bacterium was first identified in 1989 in Chile, in a farmed coho salmon (Oncorhynchus kisutch) population  and rainbow trout (Oncorhynchus mykiss) in several countries, such as Scotland, Ireland, Norway and Chile . To date, strategies for P. salmonis control and treatment are mainly based on vaccines and antibiotics. The effectiveness of both approaches has not been adequate  segregating along the genome of several animals, including aquaculture species . SNP arrP. salmonis in rainbow trout populations. Therefore, the main objectives of the current study were to i) identify genomic regions associated with resistance to P. salmonis in a farmed rainbow trout population, and ii) identify candidate genes associated with the trait.To date there are no studies aimed at identifying genomic regions or candidate genes associated with resistance to Oncorhynchus mykiss) broodstock (year-class 2011), owned by Aguas Claras  and belonging to a genetic improvement program run by Aquainnovo . This population was artificially selected for growth, appearance-related traits and carcass quality for three generations. For a detailed description about rearing conditions and population management please see The population used in this study was comprised by a rainbow trout , Infectious Pancreatic Necrosis virus (IPNV), and Renibacterium salmoninarum, and culture for Flavobacterium spp. Later, a total of 2,130 juveniles , were intraperitoneally (IP) injected with 0.2ml of a lethal dose (LD50) of a LF-89 strain of P. salmonis inoculum. Post injection, fish were equally distributed into three different tanks, considering similar family distribution into each replicate (with 5 to 9 fish per family in each tank). Environmental parameters were measured throughout the experiment and the challenge continued until the mortality curve showed a plateau. Daily mortality was recorded, and body weight was measured for each fish at time of death or at the end of the experiment (FW). Surviving fish were killed and body weight was also recorded. Fin clips from all fish were sampled and stored in 95% ethanol at -80° until they were genotyped.Fish from 105 full-sib families  with an average weight of 7.0 ± 1.5 g, were PIT-tagged for individual traceability of families. After tagging, fish were maintained in a single tank until they were transferred to Aquainnovo’s Aquaculture Technology Center Patagonia in August 2012. Fish were acclimated for 20 days in a 15mTM myDesignTM) developed by the National Center for Cool and Cold water Aquaculture at USDA and Aquagen , following the manufacturer’s instructions. Genotyping was performed using a commercial 57K SNP array (Affymetrix AxiomP < 1x10−6). Individuals with a call rate lower than 0.90 were also removed from further analyses.Quality control (QC) was assessed through Affymetrix’s Axiom Analysis Software, using default settings. Then, a second QC using Plink software , measured in days, with values ranging from 1 until the end of challenge test. Additionally, resistance to P. salmonis was also defined as binary survival (BS), with a value of 1 or 0 based on if the fish died or survived until the end of the challenge.Resistance to P. salmonis. This approach considered fish with both phenotypes and genotypes and also individuals with phenotypes but no genotypes in the analysis (y is the vector of phenotypes (for TS and BS), a is the vector of random animal effects (including the genotype information), e is the vector of residuals, and X and Z are the incidence matrices for fixed and random effects, respectively.A single-step GWAS (ssGWAS) analysis was performed to identify genomic regions associated with resistance to a and e are estimated as follow:I is the identity matrix and The variance of A linear model (AIREML) and a threshold model (THRGIBBS1F90) were used for TD and BS, respectively. Both models were fitted using BLUPF90 programs  explained by windows of 20 adjacent SNPs. Then, if a 20 SNP window explained more than 1% of the PGV, we considered that region as associated with resistance to Oncorhynchus mykiss reference genome (GCA_002163495.1). The criteria for selecting candidate genes lies in the function of the protein that encodes each gene found, mainly related to immune response, DNA repair, stress response and similar pathways.The candidate genes were identified by searching 500kb up and downstream from the SNP explaining the highest PGV within each associated window. For this purpose, we used the last version of the 2) among SNP genotypes. The pair-wise LD was estimated based on the formula proposed by Hill and Robertson (1968) and estimated for each SNP pair. Based on the physical distance among these SNP pairs, we created bins of 100kb. The decay and extent of the LD was visualized by plotting the mean r2 for each bin, from 0 to 10Mb.Whole genome linkage disequilibrium (LD) was calculated using Plink v 1.09 and measured as Pearson’s squared correlation  was estimated as the average rOncorhynchus mykiss genome are available from the online repository figshare (https://doi.org/10.6084/m9.figshare.8146076). Table S1 containing all the genes located within 1Mb windows surrounding the SNPs explaining the highest proportion of genetic variance is available at the same link mentioned above.Raw genotypes, phenotype data and the location of each SNP used in this study in the P. salmonis measured as TD and BS, and for FW are shown in P. salmonis among families in the rainbow trout population. Cumulative mortality within each replicate tank was 59.4, 65.1 and 64.7%. Mortality peaked on days 12, 15 and 19 post injection. Average final body weight was 173.80 (SD = 52.27) g. This trait ranged considerably among challenged fish, with a minimum of 46.10g and maximum 448g.Summary statistics for resistance to Variance components for TD and BS are shown in P. salmonis measured as TD and BS. We identified four genomic regions associated with resistance as TD. These regions were located on Omy03, Omy14, Omy24 and Omy27. For BS, we identified three genomic regions associated with the trait. These were found on Omy05, Omy27 and Omy30. The genomic region located on Omy27 was found to be associated with resistance to P. salmonis for both TD and BS. In both cases, this common genomic region explains the highest proportion of genetic variance for each trait, with 2.4 and 1.5% for TD and BS, respectively. The SNP explaining the highest proportion of the genetic variance (Affx-88923370) is the same for both TD and BS . A total of 26,068 SNPs remained in the set for further analyses (∼64.68%). D and BS .O. mykiss reference genome (GCA_002163495) we identified candidate genes associated with resistance to P. salmonis. Using the Omy03 for TD, we found Gluthatione S-transferease kappa 1 (gstk1) and Interleukin-11 (il11). These genes are involved in the response to oxidative stress and the immune response to bacterial infections, respectively . On Omy14, we found the Toll-like receptor 4 (tlr4) gene, which has been suggested to act as a bacteria sensor , which is part of a broad-spectrum protease