烤烟主要农艺性状对产量的遗传贡献率分析

为了研究烤烟主要农艺性状对产量的贡献,以14个品种（系）及其配制的41个杂交组合为材料,利用估算条件方差分量和预测条件遗传效应值的统计方法对多点实验数据进行了分析。结果表明,株高对产量的加性遗传方差贡献率最高,腰叶长对产量的显性遗传方差贡献率最高,各农艺性状对产量的加性X环境互作遗传方差、显性X环境互作遗传方差的贡献率均较小。对产量加性效应贡献最大的农艺性状因不同亲本而异,表明各亲本具有其独特的遗传和发育特性。多数杂交组合产量的显性效应主要受腰叶长影响,因此腰叶长可作为间接选择组合产量显性效应的指标。     

二棱大麦熟期性状的遗传研究

以甘木二条等7个二棱大麦品种进行不完全双列杂交,对其亲本、F1和F2的抽穗期,灌浆期和成熟期三个性状以1992和1995年（播种年份）的两年资料,采用加性-显性-上位性（ADAA）模型进行遗传分析．遗传方差分量的比率估算表明,三个性状都存在上位性作用．除灌浆期外,其余二性状还受显性和加性效应的作用,并以加性为主．显性效应和加性效应与环境的互作均达显著水平,基因效应的预测值表明采用P3（黔浙1号）和P4（浙农大3号）较易获得早熟后代．     

CL系列甘蔗亲本的遗传力及配合力分析

为探讨CL系列甘蔗品种作杂交亲本的遗传特点,采用3×3不完全双列杂交(NCⅡ)遗传设计,估算了7个产量和品质性状的遗传方差、一般配合力(GCA)和特殊配合力(SCA)。结果表明:锤度的遗传主要受母本加性基因效应控制,株高的遗传主要受父母本加性基因控制,而锤重的遗传主要受非加性基因效应所制约;CL83-1163作为母本,糖分配合力高,且能把高糖特性传递给后代,CL88-4730为父本,产量和品质性状的配合力大,其杂交后代表现高产高糖;根据配合力总效应(TCA),综合表现好的组合有CL83-1364×CL88-4730、CL83-1900×CL84-3152、CL83-1163×CL88-4730,可用于今后的甘蔗有性育种计划。     

甘蔗生物量育种的ADGE遗传分析

对甘蔗11个亲本品种及不完全双列杂交(NCdesignⅡ)遗传设计的30个组合的F1代实生苗生物量进行加性-显型-随机环境效应模型(ADGE)分析。结果表明:甘蔗的生物量性状遗传主要是由基因的加性、显性及加性×环境互作效应共同决定的,但基因的加性效应作用较大;甘蔗杂交亲本对其后代表型的遗传作用主要为母本的遗传效应影响;甘蔗生物量性状都具有较高的广义遗传率(h2B)和狭义遗传率(h2N),且h2B>h2N,说明了对甘蔗生物量性状在选育种早期阶段的选择效果好;通过对亲本的加性基因随机效应分析的综合,较优良的甘蔗亲本有粤糖72/426、粤糖79/177、粤糖85/177、ROC24和ROC25;根据杂交组合显性随机效应分析,认为粤糖72/426×ROC16、粤糖79/177×ROC24、粤糖79/177×ROC23及粤糖80/101×ROC22是较优良的高生物量甘蔗杂交组合,可以应用于甘蔗的高生物量育种。     

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。     

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。     

烤烟主要性状配合力和相关性研究

选用7个烤烟品种作亲本,采用Griffing方法I,利用7×7完全双列杂交,对烤烟产量、产值、上等烟比例、均价和级指等主要经济性状配合力和相关性进行研究,结果表明:各农艺性状和经济性状的遗传特性同时受基因加性效应、非加性效应以及反交效应的共同作用.从经济性状来看,以红花大金元和云烟317作亲本之一,特别是红花大金元作母本配制的杂交组合优势较强.以净叶黄作为亲本之一配制的杂交组合没有优势,显示出各组合的产量、产值等经济性状较低.     

冬小麦三种粒重性状的遗传研究

采用Griffing方法Ⅰ,利用6×6完全双列杂交,对冬小麦单株粒重、单穗粒重和千粒重三个性状的配合力、基因效应及遗传组成进行了研究,结果表明这三种粒重性状的遗传同时受基因加性效应、非加性效应和母体效应的共同作用;千粒重、株粒重、穗粒重的狭义遗传力分别为72%、63%和45%,前两个性状以基因加性效应为主,后一性状基因加性、显性效应相当;细胞质作用对千粒重影响较小,株粒重和穗粒重则存在明显的核质互作.     

六倍体小黑麦T型细胞质雄性不育体系杂种优势与配合力的研究

用具提莫菲维小麦细胞质的六倍体小黑麦的3个不育系和3个恢复系作为亲本,进行3×3不完全双列杂交,对所组配的F1代8个农艺性状的杂种优势分析结果表明,除千粒重外,其余性状出现正向超亲优势的组合较少,多数呈低亲或中亲遗传,且各组合间的差异比较显著。配合力分析表明,一般配合力与特殊配合力的方差均达到了显著水平,F1各性状均受基因加性效应和非加性效应共同作用;从总体上看,不育系A1、A2及恢复系R1、R2的一般配合力良好,其配制的组合优势较强,具一定的利用价值。对一般配合力与亲本表型值进行了相关分析,二者无显著的相关关系。     

陆地棉数量性状的遗传分析 Ⅰ.17个农艺性状的基因效应估计

由11组各含P_1、P_2、F_1、F_2、B_1和B_2家系的平均数及其方差,估计了陆地棉有关株型的10个性状和有关产量、品质的7个性状的基因效应。结果表明,株高,主茎节间长,第5、10、15果枝节间长和纤维长度的遗传,除有加性、显性效应外,尚有不可忽略的上位性效应;第1、10果枝与主茎夹角的遗传属加性-显性模型;第1果枝节间长,第5、15果枝与主茎夹角,每株铃数,单铃籽棉重,衣分,霜前籽棉、籽棉和皮棉产量则主要是加性遗传效应。因而一般地说,株型性状的遗传并不比产量性状简单。     

Genetic Analysis for F1 Yield Traits with Conditional Approach in Island Cotton (Gossypium barbadense L.)

A genetic model with additive, dominance and genotype × environment interaction effect was employed to analyze the 3-year data of F₁ hybrids from 5 × 4 diallel cross, whose parents were Island cotton and had different fruit branch types. Unconditional and conditional genetic variances were conducted for analyze genetic impacts of yield components on yield. Results of unconditional genetic variances showed that there were no additive variance of total lint yield. But conditional additive effects of total lint yield, when excluding the phenotype of boll weight, boll number at prefrost, boll number at postfrost, and lint yield at prefrost, indicated that improving the additive effects of the total lint yield was still possible. Crossing and selecting component traits with high contributive additive effects could obtain good offsprings. Yield components contributed large dominance effects to the heterosis of lint yield at prefrost and total lint yield in crosses. Yield component traits were controlled with each other. The traits having positive contributive effects could be applied to further improve target traits.     

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.     

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.     

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.     

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.     

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.