海岛棉F1产量性状的条件遗传分析

调查了海岛棉5×4不完全双列杂交实验的3年产量性状资料,运用包括加性、显性、加性×环境互作、显性×环境互作的遗传模型进行条件和非条件的遗传分析.双列杂交的亲本具有不同果枝类型.非条件遗传方差结果表明,总皮棉产量没有检测到显著的非条件加性效应方差.但是铃重、霜前铃数、霜后铃数以及霜前皮棉产量的条件分析结果发现这些性状的加性效应均对总皮棉产量的加性效应有贡献.因此,可通过这些性状改良总皮棉产量的加性效应表现.当某材料产量构成因素的加性贡献率比较高时,选择该材料作为杂交亲本可望获得具有较好总皮棉产量表现的后代.各产量构成因素的显性效应对霜前皮棉产量和总皮棉产量的杂种优势具有较大的贡献率.产量构成因素之间又相互影响.那些能够检测到显著的正向贡献率的性状将为进一步改良目标性状提供可能.本研究结果显示运用条件分析方法不仅能分析原因性状对目标性状的贡献率,还可以分析特定亲本(或组合)的某一性状对该亲本或组合目标性状的作用大小和正负,据此就可以指导某一亲本后代或组合的目标性状的间接选择.这对实际育种中具体组合的选择具有重要的意义.     

Quantitative trait loci mapping for yield and its components by using two immortalized populations of a heterotic hybrid in Gossypium hirsutum L.

Quantitative trait loci (QTL) mapping provides a powerful tool for unraveling the genetic basis of yield and yield components as well as heterosis in upland cotton. In this research, a molecular linkage map of Xiangzamian 2 (Gossypium hirsutum L.)-derived recombinant inbred lines (RILs) was reconstructed based on increased expressed sequence tag–simple sequence repeat markers. Both the RILs and immortalized F2s (IF2) developed through intermating between RILs were grown under multiple environments. Yield and yield components including seed-cotton yield, lint yield, bolls/plant, boll weight, lint percentage, seed index, lint index and fruit branch number were measured and their QTL were repeatedly identified across environments by the composite interval mapping (CIM) method. From a total of 111 non-redundant QTL, 23 were detected in both two populations. In the meantime, multi-marker joint analyses showed that 16 of these QTL had significant environmental interaction. QTL for correlated traits tended to be collocated and most of the QTL for seed-cotton yield and lint yield were associated with QTL for at least one yield component, consistent with the results observed in correlation analyses. For many QTL with significant additive effects, positive alleles from CRI12, the inferior parent with lower yield performance, were associated with trait improvement. Trait performance of IF2s and the large number of QTL with positive dominant effects implied that dominance plays an important role in the genetic basis of heterosis in Xiangzamian 2 and that non-additive inheritance is also an important genetic mode for lint percentage in the population. These QTL can provide the bases for marker-assisted breeding programs of upland cotton.     

Interspecific chromosomal effects on agronomic traits in Gossypium hirsutum by AD analysis using intermated G. barbadense chromosome substitution lines

The untapped potential of the beneficial alleles from Gossypium barbadense L. has not been well utilized in G. hirsutum L. (often referred to as Upland cotton) breeding programs. This is primarily due to genomic incompatibility and technical challenges associated with conventional methods of interspecific introgression. In this study, we used a hypoaneuploid-based chromosome substitution line as a means for systematically introgressing G. barbadense doubled-haploid line ‘3-79’ germplasm into a common Upland genetic background, inbred ‘Texas marker-1’ (‘TM-1’). We reported on the chromosomal effects, lint percentage, boll weight, seedcotton yield and lint yield in chromosome substitution CS-B (G. barbadense L.) lines. Using an additive-dominance genetic model, we studied the interaction of alleles located on two alien substituted chromosomes versus one alien substituted chromosome using a partial diallel mating design of selected CS-B lines (CS-B05sh, CS-B06, CS-B09, CS-B10, CS-B12, CS-B17 and CS-B18). Among these parents, CS-B09 and CS-B10 were reported for the first time. The donor parent 3-79, had the lowest additive effect for all of the agronomic traits. All of the CS-B lines had significant additive effects with boll weight and lint percentage. CS-B10 had the highest additive effects for lint percentage, and seedcotton and lint yield among all of the lines showing a transgressive genetic mode of inheritance for these traits. CS-B09 had greater additive genetic effects on lint yield, while CS-B06, CS-B10 and CS-B17 had superior additive genetic effects on both lint and seedcotton yield compared to TM-1 parent. The 3-79 line had the highest dominance effects for boll weight (0.513 g) and CS-B10 had the lowest dominance effect for boll weight (?0.702). Some major antagonistic genetic effects for the agronomic traits were present with most of the substituted chromosomes and chromosome arms, a finding suggested their recalcitrance to conventional breeding efforts. The results revealed that the substituted chromosomes and arms of 3-79 carried some cryptic beneficial alleles with potential to improve agronomic traits including yield, whose effects were masked at the whole genome level in 3-79.     