inhibitor, and it has been suggested to play a role in the defense against Cryptobia salmositica in rainbow trout (Zuo and Woo 1997).Among the candidate genes flanking the most important SNP on a sensor . On Omy2POU class 2 associating factor 1 (pou2af1), which has been described as a coactivator of transcription factors that regulate immunoglobulin expression of B cells (Teitell 2003), and NF-kappa-B inhibitor zeta-like (nfkbiz), a regulator of pathogen recognition, phagocytosis and production of cytokines by dendritic cells .Also, we found Omy05 we found fas ligand (faslg), whose protein has been suggested as an important mediator of anti-bacterial innate immune response, by inducing apoptosis of target cells and recruiting phagocytic cells . On the same chromosome we found Peroxiredoxin-6-like (prdx6), one of the six different isoforms that conforms the peroxiredoxins group, which are antioxidants proteins that protect cells from oxidative damage and is likely to be involved in protective response against a bacterial infection in Scophthalmus maximus .For BS, on Omy29, MAPK12 was found; previous studies described that MAPK12 is involved on the signaling pathways responsible for TNF-α secretion from rainbow trout macrophages . Glutaminase kidney isoform, mitochondrial-like (gls) was also found on Omy29, which is part of a family of enzymes that play a role in nucleotide, amino acid and urea biosynthesis .On Omy27 we found genes related with innate immune response regulation and some molecules related with metabolic processes and apoptosis. However, the SNP explaining the highest proportion of genetic variance is located within an exon of the gene Smoothelin protein 2 (Smtnl2) which remains poorly characterized both in humans and fishes, but it is believed to have a role in actin cytoskeleton organization.On P. salmonis, is shown in Table S1.The complete list of genes located within the 1Mb window flanking the SNPs explaining the highest proportion of genetic variance for resistance to P. salmonis resistance. Omy03) to 32 (Omy29). Regarding the average r2, estimations varied from 0.21 to 0.69 for almost all the associated windows. However, the genomic regions located on Omy03 and Omy14 showed a value of 0.14 and 0.18, respectively, below the 0.2 threshold suggested by  and BS (0.34). These estimates are higher than those reported in previous studies carried out for resistance to other bacterial diseases in aquaculture species, with heritabilities ranging from 0.22 to 0.38  a major genetic variability due the higher number of animals and ii) a fixation of the alleles on the coho salmon population as result of a higher number generations under artificial selection.The current rainbow trout population was founded by using 18 sires and 48 dams in 2005 . Thus, it is expected that, when comparing genome-based Best Linear Unbiased Predictor (GBLUP) method against a Bayesian approach (e.g. Bayes C), the former would show increased accuracy of the estimated breeding values over the latter . When 20K and 27K were used, Bayes C outperformed GBLUP accuracies. The authors suggested that this could be due to an oligogenic architecture of the resistance trait, or that Bayes C had higher effectiveness in capturing the linkage disequilibrium between the SNPs and a QTL when more SNPs were used.The effect of the genetic architecture of a trait (among other variables) on the accuracy of breeding values obtained through genomic selection (GS) is widely known . PreviouOmy27). The relatively low correspondence between the two traits in terms of the identified QTL might be partially explained based on the different approaches used for estimation of QTL. While for TD we fitted a linear model, for BS we fitted a threshold model. Thus, QTL detected in the observed scale in the case of TD might not be detected on the underlying scale for BS and vice versa. However, there are several genomic regions in common  below the 1% threshold for TD and BS, and other in which the threshold was surpassed only in one trait . Thus, increasing the statistical power might allow increasing the number of common regions associated to P. salmonis resistance.Although a high genetic correlation between TD and BS was found, only one common genomic region surpassed the 1% threshold for both traits  when using 1500 genotyped animals, indicating a high correspondence between true and estimated QTL. Here, we used 2047 fish with genotypes, which may be generating a similar or somewhat higher statistical power and precision than the one presented by m = 8 SNPs and decreased sharply when m = 40 SNPs. When m = 16 SNPs the estimated accuracy reached up to 0.80 ± 0.03. Thus, we assume that the likelihood of the identified QTL being true positives can be also considered high (∼0.80). Furthermore, P. salmonis.As showed by P. salmonis uses clathrin for internalization and then the actin cytoskeleton for vacuole generation , which is a member of the glutathione S-transferase family (GST), involved in cellular detoxification, and expressed in cells to reduce oxidative stress-related damage , a member of the TNF superfamily. The Fas/FasL pathway is essential for immune system regulation, including apoptosis induced by T cell activation and cytotoxic T lymphocytes , which belongs to the phosphatidylinositol family (ptdlns)  . The latptdlns 5 .nlr family card domain containing 3 (nlrc3). Previously, nlrc3 in rainbow trout in response to bacterial lipopolysaccharides (lps), specifically in the skin, liver and gills. This pattern has also been observed in Atlantic salmon during an infection with P. salmonis  is also involved in the immune response, transporting cytosolic peptides generated by the proteasome to load on MHC class I , which negatively regulates tap; generating a reduction in immune response efficiency . However, it should be noticed that these values comprises a 1Mb window-size. Similarly, We expect that in the near future, the identification and validation of causative mutations affecting some of the candidate genes presented here, by means of functional studies, will provide a better understanding of resistance against this and other infectious diseases in rainbow trout and other salmonid species. These studies will be facilitated through international collaborative initiatives such as the Functional Annotation of All Salmonid Genomes, FAASG (P. salmonis in a farmed rainbow trout population. Genes likely related with resistance were identified close to SNPs explaining the highest proportion of genetic variance. Furthermore, we identified one common genomic region associated with resistance using both a linear and binary trait. Our results show that this trait is controlled by multiple genes each with a small effect. Therefore, a genomic selection approach is suggested as the best method to improve this trait by means of artificial selection.To the best of our knowledge this is the first report identifying candidate genes related to resistance to


