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Phenotypic variation

Tìm thấy 20+ kết quả cho từ khóa "Phenotypic variation"

Comprehensive analysis of morphological variation among 24 tomato (Solanum Lycopersicum) genotypes oriented to ornamental breeding in Vietnam

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Comprehensive Analysis of Morphological Variation among 24 Tomato (Solanum Lycopersicum) Genotypes Oriented to Ornamental Breeding in Vietnam. Tomato is one of the most important vegetables cultivated in Vietnam. Twenty-four heirloom tomato genotypes were evaluated on 19 morphological traits. The results of principle component analysis indicated that three main principle components explained over 60% of the total phenotypic variation.

Variation under domestication in animal models: The case of the Mexican axolotl

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Ani- mals that are used in research experiments present differ- ent levels of phenotypic and genetic variation not only as a result of their natural evolutionary history but, in some cases, also as a consequence of domestication processes.. By means of artificial selection, unusual phenotypes and mutants can be propa- gated in captivity expanding the phenotypic variation of the domesticated populations in relation to their free-living conspecifics [6].

Linkage mapping and genome-wide association study reveals conservative QTL and candidate genes for Fusarium rot resistance in maize

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phenotypic effect in near isogenic lines when in homozygosity. [27] detected a resistance QTL with 10.2% of the phenotypic variation, but no epistatic effects were de- tected.

Mapping and validation of a major QTL for primary root length of soybean seedlings grown in hydroponic conditions

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This qRL16.1 could not explain the whole variation observed between Fendou 16 and K099. QTL analysis showed that qRL16.1 only explained 30.25 % of the total variation in the K099. Fendou 16 RIL population. In the Union × Fendou 16 population, qRL16.1 was detected between markers BARCSOYSSR_16_0698 and Sat_151, explain- ing 14 % of the phenotypic variation.

Autosomal recessive loci contribute significantly to quantitative variation of male fertility in a dairy cattle population

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In a mapping cohort of 3736 BSW bulls from which semen was collected at semen collection centers under standardized conditions, only 10% of the phenotypic variation of bull fertility was explained by autosomal var- iants. Candidate causal variants for recessive traits are fre- quently prioritized if they are compatible with the segrega- tion of the top haplotype . 6 Detailed view of a QTL for bull fertility at BTA18. inheritance of the top haplotype takes genotyping errors into account.

Identification of a major-effect QTL associated with pre-harvest sprouting in cucumber (Cucumis sativus L.) using the QTL-seq method

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This phenotypic variation in the populations indicated that PHS is a quantitative trait controlled by a major- effect QTL.. population grown in 2017 for the construction of the R- and S-pool, respectively.

Mapping QTLs for 1000-grain weight and genes controlling hull type using SNP marker in Tartary buckwheat (Fagopyrum tataricum)

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One major locus detected in all three trials was mapped in the 38.2–39.8 cM region on Chr.1, with an LOD score of and explained for of the phenotypic variation. One was located in the 14.9–22.9 cM region on Chr.1 detected in both 2017 and 2018, accounting for 3.4 and 5.0% of the phenotypic variation, respectively. 4 de- tected in all three trials, explaining 3.1–10.9% of the phenotypic variation (Table 4 and Fig.

Evaluation of genetic diversity, agronomic traits, and anthracnose resistance in the NPGS Sudan Sorghum Core collection

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Popu- lation structure of the Sudan core set explained up to 25% of the observed phenotypic variation in quantitative traits including FL, PH, PL, and PD. Further integration of the Sudan core set with other sorghum diversity panels [8, 16] and NPGS core sets [22] may increase the phenotypic diversity and stat- istical power for the complete elucidation of these com- plex traits.

Unraveling the genetic architecture for carbon and nitrogen related traits and leaf hydraulic conductance in soybean using genome-wide association analyses

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A previous QTL mapping study identified four QTLs explaining 17.7 to 24.7% of the phenotypic variation for the limited leaf hydraulic conductance trait using transpir- ation response to silver nitrate as the measurement for the trait [31]..

Combining information from genome-wide association and multi-tissue gene expression studies to elucidate factors underlying genetic variation for residual feed intake in Australian Angus cattle

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The aim of the current study was to perform a GWAS for RFI with imputed high density (770 K) genotypes in Australian Angus steers to detect significant SNPs statis- tically associated with phenotypic variation in RFI. The estimated heritability for RFI based on the 2190 steers used for the GWAS was using a gen- omic relationship matrix and after correcting for fixed effects of the contemporary groups.

