Quantitative Trait Loci Genotypes Research Articles

A simulation-based assessment of the efficiency of QTL mapping under environment and genotype x environment interaction effects.

The objective of this simulation-based study was to assess how genes, environments, and genotype x environment (GxE) interaction affect the quantitative trait loci (QTL) mapping efficiency. The simulation software performed 50 samplings of 300 recombinant inbred lines (RILs) from a F2, which were assessed in six environments. The RILs were genotyped for 977 single nucleotide polymorphisms (SNP) and phenotyped for grain yield. The average SNP density was 2 cM. We defined six QTLs and 190 minor genes. The trait heritability ranged from 30 to 80%. We fitted the single QTL model and the multiple QTL model on multiple phenotypes. The environment and complex GxE interaction effects led to a low correlation between the QTL heritability and power. The single- and across-environment analyses allowed all QTLs be declared, with an average power of 28 to 100%. In the across-environment analysis, five QTLs showed average power in the range 46 to 82%. Both models provided a good control of the false positive rate (6%, on average) and a precise localization of the QTLs (bias of 2 cM, on average). The QTL power in each environment has a high positive correlation with the range between QTL genotypes for the sum of the additive, environment, and GxE interaction effects (0.76 to 0.96). The uncertainty about the magnitude and sign of the environment and GxE interaction effects makes QTL mapping in multi-environment trials unpredictable. Unfortunately, this uncertainty has no solution because the geneticist has no control over the magnitude and sign of the environment and GxE interaction effects. However, the single- and across-environment analyses are efficient even under a low correlation between QTL heritability and power.

Comparison of genomic prediction models for general combining ability in early stages of hybrid breeding programs

AbstractThis study evaluates the impact of genomic prediction models on selecting inbred lines as parents in hybrid breeding programs. New parents in a hybrid breeding program are typically selected from early‐stage yield trials based on general combining ability (GCA) from testcrosses. Genomic studies have largely focused on predicting hybrid performance in the late stages of the breeding pipeline and largely ignored the selection of inbred lines as parents of the subsequent breeding cycles. Here, we used stochastic simulations of a maize (Zea mays L.) hybrid breeding program for 20 years to evaluate the performance of genomic prediction models for selecting parents based on their predicted GCA. Five genomic prediction models were evaluated in terms of achieved genetic gain and heterosis under two different single nucleotide polymorphism (SNP) marker densities and the true quantitative trait loci genotypes. The results show that using high‐density SNP markers generated more genetic gain and heterosis than the low‐density SNP markers. The relative performance of genomic prediction models differed across marker scenarios. For genetic gain, we observed more differences between the models at low than high marker density. For heterosis, we observed the opposite, more differences between the models at high than low marker density. Overall, models that fitted the average or additive effects specific to each heterotic pool and dominance effects provide a better fit and hence higher genetic gain in hybrid breeding programs.

A compressed variance component mixed model framework for detecting small and linked QTL-by-environment interactions.

Detecting small and linked quantitative trait loci (QTLs) and QTL-by-environment interactions (QEIs) for complex traits is a difficult issue in immortalized F2 and F2:3 design, especially in the era of global climate change and environmental plasticity research. Here we proposed a compressed variance component mixed model. In this model, a parametric vector of QTL genotype and environment combination effects replaced QTL effects, environmental effects and their interaction effects, whereas the combination effect polygenic background replaced the QTL and QEI polygenic backgrounds. Thus, the number of variance components in the mixed model was greatly reduced. The model was incorporated into our genome-wide composite interval mapping (GCIM) to propose GCIM-QEI-random and GCIM-QEI-fixed, respectively, under random and fixed models of genetic effects. First, potentially associated QTLs and QEIs were selected from genome-wide scanning. Then, significant QTLs and QEIs were identified using empirical Bayes and likelihood ratio test. Finally, known and candidate genes around these significant loci were mined. The new methods were validated by a series of simulation studies and real data analyses. Compared with ICIM, GCIM-QEI-random had 29.77 ± 18.20% and 24.33 ± 10.15% higher average power, respectively, in 0.5-3.0% QTL and QEI detection, 43.44 ± 9.53% and 51.47 ± 15.70% higher average power, respectively, in linked QTL and QEI detection, and identified 30 more known genes for four rice yield traits, because GCIM-QEI-random identified more small genes/loci, being 2.69 ± 2.37% for additional genes. GCIM-QEI-random was slightly better than GCIM-QEI-fixed. In addition, the new methods may be extended into backcross and genome-wide association studies. This study provides effective methods for detecting small-effect and linked QTLs and QEIs.