Manihot esculenta) is among the most important staple foods in the world, sustaining hundreds of millions of people in the tropics, especially in sub-Saharan Africa. Widespread genotyping makes cassava a model for clonally propagated root and tuber crops in the developing world, and provides an opportunity to study the modern benefits and consequences of historical introgression. We detected large introgressed Manihot glaziovii genome-segments in a collection of 2742 modern cassava landraces and elite germplasm, the legacy of a 1930s era breeding to combat disease epidemics. African landraces and improved varieties were, on average, 3.8% (max 13.6%) introgressed. Introgressions accounted for a significant  portion of the heritability of tested traits. M. glaziovii alleles on the distal 10 Mb of chr. 1 increased dry matter and root number. On chr. 4, introgressions in a 20 Mb region improved harvest index and brown streak disease tolerance. We observed the introgression frequency on chr. 1 double over three cycles of selection, and that later stage trials selectively excluded homozygotes from consideration as varieties. This indicates a heterozygous advantage of introgressions. However, we also found that maintaining large recombination-suppressed introgressions in the heterozygous state allowed the accumulation of deleterious mutations. We conclude that targeted recombination of introgressions would increase the efficiency of cassava breeding by allowing simultaneous fixation of beneficial alleles and purging of genetic load.Introgression of alleles from wild relatives has often been adaptive in plant breeding. However, the significance of historical hybridization events in modern breeding is often not clear. Cassava ( INTERSPECIFIC hybridization has provided an important source of adaptive genetic variation during evolution in many organisms including humans . DespiteManihot esculenta) is among the most important staple foods in the world, sustaining hundreds of millions of people in the tropics, especially in sub-Saharan Africa (http://faostat.fao.org). Cassava is a clonally propagated staple food crop, grown throughout the tropics for its starchy storage roots. In recent years, cassava has emerged from orphan-crop status as a model for plant breeding in the developing world, especially among outbreeding noncereals and vegetatively propagated root and tuber crops (Cassava (Manihot glaziovii Muell. Arg.) (The history of cassava breeding includes periodic tapping of wild congeneric relatives as sources of useful genetic variation . In the l. Arg.) .M. esculenta produced acceptable levels of resistance and storage root yield  makes cassava a model for root and tuber crops in the developing world, and provides an opportunity to study the modern benefits and consequences of historical introgression. We leveraged publicly available data (http://www.cassavabase.org) from >2742 breeding lines, land races, and local varieties, with both field phenotypes and genome-wide marker records  in African cassava breeding , a few other wild relatives, and 200 cultivated M. esculenta samples genotyped at ∼28 M SNPs. HapMapII was the basis for identifying introgression diagnostic markers (IDMs), which could be used to detect M. glaziovii introgressions in the genome of additional samples. The rest of the germplasms we analyzed were genotyped using the genotyping-by-sequencing (GBS) approach , International Institute of Tropical Agriculture , and National Crops Resources Research Institute .The genomic selection (GS) training populations for the three programs, IITA, NRCRI (NR), and NaCRRI (UG), have all been previously described  and Locai.e., three training populations plus assorted landraces) was assembled. Sites were removed if >80% had zero reads. Imputation was done with Beagle v4.0. The imputed data were filtered, keeping sites with allelic R-square (INFO/AR2 field of the VCF) ≥0.3. The imputed and filtered reference set was then used to impute the remaining IITA GS progeny.Except for the CIAT collection, our GBS samples were derived from the following pipeline: a reference panel of 4629 accessions, consisting of all African germplasm available that were not classified as GS progenies  . For those data, before imputation, genotype calls were allowed only if a minimum of one read and a maximum of 50 reads were present for each individual at each site. We removed loci with >80% missing and an average mean depth >120. We also thinned markers within 5 bp of each other. These quality control procedures were implemented using the VCFtools (v0.1.14) software package  .M. glaziovii genome segments. Our approach relies on the comparison of a set of pure  M. esculenta (Me reference) to a collection of M. glaziovii (Mg reference). From comparison of these two reference panels, we identified introgression diagnostic markers (IDM) that can be used for detecting M. glaziovii segments in an admixed sample. We used two criteria to classify a single nucleotide polymorphism (SNP) as an introgression diagnostic. Either the SNP is fixed for different alleles in the two (Me and Mg) reference panels (“Strict” IDMs), or the SNP must be fixed in the Me reference but polymorphic in the Mg sample (“GlazPoly” IDMs). The rationale for “GlazPoly” IDMs is that, if we identify alleles that are only present in the M. glaziovii panel and not in the pure M. esculenta, then finding the M. glaziovii alleles at those sites in an introgressed individual would be diagnostic, or at least contribute to our confidence, that an M. glaziovii genome segment was present.We used an ancestry informative marker approach, similar to that of M. glaziovii reference panel, we selected seven samples  that were marked as M. glaziovii in HapMapII . Specifically, we calculated the mean, median, maximum, and SD of MeanGlazDist variable for each window . We noted some outlier windows at the end of chromosomes, where all summary statistics are very close to zero. We exclude these windows from further consideration by filtering cases with MeanGlazDist <200. We then select as Me reference for each window, the 10 clones with greatest MeanGlazDist in that window.Before making decisions about which cassava clones were genetically distant enough from In order to apply IDMs for detecting introgressions in the GBS data, we restrict our downstream analyses to the 149,098 GBS sites intersecting both GBS and WGS datasets.Introgression tagging variants” below), were included only in the “all markers” analysis. Only the populations UG, GG, NR, and LG (N = 2742) and SNPs with minor allele frequency (MAF) >0.01 were included in the PCAs. PCA was conducted using the prcomp R function, with center and scale arguments set as TRUE.We conducted a principal components analysis (PCA) on three marker sets: all markers (IDM+non-IDM), IDM markers, and non-IDM markers. For this analysis, non-IDM markers that were later determined to be in strong LD with the set of IDM . Doing this enables us to interpret eigenvector coefficients (PC loadings) and SNP effects [see section on genome-wide association analysis (GWAS) below] for those markers relative to introgressions. For example, a positive