Phenotypic Variance
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2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Jianhua Zhao ◽  
Haoxia Li ◽  
Yuhui Xu ◽  
Yue Yin ◽  
Ting Huang ◽  
...  

Abstract Background Lycium Linn. (Solanaceae) is a genus of economically important plants producing fruits and leaves with high nutritional value and medicinal benefits. However, genetic analysis of this plant and molecular breeding for quality improvement are limited by the lack of sufficient molecular markers. Results In this study, two parental strains, ‘Ningqi No. 1’ (Lycium barbarum L.) and ‘Yunnan Gouqi’ (Lycium yunnanense Kuang et A.M. Lu), and 200 F1 hybrid individuals were resequenced for genetic analysis. In total, 8,507 well-selected SNPs were developed, and a high-density genetic map (NY map) was constructed with a total genetic distance of 2,122.24 cM. A consensus genetic map was established by integrating the NY map and a previously published genetic map (NC map) containing 15,240 SNPs, with a total genetic distance of 3,058.19 cM and an average map distance of 0.21 cM. The 12 pseudochromosomes of the Lycium reference genome were anchored using this consensus genetic map, with an anchoring rate of 64.3%. Moreover, weak collinearities between the consensus map and the pepper, potato, and tomato genomes were observed. Twenty-five stable QTLs were identified for leaf- and fruit-related phenotypes, including fruit weight, fruit longitude, leaf length, the fruit index, and the leaf index; these stable QTLs were mapped to four different linkage groups, with LOD scores ranging from 2.51 to 19.37 and amounts of phenotypic variance explained from 6.2% to 51.9%. Finally, 82 out of 188 predicted genes underlying stable QTLs for fruit-related traits were differentially expressed according to RNA-seq analysis. Conclusions A chromosome-level assembly can provide a foundation for further functional genomics research for wolfberry. The genomic regions of these stably expressed QTLs could be used as targets for further fine mapping and development of molecular markers for marker-assisted selection (MAS). The present study provided valuable information on saturated SNP markers and reliable QTLs for map-based cloning of functional genes related to yield and morphological traits in Lycium spp.


PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0254526
Author(s):  
Ravindra Ramrao Kale ◽  
Ch. V. Durga Rani ◽  
M. Anila ◽  
H. K. Mahadeva Swamy ◽  
V. P. Bhadana ◽  
...  

With an objective of mapping novel low soil P (Phosphorus) tolerance loci in the non-Pup1 type donor rice line, Wazuhophek, we screened a recombinant inbred line (RIL) mapping population consisting of 330 lines derived from the cross Wazuhophek x Improved Samba Mahsuri (which is highly sensitive to low soil P) in a plot with low soil P for tolerance associated traits. Molecular mapping with SSR markers revealed a total of 16 QTLs (seven major and nine minor QTLs), which are associated with low soil P tolerance related traits. Interestingly, a QTL hotspot, harbouring 10 out of 16 QTLs were identified on the short arm of chromosome 8 (flanked by the makers RM22554 and RM80005). Five major QTLs explaining phenotypic variance to an extent of 15.28%, 17.25%, 21.84%, 20.23%, and 18.50%, associated with the traits, plant height, shoot length, the number of productive tillers, panicle length and yield, respectively, were located in the hotspot. Two major QTLs located on chromosome 1, associated with the traits, total biomass and root to shoot ratio, explaining 15.44% and 15.44% phenotypic variance, respectively were also identified. Complex epistatic interactions were observed among the traits, grain yield per plant, days to 50% flowering, dry shoot weight, and P content of the seed. In-silico analysis of genomic regions flanking the major QTLs revealed the presence of key putative candidate genes, possibly associated with tolerance.


2021 ◽  
Author(s):  
Alex Hubbe ◽  
Guilherme Garcia ◽  
Harley Sebastiao ◽  
Arthur Porto ◽  
Fabio Andrade Machado ◽  
...  

Understanding how development changes the genetic covariance of complex phenotypes is fundamental for the study of evolution. If the genetic covariance changes dramatically during postnatal ontogeny, one cannot infer confidently evolutionary responses based on the genetic covariance estimated from a single postnatal ontogenetic stage. Mammalian skull morphology is a common model system for studying the evolution of complex structures. These studies often involve estimating covariance between traits based on adult individuals. There is robust evidence that covariances changes during ontogeny. However, it is unknown whether differences in age-specific covariances can, in fact, bias evolutionary analyses made at subadult ages. To explore this issue, we sampled two marsupials from the order Didelphimorphia, and one precocial and one altricial placental at different stages of postnatal ontogeny. We calculated the phenotypic variance-covariance matrix (P-matrix) for each genus at these postnatal ontogenetic stages. Then, we compared within genus P-matrices and also P-matrices with available congeneric additive genetic variance-covariance matrices (G-matrices) using Random Skewers and the Krzanowsky projection methods. Our results show that the structural similarity between matrices is in general high (> 0.7). Our study supports that the G-matrix in therian mammals is conserved during most of the postnatal ontogeny. Thus it is feasible to study life-history changes and evolutionary responses based on the covariance estimated from a single ontogenetic stage. Our results also suggest that at least for some marsupials the G-matrix varies considerably prior to weaning, which does not invalidate our previous conclusion because specimens at this stage would experience striking differences in selective regimes than during later ontogenetic stages.


