scholarly journals Quantitative trait loci for starch-corrected chip color after harvest, cold storage and after reconditioning mapped in diploid potato

2019 ◽  
Vol 295 (1) ◽  
pp. 209-219 ◽  
Author(s):  
Dorota Sołtys-Kalina ◽  
Katarzyna Szajko ◽  
Iwona Wasilewicz-Flis ◽  
Dariusz Mańkowski ◽  
Waldemar Marczewski ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Cold Storage ◽  
Quantitative Trait ◽  
Genetic Factors ◽  
Starch Content ◽  
Chip Color ◽  
Diploid Potato ◽  
Trait Loci ◽  
On Chip ◽  
Variance Explained

Abstract The objective of this study was to map the quantitative trait loci (QTLs) for chip color after harvest (AH), cold storage (CS) and after reconditioning (RC) in diploid potato and compare them with QTLs for starch-corrected chip color. Chip color traits AH, CS, and RC significantly correlated with tuber starch content (TSC). To limit the effect of starch content, the chip color was corrected for TSC. The QTLs for chip color (AH, CS, and RC) and the starch-corrected chip color determined with the starch content after harvest (SCAH), after cold storage (SCCS) and after reconditioning (SCRC) were compared to assess the extent of the effect of starch and the location of genetic factors underlying this effect on chip color. We detected QTLs for the AH, CS, RC and starch-corrected traits on ten potato chromosomes, confirming the polygenic nature of the traits. The QTLs with the strongest effects were detected on chromosomes I (AH, 0 cM, 11.5% of variance explained), IV (CS, 43.9 cM, 12.7%) and I (RC, 49.7 cM, 14.1%). When starch correction was applied, the QTLs with the strongest effects were revealed on chromosomes VIII (SCAH, 39.3 cM, 10.8% of variance explained), XI (SCCS, 79.5 cM, 10.9%) and IV (SCRC, 43.9 cM, 10.8%). Applying the starch correction changed the landscape of QTLs for chip color, as some QTLs became statistically insignificant, shifted or were refined, and new QTLs were detected for SCAH. The QTLs on chromosomes I and IV were significant for all traits with and without starch correction.

scholarly journals On the Differences Between Maximum Likelihood and Regression Interval Mapping in the Analysis of Quantitative Trait Loci

Genetics ◽  
2000 ◽  
Vol 156 (2) ◽  
pp. 855-865 ◽  
Author(s):  
Chen-Hung Kao
Keyword(s):  
Quantitative Trait Loci ◽  
Maximum Likelihood ◽  
Qtl Mapping ◽  
Quantitative Trait ◽  
Mean Squared Error ◽  
Interval Mapping ◽  
Size Difference ◽  
Ratio Test ◽  
Trait Loci ◽  
Variance Explained

AbstractThe differences between maximum-likelihood (ML) and regression (REG) interval mapping in the analysis of quantitative trait loci (QTL) are investigated analytically and numerically by simulation. The analytical investigation is based on the comparison of the solution sets of the ML and REG methods in the estimation of QTL parameters. Their differences are found to relate to the similarity between the conditional posterior and conditional probabilities of QTL genotypes and depend on several factors, such as the proportion of variance explained by QTL, relative QTL position in an interval, interval size, difference between the sizes of QTL, epistasis, and linkage between QTL. The differences in mean squared error (MSE) of the estimates, likelihood-ratio test (LRT) statistics in testing parameters, and power of QTL detection between the two methods become larger as (1) the proportion of variance explained by QTL becomes higher, (2) the QTL locations are positioned toward the middle of intervals, (3) the QTL are located in wider marker intervals, (4) epistasis between QTL is stronger, (5) the difference between QTL effects becomes larger, and (6) the positions of QTL get closer in QTL mapping. The REG method is biased in the estimation of the proportion of variance explained by QTL, and it may have a serious problem in detecting closely linked QTL when compared to the ML method. In general, the differences between the two methods may be minor, but can be significant when QTL interact or are closely linked. The ML method tends to be more powerful and to give estimates with smaller MSEs and larger LRT statistics. This implies that ML interval mapping can be more accurate, precise, and powerful than REG interval mapping. The REG method is faster in computation, especially when the number of QTL considered in the model is large. Recognizing the factors affecting the differences between REG and ML interval mapping can help an efficient strategy, using both methods in QTL mapping to be outlined.