loading for an IDM SNP means individuals at the high end of that principal component are more likely to have a M. glaziovii diagnostic allele at that site than individuals at the low end.The PCA and many other downstream analyses were conducted on allelic dosage matrices, where the genotypes for each individual (rows) and each SNP (columns) is represented as 0, 1, 2. Note that our dataset contains noninteger values between 0 and 2, representing the uncertainty of imputation. For the non-IDM and tag-IDM, the counted allele is the default for the M. glaziovii dosage across IDMs in 250 kb, nonoverlapping windows across the genome . The window-based dosages were used to generate genome-wide maps of introgression status for each sample and for GWAS (see below).GBS  producesMarker genetic distances were interpolated using the consensus genetic map from the M. glaziovii allele frequency and homozygosity rate. For each population, these summaries were made both on a per IDM and a per individual basis. Further, we examined differences between populations on a genome-wide basis as well as in focal introgression regions on chr. 1 (from 25 Mb+) and chr. 4 (5–25 Mb).There were eight data sets  for which we wanted to compare introgression status. We computed both the M. glaziovii and M. esculenta. Before running HAPMIX, we used HapCUT  that were most genetically distant from the eight M. glaziovii samples based on IDMs analysis. To define them, we simply summed the MeanGlazDist values across each individual’s genome and selected the top 10. They were: CW45617, CPCR15B91, CPCR27B7, CPCR27B17, MBRA685, UG08S0P003, CR4442, CPCR24B3, BRA8565, and UG08S0P005. The remaining 207 M. esculenta served as our admixed sample.We needed two reference populations: reference population 1, consisting of individuals related to the true T; (2–3) the rates at which there is copying of ancestry segments from the “wrong” population, M. esculenta x M. glaziovii hybrids in HapMapII (results not shown). For each hybrid, when tuned properly, HAPMIX should infer the presence of one M. glaziovii allele at each site . We selected the following parameter settings for HAPMIX: HAPMIX requires specification of nine model parameters: (1) the average number of generations since admixture, M. glaziovii genome segments on the germplasm. We used genomic mixed models to estimate the amount of genetic variance attributable to introgression regions relative to the rest-of-the-genome  and maxLD . For each threshold, we partitioned the SNPs into IDM, non-IDM, and tag-IDM, accordingly. We then constructed four kinship matrices using the  package : tag-IDMWe chose to use a LDscore threshold of 500, because they were more similar in the kinship they measured to the IDM than to the non-IDM . By redesignating these originally non-IDM SNP as tag-IDM and including them in the kinship matrix with IDMs, we reduced the correlation of IDM and non-IDM kinships to 0.30. We included tag-IDM in the IDM kinship matrices used in all subsequent analyses. With this procedure, we hoped to improve our ability to distinguish introgression-associated (IDM + tag-IDM) from the rest of the genetic variance (non-IDM) in key cassava traits.Datasets above) have GS programs as part of a project called Next Generation Cassava Breeding (www.nextgencassava.org). For the NR and UG populations, the trials included in our analyses comprise the GS training populations (TPs). For IITA, trials from both the GG  and LG  populations were included. There were a total of 2742 phenotyped clones in the dataset.For this study, we compiled data from 68 field trials , which were scored for nine traits. NaCRRI, NRCRI, and IITA , clones (Nclone), reps (Nrep), and the ratio of Nobs/Nclone (ObsToCloneRatio) per-Institute-per-Trial (Table S6).With the exception of the LG dataset, versions of most of these data have been analyzed in other publications , fresh root weight (RTWT), fresh shoot weight (SHTWT), root number per plot (RTNO), and harvest index (HI). MCMDS and MCBSDS are the mean of measurements taken at up to three time points throughout the season: 1, 3, and 6 months after planting (MAP). Dry matter content is the percentage of dry root weight relative to fresh root weight (RTWT). At IITA, DM was measured by drying 100 g of fresh roots in an oven whereas at NRCRI and NaCRRI, the specific gravity method (http://www.cropontology.org/ontology/CO_334) and our previous publications for additional details  (Table S6).y is a X is the n x p design matrix relating observations to corresponding p levels of the fixed-effects. The p x 1 vector β contains the fixed-effect estimates. The n x q design matrix Z related the records in y to the q levels of the random effects vector g, in this case g is the genotype (unique cassava clone) effect. The ϵ is the residual or error term.We fit linear mixed-models to the field trial data described above . The genA.mat function in the rrBLUP R package  are, in this case, often called genomic-estimated breeding values (GEBVs).We assume the following about the random effects vector: package . I is thM. glaziovii alleles Z is the design matrix for both We want to partition the total heritability For simplicity, we will sometimes refer to the two models described above as the ALL and the PARTITIONED models, respectively.Our first analysis was on a per-trial basis, where a “trial” is defined as a unique experiment planted in a single location-year.In addition to the two genetic models described above, for each trial, we fit three additional models:lm function in R (v3.4.3). For the rest of the models described above, we fit them with the mmer mixed-model solver function in the sommer (v3.1) R package .Since a trial is a single location-year, the only fixed-effect was the number of plant stands harvested (NOHAV), fit for RTWT, RTNO, and SHTWT  as null model refers to a model “nested” within the full model, i.e., with one or more random-effect dropped.In the case of nested model comparisons described below, we conducted likelihood ratio tests (LRT) to determine the significance of individual random effects. Using the likelihoods from the models describe above, we compared Each genetic model was compared to the NULL (nongenetic) model Using the results of the trial-by-trial modeling, we first flagged any trait-trial for which there was not at least one genetic model with significant We used the individual results from per-trial analyses, as described above, to identify and filter out data with very low genetic signal (Table S6). Having curated our dataset as described above, we combined data across trials (within institutes) to achieve larger sample sizes and more replications per clone.i.i.d. random effects for location-year-trial  and location-year-trial-rep .For each Trait-Institute data set, we fit the genetic models ALL, PARTITIONED and non-IDM described above for per-trial analysis. Since the grouped datasets have multiple locations, years, and replications, we added In addition to There were 38K IDMs. Any partition of the genome with this number of SNPs is likely to