Optimal utilization of non‐additive quantitative trait locus in animal breeding programs

In a previous study on maize (Zea mays, L.) several quantitative trait loci (QTL) showing high dominance-additive ratio for agronomic traits were identified in a population of recombinant inbred lines derived from B73 × H99. For four of these mapped QTL, namely 3.05, 4.10, 7.03 and 10.03 according to their chromosome and bin position, families of near-isogenic lines (NILs) were developed, i.e., couples of homozygous lines nearly identical except for the QTL region that is homozygote either for the allele provided by B73 or by H99. For two of these QTL (3.05 and 4.10) the NILs families were produced in two different genetic backgrounds. The present research was conducted in order to: (i) characterize these QTL by estimating additive and dominance effects; (ii) investigate if these effects can be affected by genetic background, inbreeding level and environmental growing conditions (low vs. high plant density). The six NILs’ families were tested across three years and in three Experiments at different inbreeding levels as NILs per se and their reciprocal crosses (Experiment 1), NILs crossed to related inbreds B73 and H99 (Experiment 2) and NILs crossed to four unrelated inbreds (Experiment 3). Experiment 2 was conducted at two plant densities (4.5 and 9.0 plants m-2). Results of Experiments 1 and 2 confirmed previous findings as to QTL effects, with dominance-additive ratio superior to 1 for several traits, especially for grain yield per plant and its component traits; as a tendency, dominance effects were more pronounced in Experiment 1. The QTL effects were also confirmed in Experiment 3. The interactions involving QTL effects, families and plant density were generally negligible, suggesting a certain stability of the QTL. Results emphasize the importance of dominance effects for these QTL, suggesting that they might deserve further studies, using NILs’ families and their crosses as base materials.

Simple SummaryThe relative growth of body components and metabolic traits relative to body weights are phenotypically characterized using joint allometric scaling models, and random regression models (RRMs) are constructed to map quantitative trait loci (QTLs) for allometries of body compositions and metabolic traits in broilers. Prior to statistically inferring the QTLs for the allometric scalings, the QTL candidates in RRMs are obtained by rapidly shrinking most of marker genetic effects to zero with the LASSO technique. Referred to as real joint allometric scaling models, statistical utility of the so-called LASSO-RRM mapping method is demonstrated by computer simulation analysis. Using the F2 population by crossing broiler × Fayoumi, we formulate optimal joint allometric scaling models of fat, shank weight (shank-w) and liver as well as thyroxine (T4) and glucose (GLC) to body weights. For body compositions, a total of 9 QTLs, including 4 additive and 5 dominant, were detected to control the allometric scalings of fat, shank-w and liver to body weights; while for metabolic traits, total 10 QTLs, were mapped to govern the allometries of T4 and GLC to body weights, among which 6 QTLs were of dominant genetic effect. The detected QTLs or highly linked markers can be used to regulate relative growths for meat quality traits to body weight in marker-assisted breeding of broilers.In animal breeding, body components and metabolic traits always fall behind body weights in genetic improvement, which leads to the decline in standards and qualities of animal products. Phenotypically, the relative growth of multiple body components and metabolic traits relative to body weights are characterized by using joint allometric scaling models, and then random regression models (RRMs) are constructed to map quantitative trait loci (QTLs) for relative grwoth allometries of body compositions and metabolic traits in chicken. Referred to as real joint allometric scaling models, statistical utility of the so-called LASSO-RRM mapping method is given a demonstration by computer simulation analysis. Using the F2 population by crossing broiler × Fayoumi, we formulated optimal joint allometric scaling models of fat, shank weight (shank-w) and liver as well as thyroxine (T4) and glucose (GLC) to body weights. For body compositions, a total of 9 QTLs, including 4 additive and 5 dominant QTLs, were detected to control the allometric scalings of fat, shank-w, and liver to body weights; while a total of 10 QTLs of which 6 were dominant, were mapped to govern the allometries of T4 and GLC to body weights. We characterized relative growths of body compositions and metabolic traits to body weights in broilers with joint allometric scaling models and detected QTLs for the allometry scalings of the relative growths by using RRMs. The identified QTLs, including their highly linked genetic markers, could be used to order relative growths of the body components or metabolic traits to body weights in marker-assisted breeding programs for improving the standard and quality of broiler meat products.