Empirical Nonparametric Bootstrap Strategies in Quantitative Trait Loci Mapping: Conditioning on the Genetic Model

Genetics ◽  
1998 ◽  
Vol 148 (1) ◽  
pp. 525-535
Author(s):  
Claude M Lebreton ◽  
Peter M Visscher
Keyword(s):  
Quantitative Trait Loci ◽  
Confidence Interval ◽  
Confidence Intervals ◽  
Quantitative Trait ◽  
Genetic Factors ◽  
Genetic Model ◽  
Original Data ◽  
Nonparametric Bootstrap ◽  
Test Protocol ◽  
Trait Loci

AbstractSeveral nonparametric bootstrap methods are tested to obtain better confidence intervals for the quantitative trait loci (QTL) positions, i.e., with minimal width and unbiased coverage probability. Two selective resampling schemes are proposed as a means of conditioning the bootstrap on the number of genetic factors in our model inferred from the original data. The selection is based on criteria related to the estimated number of genetic factors, and only the retained bootstrapped samples will contribute a value to the empirically estimated distribution of the QTL position estimate. These schemes are compared with a nonselective scheme across a range of simple configurations of one QTL on a one-chromosome genome. In particular, the effect of the chromosome length and the relative position of the QTL are examined for a given experimental power, which determines the confidence interval size. With the test protocol used, it appears that the selective resampling schemes are either unbiased or least biased when the QTL is situated near the middle of the chromosome. When the QTL is closer to one end, the likelihood curve of its position along the chromosome becomes truncated, and the nonselective scheme then performs better inasmuch as the percentage of estimated confidence intervals that actually contain the real QTL's position is closer to expectation. The nonselective method, however, produces larger confidence intervals. Hence, we advocate use of the selective methods, regardless of the QTL position along the chromosome (to reduce confidence interval sizes), but we leave the problem open as to how the method should be altered to take into account the bias of the original estimate of the QTL's position.

Quantitative trait loci underlying root yield and starch content in an F1 derived cassava population (Manihot esculenta Crantz)

2016 ◽  
Vol 155 (4) ◽  
pp. 569-581 ◽  
Author(s):  
S. SRAPHET ◽  
A. BOONCHANAWIWAT ◽  
T. THANYASIRIWAT ◽  
R. THAIKERT ◽  
S. WHANKAEW ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Candidate Genes ◽  
Quantitative Trait ◽  
Manihot Esculenta ◽  
Starch Content ◽  
Glycosyl Hydrolases ◽  
Root Yield ◽  
Manihot Esculenta Crantz ◽  
Trait Loci ◽  
Gene Expression Studies

SUMMARYCassava (Manihot esculenta Crantz) root yield measured as fresh weight (hereafter root yield) is declining in much of Asia and Africa. The current study aimed to identify quantitative trait loci (QTL) underlying both root and starch fresh weights in F1 cassava. Eight QTL were associated with root yield, underlying 12·9–40·0% of the phenotypic variation (PVE). Nine QTL were associated with starch content, underlying 11·3–27·3% of the PVE. Quantitative trait loci were identified from four different environments that encompassed two locations and 3 years. Consistent QTL for root yield, YLD5_R11 and YLD8_L09 on linkage group (LG) 16, were detected across years and locations. Quantitative trait loci for starch content, ST3_R09, ST6_R10 and ST7_R11 on LG 11, were found across 3 years. Co-localization of QTL for both traits with positive correlation was detected between YLD3_R10 and ST5_R10 on LG 9. Candidate genes within the QTL that were consistent across multiple environments were identified based on cassava genome sequences. Genes predicted to encode for glycosyl hydrolases, uridine 5’-diphospho-(UDP)-glucuronosyl transferases and UDP-glucosyl transferases were found among the 44 genes located within the region containing the QTL controlling starch content. Sixteen genes predicted to encode proteins that were possibly associated with root yield were identified. The QTL controlling root yield and starch content in the current study will be useful for molecular breeding of cassava through marker-assisted selection. The identification of candidate genes underlying both traits will be useful both as markers and for gene expression studies.

scholarly journals The contribution of quantitative trait loci and neutral marker loci to the genetic variances and covariances among quantitative traits in random mating populations.

