The relation between the genetic architecture of quantitative traits and long-term genetic response

The genetic architecture of a quantitative trait refers to the number of genetic variants, allele frequencies, and effect sizes of variants that affect a trait and their mode of gene action. This study was conducted to investigate the effect of four shapes of allelic frequency distributions (constant, uniform, L-shaped and U-shaped) and different number of trait-affecting loci (50, 100, 200, 500) on allelic frequency changes, long term genetic response, and maintaining genetic variance. To this end, a population of 440 individuals composed of 40 males and 400 females as well as a genome of 200 cM consisting of two chromosomes and with a mutation rate of 2.5?×?10^?5 per locus was simulated. Selection of superior animals was done using best linear unbiased prediction (BLUP) with assumption of infinitesimal model. Selection intensity was constant over 30 generations of selection. The highest genetic progress obtained when the allelic frequency had L-shaped distribution and number of trait-affecting loci was high (500). Although quantitative genetic theories predict the extinction of genetic variance due to artificial selection in long time, our results showed that under L- and U-shapped allelic frequency distributions, the additive genetic variance is persistent after 30 generations of selection. Further, presence or absence of selection limit can be an indication of low (<50) or high (>100) number of trait-affecting loci, respectively. It was concluded that the genetic architecture of complex traits is an important subject which should be considered in studies concerning long-term response to selection.     

Predicting the response to simultaneous selection: genetic architecture and physiological constraints

A great deal is known about the evolutionary significance of body size and development time. They are determined by the nonlinear interaction of three physiological traits: two hormonal events and growth rate (GR). In this study we investigate how the genetic architecture of the underlying three physiological traits affects the simultaneous response to selection on the two life-history traits in the hawkmoth Manduca sexta. The genetic architecture suggests that when the two life-history traits are both selected in the same direction (to increase or decrease) the response to selection is primarily determined by the hormonal mechanism. When the life-history traits are selected in opposite directions (one to increase and one to decrease) the response to selection is primarily determined by factors that affect the GR. To determine how the physiological traits affect the response to selection of the life-history traits, we simulated the predicted response to 10 generations of selection. A total of 83% of our predictions were supported by the simulation. The main components of this physiological framework also exist in unicellular organisms, vertebrates, and plants and can thus provide a robust framework for understanding how underlying physiology can determine the simultaneous evolution of life-history traits.     

Characterization of QTL for oil content in maize kernel

Kernel oil content in maize is a complex quantitative trait. Phenotypic variation in kernel oil content can be dissected into its component traits such as oil metabolism and physical characteristics of the kernel, including embryo size and embryo-to-endosperm weight ratio (EEWR). To characterize quantitative trait loci (QTL) for kernel oil content, a recombinant inbred population derived from a cross between normal line B73 and high-oil line By804 was genotyped using 228 molecular markers and phenotyped for kernel oil content and its component traits [embryo oil content, embryo oil concentration, EEWR, embryo volume, embryo width, embryo length, and embryo width-to-length ratio (EWLR)]. A total of 58 QTL were identified for kernel oil content and its component traits in 26 genomic regions across all chromosomes. Eight main-effect QTL were identified for kernel oil content, embryo oil content, embryo oil concentration, EEWR, embryo weight, and EWLR, each accounting for over 10?% of the phenotypic variation in six genomic regions. Over 90?% of QTL identified for kernel oil content co-localized with QTL for component traits, validating their molecular contribution to kernel oil content. On chromosome 1, the QTL that had the largest effect on kernel oil content (qKO1-1) was associated with embryo width; on chromosome 9, the QTL for kernel oil content (qKO9) was related to EEWR (qEEWR9). Embryo oil concentration and embryo width were identified as the most important component traits controlling the second largest QTL for kernel oil content on chromosome 6 (qKO6) and a minor QTL for kernel oil content on chromosome 5 (qKO5-2), respectively. The dissection of kernel oil QTL will facilitate future cloning and/or functional validation of kernel oil content, and help to elucidate the genetic basis of kernel oil content in maize.     

Analysis of the genetic architecture of maize kernel size traits by combined linkage and association mapping

Kernel size‐related traits are the most direct traits correlating with grain yield. The genetic basis of three kernel traits of maize, kernel length (KL), kernel width (KW) and kernel thickness (KT), was investigated in an association panel and a biparental population. A total of 21 single nucleotide polymorphisms (SNPs) were detected to be most significantly (P < 2.25 × 10^?6) associated with these three traits in the association panel under four environments. Furthermore, 50 quantitative trait loci (QTL) controlling these traits were detected in seven environments in the intermated B73 × Mo17 (IBM) Syn10 doubled haploid (DH) population, of which eight were repetitively identified in at least three environments. Combining the two mapping populations revealed that 56 SNPs (P < 1 × 10^?3) fell within 18 of the QTL confidence intervals. According to the top significant SNPs, stable‐effect SNPs and the co‐localized SNPs by association analysis and linkage mapping, a total of 73 candidate genes were identified, regulating seed development. Additionally, seven miRNAs were found to situate within the linkage disequilibrium (LD) regions of the co‐localized SNPs, of which zma‐miR164e was demonstrated to cleave the mRNAs of Arabidopsis CUC1, CUC2 and NAC6 in vitro. Overexpression of zma‐miR164e resulted in the down‐regulation of these genes above and the failure of seed formation in Arabidopsis pods, with the increased branch number. These findings provide insights into the mechanism of seed development and the improvement of molecular marker‐assisted selection (MAS) for high‐yield breeding in maize.     