In the process of crossbreeding, the linkage disequilibria between the quantitative trait loci (QTL) and their linked markers were reduced gradually with increasing generations. To study the potential of QTL mapping using the crossbred population, we presented a mixed effect model that treated the mean allelic value of the different founder populations as the fixed effect and the allelic deviation from the population mean as random effect. It was assumed that there were fifty QTLs having effect on the trait variation, the population mean and variance were divided to each QTL in founder generation in our model. Only the additive effect was considered in this model for simulation. Six schemes (S1-S6) of crossbreeding were studied. The selection index was used to evaluate the synthetic breeding value of two traits of the individual in the scheme of S2, S4 and S6, and the individuals with high selection index were chosen as the parents of the next generation. Random selection was used in the scheme of S1, S3 and S5. In this study, we premised a QTL explained 40% of the genetic variance was located in a region of 20 cM by the linkage analysis previously. The log likelihood ratio (log LR) was calculated to determine the presence of a QTL at the particular chromosomal position in each of the generations from the fourth to twentieth. The profiles of log LR and the number of the highest log LR located in the region of 5, 10 and 20 cM were compared between different generations and schemes. The profiles and the correct number reduced gradually with the generations increasing in the schemes of S2, S4 and S6, but both of them increased in the schemes of S1, S3 and S5. From the results, we concluded that the crossbreeding population undergoing random selection was suitable for improving the resolution of QTL mapping. Even experiencing index selection, there was still enough variation existing within the crossbred population before the fourteenth generation that could be used to refine the location of QTL in the chromosome region.

Mapping studies using populations with introgressed marker-defined genomic regions are continuously increasing knowledge about quantitative trait loci (QTL) that correlate with variation in important crop traits. This knowledge is useful for plant breeding, although combining desired traits in one genotype might be complicated by the mode of inheritance and co-localization of QTL with antagonistic effects, and by physiological trade-offs, and feed-back or feed-forward mechanisms. Therefore, integrating advances at the genetic level with insight into influences of environment and crop management on crop performance remains difficult. Whereas mapping studies can pinpoint correlations between QTL and phenotypic traits for specific conditions, ignoring or overlooking the importance of environment or crop management can jeopardize the relevance of such assessments. Here, we focus on fruit load (a measure determining competition among fruits on one plant) and its strong modulation of QTL effects on fruit size and composition. Following an integral approach, we show which fruit traits are affected by fruit load, to which underlying processes these traits can be linked, and which processes at lower and higher integration levels are affected by fruit load (and subsequently influence fruit traits). This opinion paper (i) argues that a mechanistic framework to interpret interactions between fruit load and QTL effects is needed, (ii) pleads for consideration of the context of agronomic management when detecting QTL, (iii) makes a case for incorporating interacting factors in the experimental set-up of QTL mapping studies, and (iv) provides recommendations to improve efficiency in QTL detection and use, with particular focus on model-based marker-assisted breeding.

BackgroundWe applied a range of genome-wide association (GWA) methods to map quantitative trait loci (QTL) in the simulated dataset provided by the QTLMAS2009 workshop to derive a comprehensive set of results. A Gompertz curve was modelled on the yield data and showed good predictive properties. QTL analyses were done on the raw measurements and on the individual parameters of the Gompertz curve and its predicted growth for each interval. Half-sib and variance component linkage analysis revealed QTL with different modes of inheritance but with low resolution. This was complemented by association studies using single markers or haplotypes, and additive, dominance, parent-of-origin and epistatic QTL effects. All association analyses were done on phenotypes pre-corrected for pedigree effects. These methods detected QTL positions with high concordance to each other and with greater refinement of the linkage signals. Two-locus interaction analysis detected no epistatic pairs of QTL. Overall, using stringent thresholds we identified QTL regions using linkage analyses, corroborated by 6 individual SNPs with significant effects as well as two putatively imprinted SNPs.ConclusionsWe obtained consistent results across a combination of intra- and inter- family based methods using flexible linear models to evaluate a variety of models. The Gompertz curve fitted the data really well, and provided complementary information on the detected QTL. Retrospective comparisons of the results with actual data simulated showed that best results were obtained by including both yield and the parameters from the Gompertz curve despite the data being simulated using a logistic function.