explain a significant portion of the genetic variance based on that fact alone. We compared the variance explained by the partition according to IDM status to three random sets of SNPs of the same sample size (Tables S7 andS8).Several previous studies have shown that the primary introgression regions on chromosomes 1 and 4 are characterized by strong, relatively long-range LD  analysis implemented in gcta (v1.25.2) , excluding those with MAF <5%. We ran the mixed-linear model association  as in the multi-trial analysis.As phenotypic responses for both GWAS, we supplied BLUPs for each clone. Using the mmer in sommer. We also fit 15 randomly selected partitions of We measured the importance of introgression regions for breeding value prediction with fivefold cross-validation. We fit the nonpartitioned (ALL), PARTITIONED, and IDMnull models again using ications  and is bThe cross-validation was set-up such that each of the trait-institute data set were divided up into 10 different random partitions of five approximately equal parts. For each model on each fivefold partition of the data, five predictions were made, in which four-fifths of the clones’ phenotypes were included, and one-fifth were left out as a test-set. Prediction accuracy was measured as the correlation among the test-set individuals of their BLUP (as used in GWAS) and their GEBV. For the PARTITIONED model, accuracy was measured for the total GEBV (IDM+non-IDM BLUPs).http://www.cassavabase.org of the number of field plots planted as of 22 January 2019 for each of the GS progeny (Table S2). We reserve phenotypic records for these field plots for a future study.We downloaded records from We extracted the dosage of deleterious alleles at 9779 of the 22,495 putative deleterious mutations identified by ftp://ftp.cassavabase.org/manuscripts/Wolfe_et_al_2019/. Supplemental Figures and Tables files are available at figshare: https://doi.org/10.25386/genetics.9897749.The authors state that all data necessary for confirming the conclusions presented in the article are represented fully within the article. Raw unimputed and imputed genotype datasets, downstream analytical results, and high resolution maps of introgressions are publically available on the Cassavabase FTP: M. glaziovii genome segments in cultivated cassava samples, we defined IDMs across the genome. When we computed genetic distances from M. glaziovii, in order to determine the panel of nonintrogressed M. esculenta, we observed elevated variability among HapMapII clones in their genetic distance from M. glaziovii on chromosome 1 from Mb 25 to the end of the chromosome . This area corresponds to a region shown previously to be segregating for M. glaziovii introgressions  or fixed in the M. esculenta reference panel, but polymorphic among the M. glaziovii . At each IDM locus, therefore, we could identify an allele that was putatively derived from M. glaziovii and would be diagnostic of introgression if found in a cultivated cassava genome (Table S1). The IDMs were distributed similarly across the genome compared to the rest of the markers in our dataset .In order to detect introgressed M. glaziovii diagnostic allele. The resulting loadings (eigenvector coefficients) for markers on PC1 (21% variance explained) are strongest for IDMs on the last 10 Mb of chr. 1, while the strongest loadings on PC2 (9%) are at IDMs spanning the majority of chr. 4  among the African breeding germplasm as a whole  and chr. 4 from 5 to 25 Mb . The genWhen we isolate the introgressions on chrs. 1 and 4, which appear to be the same as previously identified by M. glaziovii alleles per individual increased from 0.03 in LG to 0.042 in GG, and then more than doubled in the GS progeny, with C1 at 0.095, C2, and C3 at 0.12  to IITA improved varieties (GG) and three successive generations of genomic selection progeny , which descend from parents selected initially from the GG. The most notable changes we observed were on chrs. 1 and 4 . Genome-M. glaziovii haplotypes, with a mean homozygosity rate of only 1% genome-wide  as a metric of the level of advancement each progeny had attained. We found that while heterozygosity for introgressions was acceptable . Although, we note that M. glaziovii segments on Chr. 1 tend to appear smaller in the HAPMIX results.We also used HAPMIX  conducted on 2742 genotyped clones in our study populations (Table S5). To these data, we fit linear mixed-models with two random-genetic effects, kinship measured using IDM markers and kinship by non-IDM markers. The estimated genetic variances partitioned the heritability into two components: one due to introgression regions We first investigated the amount of LD between introgressed and nonintrogressed regions. Using the procedure described in the methods, we reclassified 1413 SNPs, primarily located on chr. 1 and 4 , that were more similar in the kinship they measured to the IDM than to the non-IDM . Redesignating these SNPs as tag-IDM reduced the correlation of IDM and non-IDM kinships from 0.37 to 0.30. We therefore included tag-IDM in the IDM kinship matrices used in all subsequent analyses.The second preliminary analysis we did was to analyze each trial separately, in part to check the quality of the data before combining into a larger, multi-trial analysis. Based on a likelihood ratio test, we chose to remove 31 trait-trials that did not show evidence of significant genetic variance except cassava bacterial blight and mosaic disease severity. In all of these cases, the PARTITIONED model had an AIC >2 units smaller than the nonpartitioned one. The proportion of the heritability accounted for by significant introgressions was as high as 56%  to achieve large overall sample sizes  and increase the average number of replications per clone . We fit the three models for each trait and analyzed each breeding program’s data separately. Out of 19 Trait-Institute analyses, 10 had significant genetic variance from introgressions. In fact, introgression regions appear important for every trait One-third of the SNPs in our study were classified as IDMs (including tag-IDMs). We compared the variance explained by our IDM-defined partition, to three random genome partitions of the same size (Table S7). For the random samples, the correlation of GRMs was >0.99, but was only 0.30 for the IDM-defined partition (Table S8). The IDM-defined partition explained an average of 20% of the total genetic variance, in comparison to 37% for the random partitions, which is closer to proportional with the total number of markers .Most of the cases with significant We know from previous studies in cassava, and confirm here , that the introgression regions on chromosomes 1 and 4 are characterized by strong, relatively long-range LD , described in more detail in Methods. There were Bonferroni-significant IDM and/or DoseGlaz for all traits except bacterial blight (MCBBS)  and RTNO  (Table S18). For MCBSDS, the Chr. 4 QTL includes DoseGlaz and IDM, covering most of the introgression region, from 12.6 to 23.4 Mb. For SHTWT and HI however, the region spanned only from 