Association Mapping for Epistasis and Environmental Interaction of Yield Traits in 323 Cotton Cultivars under 9 Different Environments

Improving yield is a major objective for cotton breeding schemes, and lint yield and its three component traits (boll number, boll weight and lint percentage) are complex traits controlled by multiple genes and various environments. Association mapping was performed to detect markers associated with these four traits using 651 simple sequence repeats (SSRs). A mixed linear model including epistasis and environmental interaction was used to screen the loci associated with these four yield traits by 323 accessions of Gossypium hirsutum L. evaluated in nine different environments. 251 significant loci were detected to be associated with lint yield and its three components, including 69 loci with individual effects and all involved in epistasis interactions. These significant loci explain ∼ 62.05% of the phenotypic variance (ranging from 49.06% ∼ 72.29% for these four traits). It was indicated by high contribution of environmental interaction to the phenotypic variance for lint yield and boll numbers, that genetic effects of SSR loci were susceptible to environment factors. Shared loci were also observed among these four traits, which may be used for simultaneous improvement in cotton breeding for yield traits. Furthermore, consistent and elite loci were screened with −Log₁₀ (P-value) >8.0 based on predicted effects of loci detected in different environments. There was one locus and 6 pairs of epistasis for lint yield, 4 loci and 10 epistasis for boll number, 15 loci and 2 epistasis for boll weight, and 2 loci and 5 epistasis for lint percentage, respectively. These results provided insights into the genetic basis of lint yield and its components and may be useful for marker-assisted breeding to improve cotton production.     

Genetic dissection of chromosome substitution lines of cotton to discover novel Gossypium barbadense L. alleles for improvement of agronomic traits

We recently released a set of 17 chromosome substitution (CS-B) lines (2n = 52) that contain Gossypium barbadense L. doubled-haploid line ‘3-79’ germplasm systematically introgressed into the Upland inbred ‘TM-1’ of G. hirsutum (L.). TM-1 yields much more than 3-79, but cotton from the latter has superior fiber properties. To explore the use of these quasi-isogenic lines in studying gene interactions, we created a partial diallel among six CS-B lines and the inbred TM-1, and characterized their descendents for lint percentage, boll weight, seedcotton yield and lint yield across four environments. Phenotypic data on the traits were analyzed according to the ADAA genetic model to detect significant additive, dominance, and additive-by-additive epistasis effects at the chromosome and chromosome-by-chromosome levels of CS-B lines. For example, line 3-79 had the lowest boll weight, seedcotton yield and lint yield, but CS-B22Lo homozygous dominance genetic effects on seedcotton and lint yield were nearly four times those of TM-1, and its hybrids with TM-1 had the highest additive-by-additive epistatic effects on seedcotton and lint yield. CS-B14sh, 17, 22Lo and 25 produced positive homozygous dominance effects on lint yield, whereas doubly heterozygous combinations of CS-B14sh with CS-B17, 22Lo and 25 produced negative dominance effects, suggesting that epistatic effects between genes in these chromosomes strongly affect lint yield. The results underscore the opportunities to systematically identify genomic regions harboring genes that impart agronomically significant effects via epistatic interactions. The chromosome-by-chromosome approach significantly complements other strategies to detect and quantify epistatic interaction effects, and the quasi-isogenic nature of families and lines from CS-B intermatings will facilitate high-resolution localization, development of markers for selection and map-assisted identification of genes involved in strong epistatic effects.     