Sensitivity to the locomotor stimulant properties of drugs of abuse is a heritable trait and its genetic basis may be shared with alleles that modulate activation of the mesolimbic reward pathway. We previously used mouse lines derived from C57BL/6J and DBA/2J alleles that were selected for high and low methamphetamine (MA) sensitivity and identified a genome‐wide significant quantitative trait locus (QTL) on chromosome 11. In order to further pursue this QTL, we utilized the power of an F2 cross and the iterative nature of interval‐specific congenic lines in an effort to identify the causal gene(s) underlying this QTL. The results of F2 mice and the larger congenic lines revealed two large‐effect QTLs for MA‐induced locomotor activity; however, the phenotypic results of the smaller subcongenic lines revealed additional smaller effect QTLs with different modes of inheritance. One subcongenic locus converged remarkably well with the QTL peak in the F2 cross and altered methamphetamine sensitivity in a time‐dependent manner. We generated eight sub‐subcongenic lines derived from this subcongenic line (17 congenic lines total). Owing to a fortuitous recombination event, we identified and replicated a 0.23 Mb region spanning 50.17–50.40 Mb that was critical for MA sensitivity. This region contains only three genes (Rufy1, Hnrnph1, and Cby3) and only two of these genes are expressed in the brain (Rufy1 and Hnrnph1). This study exemplifies how a careful and detailed use of congenic lines can yield narrow QTL intervals and a tractable number of genes for quantitative trait gene identification.

Maximizing fruit size is critical for profitable sweet cherry (Prunus avium L.) production. Yet, despite its importance, little is known about the genetic control of fruit size. The objective of this study was to identify quantitative trait loci (QTLs) for fruit size and two essential components of fruit size, mesocarp cell number and size. This study utilized a double pseudo-testcross population derived from reciprocal crosses between a sweet cherry cultivar with ~8 g fruit, “Emperor Francis” (EF), and a wild forest sweet cherry selection with ~2 g fruit, “New York 54” (NY). A total of 190 F1 progeny previously utilized for the construction of the linkage maps were evaluated in 2006 and 2007 for fruit weight, length, and diameter; mesocarp cell number and length; and pit length and diameter. In 2008, a subset of this population was again evaluated for fruit weight. Correlation analysis revealed that the three fruit size traits were highly correlated with each other, and mesocarp cell number, not cell length, was correlated with fruit size. Three QTLs were identified for each fruit size trait, and one QTL was identified for mesocarp cell number. Fruit size QTLs were found on linkage group 2 on the EF map (EF 2) and linkage groups 2 and 6 on the NY map (NY 2 and NY 6). On EF 2, the cell number QTL clustered with the fruit size QTL, suggesting that the underlying basis of the fruit size increase associated with this QTL was an increase in mesocarp cell number. On NY 6, pit length and diameter QTLs clustered with those for fruit size, suggesting that the underlying morphological basis of this fruit size QTL is the difference in pit size.

A method was derived to estimate effects of quantitative trait loci (QTL) using incomplete genotype information in large outbreeding populations with complex pedigrees. The method accounts for background genes by estimating polygenic effects. The basic equations used are very similar to the usual linear mixed model equations for polygenic models, and segregation analysis was used to estimate the probabilities of the QTL genotypes for each animal. Method R was used to estimate the polygenic heritability simultaneously with the QTL effects. Also, initial allele frequencies were estimated. The method was tested in a simulated data set of 10,000 animals evenly distributed over 10 generations, where 0, 400 or 10,000 animals were genotyped for a candidate gene. In the absence of selection, the bias of the QTL estimates was < 2%. Selection biased the estimate of the Aa genotype slightly, when zero animals were genotyped. Estimates of the polygenic heritability were 0.251 and 0.257, in absence and presence of selection, respectively, while the simulated value was 0.25. Although not tested in this study, marker information could be accommodated by adjusting the transmission probabilities of the genotypes from parent to offspring according to the marker information. This renders a QTL mapping study in large multi-generation pedigrees possible.