22.35 to 25.1 Mb and there was a single significant marker for RTNO and RTWT nearby at 17.9 Mb. Effects on chr. 4 of M. glaziovii alleles for brown streak disease appear protective . For SHTWT [units: ln(kg/plot)] and HI (units: proportion 0–1) mean M. glaziovii effects were −0.085 and 0.023, respectively. In addition, there was one DoseGlaz significant for MCBSDS on chr. 5 and one on chr. 12. The sig. DoseGlaz on chr. 12 was estimated to increase disease susceptibility with an effect-size of 1.22 . Note that RTNO and SHTWT effects are on the natural log scale.On chr. 1 between 24.0 and 31.9 Mb, significant IDM and GS is becoming an important part of modern cassava breeding  relative to the PARTITIONED and 0.005 (max 0.03) relative to the ALL models . These cFinally, we observed that the size of the impact on prediction accuracy (measured from comparing ALL and IDMnull models) scaled with the artition .M. glaziovii allelic effects; however, we found neither a beneficial effect nor a significant genetic variance for CMD. In a previous article, focused on GWAS for CMD, we noted an absence of major effect QTL other than CMD2, a dominant, possibly multi-allelic locus . Introgression-derived CMD resistance has previously been suggested to be weak (relative to CMD2), “recessive” and “polygenic”  was to combat CMD . We obseM. glaziovii segments are common in African breeding germplasm  wild ancestors; the four others were heterozygous for the same segments on chrs. 1 and 4 that the African germplasm have. To date, we have not been able to trace the pedigree or otherwise ascertain the origin of these clones.By comparison of African to Latin America clones, we believe our evidence supports the origin of the chr. 1 and 4 M. glaziovii introgressions in cassava, we estimated that recombination is reduced in the introgressed regions on chr. 1 and 4 by 14 and 71% respectively, compared to the rest of the genome [Figures S6, S9, and Table S21; The suppression of recombination, often due to structural variants like inversions, is often a consequence of hybridization between crops and their wild relatives  represent those that successfully germinated and were vigorous enough early on to warrant genotyping. We discovered that M. glaziovii homozygotes were excluded from later stage field trials early in the process (Heliconius butterflies (In clonally propagated crops, selection for advancement during variety trials is using total genetic merit rather than breeding value based on performance in a series of field trials with progressively more replicates, locations, and increasing plot size. The GS progeny that we analyzed (and thus the sample in which we observed an initial increase in  process . This in process  and wingIntrogression-associated inbreeding depression is thus a critical topic for future investigation. At present, cassava breeders are maintaining introgression heterozygosity at great cost, through a multi-stage selection process. First, favored crosses between heterozygous parents generates many unsuitable offspring, which are homozygous for introgressions and suffer some as yet unquantified inbreeding depression. Subsequently, field evaluations are required to identify and purge these individuals. We propose that targeted recombination of introgression segments would increase the rate of gain and sustainability of cassava breeding by allowing simultaneous fixation of beneficial alleles and purging of genetic load.Taken together, our results point to the continued importance of wild alleles in cassava, one of the most important staple foods in the developing world, and a model for other clonally propagated root and tuber crops. We present an example of both the benefits and consequences of historical introgression for modern crop breeding. Our methods and the breeding implications we highlight will therefore provide a valuable example for other crops.


The 100-pod fresh weight (PFW), 100-seed fresh weight (SFW), 100-seed dry weight (SDW) and moisture content of fresh seeds (MCFS) at the R6 stage are crucial factors for vegetable soybean yield. However, the genetic basis of yield at the R6 stage remains largely ambiguous in soybean.Glyma.16g018200, Glyma.16g018300, Glyma.05g243400, Glyma.05g244100 and Glyma.05g245300 were regarded as candidate genes associated with PFW and MCFS.To better understand the molecular mechanism underlying yield, we investigated four yield-related traits of 133 soybean landraces in two consecutive years and conducted a genome-wide association study (GWAS) using 82,187 single nucleotide polymorphisms (SNPs). The GWAS results revealed a total of 14, 15, 63 and 48 SNPs for PFW, SFW, SDW and MCFS, respectively. Among these markers, 35 SNPs were repeatedly identified in all evaluated environments , and most co-localized with yield-related QTLs identified in previous studies. AX-90496773 and AX-90460290 were large-effect markers for PFW and MCFS, respectively. The two markers were stably identified in all environments and tagged to linkage disequilibrium (LD) blocks. Six potential candidate genes were predicted in LD blocks; five of them showed significantly different expression levels between the extreme materials with large PFW or MCFS variation at the seed development stage. Therefore, the five genes These results provide useful information for the development of functional markers and exploration of candidate genes in vegetable soybean high-yield breeding programs.The online version of this article (10.1186/s12863-019-0737-9) contains supplementary material, which is available to authorized users. Glycine max (L.) Merr.) is a widely cultivated oil crop worldwide. Soybean seeds are used to supply edible oil and serve as a source of high-quality plant protein [h2 than does grain yield, and several quantitative trait loci (QTLs) are commonly associated with grain yield and grain moisture [Soybean  reveal the genetic basis of yield-related traits in soybean at the R6 stage and (2) provide valuable markers and candidate genes for the molecular breeding of vegetable soybean.A total of 133 soybean landraces came from the soybean mini core collection, and the soybean mini core collection were selected from 23,587 soybean germplasms in the Chinese National Soybean GeneBank. Thus the 133 soybean landraces had abundant genetic diversity and were suitable for association analysis . The 133These germplasms included abundant genetic diversity due to geographic, climatic and cultivation factors present in China and could be used for GWAS analysis. They were planted at the Jiangpu Experimental Station of the Agricultural University of Nanjing, China (32.04°N 118.63°E) in late June 2015 and 2016, according to a completely randomized block design, with two years and three replications. Planting was performed with two rows per plot and 40 plants per row, with plant spacing of 10 cm and row spacing of 50 cm.Four yield-related traits, the 100-pod fresh weight (PFW), 100-seed fresh weight (SFW), 100-seed dry weight (SDW) and moisture content of fresh seeds (MCFS), were investigated at the