云南香型软米水稻资源农艺性状遗传效应研究

选取5份云南地方香型软米水稻种质资源和6份自育香型软米保持系按5×6不完全双列杂交设计(NCⅡ)配制成30个组合,采用加性-显性-上位性遗传模型,分析云南香型软米11个农艺性状的遗传效应。结果表明,云南香型软米多数农艺性状的遗传主要受加×加上位性效应、加性×环境效应、显性×环境效应的影响,还存在不同程度的加性效应和显性效应,单株产量受基因加性效应、显性效应、加×加上位性效应、加性×环境效应、显性×环境效应的影响;株高、有效穗的遗传率以普通狭义遗传率为主,其他性状的普通狭义遗传率和互作狭义遗传率均达极显著水平;产量构成性状之间存在不同类型和不同程度的遗传相关,多数性状之间以加×加上位性、加性×环境和显性×环境互作效应显著。     

Improvement of Prediction Ability for Genomic Selection of Dairy Cattle by Including Dominance Effects

Dominance may be an important source of non-additive genetic variance for many traits of dairy cattle. However, nearly all prediction models for dairy cattle have included only additive effects because of the limited number of cows with both genotypes and phenotypes. The role of dominance in the Holstein and Jersey breeds was investigated for eight traits: milk, fat, and protein yields; productive life; daughter pregnancy rate; somatic cell score; fat percent and protein percent. Additive and dominance variance components were estimated and then used to estimate additive and dominance effects of single nucleotide polymorphisms (SNPs). The predictive abilities of three models with both additive and dominance effects and a model with additive effects only were assessed using ten-fold cross-validation. One procedure estimated dominance values, and another estimated dominance deviations; calculation of the dominance relationship matrix was different for the two methods. The third approach enlarged the dataset by including cows with genotype probabilities derived using genotyped ancestors. For yield traits, dominance variance accounted for 5 and 7% of total variance for Holsteins and Jerseys, respectively; using dominance deviations resulted in smaller dominance and larger additive variance estimates. For non-yield traits, dominance variances were very small for both breeds. For yield traits, including additive and dominance effects fit the data better than including only additive effects; average correlations between estimated genetic effects and phenotypes showed that prediction accuracy increased when both effects rather than just additive effects were included. No corresponding gains in prediction ability were found for non-yield traits. Including cows with derived genotype probabilities from genotyped ancestors did not improve prediction accuracy. The largest additive effects were located on chromosome 14 near DGAT1 for yield traits for both breeds; those SNPs also showed the largest dominance effects for fat yield (both breeds) as well as for Holstein milk yield.     

Estimates of genetic variance in an F2 maize population

Maize (Zea mays L.) breeders have used several genetic-statistical models to study the inheritance of quantitative traits. These models provide information on the importance of additive, dominance, and epistatic genetic variance for a quantitative trait. Estimates of genetic variances are useful in understanding heterosis and determining the response to selection. The objectives of this study were to estimate additive and dominance genetic variances and the average level of dominance for an F2 population derived from the B73 x Mo17 hybrid and use weighted least squares to determine the importance of digenic epistatic variances relative to additive and dominance variances. Genetic variances were estimated using Design III and weighted least squares analyses. Both analyses determined that dominance variance was more important than additive variance for grain yield. For other traits, additive genetic variance was more important than dominance variance. The average level of dominance suggests either overdominant gene effects were present for grain yield or pseudo-overdominance because of linkage disequilibrium in the F2 population. Epistatic variances generally were not significantly different from zero and therefore were relatively less important than additive and dominance variances. For several traits estimates of additive by additive epistatic variance decreased estimates of additive genetic variance, but generally the decrease in additive genetic variance was not significant.     