Regression based fast multi-trait genome-wide QTL analysis.

Multivariate simple interval mapping (SIM) is one of the most popular approaches for multiple quantitative trait locus (QTL) analysis. Both maximum likelihood (ML) and least squares (LS) multivariate regression (MVR) are widely used methods for multi-trait SIM. ML-based MVR (MVR-ML) is an expectation maximization (EM) algorithm based iterative and complex time-consuming approach. Although the LS-based MVR (MVR-LS) approach is not an iterative process, the calculation of likelihood ratio (LR) statistic in MVR-LS is also a time-consuming complex process. We have introduced a new approach (called FastMtQTL) for multi-trait QTL analysis based on the assumption of multivariate normal distribution of phenotypic observations. Our proposed method can identify almost the same QTL positions as those identified by the existing methods. Moreover, the proposed method takes comparatively less computation time because of the simplicity in the calculation of LR statistic by this method. In the proposed method, LR statistic is calculated only using the sample variance-covariance matrix of phenotypes and the conditional probability of QTL genotype given the marker genotypes. This improvement in computation time is advantageous when the numbers of phenotypes and individuals are larger, and the markers are very dense resulting in a QTL mapping with a bigger dataset.

Mapping the Effects of Genetic Variation on Chromatin State and Gene Expression Reveals Loci That Control Ground State Pluripotency.

Mouse embryonic stem cells (mESCs) cultured in the presence of LIF occupy a ground state with highly active pluripotency-associated transcriptional and epigenetic circuitry. However, ground state pluripotency in some inbred strain backgrounds is unstable in the absence of ERK1/2 and GSK3 inhibition. Using an unbiased genetic approach, we dissect the basis of this divergent response to extracellular cues by profiling gene expression and chromatin accessibility in 170 genetically heterogeneous mESCs. We map thousands of loci affecting chromatin accessibility and/or transcript abundance, including 10 QTL hotspots where genetic variation at a single locus coordinates the regulation of genes throughout the genome. For one hotspot, we identify a single enhancer variant ∼10 kb upstream of Lifr associated with chromatin accessibility and mediating a cascade of molecular events affecting pluripotency. We validate causation through reciprocal allele swaps, demonstrating the functional consequences of noncoding variation in gene regulatory networks that stabilize pluripotent states invitro.

Assessing trait contribution and mapping novel QTL for salinity tolerance using the Bangladeshi rice landrace Capsule