Plant Disease ◽  
2021 ◽  
Author(s):  
Cai Sun ◽  
Yike Liu ◽  
Qiang Li ◽  
Baotong Wang ◽  
Shuhui Chen ◽  
...  

Wheat stripe rust, an airborne fungal disease and caused by Puccinia striiformis Westend. f. sp. tritici (Pst), is one of the most devastating diseases on wheat. It is the most effective and economical measure for the diseases control to identify high-level resistance genes and apply in wheat breeding. Chinese wheat cultivar Xike01015 presents high levels of all stage resistance (ASR) to the current predominant Pst race CYR33. In this study, a single dominant gene, designated as YrXk, was identified in Xike01015 conferring resistance to CYR33 with genetic analysis of F2 and BC1 population from cross of Mingxian169 (susceptible) and Xike01015. The specific length amplified fragment sequencing (SLAF-seq) strategy was used to construct linkage map in the F2 population. QTL analysis mapped YrXk to a 12.4 Mb segment on chromosome1BS, explaining over 86.96% phenotypic variance. Gene annotation in the QTL region identified three differential expressed candidate genes , TraesCS1B02G168600.1, TraesCS1B02G170200.1, and TraesCS1B02G172400.1. The qRT-PCR results displayed that TraesCS1B02G170200.1 and TraesCS1B02G168600.1 significantly up-regulated and down-regulated, respectively, and TraesCS1B02G170200.1 slightly up-regulated after changed with CYR33 in the seedling stage, which indicating these genes may function in wheat resistance to stripe rust. The results of this study can be used in wheat breeding for improving resistance to stripe rust.


2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Qianqian Zhang ◽  
Florian Privé ◽  
Bjarni Vilhjálmsson ◽  
Doug Speed

AbstractMost existing tools for constructing genetic prediction models begin with the assumption that all genetic variants contribute equally towards the phenotype. However, this represents a suboptimal model for how heritability is distributed across the genome. Therefore, we develop prediction tools that allow the user to specify the heritability model. We compare individual-level data prediction tools using 14 UK Biobank phenotypes; our new tool LDAK-Bolt-Predict outperforms the existing tools Lasso, BLUP, Bolt-LMM and BayesR for all 14 phenotypes. We compare summary statistic prediction tools using 225 UK Biobank phenotypes; our new tool LDAK-BayesR-SS outperforms the existing tools lassosum, sBLUP, LDpred and SBayesR for 223 of the 225 phenotypes. When we improve the heritability model, the proportion of phenotypic variance explained increases by on average 14%, which is equivalent to increasing the sample size by a quarter.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Giovanni Bittante ◽  
Simone Savoia ◽  
Alessio Cecchinato ◽  
Sara Pegolo ◽  
Andrea Albera

AbstractSpectroscopic predictions can be used for the genetic improvement of meat quality traits in cattle. No information is however available on the genetics of meat absorbance spectra. This research investigated the phenotypic variation and the heritability of meat absorbance spectra at individual wavelengths in the ultraviolet–visible and near-infrared region (UV–Vis-NIR) obtained with portable spectrometers. Five spectra per instrument were taken on the ribeye surface of 1185 Piemontese young bulls from 93 farms (13,182 Herd-Book pedigree relatives). Linear animal model analyses of 1481 single-wavelengths from UV–Vis-NIRS and 125 from Micro-NIRS were carried out separately. In the overlapping regions, the proportions of phenotypic variance explained by batch/date of slaughter (14 ± 6% and 17 ± 7%,), rearing farm (6 ± 2% and 5 ± 3%), and the residual variances (72 ± 10% and 72 ± 5%) were similar for the UV–Vis-NIRS and Micro-NIRS, but additive genetics (7 ± 2% and 4 ± 2%) and heritability (8.3 ± 2.3% vs 5.1 ± 0.6%) were greater with the Micro-NIRS. Heritability was much greater for the visible fraction (25.2 ± 11.4%), especially the violet, blue and green colors, than for the NIR fraction (5.0 ± 8.0%). These results allow a better understanding of the possibility of using the absorbance of visible and infrared wavelengths correlated with meat quality traits for the genetic improvement in beef cattle.


2021 ◽  
Vol 22 (13) ◽  
pp. 7188
Author(s):  
T. Danakumara ◽  
Jyoti Kumari ◽  
Amit Kumar Singh ◽  
Subodh Kumar Sinha ◽  
Anjan Kumar Pradhan ◽  
...  