R6 growth stage during which the pods contain full-size green beans. At least fifty pods were harvested for each replication in each year. The pods were then weighed on the electronic scale, and PFW (g) was calculated. Next, the pod husks were stripped, and seed weight was measured to determine SFW (g). The seeds were then killed by heating at 110 °C for 30 min and dried at 65 °C to a constant weight to obtain the SDW (g). Finally, MCFS (%) was calculated using the following formula.yijk = μ + αi + βj + γkj + (αβ)ij + εijk, where μ is the overall mean, αi is the genetic effect of the ith genotype, βj is the effect of the jth environment, γkj is the random effect of the kth replicate in the jth environment, (αβ)ij is the interaction effect between the ith genotype and the jth environment, and εijk is the residual. As sources of variation, the environment, genotype, replication within environment, and genotype × environment were treated as random effects. The formula for calculating broad-sense heritability is:Statistical analyses for all traits were performed using SAS version 9.4 . Analysiσ2g is the genotypic variance, σ2ge is the genotype by environment interaction variance, α2ε is the error variance, n is the number of environments, and r is the number of replications. All of the above variance values can be calculated using the REML method for the SAS VARCOMP procedure.The association panel was genotyped using a 180 K AXIOM® SoyaSNP array , and a tWe used PLINK V1.07 to perform SNP filtering by setting the MAF to 0.2 and the call rate to 0.1. The remaining data contained 8270 SNPs, which were used to construct a population structure in STRUCTURE 2.3.4. The number of subgroups (K) was set from 1 to 6, with 4 replications. The length of the burn-in period was set to 10,000, and the number of Monte Carlo Markov Chain (MCMC) replications was set to 100,000. The suitable K in this population was determined by the log probability of the data LnP(D) and delta K. In previous studies, the mini core collection was divided into two or three distinct subgroups depending on the markers used in the tests , 27, 28.r2) for estimating the degree of LD between pairwise SNPs (MAF > 0.2) were calculated using PLINK V1.07, and a figure showing average LD decay was drawn with R [r2 decreased to half its maximum value [A total of 82,187 SNPs (MAF > 0.05) were employed to conduct principal component analysis (PCA) and construct a neighbor-joining (NJ) phylogenetic tree using PLINK V1.07 and PHYLIP. The kinship matrix was assessed using TASSEL V5.2.15 to determine the relatedness among individuals based on the SNP dataset . Linkagen with R . The LD um value .P ≤ 1/82,187, or –Log10(P) ≥ 4.91 [The population structure and relative kinship in natural populations always result in a high level of spurious positives in association mapping . After a) ≥ 4.91 .http://www.phytozome.net). Based on the soybean genomic annotations and expression data, potential candidate genes were predicted.To identify candidate genes underlying the association signals, we selected significant SNPs associated with large-effect QTLs to search candidate genes in their candidate regions. The candidate regions were defined by the average LD decay distance or the LD block. The soybean reference genome was Wm82.a2.v1, and the functional annotations and tissue expression of genes located in the candidate regions were obtained from Phytozome (http://www.phytozome.net). The qRT-PCR primers were designed with Primer Premier 5.0 and were listed in Additional file GmEF1β (GenBank ID AK286947.1) was selected as the control gene, and the qRT-PCR assays were conducted three times using a Light Cycler 480 instrument. The relative expression level of the candidate genes was calculated using the comparative 2−△△CT method [To determine the expression of potential candidate genes, we used quantitative real-time PCR (qRT-PCR) to analyze their expression in extreme materials with large phenotypic differences. Based on the phenotypic data in 2015 and 2016, the materials (ZDD21907 (PFW 198 g), ZDD20532 (PFW 39 g), ZDD01983 (MCFS 75.5%) and ZDD02315 (MCFS 61.7%)) showed stable and large phenotypic differences, therefore we chosen them as the extreme materials and cultivated in the field. Three replicate biological samples were collected in liquid nitrogen at three stages during soybean seed development , R6  and R7 ), as defined by Fehr (1977) . Total RT method . Statisth2) was calculated for the four traits. All traits presented an h2 above 82%, suggesting that genetic effects play a predominant role in the phenotype variation of these traits  above 0.914. MCFS showed a significant positive correlation with PFW and SFW  but showed a nonsignificant negative correlation with SDW, suggesting that MCFS is an important factor influencing the yield of fresh pods.A total of 133 soybean landraces were planted in two consecutive years, and four yield-related traits were investigated. The average values of these traits across the two years showed a continuous distribution in the GWAS panel of 133 soybean landraces, with a wide range of variation Table h was calcA total of 82,187 high-quality SNPs  were used for a GWAS of the four traits, with an average marker density of 11.76 kb/SNP at the genome-wide scale. The lowest marker density (16.28 kb/SNP) was found on Chr.14, and the highest marker density (9.57 kb/SNP) was found on Chr.16. Thus, the markers were unevenly distributed throughout the genome   did not plateau at a single k value but instead continued to increase with relatively constant increments. Calculation of Delta K revealed a sharp peak at k = 2; therefore, the 133 soybean landraces were divided into two subgroups, designated subgroup1 and subgroup2 Fig. a and c. P values from the GLM(Q) model showed the greatest deviation from the expected P values assuming that no association exists, followed by the GLM (PCA) model. The P values from the MLM (Q + K) and MLM (PCA + K) models were similar and close to the expected P values, and the effects of the MLM (Q + K) and MLM (PCA + K) models on the controlling false associations were similar  and MLM (Q + K) models were lower than the expected P values, suggesting that the two models excessively corrected the observed P values; thus, no significant associations were identified. The observed P values from the GLM (PCA) and GLM (Q) models were higher than the expected P values, and the observed P values from GLM (PCA) were much closer to the expected P values than those from the GLM (Q) model, indicating that the GLM (PCA) model could effectively control false-positive associations and avoid false-negative associations. Thus, for PFW and SFW, the MLM (Q + K) model was chosen for subsequent association analyses, whereas for SDW and MCFS, the GLM (PCA) model was selected.Association analyses for the four yield-related traits were performed to evaluate the effects of different models on the control of false associations. For PFW and SFW, the observed lar Fig. . For SDW10(P) > 4.91) were evaluated for the four yield-related