The soilborne oomycete Phytophthora cinnamomi-which causes root rot, trunk cankers, and stem lesions on an estimated 5,000 plant species worldwide-is a lethal pathogen of American chestnut (Castanea dentata) as well as many other woody plant species. P. cinnamomi is particularly damaging to chestnut and chinquapin trees (Castanea spp.) in the southern portion of its native range in the United States due to relatively mild climatic conditions that are conductive to disease development. Introduction of resistant genotypes is the most practical solution for disease management in forests because treatment with fungicides and eradication of the pathogen are neither practical nor economically feasible in natural ecosystems. Using backcross families derived from crosses of American chestnuts with two resistant Chinese chestnut cultivars Mahogany and Nanking, we constructed linkage maps and identified quantitative trait loci (QTLs) for resistance to P. cinnamomi that had been introgressed from these Chinese chestnut cultivars. In total, 957 plants representing five cohorts of three hybrid crosses were genotyped by sequencing and phenotyped by standardized inoculation and visual examination over a 6-year period from 2011 to 2016. Eight parental linkage maps comprising 7,715 markers were constructed, and 17 QTLs were identified on four linkage groups (LGs): LG_A, LG_C, LG_E, and LG_K. The most consistent QTLs were detected on LG_E in seedlings from crosses with both 'Mahogany' and 'Nanking' and LG_K in seedlings from 'Mahogany' crosses. Two consistent large and medium effect QTLs located ∼10 cM apart were present in the middle and at the lower end of LG_E; other QTLs were considered to have small effects. These results imply that the genetic architecture of resistance to P. cinnamomi in Chinese chestnut × American chestnut hybrid progeny may resemble the P. sojae-soybean pathosystem, with a few dominant QTLs along with quantitatively inherited partial resistance conferred by multiple small-effect QTLs.

Powdery mildew, caused by the ascomy- cete fungus Podosphaera leucotricha, is one of the most damaging diseases of apple worldwide. Poly- genically determined resistance might contribute to a significant increase of resistance to this disease in new cultivars. A quantitative trait locus (QTL) anal- ysis was performed in an F1 progeny derived from a cross between the apple cultivar Discovery and the apple hybrid TN10-8. Powdery mildew incidence was assessed during four years (five seasons) in spring and/or autumn in a French local orchard. Seven additive and/or dominant QTLs were detected over the five seasons, with effects (R 2 ) ranging from 7.5% to 27.4% of the progeny phenotypic variation. Two QTLs, on linkage groups (LGs) 2 and 13, were con- sistently identified and accounted together from 29% to 37% of the phenotypic variation according to the year of assessment. The other QTLs were identified during one (LGs 1, 14), two (LG10), or three (LGs 8, 17) seasons. Their instability indicated a changing genetic determinism according to the year of assess- ment, for which several hypotheses may be put for- ward. The QTLs on LGs 2 and 8 mapped close to clusters of resistance gene analogs (RGAs) and major genes for resistance to mildew or apple scab previ- ously identified. The stable QTLs identified on LGs 2 and 13, together with the strong effect QTL located on LG 8, are of special interest for breeding purposes, especially if combined with other major resistance genes.

본 연구는 콩 cyst 선충 race 14에 대한 저항성 QTLs 구명을 목적으로 한 바 결과를 요약하면 다음과 같다. 1.회귀분석 결과 30개의 marker들(29 RAPD, 1 RFLP)에서 cyst 선충 race 14의 저항성에 대한 유의성이 인정되었다. 2.MAPMAKER/QTL 분석 결과 2개의 QTL들이 구명되었는데, 이 QTL들은 2개의 linkage groups (LGC-7 와 LGC-9)에 위치하였으며, 모두 우성유전 양상을 나타내었다. 3.다중회귀분석 결과 2개의 marker들(B15²와 H06¹)로 구성된 조합에서 가장 높은 표현적 변이의 값(22.9%)을 나타내었다. 콩 cyst 선충 race 14에 대한 표현적 변이를 충분히 설명하기 위해서는 지속적인 QTL 구명 연구가 요구된다.