The genetic architecture of nodal root number in maize

The maize nodal root system plays a crucial role in the development of the aboveground plant and determines the yield via the uptake of water and nutrients in the field. However, the genetic architecture of the maize nodal root system is not well understood, and it has become the ‘dark matter’ of maize genetics. Here, a large teosinte‐maize population was analyzed, and high‐resolution mapping revealed that 62 out of 133 quantitative trait loci (QTLs), accounting for approximately half of the total genetic variation in nodal root number, were derived from QTLs for flowering time, which was further validated through a transgenic analysis and a genome‐wide association study. However, only 16% of the total genetic variation in nodal root number was derived from QTLs for plant height. These results gave a hint that flowering time played a key role in shaping nodal root number via indirect selection during maize domestication. Our results also supported that more aerial nodal roots and fewer crown roots might be favored in temperate maize, and this root architecture might efficiently improve root‐lodging resistance and the ability to take up deep water and nitrogen under dense planting.     

Predicting long-term response to selection

Lande's equation for predicting the response of trait means to a shift in optimal trait values is tested using a stochastic simulation model. The simulated population is finite, and each individual has a finite number of loci. Therefore, selection may cause allele frequencies and distributions to change over time. Since the equation assumes constant genetic parameters, the degree to which such allelic changes affect predictions can be examined. Predictions are based only on information available at generation zero of directional selection. The quality of the predictions depends on the nature of allelic distributions in the original population. If allelic effects are approximately normally distributed, as assumed in Lande's Gaussian approximation to the continuum-of-alleles model, the predictions are very accurate, despite small changes in the G matrix. If allelic effects have a leptokurtic distribution, as is likely in Turelli's 'house-of-cards' approximation, the equation underestimates the rate of response and correlated response, and overestimates the time required for the trait means to reach their equilibrium values. Models with biallelic loci have limits as to the amount of trait divergence possible, since only two allelic values are available at each of a finite set of loci. If the new optimal trait values lie within these limits, predictions are good, if not, singularity in the G matrix results in suboptimal equilibria, despite the presence of genetic variance for each individual trait.     

Sporophytic response to pollen selection for Alachlor tolerance in maize

In order to assess the efficiency of male gametophytic selection (MGS) for crop improvement, pollen selection for tolerance to herbicide was applied in maize. The experiment was designed to test the parallel reactivity to Alachlor of pollen and plants grown in controlled conditions or in the field, the response to pollen selection in the sporophytic progeny, the response to a second cycle of MGS, and the transmission of the selected trait to the following generations. The results demonstrated that pollen assay can be used to predict Alachlor tolerance under field conditions and to monitor the response to selection. A positive response to selection applied to pollen in the sporophytic progeny was obtained in diverse genetic backgrounds, indicating that the technique can be generally included in standard breeding programs; the analysis of the data produced in a second selection cycle indicated that the selected trait is maintained in the next generation.     

Expression of fungal diacylglycerol acyltransferase2 genes to increase kernel oil in maize

Maize (Zea mays) oil has high value but is only about 4% of the grain by weight. To increase kernel oil content, fungal diacylglycerol acyltransferase2 (DGAT2) genes from Umbelopsis (formerly Mortierella) ramanniana and Neurospora crassa were introduced into maize using an embryo-enhanced promoter. The protein encoded by the N. crassa gene was longer than that of U. ramanniana. It included 353 amino acids that aligned to the U. ramanniana DGAT2A protein and a 243-amino acid sequence at the amino terminus that was unique to the N. crassa DGAT2 protein. Two forms of N. crassa DGAT2 were tested: the predicted full-length protein (L-NcDGAT2) and a shorter form (S-NcDGAT2) that encoded just the sequences that share homology with the U. ramanniana protein. Expression of all three transgenes in maize resulted in small but statistically significant increases in kernel oil. S-NcDGAT2 had the biggest impact on kernel oil, with a 26% (relative) increase in oil in kernels of the best events (inbred). Increases in kernel oil were also obtained in both conventional and high-oil hybrids, and grain yield was not affected by expression of these fungal DGAT2 transgenes.     