traits using the means across 2 years and within individual years, respectively . For SFW, fifteen significant SNPs were detected  on SFW, with variance of 1.74 g between alleles. Sixty-three SNPs were significantly associated with SDW  on SDW, associated with a difference of 5.81 g between alleles. For MCFS, a total of forty-eight SNPs were identified . Of these, twenty SNPs were repeatedly detected in all environments, and all were located in a range of 164 kb  on chromosome 5 (Table R2 = 21.56%) on MCFS, associated with a difference of 3.51% between alleles. Altogether, thirty-five markers were repeatedly associated with one of the four yield-related traits in all environments. In addition, four markers  were commonly associated with both PFW and SFW, and two markers (AX-90328574 and AX-90496773) were associated with both PFW and SDW in all environments , we were able to initially predict potential candidate genes for PFW and MCFS. A total of six genes were considered potential candidate genes, and the functional annotations of these genes are listed in Table Glyma.16g018200 and Glyma.16g018300 showed significant differences in expression between ZDD21907 (PFW 198 g) and ZDD20532 (PFW 39 g) at the R5 and R6 stages (P ≤ 0.01)  and ZDD02315 (MCFS 61.7%) at the R5, R6 and R7 stages   on chromosome 16, and the MCFS marker AX-90460290  on chromosome 5. Compared with the alternative alleles, the PFW of the materials carrying the favorable allele (AA) at AX-90496773 was 16.33g higher than the materials carrying the unfavorable allele (GG), the MCFS of the materials carrying the favorable allele (GG) at AX-90460290 was 3.51% higher than the materials carrying the unfavorable allele (AA)  across the entire genome, which was within the reported range (90 kb ~ 574 kb), but slightly lower than the previously reported distance of 130 kb in cultivated soybean [The acceptable distance between the markers and the candidate genes was determined based on LD, which varies with species and populations . In this soybean . Greater soybean . Moreove soybean . On averhttp://www.soybase.org/). In addition, many QTLs have been identified for several traits that are highly related to yield, such as seed size, flowering time, maturity and plant height. These QTLs could be used to confirm the loci identified by GWAS. In this study, the genetic bases of four yield-related traits at the R6 stage were analyzed using association mapping, and a total of 116 significant SNPs were identified  protein family, which may participate in the post-transcriptional regulation of genes, including pre-mRNA splicing and the cellular localization and stability maintenance of RNA [Glyma.16g018300 is homologous to AT1g01090, and the proteins encoded by these genes share 80.3% amino acid sequence identity. Glyma.16g018300 encodes the pyruvate dehydrogenase E1 component alpha subunit and may be involved in two pathways, PWY-5173 (acetyl-CoA biosynthesis) and PWY-5464 . In Arabidopsis thaliana, the WRINKLED1 (WRI1) transcription factor plays a role of utmost importance during oil accumulation in maturing seeds, and AT1g01090 is the putative target gene of WRI1 in the fatty acid synthesis pathway [Glyma.05g243400, Glyma.05g244100 and Glyma.05g245300 are candidate genes for MCFS, and Glyma.05g243400 and Glyma.05g244100 are located 118 kb and 73 kb upstream of the large-effect marker AX-90460290, respectively. Glyma.05g243400 is homologous to AT1g1870, which encodes a putative EF-1-α-related GTP-binding protein. The vacuole is an essential organelle for plant life and plays important roles in storage , digestion, pH and ion homeostasis, turgor pressure maintenance, biotic and abiotic defense responses, toxic compound sequestration, and pigmentation [A. thaliana predicted that AT1g1870 may be related to vacuolar membrane fusion and remodeling [Glyma.05g244100 shares 83.2% amino acid sequence identity with MOTHER OF FT AND TFL1 (MFT), which encodes a phosphatidylethanolamine-binding protein that regulates seed germination via the ABA and GA signaling pathways in Arabidopsis thaliana [Glyma.05g245300 is homologous to the AT1g73660 gene, encoding a Raf-like MAPKKK. In Arabidopsis thaliana, the AT1g73660-encoded MAPKKK is a negative regulator of salt tolerance and may regulate targets involved in the salt stress response [In this study, a total of five candidate genes associated with PFW and MCFS at the R6 stage were predicted within the LD blocks of two markers of large effect Fig. . Among te of RNA . Glyma.1entation . Analysiresponse . In the Glyma.16g018200, Glyma.16g018300 Glyma.05g243400, Glyma.05g244100 and Glyma.05g245300, are proposed as the candidate genes for PFW and MCFS, but further investigation is needed for verification of this hypothesis. These results provide insights into the yield improvement of vegetable soybean.In this study, we identified 14, 15, 63 and 48 markers associated with PFW, SFW, SDW and MCFS, respectively, via GWAS. Most markers co-localized with previously reported yield-related QTLs. We were particularly interested in the large-effect markers AX-90496773 and AX-90460290, which had an impact on yield-related traits at the R6 stage. According to genetic annotation and expression analyses, a total of five putative genes, including Additional file 1:Table S1. The ecological distribution of 133 soybean landraces. (XLSX 14 kb)Additional file 2:Table S2. qRT-PCR primers. (XLSX 9 kb)Additional file 3:Table S3. Summary of the polymorphic markers on the 20 chromosomes of Glycine max. (XLSX 10 kb)Additional file 4:Table S4. The ecological distribution of 133 soybean landraces from different subgroups. (XLSX 9 kb)Additional file 5:Table S5. SNPs significantly associated with the four yield-related traits. (XLSX 22 kb)Additional file 6:Figure S1. Manhattan and quantile–quantile (QQ) plots of the GWAS for 100-pod fresh weight (PFW) in soybean at the R6 stage. The horizontal blue line indicates the genome-wide significance threshold (−log10(P) > 4.91); a, b and c represent 2015, 2016 and the means across the two years, respectively. (TIF 10418 kb)Additional file 7:Figure S2. Manhattan and quantile–quantile (QQ) plots of the GWAS for 100-seed fresh weight (SFW) in soybean at the R6 stage. The horizontal blue line indicates the genome-wide significance threshold (−log10(P) > 4.91). a, b and c represent 2015, 2016 and the means across the two years, respectively. (TIF 5303 kb)Additional file 8:Figure S3. Manhattan and quantile–quantile (QQ) plots of GWAS for 100-seed dry weight (SDW) in soybean at the R6 stage. The horizontal blue line indicates the genome-wide significance threshold (−log10(P) > 4.91). a, b and c represent 2015, 2016 and the means across the two years, respectively. (TIF 5874 kb)Additional file 9:Figure S4. Manhattan and quantile–quantile (QQ) plots of GWAS for moisture content of fresh seeds (MCFS) in soybean at the R6 stage. The horizontal blue line indicates the genome-wide significance threshold (−log10(P) > 4.91). a, b and c represent 2015, 2016 and the means across the two years, respectively. (TIF 10454 kb)