Dissecting total genetic variance into additive and dominance components of purebred and crossbred pig traits

The partition of the total genetic variance into its additive and non-additive components can differ from trait to trait, and between purebred and crossbred populations. A quantification of these genetic variance components will determine the extent to which it would be of interest to account for dominance in genomic evaluations or to establish mate allocation strategies along different populations and traits. This study aims at assessing the contribution of the additive and dominance genomic variances to the phenotype expression of several purebred Piétrain and crossbred (Piétrain × Large White) pig performances. A total of 636 purebred and 720 crossbred male piglets were phenotyped for 22 traits that can be classified into six groups of traits: growth rate and feed efficiency, carcass composition, meat quality, behaviour, boar taint and puberty. Additive and dominance variances estimated in univariate genotypic models, including additive and dominance genotypic effects, and a genomic inbreeding covariate allowed to retrieve the additive and dominance single nucleotide polymorphism variances for purebred and crossbred performances. These estimated variances were used, together with the allelic frequencies of the parental populations, to obtain additive and dominance variances in terms of genetic breeding values and dominance deviations. Estimates of the Piétrain and Large White allelic contributions to the crossbred variance were of about the same magnitude in all the traits. Estimates of additive genetic variances were similar regardless of the inclusion of dominance. Some traits showed relevant amount of dominance genetic variance with respect to phenotypic variance in both populations (i.e. growth rate 8%, feed conversion ratio 9% to 12%, backfat thickness 14% to 12%, purebreds-crossbreds). Other traits showed higher amount in crossbreds (i.e. ham cut 8% to 13%, loin 7% to 16%, pH semimembranosus 13% to 18%, pH longissimus dorsi 9% to 14%, androstenone 5% to 13% and estradiol 6% to 11%, purebreds-crossbreds). It was not encountered a clear common pattern of dominance expression between groups of analysed traits and between populations. These estimates give initial hints regarding which traits could benefit from accounting for dominance for example to improve genomic estimated breeding value accuracy in genetic evaluations or to boost the total genetic value of progeny by means of assortative mating.     

Genomic analysis of dominance effects on milk production and conformation traits in Fleckvieh cattle

Background

Estimates of dominance variance in dairy cattle based on pedigree data vary considerably across traits and amount to up to 50% of the total genetic variance for conformation traits and up to 43% for milk production traits. Using bovine SNP (single nucleotide polymorphism) genotypes, dominance variance can be estimated both at the marker level and at the animal level using genomic dominance effect relationship matrices. Yield deviations of high-density genotyped Fleckvieh cows were used to assess cross-validation accuracy of genomic predictions with additive and dominance models. The potential use of dominance variance in planned matings was also investigated.

Results

Variance components of nine milk production and conformation traits were estimated with additive and dominance models using yield deviations of 1996 Fleckvieh cows and ranged from 3.3% to 50.5% of the total genetic variance. REML and Gibbs sampling estimates showed good concordance. Although standard errors of estimates of dominance variance were rather large, estimates of dominance variance for milk, fat and protein yields, somatic cell score and milkability were significantly different from 0. Cross-validation accuracy of predicted breeding values was higher with genomic models than with the pedigree model. Inclusion of dominance effects did not increase the accuracy of the predicted breeding and total genetic values. Additive and dominance SNP effects for milk yield and protein yield were estimated with a BLUP (best linear unbiased prediction) model and used to calculate expectations of breeding values and total genetic values for putative offspring. Selection on total genetic value instead of breeding value would result in a larger expected total genetic superiority in progeny, i.e. 14.8% for milk yield and 27.8% for protein yield and reduce the expected additive genetic gain only by 4.5% for milk yield and 2.6% for protein yield.

Conclusions

Estimated dominance variance was substantial for most of the analyzed traits. Due to small dominance effect relationships between cows, predictions of individual dominance deviations were very inaccurate and including dominance in the model did not improve prediction accuracy in the cross-validation study. Exploitation of dominance variance in assortative matings was promising and did not appear to severely compromise additive genetic gain.     

