Developments in quantitative genetics methodology as applied to national genetic improvement programs for swine

Genetic selection in pigs through BLUP was very successful. However, strong selection mainly on growth and number of born alive decreased fitness and reduced environmental changes that animals can tolerate especially under suboptimal environments. Additional challenges are genetic differences between purebreds (selected animals) and crossbreds (commercial animals), and possibly different environments for these groups of animals. A successful genetic selection at this time requires comprehensive data for all levels of the pyramid, multitrait models for a variety of traits including categorical and survival, and software that can implement complicated models while supporting large data sets. Many projects in pig genetic evaluation are carried out at the University of Georgia. Those studies are supported by software family called BGF90.

Detecting polygenic selection in marine populations by combining population genomics and quantitative genetics approaches

Highly fecund marine species with dispersive life-history stages often display large population sizes and wide geographic distribution ranges. Consequently, they are expected to experience reduced genetic drift, efficient selection fueled by frequent adaptive mutations, and high migration loads. This has important consequences for understanding how local adaptation proceeds in the sea. A key issue in this regard, relates to the genetic architecture underlying fitness traits. Theory predicts that adaptation may involve many genes but with a high variance in effect size. Therefore, the effect of selection on allele frequencies may be substantial for the largest effect size loci, but insignificant for small effect genes. In such a context, the performance of population genomic methods to unravel the genetic basis of adaptation depends on the fraction of adaptive genetic variance explained by the cumulative effect of outlier loci. Here, we address some methodological challenges associated with the detection of local adaptation using molecular approaches. We provide an overview of genome scan methods to detect selection, including those assuming complex demographic models that better describe spatial population structure. We then focus on quantitative genetics approaches that search for genotype-phenotype associations at different genomic scales, including genome-wide methods evaluating the cumulative effect of variants. We argue that the limited power of single locus tests can be alleviated by the use of polygenic scores to estimate the joint contribution of candidate variants to phenotypic variation.

Hyperspectral reflectance-based phenotyping for quantitative genetics in crops: Progress and challenges

Many biochemical and physiological properties of plants that are of interest to breeders and geneticists have extremely low throughput and/or can only be measured destructively.This has limited the use of information on natural variation in nutrient and metabolite abundance,as well as photosynthetic capacity in quantitative genetic contexts where it is necessary to collect data from hundreds or thousands of plants.A number of recent studies have demonstrated the potential to estimate many of these traits from hyperspectral reflectance data,primarily in ecophysiological contexts.Here,we summarize recent advances in the use of hyperspectral reflectance data for plant phenotyping,and discuss both the potential benefits and remaining challenges to its application in plant genetics contexts.The performances of previously published models in estimating six traits fromhyperspectral reflectance data in maizewere evaluated on newsample datasets,and the resulting predicted trait values shown to be heritable(e.g.,explained by genetic factors)were estimated.The adoption of hyperspectral reflectance-based phenotyping beyond its current uses may accelerate the study of genes controlling natural variation in biochemical and physiological traits.

Dissection of Genetic Effects of Quantitative Trait Loci(QTL) in Transgenic Cotton

When alien DNA inserts into cotton genome in multi-copy manner,several QTL in cotton genome are disrupted,which are called dQTL in this study.Transgenic mutant line is near-isogenic to its recipient which is divergent for the dQTL from remaining QTL.So,a set of data from a

Selection for growth performance of tank-reared Pacific white shrimp, Litopenaeus vannamei

Seven growth-related traits were measured to assess the selection response and genetic parameters of the growth of Pacific white shrimp, Litopenaeus vannamei, which had been domesticated in tanks for more than four generations. Phenotypic and genetic parameters were evaluated and fitted to an animal model. Realized response was measured from the difference between the mean growth rates of selected and control families. Realized heritability was determined from the ratio of the selection responses and selection differentials. The animal model heritability estimate over generations was 0.44±0.09 for body weight (BW), and ranged from 0.21±0.08 to 0.37±0.06 for size traits. Genetic correlations of phenotypic traits were more variable (0.51-0.97), although correlations among various traits were high (>0.83). Across generations, BW and size traits increased, while selection response and heritability gradually decreased. Selection responses were 12.28%-23.35% for harvest weight and 3.58%-13.53% for size traits. Heritability estimates ranged from 0.34±0.09 to 0.48±0.15 for harvest weight and 0.17±0.01-0.38±0.11 for size traits. All phenotypic and genetic parameters differed between various treatments. To conclude, the results demonstrated a potential for mass selection of growth traits in L. vannamei. A breeding scheme could use this information to integrate the effectiveness constituent traits into an index to achieve genetic progress.

Bioinformatic Tools for Polyploid Crops

Polyploidy is common among agriculturally important crops. Popular genetic methods and their implementations cannot always be applied to polyploid genetic data. We give an overview about available tools and their limitations in terms of levels of ploidy, auto- and allo-ploidy. The main classes of tools are genotype calling, linkage mapping and haplotyping. The usability of the tools is discussed with a focus on their applicability to data sets produced by state of the art technologies. We show that many challenges remain until the toolset for polyploidy provides similar functionalities as those which are already available for diploids. Some tools have been developed over a decade ago and are now outdated. In addition, we discuss necessary steps to overcome this shortage in the future.