BackgroundSalinity is one of the most widespread abiotic stresses affecting rice productivity worldwide. The purpose of this study was to establish the relative importance of different traits associated with salinity tolerance in rice and to identify new quantitative trait loci (QTL) conferring tolerance to salinity at seedling stage. A total of 231 F2:3 plants derived from a cross between a sensitive variety BRRI dhan29 (BR29 hereafter) and Capsule, a salt tolerant Bangladeshi indica landrace, were evaluated under salt stress in a phytotron.ResultsOut of the 231 F2 plants, 47 highly tolerant and 47 most sensitive lines were selected, representing the two extreme tails of the phenotypic distribution. These 94 plants were genotyped for 105 simple sequence repeat (SSR) and insertion/deletion (InDel) markers. A genetic linkage map spanning approximately 1442.9 cM of the 12 linkage groups with an average marker distance of 13.7 cM was constructed. QTL were identified on the long arm of chromosome 1 for Na+ concentration, K+ concentration, Na+-K+ ratio and survival; chromosome 3 for Na+ concentration, survival and overall phenotypic evaluation using the Standard Evaluation system (SES); and chromosome 5 for SES. A total of 6 pairwise epistatic interactions were also detected between QTL-linked and QTL-unlinked regions. Graphical genotyping indicated an association between the phenotypes of the extreme families and their QTL genotypes. Path coefficient analysis revealed that Na+ concentration, survival, Na+-K+ ratio and the overall phenotypic performance (SES score) are the major traits associated with salinity tolerance of Capsule.ConclusionsCapsule provides an alternative source of salinity tolerance aside from Pokkali and Nona Bokra, the two Indian salt tolerant landraces traditionally used for breeding salt tolerant rice varieties. Pyramiding the new QTL identified in this study with previously discovered loci, such as Saltol, will facilitate breeding varieties that are highly tolerant of salt stress.

Determination of quantitative trait nucleotides by concordance analysis between quantitative trait loci and marker genotypes of US Holsteins

Experimental designs that exploit family information can provide substantial predictive power in quantitative trait nucleotide discovery projects. Concordance between quantitative trait locus genotype as determined by the a posteriori granddaughter design and marker genotype was determined for 30 trait-by-chromosomal segment effects segregating in the US Holstein population with probabilities of <10-20 to accept the null hypotheses of no segregating gene affecting the trait within the chromosomal segment. Genotypes for 83 grandsires and 17,217 sons were determined by either complete sequence or imputation for 3,148,506 polymorphisms across the entire genome; 471 Holstein bulls had a complete genome sequence, including 64 of the grandsires. Complete concordance was obtained only for stature on chromosome 14 and daughter pregnancy rate on chromosome 18. For each quantitative trait locus, effects of the 30 polymorphisms with highest concordance scores for the analyzed trait were computed by stepwise regression for predicted transmitting abilities of 26,750 bulls with progeny test and imputed genotypes. Effects for stature on chromosome 11, daughter pregnancy rate on chromosome 18, and protein percentage on chromosome 20 met 3 criteria: complete or almost complete concordance, nominal significance of the polymorphism effect after correction for all other polymorphisms, and marker coefficient of determination >40% of total multiple-regression coefficient of determination for the 30 polymorphisms with highest concordance. An intronic variant marker on chromosome 5 at 93,945,738 bp explained 7% of variance for fat percentage and 74% of total multiple-marker regression variance but was concordant for only 24 of 30 families. The missense polymorphism Phe279Tyr in GHR at 31,909,478 bp on chromosome 20 was confirmed as the causative mutation for fat and protein concentration. For effect on fat percentage on chromosome 14, 12 additional missense polymorphisms were found that had almost complete concordance with the suggested causative polymorphism (missense mutation Ala232Glu in DGAT1). The only polymorphism found likely to improve predictive power for genomic evaluation of dairy cattle was on chromosome 15; that polymorphism had a frequency of 0.45 for the allele with economically positive effects on all production traits.

Joint parameter estimation in the QTL mapping of ordinal traits

With the rapid development of statistical genetics, the deep researches of ordinal traits have been gradually emphasized. The data of these traits bear relatively less information than those of continuous phenotypes, therefore it is more complex to map the quantitative trait loci (QTL) of ordinal traits. In this paper, the multiple-interval mapping method is considered in the genetic mapping of ordinal traits. By combining threshold model and statistical model, we build a cumulative logistic regression model to express the relationship between the ordinal data and the QTL genotypes. In order to make the interval mapping more straightforward, we treat the recombination rates as unknown parameters, and then simultaneously obtain the estimates of QTL positions, QTL effects and threshold parameters via the EM algorithm. We perform simulation experiments to investigate and compare the proposed method. We also present a real example to test the reasonableness of the considered model and estimate both model parameters and QTL parameters. Both results of simulations and example show that the method we proposed is reasonable and effective.