Genetic changes in plant growth and their associations with chromosomes from <Emphasis Type="Italic">Gossypium barbadense</Emphasis> L. in <Emphasis Type="Italic">G. hirsutum</Emphasis> L

Cotton (Gossypium spp.) plant growth is an important time-specific agronomic character that supports the development of squares, flowers, boll retention, and yield. With the use of a mixed linear model approach, we investigated 14 cotton chromosome substitution (CS-B) lines and their chromosome-specific F₂ hybrids for genetic changes in plant growth that was measured during the primary flowering time under two environments. The changes in additive and dominance variances for plant height and number of mainstem nodes are reported, showing that additive effects for these two traits were a key genetic component after initial flowering occurred in the field. Time-specific genetic variance components were also detected where phenotypic values observed at time t were conditioned on the events occurring at time t − 1, demonstrating new genetic variations arising at several time intervals during plant growth. Results also revealed that plant height and number of nodes shared some common influence due to additive effects during plant development. With the comparative analyzes, chromosomes associated with the genetic changes in plant growth were detected. Therefore, these results should add new understanding of the genetics underlying these time-specific traits. Mention of trademark or proprietary product does not constitute a guarantee or warranty of the product by the United States Department of Agriculture and does not imply its approval to the exclusion of other products that may also be suitable.     

数量性状的核质互作遗传效应分析:二倍体植株遗传模型

提出了能分析二倍体植株数量性状核质互作效应的遗传模型,该模型把控制数量性状总的遗传效应分为核效应、质效应和核质互作效应,以及它们分别与环境作用的效应。其中,核质互作效应可进一步分解为加性核质互作与显性核质互作。基于平衡与非平衡两种双列杂交试验设计,蒙特卡罗模拟结果表明:采用混合线性模型方法进行统计分析,可以有效地估计各项遗传效应值及其方差分量。此外,运用该模型对棉花的4个数量性状(单株铃数、衣分、2.5%跨长和麦克隆值)进行了遗传分析。     

Genetic effects of individual chromosomes in cotton cultivars detected by using chromosome substitution lines as genetic probes

Determination of chromosomes or chromosome arms with desirable genes in different inbred lines and/or crosses should provide useful genetic information for crop improvement. In this study, we applied a modified additive-dominance model to analyze a data set of 13 cotton chromosome substitution lines and their recurrent parent TM-1, five commercial cultivars, and their 70 F(2) hybrids. The chromosome additive and dominance variance components for eight agronomic and fiber traits were determined. On average, each chromosome or chromosome arm was associated with 6.5 traits in terms of additive and/or dominance effects. The chromosomes or chromosome arms, which contributed significant additive variances for the traits investigated, included 2, 16, 18, 25, 5sh (short arm), 14sh, 15sh, 22sh, and 22Lo (long arm). Chromosome additive effects were also predicted in this study. The results showed that CS-B 25 was favorably associated with several fiber traits, while FM966 was favorably associated with both yield and fiber traits with alleles on multiple chromosomes or chromosome arms. Thus, this study should provide valuable genetic information on pure line development for several improved traits such as yield and fiber quality.     

烤烟主要农艺性状的遗传与相关分析

利用包括基因型与环境互作的加性-显性遗传模型,对14个烤烟品种(系)及其配制的41个杂交组合在4个环境下的7个农艺性状表现进行遗传分析。结果表明,株高、节距、腰叶宽主要受加性效应控制,叶数、腰叶长受显性×环境互作效应影响最大,茎围以加性×环境互作效应、显性×环境互作效应为主,产量以加性效应、显性×环境互作效应为主。适应当地生态条件的品种（系）具有较高的正向加性效应。许多组合的显性主效应及在各试验点的显性×环境互作效应在方向上不尽一致,杂交组合的选配宜针对特定的生态环境进行。性状相关分析表明,大多数成对性状的各项相关系数为正值,且多以加性遗传相关为主,可利用株高对产量进行间接选择。
     

Mapping QTL for Biomass Yield and Its Components in Rice (Oryza sativa L.)