Forward and reverse response to artificial selection

Summary The effect of t generations of reverse selection after t generations of forward selection can be described by expressing the change in the metric mean resulting from reverse selection (R) interms ofthe change in the metric mean due to the previous forward selection (x). An additive model of artificial selection in a population of effective size N with no natural selection has been considered.If reverse selection is continued for as many generations as the previous forward selection (t=t), then the ratio R/x equals 1 – F where F is the inbreeding coefficient for a neutral locus at generation t and is estimated as [1–(1–1/2N)^t]. The result of a single generation of reverse selection (t=1) following t generations of forward selection can be described in terms of the ratio NR₁/Dx where R₁ is the response to the first generation of reverse selection. The value of NR₁/x is expected to be (1–F) /2F.For any period of reverse selection following any period of forward selection, the value of R/x never exceeds t /t, and tends to decrease exponentially from this value as t increases.     

The evolution of genetic architecture under frequency-dependent disruptive selection

We propose a model to analyze a quantitative trait under frequency-dependent disruptive selection. Selection on the trait is a combination of stabilizing selection and intraspecific competition, where competition is maximal between individuals with equal phenotypes. In addition, there is a density-dependent component induced by population regulation. The trait is determined additively by a number of biallelic loci, which can have different effects on the trait value. In contrast to most previous models, we assume that the allelic effects at the loci can evolve due to epistatic interactions with the genetic background. Using a modifier approach, we derive analytical results under the assumption of weak selection and constant population size, and we investigate the full model by numerical simulations. We find that frequency-dependent disruptive selection favors the evolution of a highly asymmetric genetic architecture, where most of the genetic variation is concentrated on a small number of loci. We show that the evolution of genetic architecture can be understood in terms of the ecological niches created by competition. The phenotypic distribution of a population with an adapted genetic architecture closely matches this niche structure. Thus, evolution of the genetic architecture seems to be a plausible way for populations to adapt to regimes of frequency-dependent disruptive selection. As such, it should be seen as a potential evolutionary pathway to discrete polymorphisms and as a potential alternative to other evolutionary responses, such as the evolution of sexual dimorphism or assortative mating.     

Fatty acid composition of oil during maize kernel development

Dry wt, total oil and fatty acid composition of the oil was determined during kernel development of three maize inbreds. There was significant variability among these inbreds for duration and rate of dry wt, oil and fatty acid accumulation. Relative quantities of the component fatty acids changed as the kernels developed. Palmitic and linolenic decreased while oleic and stearic increased with maturity. Inbred C103, the higher oil inbred, appeared to accumulate oil over a longer period of time while inbred Hy2, the lower oil inbred, accumulated oil over a shorter period of time. However, the latter had higher daily rates of synthesis for some of the unsaturated fatty acids.     

Genomic screening for artificial selection during domestication and improvement in maize

BACKGROUND: Artificial selection results in phenotypic evolution. Maize (Zea mays L. ssp. mays) was domesticated from its wild progenitor teosinte (Zea mays subspecies parviglumis) through a single domestication event in southern Mexico between 6000 and 9000 years ago. This domestication event resulted in the original maize landrace varieties. The landraces provided the genetic material for modern plant breeders to select improved varieties and inbred lines by enhancing traits controlling agricultural productivity and performance. Artificial selection during domestication and crop improvement involved selection of specific alleles at genes controlling key morphological and agronomic traits, resulting in reduced genetic diversity relative to unselected genes. SCOPE: This review is a summary of research on the identification and characterization by population genetics approaches of genes affected by artificial selection in maize. CONCLUSIONS: Analysis of DNA sequence diversity at a large number of genes in a sample of teosintes and maize inbred lines indicated that approx. 2 % of maize genes exhibit evidence of artificial selection. The remaining genes give evidence of a population bottleneck associated with domestication and crop improvement. In a second study to efficiently identify selected genes, the genes with zero sequence diversity in maize inbreds were chosen as potential targets of selection and sequenced in diverse maize landraces and teosintes, resulting in about half of candidate genes exhibiting evidence for artificial selection. Extended gene sequencing demonstrated a low false-positive rate in the approach. The selected genes have functions consistent with agronomic selection for plant growth, nutritional quality and maturity. Large-scale screening for artificial selection allows identification of genes of potential agronomic importance even when gene function and the phenotype of interest are unknown. These approaches should also be applicable to other domesticated species if specific demographic conditions during domestication exist.     

The joint effects of kin, multilevel selection and indirect genetic effects on response to genetic selection

Kin and levels-of-selection models are common approaches for modelling social evolution. Indirect genetic effect (IGE) models represent a different approach, specifying social effects on trait values rather than fitness. We investigate the joint effect of relatedness, multilevel selection and IGEs on response to selection. We present a measure for the degree of multilevel selection, which is the natural partner of relatedness in expressions for response. Response depends on both relatedness and the degree of multilevel selection, rather than only one or the other factor. Moreover, response is symmetric in relatedness and the degree of multilevel selection, indicating that both factors have exactly the same effect. Without IGEs, the key parameter is the product of relatedness and the degree of multilevel selection. With IGEs, however, multilevel selection without relatedness can explain evolution of social traits. Thus, next to relatedness and multilevel selection, IGEs are a key element in the genetical theory of social evolution.