Quantitative trait loci analysis of melon (Cucumis melo L.) domestication-related traits.

Loci on LGIV, VI, and VIII of melon genome are involved in the control of fruit domestication-related traits and they are candidate to have played a role in the domestication of the crop. The fruit of wild melons is very small (20-50g) without edible pulp, contrasting with the large size and high pulp content of cultivated melon fruits. An analysis of quantitative trait loci (QTL) controlling fruit morphology domestication-related traits was carried out using an in vitro maintained F2 population from the cross between the Indian wild melon "Trigonus" and the western elite cultivar 'Piel de Sapo'. Twenty-seven QTL were identified in at least two out of the three field trials. Six of them were also being detected in BC1 and BC3 populations derived from the same cross. Ten of them were related to fruit morphological traits, 12 to fruit size characters, and 5 to pulp content. The Trigonus alleles decreased the value of the characters, except for the QTL at andromonoecious gene at linkage group (LG) II, and the QTL for pulp content at LGV. QTL genotypes accounted for a considerable degree of the total phenotypic variation, reaching up to 46%. Around 66% of the QTL showed additive gene action, 19% exhibited dominance, and 25% consisted of overdominance. The regions on LGIV, VI, and VIII included the QTL with more consistent and strong effects on domestication-related traits. QTLs on those regions were validated in BC2S1, BC2S2, and BC3 families, with "Trigonus" allele decreasing the fruit morphological traits in all cases. The validated QTL could represent loci involved in melon domestication, although further experiments as genomic variation studies across wild and cultivated genotypes would be necessary to confirm this hypothesis.

Genome-wide identification of markers for selecting higher oil content in oil palm

BackgroundOil palm (Elaeis guineensis, Jacq.) is the most important source of edible oil. The improvement of oil yield is currently slow in conventional breeding programs due to long generation intervals. Marker-assisted selection (MAS) has the potential to accelerate genetic improvement. To identify DNA markers associated with oil content traits for MAS, we performed quantitative trait loci (QTL) mapping using genotyping by sequencing (GBS) in a breeding population derived from a cross between Deli Dura and Ghana Pisifera, containing 153 F1 trees.ResultsWe constructed a high-density linkage map containing 1357 SNPs and 123 microsatellites. The 16 linkage groups (LGs) spanned 1527 cM, with an average marker space of 1.03 cM. One significant and three suggestive QTL for oil to bunch (O/B) and oil to dry mesocarp (O/DM) were mapped on LG1, LG8, and LG10 in a F1 breeding population, respectively. These QTL explained 7.6–13.3% of phenotypic variance. DNA markers associated with oil content in these QTL were identified. Trees with beneficial genotypes at two QTL for O/B showed an average O/B of 30.97%, significantly (P < 0.01) higher than that of trees without any beneficial QTL genotypes (average O/B of 28.24%). QTL combinations showed that the higher the number of QTL with beneficial genotypes, the higher the resulting average O/B in the breeding population.ConclusionsA linkage map with 1480 DNA markers was constructed and used to identify QTL for oil content traits. Pyramiding the identified QTL with beneficial genotypes associated with oil content traits using DNA markers has the potential to accelerate genetic improvement for oil yield in the breeding population of oil palm.

Functional confirmation of PLAG1 as the candidate causative gene underlying major pleiotropic effects on body weight and milk characteristics

A major pleiotropic quantitative trait locus (QTL) located at ~25 Mbp on bovine chromosome 14 affects a myriad of growth and developmental traits in Bos taurus and indicus breeds. These QTL have been attributed to two functional variants in the bidirectional promoter of PLAG1 and CHCHD7. Although PLAG1 is a good candidate for mediating these effects, its role remains uncertain given that these variants are also associated with expression of five additional genes at the broader locus. In the current study, we conducted expression QTL (eQTL) mapping of this region using a large, high depth mammary RNAseq dataset representing 375 lactating cows. Here we show that of the seven previously implicated genes, only PLAG1 and LYN are differentially expressed by QTL genotype, and only PLAG1 bears the same association signature of the growth and body weight QTLs. For the first time, we also report significant association of PLAG1 genotype with milk production traits, including milk fat, volume, and protein yield. Collectively, these data strongly suggest PLAG1 as the causative gene underlying this diverse range of traits, and demonstrate new effects for the locus on lactation phenotypes.