Estimation of Genetic Parameters of Turiacu Pineapple Clones and Genetic Correlation between Traits

This study aimed to estimate genetic parameters of Turiaçu pineapple clones using mixed models;and estimate genetic correlation coefficient between traits, to provide important information to breeding programs. For this, 19 pineapple plants were visually selected and its slips were collected and planted in a single environment and nine traits were evaluated. The genetic parameters of most evaluated traits presented significant differences between the 19 clones. Some traits, such as Fusarium wilt resistance, showed high magnitudes of heritability. Clones did not differ in relation to total titratable acidity (TTA) and total soluble solids (TSS). However, these traits, which have implications to fruit quality, showed to be negatively correlated to fruit mass (-0.46 and -0.67). The results indicate that the population of Turiaçu pineapple is suitable for selection, especially for Fusarium wilt resistance. Also, Turiaçu clones can be used in breeding programs of pineapple.

Identification of genomic regions determining flower and pod numbers development in soybean (Glycine max L.)

Flower and pod numbers per plant are important agronomic traits underlying soybean yield. So far quantitative trait loci （QTL） de- tected for flower and pod-related traits have mainly focused on the final stage, and might therefore have ignored genetic effects expressed during a specific developmental stage. Here, dynamic expressions of QTL for flower and pod numbers were identified using 152 recom- binant inbred lines （RILs） and a linkage map of 306 markers. Wide genetic variation was found among RILs; 17 unconditional and 18 conditional QTL were detected for the two traits at different developmental stages over two years. Some QTL were detected only at one stage and others across two or more stages, indicating that soybean flower and pod numbers development may be governed by time-dependent gene expression. Three main QTL （qfn-Chrl8-2, qfn-Chr20-1, and qfn-Chr19） were detected for flower number, and two main QTL （qpn-Chrll and qpn-Chr20） were detected for pod number. The phenotypic variation explained by them ranged from 6.1% to 34.7%. The markers linked to these QTL could be used in marker-assisted selection for increasing soybean flower and pod numbers, with the ultimate aim of increasing soybean yield. Comparison of the QTL regions for flower and pod numbers traits with the related genes reported previously showed that seven and four related genes were located in the QTL regions of qfn-Chr11 and qfn-Chr19, respectively. These results provide a basis for free mapping and cloning of flower and pod development-related genes.

Evolutionary responses to climate change and contaminants： Evidence and experimental approaches

A fundamental objective within ecotoxicology lies in understanding and predicting effects of contaminants. This ob- jective is made more challenging when global climate change is considered as an environmental stress that co-occurs with con- taminant exposure. In this multi-stressor context, evolutionary processes are particularly important. In this paper, we consider several non-＂omic＂ approaches wherein evolutionary responses to stress have been studied and discuss those amenable to a mul- tiple stressor context. Specifically, we discuss common-garden designs, artificial and quasi-natural selection, and the estimation of adaptive potential using quantitative genetics as methods for studying evolutionary responses to contaminants and climate change in the absence of expensive molecular tools. While all approaches shed light on potential evolutionary impacts of stressor exposure, they also have limitations. These include logistical constraints, difficulty extrapolating to real systems, and responses tied strongly to specific taxa, populations, and/or testing conditions. The most effective way to lessen these inherent limitations is likely through inclusion of complementary physiological and molecular tools, when available. We believe that an evolutionary context to the study of contaminants and global climate change is a high priority in ecotoxicology and we outline methods that can be implemented by almost any researcher but will also provide valuable insights [Current Zoology 61 （4）： 690-701, 2015].

Genome–Phenome Wide Association in Maize and Arabidopsis Identifies a Common Molecular and Evolutionary Signature

Linking natural genetic variation to trait variation can help determine the functional roles ofdifferent genes.Variations of one or several traits are often assessed separately.High-throughput phenotyping and data mining can capture dozens or hundreds of traits from the same individuals.Here,we test the association between markers within a gene and many traits simultaneously.This genome–phenome wide association study(GPWAS)is both a multi-marker and multi-trait test.Genes identified using GPWAS with 260 phenotypic traits in maize were enriched for genes independently linked to phenotypic variation.Traits associated with classical mutants were consistent with reported phenotypes for mutant alleles.Genes linked to phenomic variation in maize using GPWAS shared molecular,population genetic,and evolutionary features with classical mutants in maize.Genes linked to phenomic variation in Arabidopsis using GPWAS are significantly enriched in genes with known loss-of-function phenotypes.GPWAS may be an effective strategy to identify genes in which loss-of-function alleles produce mutant phenotypes.The shared signatures present in classical mutants and genes identified using GPWAS may be markers for genes with a role in specifying plant phenotypes generally or pleiotropy specifically.