Cultivars with efficient root systems play a major role in enhancing resource use efficiency, particularly water absorption, and thus in drought tolerance. In this study, a diverse wheat association panel of 136 wheat accessions including mini core subset was genotyped using Axiom 35k Breeders’ Array to identify genomic regions associated with seedling stage root architecture and shoot traits using multi-locus genome-wide association studies (ML-GWAS). The association panel revealed a wide variation of 1.5- to 50- fold and were grouped into six clusters based on 15 traits. Six different ML-GWAS models revealed 456 significant quantitative trait nucleotides (QTNs) for various traits with phenotypic variance in the range of 0.12–38.60%. Of these, 87 QTNs were repeatedly detected by two or more models and were considered reliable genomic regions for the respective traits. Among these QTNs, eleven were associated with average diameter and nine each for second order lateral root number (SOLRN), root volume (RV) and root length density (RLD). A total of eleven genomic regions were pleiotropic and each controlled two or three traits. Some important candidate genes such as Formin homology 1, Ubiquitin-like domain superfamily and ATP-dependent 6-phosphofructokinase were identified from the associated genomic regions. The genomic regions/genes identified in this study could potentially be targeted for improving root traits and drought tolerance in wheat.


Agriculture ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 604
Author(s):  
Iván Agea ◽  
María de la Luz García ◽  
María-José Argente

A divergent selection for litter size residual variability has been carried out in rabbits during 12 generations. Litter size residual variability was estimated as phenotypic variance of litter size within females after correcting for the year-season and the parity-lactation status effects. Stress causes an increase in core body temperature. Infrared thermography (IRT) has been shown to be a useful technique for identifying changes in body temperature emissivity. The aim of this work is to study the correlated response to selection for litter size residual variability in body temperature emissivity at natural mating. Natural mating can be considered a stressful stimulus for does. Temperature was measured in the eyeball by IRT before mating (basal temperature) and after 5 min, 30 min, and 60 min in does of the lines selected to decrease and to increase litter size residual variability (i.e., the Low and the High lines). Both lines showed similar basal temperature. Eyeball temperature was increased slightly in the Low line from basal state to 5 min after stressful stimulus (from 35.69 °C to 36.32 °C), and this increase remained up to 60 min after stress (36.55 °C). The High line showed a higher temperature than the Low line at 30 min (+0.96 °C, p = 0.99). At 60 min, temperature was similar between lines. The evolution of temperature was different between lines: the High line reached the peak of temperature later than the Low line (at 30 min vs. 5 min), and its peak was higher compared to the Low line (36.95 °C vs. 36.32 °C). In conclusion, the does selected for reducing litter size variability showed a lower increase in temperature after a stressful stimulus, therefore showing lower stress and consequently better welfare.


Agronomy ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 1298
Author(s):  
Júlia Halász ◽  
Attila Hegedűs ◽  
Ildikó Karsai ◽  
Ágnes Tósaki ◽  
László Szalay

Knowledge of dormancy traits are important in peach breeding. Traditional method selection of seedlings takes a long time because of the juvenile period of plants; therefore, novel application of marker assisted selection methods are needed to accelerate this work. The aims of this study were to test the extent of variability in the PpSOC1 gene among 16 peach cultivars and to establish whether the variability of SOC1 can be used as a functional marker for the timing of endodormancy break based on a 14-year phenology evaluation covering nine consecutive phenology phases, from string stage to ripening. Based on an SSR motif of SOC1, three allele categories were detected: one peach cultivar was heterozygous (203/209), while five of the 15 homozygous cultivars carried a 203 bp allele and the remainder were characterized with 218 bp. There were significant correlations between the PpSOC1 alleles and the various phenology phases, the strongest one being observed at the string stage, marking the end of endodormancy. At this stage, PpSOC1 explained 82.6% of the phenotypic variance; cultivars with the 203 allele reached the string stage 11.7 days earlier than those with 218 bp allele. This finding makes the PpSOC1 screening a valuable method in breeding.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Camila U. Braz ◽  
Troy N. Rowan ◽  
Robert D. Schnabel ◽  
Jared E. Decker

AbstractUnderstanding genotype-by-environment interactions (G × E) is crucial to understand environmental adaptation in mammals and improve the sustainability of agricultural production. Here, we present an extensive study investigating the interaction of genome-wide SNP markers with a vast assortment of environmental variables and searching for SNPs controlling phenotypic variance (vQTL) using a large beef cattle dataset. We showed that G × E contribute 10.1%, 3.8%, and 2.8% of the phenotypic variance of birth weight, weaning weight, and yearling weight, respectively. G × E genome-wide association analysis (GWAA) detected a large number of G × E loci affecting growth traits, which the traditional GWAA did not detect, showing that functional loci may have non-additive genetic effects regardless of differences in genotypic means. Further, variance-heterogeneity GWAA detected loci enriched with G × E effects without requiring prior knowledge of the interacting environmental factors. Functional annotation and pathway analysis of G × E genes revealed biological mechanisms by which cattle respond to changes in their environment, such as neurotransmitter activity, hypoxia-induced processes, keratinization, hormone, thermogenic and immune pathways. We unraveled the relevance and complexity of the genetic basis of G × E underlying growth traits, providing new insights into how different environmental conditions interact with specific genes influencing adaptation and productivity in beef cattle and potentially across mammals.


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