Functional Mapping of Multiple Dynamic Traits

Many biological phenomena undergo developmental changes in time and space. Functional mapping, which is aimed at mapping genes that affect developmental patterns, is instrumental for studying the genetic architecture of biological changes. Often biological processes are mediated by a network of developmental and physiological components and, therefore, are better described by multiple phenotypes. In this article, we develop a multivariate model for functional mapping that can detect and characterize quantitative trait loci (QTLs) that simultaneously control multiple dynamic traits. Because the true genotypes of QTLs are unknown, the measurements for the multiple dynamic traits are modeled using a mixture distribution. The functional means of the multiple dynamic traits are estimated using the nonparametric regression method, which avoids any parametric assumption on the functional means. We propose the profile likelihood method to estimate the mixture model. A likelihood ratio test is exploited to test for the existence of pleiotropic effects on distinct but developmentally correlated traits. A simulation study is implemented to illustrate the finite sample performance of our proposed method. We also demonstrate our method by identifying QTLs that simultaneously control three dynamic traits of soybeans. The three dynamic traits are the time-course biomass of the leaf, the stem, and the root of the whole soybean. The genetic linkage map is constructed with 950 microsatellite markers. The new model can aid in our comprehension of the genetic control mechanisms of complex dynamic traits over time.

Contributions of linkage disequilibrium and co-segregation information to the accuracy of genomic prediction.

BackgroundTraditional genomic prediction models using multiple regression on single nucleotide polymorphisms (SNPs) genotypes exploit associations between genotypes of quantitative trait loci (QTL) and SNPs, which can be created by historical linkage disequilibrium (LD), recent co-segregation (CS) and pedigree relationships. Results from field data analyses show that prediction accuracy is usually much higher for individuals that are close relatives of the training population than for distantly related individuals. A possible reason is that historical LD between QTL and SNPs is weak and, for close relatives, prediction accuracy of SNP models is mainly contributed by pedigree relationships and CS. Information from pedigree relationships decreases fast over generations and only contributes to within-family prediction. Information from CS is affected by family structures and effective population size, and can have a substantial contribution to prediction accuracy when modeled explicitly.ResultsIn this study, a method to explicitly model CS was developed by following the transmission of putative QTL alleles using allele origins at SNPs. Bayesian hierarchical models that combine information from LD and CS (LD-CS model) were developed for genomic prediction in pedigree populations. Contributions of LD and CS information to prediction accuracy across families and generations without retraining were investigated in simulated half-sib datasets and deep pedigrees with different recent effective population sizes, respectively. Results from half-sib datasets showed that when historical LD between QTL and SNPs is low, accuracy of the LD model decreased when the training data size is increased by adding independent sire families, but accuracies from the CS and LD-CS models increased and plateaued rapidly. Results from deep pedigree datasets show that the LD model had high accuracy across generations only when historical LD between QTL and SNPs was high. Modeling CS explicitly resulted in higher accuracy than the LD model across generations when the mating design generated many close relatives.ConclusionsOur results suggest that modeling CS explicitly improves accuracy of genomic prediction when historical LD between QTL and SNPs is low. Modeling both LD and CS explicitly is expected to improve accuracy when recent effective population size is small, or when the training data include many independent families.Electronic supplementary materialThe online version of this article (doi:10.1186/s12711-016-0255-4) contains supplementary material, which is available to authorized users.