Addicive effects, additive by additive epistatic effects, and their environmental interactions of QTLs are important genetic components of quantitative traits. Genetic architecture underlying rice biomass yield and its two component traits (straw yield and grain yield) were analyzed for a population of 125 DH lines from an inter-subspecific cross of IR64/Azucena. The mixed-model based composite interval mapping approach (MCIM) was used to detect QTLs, There were 12 QTLs detected with additive main effects, 27 QTLs involved in digenic interaction with aa and/or aae effects, and 18 QTLs affected by environments with ae and/or aae effects. It was revealed that epistatic effects and QE interaction effects existed on biomass yield and its component traits in rice. In addition, the genetic basis of relationships among these traits were investigated. Four QTLs and one pair of epistatic QTLs were detected to be responsible for the positive correlation between biomass yield and straw yield. Three QTLs might be responsible for the negative correlation between straw yield and grain yield. This result could partially explain the genetic basis of correlation among the three traits, and provide useful information for genetic improvement of these traits by marker-assisted selection.     

综合性状及其分量的多元条件分析

提出基于混合线性模型的多元条件分析方法,用于分析复杂综合性状的分量对其目标性状的贡献。定义了贡献率和贡献遗传效应两个概念,贡献率测定给定分量性状的遗传变异对目标性状的贡献比率,贡献遗传效应衡量给定分量性状的遗传效应对目标性状的贡献值。运用所提出的新方法分析了棉花籽棉产量的3个分量对产量的贡献。     

陆地棉开花成铃性状的遗传研究Ⅲ.不同发育阶段的遗传规律

对4个陆地棉品种(系)双列杂交实验的2年观察资料按包括基因型×环境互作的加性-显性遗传模型进行不同发育阶段开花成铃规律的遗传分析。方差分析表明,开花成铃早期主要受显性效应控制,至中后期加性效应作用逐渐增强,基因型×环境互作效应相对较小。不同发育阶段平均开花成铃数与总铃数的相关分析表明,8月1日前加性相关系数为负数或零值,但存在显著或极显著的显性正相关,8月1日后则相反。不同发育阶段平均开花成铃数的条件遗传分析发现不同时期的基因活动强度不同,7月下旬及8月上中旬最大;检测间隔(t-k)对探讨花铃期基因活动规律有重要作用;选择调查周期时应兼顾实验目的、实验环境条件、入选性状及所处的发育阶段。     

Analysis of Conditional Genetic Effects and Variance Components in Developmental Genetics

A genetic model with additive-dominance effects and genotype X environment interactions is presented for quantitative traits with time-dependent measures. The genetic model for phenotypic means at time t conditional on phenotypic means measured at previous time (t - 1) is defined. Statistical methods are proposed for analyzing conditional genetic effects and conditional genetic variance components. Conditional variances can be estimated by minimum norm quadratic unbiased estimation (MINQUE) method. An adjusted unbiased prediction (AUP) procedure is suggested for predicting conditional genetic effects. A worked example from cotton fruiting data is given for comparison of unconditional and conditional genetic variances and additive effects.     

Genetics of tassel branch number in maize and its implications for a selection program for small tassel size

Summary Tassel branch numbers of six crosses of maize (Zea mays L.) were analyzed to determine inheritance of this trait. Generation mean analyses were used to estimate genetic effects, and additive and nonadditive components of variance were calculated and evaluated for bias due to linkage. Both narrow-sense and broad-sense heritabilities were estimated. Additive genetic variance estimates were significant in five of the six crosses, whereas estimates of variance due to nonadditive components were significant in only three crosses. Additionally, estimates of additive variance components usually were larger than corresponding nonadditive components. There was no evidence for linkage bias in these estimates. Estimates of additive genetic effects were significant in four of six crosses, but significant dominance, additive × additive and additive × dominance effects also were detected. Additive, dominance, and epistatic gene action, therefore, all influenced the inheritance of tassel branch number, but additive gene action was most important. Both narrow-sense and broadsense heritability estimates were larger than those reported for other physiological traits of maize and corroborated conclusions concerning the importance of additive gene action inferred from analyses of genetic effects and variances. We concluded that selection for smalltasseled inbreds could be accomplished most easily through a mass-selection and/or pedigree-selection system. Production of a small-tasseled hybrid would require crossing of two small-tasseled inbreds. We proposed two genetic models to explain unexpected results obtained for two crosses. One model involved five interacting loci and the other employed two loci displaying only additive and additive × additive gene action.Journal Paper No. J-9231 of the Iowa Agriculture and Home Economics Experiment Station, Ames, Iowa 50011. Project No. 2152