Globe artichoke (Cynara cardunculus var. scolymus), whose immature inflorescences (capitula) are consumed as a vegetable all over the world, contributes significantly to the agricultural economy of the Mediterranean basin. Here, we describe a QTL (quantitative trait loci) analysis aimed at elucidating the mode of inheritance of seven main and first-order capitulum traits. Mapping was carried out in an F1 population obtained by crossing a globe artichoke with a cultivated cardoon (C. cardunculus var. altilis). A total of 100 QTL associated with the seven capitulum traits were mapped to 23 chromosomal regions, scattered over 12 of the 17 linkage groups. Among these, 73 were expressed in both growing seasons, while the others were only detected in one season. Up to nine QTL per trait were identified, and major QTL, responsible for some 20 % of the phenotypic variation, were detected for capitulum length, diameter, shape index and fresh weight. The QTL for correlated traits frequently co-localized, most likely due to pleiotropy. This study represents the first report on yield traits QTL in globe artichoke. The QTL identified, along with linked markers, particularly those located in four hot-spot QTL regions are of practical interest for crop improvement based on marker-assisted breeding.

Breeding indigenous African taurine cattle tolerant to trypanosomosis is a straightforward approach to control costs generated by this disease. A recent study identified quantitative trait loci (QTL) underlying trypanotolerance traits in experimental crosses between tolerant N'Dama and susceptible Boran zebu cattle. As trypanotolerance is thought to result from local adaptation of indigenous cattle breeds, we propose an alternative and complementary approach to study the genetic architecture of this trait, based on the identification of selection signatures within QTL or candidate genes. A panel of 92 microsatellite markers was genotyped on 509 cattle belonging to four West African trypanotolerant taurine breeds and 10 trypanosusceptible European or African cattle breeds. Some of these markers were located within previously identified QTL regions or candidate genes, while others were chosen in regions assumed to be neutral. A detailed analysis of the genetic structure of these different breeds was carried out to confirm a priori grouping of populations based on previous data. Tests based on the comparison of the observed heterozygosities and variances in microsatellite allelic size among trypanotolerant and trypanosusceptible breeds led to the identification of two significantly less variable microsatellite markers. BM4440, one of these two outlier loci, is located within the confidence interval of a previously described QTL underlying a trypanotolerance-related trait. Detection of selection signatures appears to be a straightforward approach for unravelling the molecular determinism of trypanosomosis pathogenesis. We expect that a whole genome approach will help confirm these results and achieve a higher resolving power.

This book describes the theory and practice of methods for quantitative trait loci (QTL) detection and analysis, as well as marker assisted selection, in animal genetics and breeding. It is divided into 16 chapters: historical overview; principles of parameter estimation; random and fixed effects (the mixed model); experimental designs to detect QTL (generation of linkage disequilibrium); QTL parameter estimation for crosses between inbred lines; advanced statistical methods for QTL detection and parameter estimation; analysis of QTL as random effects; statistical power to detect QTL and parameter confidence interval; optimization of experimental designs; fine mapping of QTL; complete genome QTL scans (the problem of multiple comparisons); multiple trait QTL analysis; principles of selection index and traditional breeding programmes; marker-assisted selection (theory); marker-assisted selection (results of simulation studies); and marker-assisted introgression. The book is aimed at graduate students and research workers in quantitative genetics and animal breeding. There is an author index and a detailed subject index.

Genome wide quantitative trait loci (QTL) mapping was conducted in Arabidopsis thaliana using F2 mapping population (Col-0 × Don-0) and SNPs markers. A total of five linkage groups were obtained with number of SNPs varying from 45 to 59 per linkage group. The composite interval mapping detected a total of 36 QTLs for 15 traits and the number of QTLs ranged from one (root length, root dry biomass, cauline leaf width, number of internodes and internode distance) to seven (for bolting days). The range of phenotypic variance explained (PVE) and logarithm of the odds ratio of these 36 QTLs was found be 0.19-38.17% and 3.0-6.26 respectively. Further, the epistatic interaction detected one main effect QTL and four epistatic QTLs. Five major QTLs viz. Qbd.nbri.4.3, Qfd.nbri.4.2, Qrdm.nbri.5.1, Qncl.nbri.2.2, Qtd.nbri.4.1 with PVE > 15.0% might be useful for fine mapping to identify genes associated with respective traits, and also for development of specialized population through marker assisted selection. The identification of additive and dominant effect QTLs and desirable alleles of each of above mentioned traits would also be important for future research.