Systems genetic and pharmacological analysis identifies candidate genes underlying mechanosensation in the von Frey test.

Mechanical sensitivity is commonly affected in chronic pain and other neurological disorders. To discover mechanisms of individual differences in punctate mechanosensation, we performed quantitative trait locus (QTL) mapping of the response to von Frey monofilament stimulation in BXD recombinant inbred (BXD) mice. Significant loci were detected on mouse chromosome (Chr) 5 and 15, indicating the location of underlying polymorphisms that cause heritable variation in von Frey response. Convergent evidence from public gene expression data implicates candidate genes within the loci: von Frey thresholds were strongly correlated with baseline expression of Cacna2d1, Ift27 and Csnk1e in multiple brain regions of BXD strains. Systemic gabapentin and PF-670462, which target the protein products of Cacna2d1 and Csnk1e, respectively, significantly increased von Frey thresholds in a genotype-dependent manner in progenitors and BXD strains. Real-time polymerase chain reaction confirmed differential expression of Cacna2d1 and Csnk1e in multiple brain regions in progenitors and showed differential expression of Cacna2d1 and Csnk1e in the dorsal root ganglia of the progenitors and BXD strains grouped by QTL genotype. Thus, linkage mapping, transcript covariance and pharmacological testing suggest that genetic variation affecting Cacna2d1 and Csnk1e may contribute to individual differences in von Frey filament response. This study implicates Cacna2d1 and Ift27 in basal mechanosensation in line with their previously suspected role in mechanical hypersensitivity. Csnk1e is implicated for von Frey response for the first time. Further investigation is warranted to identify the specific polymorphisms involved and assess the relevance of these findings to clinical conditions of disturbed mechanosensation.

Coupling of MIC-3 overexpression with the chromosomes 11 and 14 root-knot nematode (RKN) (Meloidogyne incognita) resistance QTLs provides insights into the regulation of the RKN resistance response in Upland cotton (Gossypium hirsutum)

Genetic analysis of MIC-3 transgene with RKN resistance QTLs provides insight into the resistance regulatory mechanism and provides a framework for testing additional hypotheses. Resistance to root-knot nematode (RKN) (Meloidogyne incognita) in Upland cotton (Gossypium hirsutum) is mediated by two major quantitative trait loci (QTL) located on chromosomes 11 and 14. The MIC-3 (Meloidogyne Induced Cotton3) protein accumulates specifically within the immature galls of RKN-resistant plants that possess these QTLs. Recently, we showed that MIC-3 overexpression in an RKN-susceptible cotton genotype suppressed RKN egg production but not RKN-induced root galling. In this study, the MIC-3 overexpression construct T-DNA in the single-copy transgenic line '14-7-1' was converted into a codominant molecular marker that allowed the marker assisted selection of F2:3 cotton lines, derived from a cross between 14-7-1 and M-240 RNR, having all possible combinations of the chromosomes 11 and 14 QTLs with and without the MIC-3 overexpression construct. Root-knot nematode reproduction (eggs g(-1) root) and severity of RKN-induced root galling were assessed in these lines. We discovered that the addition of MIC-3 overexpression suppressed RKN reproduction in lines lacking both resistance QTLs and in lines having only the chromosome 14 QTL, suggesting an additive effect of the MIC-3 construct with this QTL. In contrast, MIC-3 overexpression did not improve resistance in lines having the single chromosome 11 QTL or in lines having both resistance QTLs, suggesting an epistatic interaction between the chromosome 11 QTL and the MIC-3 construct. Overexpression of MIC-3 did not affect the severity of RKN-induced root galling regardless of QTL genotype. These data provide new insights into the relative order of action of the chromosomes 11 and 14 QTLs and their potential roles in regulating MIC-3 expression as part of the RKN resistance response.