Identification of QTL for resistance to root rot in sweetpotato (Ipomoea batatas (L.) Lam) with SSR linkage maps

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We mapped seven stable QTLs, which ex- plained of the phenotypic variation in root rot resistance. Accordingly, we speculated that the root rot resistance of sweetpotato may be controlled by sev- eral major QTLs. 47.42 and 47.38% of the phenotypic variation, and two QTLs for late leaf spot, explaining 47.63 and 34.03% of phenotypic variation in peanut [28].

GWAS and co-expression network combination uncovers multigenes with close linkage effects on the oleic acid content accumulation in Brassica napus

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Additionally, whole-genome sequencing of 50 rapeseed accessions revealed three genes (BnmtACP2-A02, BnABCI13-A02 and BnECI1-A02) in the A02 chromosome haplotype region and two genes (BnFAD8-C02 and BnSDP1-C02) in the C02 chromosome haplotype region that were closely linked to oleic acid content phenotypic variation.

Gene expression predictions and networks in natural populations supports the omnigenic theory

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On top of the DNA sequence, the superposing layer of transcriptomics adds up the inter- mediate pattern of gene interactions and physiological epistasis, before the final level of phenotypic expression [39]. Our analytical approach, by looking at the specific roles of genes with different networking connectivities, high- lights the importance of the gene system as a whole in explaining phenotypic variation rather than that of par- ticular sets of genes.

A fast-linear mixed model for genome-wide haplotype association analysis: Application to agronomic traits in maize

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and chr4.S explained 7.33 and 7.38% of the phenotypic variation, respectively. The four haplotype alleles accounted for 0.54 to 10.16% of the phenotypic variation, while the three haplotype blocks accounted for and 10.69%, which are quite larger than the corresponding SNPs or haplotype alleles detected.. Additionally, all the detected genetic units were mapped on the annotated genes, especially Chr3Block4589 on two genes with known biological meaning..

High-density genetic linkage map construction and cane cold hardiness QTL mapping for Vitis based on restriction siteassociated DNA sequencing

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A major QTL related to LTP was identified on LG15, corresponding to the confidence interval of 52.42 cM–68.94 cM, explained 7.33% of the total phenotypic variation (Table 4 and Fig. QTL related to LTX was identified on LG2, corresponding to the confidence interval of 59.32 cM–74.88 cM, explained 9.38% of the total phenotypic variation (Table 5 and Fig. R 2 represents the individual contribution of one QTL to the variation in cold hardiness. Cold hardiness phenotypic determination.

Genetic mapping for agronomic traits in a MAGIC population of common bean (Phaseolus vulgaris L.) under drought conditions

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Significant markers identified in genome wide association studies, genetic and physical position, p value, allele frequency, phenotypic effect and founder genotypes associated with 9 traits in the MAGIC population evaluated in and 2016.. Details of QTL identified by interval mapping, genetic and physical position, LOD, phenotypic variation explained, and founders ’ allelic effects mapped for 7 traits in the MAGIC population evaluated in and 2016..

Genome wide association mapping for heat tolerance in sub-tropical maize

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The maximum phenotypic variance was explained by PH (21.10%) followed by AD (15.64%) and GY (15.00%) under normal conditions while ASI (17.63%) showed highest phenotypic variation followed by EH (11.63%) under heat stress.. A total of 20 and 6 significant haplotypes were de- tected, which control more than one trait under normal and heat stress conditions, respectively (Tables 6). and PH, EH and GY, respectively, under normal conditions.

Characterizing the oligogenic architecture of plant growth phenotypes informs genomic selection approaches in a common wheat population

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Identified heading date and adult plant height QTL were fit in a multiple regression model to estimate the proportion of phenotypic variation in plant height on a given day associated with each QTL. Variation in simulated genotype values were normalized by total QTL variation explained, and plotted over time to assess the relative importance of QTL in variation in plant height over time (Fig.

Genome-wide association screening and verification of potential genes associated with root architectural traits in maize (Zea mays L.) at multiple seedling stages

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Results: Using inclusive composite interval mapping, 8 QTLs accounting for of the phenotypic variation in root traits, were detected on chromosomes 1 (qRDW v3 -1-1 and qRDW/SDW v qRBN v qSUA v1 -4-1, qSUA v2 -4-1, and qROV v2 -4-1), and 10 (qTRL v1 -10-1, qRBN v1 -10-1). 17 SNPs were repeatedly detected from at least two growth stages, with several SNPs associated with multiple traits stably identified at all evaluated stages.

Evolution of cis- and trans-regulatory divergence in the chicken genome between two contrasting breeds analyzed using three tissue types at one-day-old

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Both cis- and trans-regulatory variation are play key roles in phenotypic variation [1, 8–10]. Therefore, it is critical to investigate gene regulatory divergence in birds.. The rapid change under domestication offers a unique model for revealing the relative importance of the cis- and trans-regulatory variation underlying phenotypic change.