Genetic analysis of salt tolerance in a progeny derived from the citrus rootstocks Cleopatra mandarin and trifoliate orange

A total of 60 traits that could be related to salt tolerance were genetically analyzed using nucellar plants as repetitions of apomictic hybrids in a reference population derived from two common citrus rootstocks, Cleopatra mandarin (salt tolerant) and trifoliate orange (salt sensitive), in two experiments differing in duration (1 versus 3 years) [NaCl] (30 versus 25 mM) and environmental control (greenhouse versus screenhouse). In both experiments, the trifoliate parent always showed less aerial vegetative growth than Cleopatra, and under salinity, the trifoliate parent showed higher Na+ and Cl− leaf concentrations than the salt-tolerant parent. Salinity affected the relationships among traits, particularly those involving leaf water potential; leaf concentrations of Cl−, K+, B and Fe; and root [Na+]. Most traits showed heritabilities below 0.6, and their quantitative trait locus (QTL) analyses were carried out using three mapping procedures to obtain complementary genetic information on trait inheritance. A total of 98 QTLs were detected by interval mapping and multiple QTL mapping procedures. Fresh and dried weights of the leaf, studied in both experiments, showed common QTLs, remarking their repeatability. A cluster of QTLs governing plant vigour and leaf boron concentration pointed a genomic region in linkage group 3 as the most relevant one to improve salt tolerance using the Cleopatra parent as donor. Besides, a QTL genotype in linkage group 7, associated with the smallest leaf water potential and defoliation index under salinity, corresponded to the highest leaf [Na+] and the largest leaf area, suggesting the presence of a putative tissue salt tolerance QTL.

Accuracy of prediction of simulated polygenic phenotypes and their underlying quantitative trait loci genotypes using real or imputed whole-genome markers in cattle.

BackgroundMore accurate genomic predictions are expected when the effects of QTL (quantitative trait loci) are predicted from markers in close physical proximity to the QTL. The objective of this study was to quantify to what extent whole-genome methods using 50 K or imputed 770 K SNPs (single nucleotide polymorphisms) could predict single or multiple QTL genotypes based on SNPs in close proximity to those QTL.MethodsPhenotypes with a heritability of 1 were simulated for 2677 Hereford animals genotyped with the BovineSNP50 BeadChip. Genotypes for the high-density 770 K SNP panel were imputed using Beagle software. Various Bayesian regression methods were used to predict single QTL or a trait influenced by 42 such QTL. We quantified to what extent these predictions were based on SNPs in close proximity to the QTL by comparing whole-genome predictions to local predictions based on estimates of the effects of variable numbers of SNPs i.e. ±1, ±2, ±5, ±10, ±50 or ±100 that flanked the QTL.ResultsPrediction accuracies based on local SNPs using whole-genome training for single QTL with the 50 K SNP panel and BayesC0 ranged from 0.49 (±1 SNP) to 0.75 (±100 SNPs). The minimum number of local SNPs for an accurate prediction is ±10 SNPs. Prediction accuracies that were based on local SNPs only were higher than those based on whole-genome SNPs for both 50 K and 770 K SNP panels. For the 770 K SNP panel, prediction accuracies were higher than 0.70 and varied little i.e. between 0.73 (±1 SNP) and 0.77 (±5 SNPs). For the summed 42 QTL, prediction accuracies were generally higher than for single QTL regardless of the number of local SNPs. For QTL with low minor allele frequency (MAF) compared to QTL with high MAF, prediction accuracies increased as the number of SNPs around the QTL increased.ConclusionsThese results suggest that with both 50 K and imputed 770 K SNP genotypes the level of linkage disequilibrium is sufficient to predict single and multiple QTL. However, prediction accuracies are eroded through spuriously estimated effects of SNPs that are distant from the QTL. Prediction accuracies were higher with the 770 K than with the 50 K SNP panel.

Using Targeted Resequencing for Identification of Candidate Genes and SNPs for a QTL Affecting the pH Value of Chicken Meat.

Using targeted genetical genomics, a quantitative trait locus (QTL) affecting the initial postmortem pH value of chicken breast muscle (Pectoralis major) on chromosome 1 (GGA1) recently was fine-mapped. Thirteen genes were present in the QTL region of approximately 1 Mb. In this study, 10 birds that were inferred to be homozygous for either the high (QQ) or low (qq) QTL allele were selected for resequencing. After enrichment for 1 Mb around the QTL region, >500 × coverage for the QTL region in each of the 10 birds was obtained. In total 5056 single-nucleotide polymorphisms (SNPs) were identified for which the genotypes were consistent with one of the QTL genotypes. We used custom tools to identify putative causal mutations in the mapped QTL region from these SNPs. Four nonsynonymous SNPs differentiating the two QTL genotype groups were identified within four local genes (PRDX4, EIF2S3, PCYT1B, and E1BTD2). Although these are likely candidate SNPs to explain the QTL effect, 54 additional consensus SNPs were detected within gene-related regions (untranslated regions, splicing sites CpG island, and promoter regions) for the QQ birds and 71 for the qq birds. These could also play a role explaining the observed QTL effect. The results provide an important step for prioritizing among a large amount of candidate mutations and significantly contribute to the understanding of the genetic mechanisms affecting the initial postmortem pH value of chicken muscle.

Bayesian Analysis for Genetic Architectures of Body Weights and Morphological Traits Using Distorted Markers in Japanese Flounder Paralichthys olivaceus.

A doubled haploids (DH) population of 160 individuals is constructed by using mitotic gynogenetics for the study of genetic architectures of the body weight and morphological traits in Japanese flounder. Each DH individual is genotyped for 458 SSR markers, 222 of which segregate distortionally. By modifying conditional probabilities of quantitative trait locus (QTL) genotypes on the distorted flanking markers, Bayesian model selection is used to dissect genetic architectures for the traits. As a result, we identify 42 main-effect QTLs on chromosomes 5, 6, 7, 8, 9, 10, 15, 20, 21, 22, and 59 pairs of interacting QTLs. Among these detected QTLs, the largest interacting QTL is between chromosome 6 and chromosome 9 and accounts for 25.196 % of phenotypic variance for body weights and in a similar trend. Also, many QTLs show pleiotropic effects. The QTL on chromosome 9 simultaneously governs seven traits, BL, BH, FL, HL, PFL, HW, and CW. As compared to method using the uncorrected conditional probabilities of QTL genotypes, our method using corrected conditional probabilities can detect more interacting QTLs for the traits.

Epithelial Cadherin Determines Resistance to Infectious Pancreatic Necrosis Virus in Atlantic Salmon.

Infectious pancreatic necrosis virus (IPNV) is the cause of one of the most prevalent diseases in farmed Atlantic salmon (Salmo salar). A quantitative trait locus (QTL) has been found to be responsible for most of the genetic variation in resistance to the virus. Here we describe how a linkage disequilibrium-based test for deducing the QTL allele was developed, and how it was used to produce IPN-resistant salmon, leading to a 75% decrease in the number of IPN outbreaks in the salmon farming industry. Furthermore, we describe how whole-genome sequencing of individuals with deduced QTL genotypes was used to map the QTL down to a region containing an epithelial cadherin (cdh1) gene. In a coimmunoprecipitation assay, the Cdh1 protein was found to bind to IPNV virions, strongly indicating that the protein is part of the machinery used by the virus for internalization. Immunofluorescence revealed that the virus colocalizes with IPNV in the endosomes of homozygous susceptible individuals but not in the endosomes of homozygous resistant individuals. A putative causal single nucleotide polymorphism was found within the full-length cdh1 gene, in phase with the QTL in all observed haplotypes except one; the absence of a single, all-explaining DNA polymorphism indicates that an additional causative polymorphism may contribute to the observed QTL genotype patterns. Cdh1 has earlier been shown to be necessary for the internalization of certain bacteria and fungi, but this is the first time the protein is implicated in internalization of a virus.