Genetics ◽  
1995 ◽  
Vol 139 (1) ◽  
pp. 445-455 ◽  
Author(s):  
A Ruiz ◽  
A Barbadilla
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Quantitative Traits ◽  
Random Mating ◽  
Unbiased Estimation ◽  
Limited Information ◽  
Trait Loci ◽  
Marker Loci ◽  
Neutral Marker ◽  
Variance Explained

Abstract Using Cockerham's approach of orthogonal scales, we develop genetic models for the effect of an arbitrary number of multiallelic quantitative trait loci (QTLs) or neutral marker loci (NMLs) upon any number of quantitative traits. These models allow the unbiased estimation of the contributions of a set of marker loci to the additive and dominance variances and covariances among traits in a random mating population. The method has been applied to an analysis of allozyme and quantitative data from the European oyster. The contribution of a set marker loci may either be real, when the markers are actually QTLs, or apparent, when they are NMLs that are in linkage disequilibrium with hidden QTLs. Our results show that the additive and dominance variances contributed by a set of NMLs are always minimum estimates of the corresponding variances contributed by the associated QTLs. In contrast, the apparent contribution of the NMLs to the additive and dominance covariances between two traits may be larger than, equal to or lower than the actual contributions of the QTLs. We also derive an expression for the expected variance explained by the correlation between a quantitative trait and multilocus heterozygosity. This correlation explains only a part of the genetic variance contributed by the markers, i.e., in general, a combination of additive and dominance variances and, thus, provides only very limited information relative to the method supplied here.

Analysis of quantitative trait loci (QTLs) and quantitative trait alleles (QTAs) for potato tuber yield and starch content

1998 ◽  
Vol 97 (5-6) ◽  
pp. 834-846 ◽  
Author(s):  
R. Schäfer-Pregl ◽  
E. Ritter ◽  
L. Concilio ◽  
J. Hesselbach ◽  
L. Lovatti ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Potato Tuber ◽  
Quantitative Trait ◽  
Tuber Yield ◽  
Starch Content ◽  
Trait Loci

Mapping quantitative trait loci controlling soybean seed starch content in an interspecific cross of ‘Williams 82’ (Glycine max ) and ‘PI 366121’ (Glycine soja )

2017 ◽  
Vol 136 (3) ◽  
pp. 379-385 ◽  
Author(s):  
Sanjeev K. Dhungana ◽  
Krishnanand P. Kulkarni ◽  
Cheol W. Park ◽  
Hyun Jo ◽  
Jong T. Song ◽  
...  
Keyword(s):  
Glycine Max ◽  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Glycine Soja ◽  
Starch Content ◽  
Interspecific Cross ◽  
Soybean Seed ◽  
Trait Loci

scholarly journals Searching for genetic factors of fatty liver in SMXA-5 mice by quantitative trait loci analysis under a high-fat diet

2007 ◽  
Vol 48 (9) ◽  
pp. 2039-2046 ◽  
Author(s):  
Mayumi Kumazawa ◽  
Misato Kobayashi ◽  
Fusayo Io ◽  
Takahiro Kawai ◽  
Masahiko Nishimura ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Fatty Liver ◽  
Quantitative Trait ◽  
High Fat Diet ◽  
Genetic Factors ◽  
High Fat ◽  
Trait Loci

scholarly journals Quantitative trait loci influencing pentacyclic triterpene composition in apple fruit peel

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
John T. Christeller ◽  
Tony K. McGhie ◽  
Jason W. Johnston ◽  
Bridie Carr ◽  
David Chagné
Keyword(s):  
Quantitative Trait Loci ◽  
Cold Storage ◽  
Quantitative Trait ◽  
Apple Fruit ◽  
Fruit Peel ◽  
Pentacyclic Triterpene ◽  
Trait Loci ◽  
Variation Explained ◽  
Apple Peels ◽  
Unique Compound

AbstractThe chemical composition of pentacyclic triterpenes was analysed using a ‘Royal Gala’ x ‘Granny Smith’ segregating population in 2013 and 2015, using apple peels extracted from mature fruit at harvest and after 12 weeks of cold storage. In 2013, 20 compound isoforms from nine unique compound classes were measured for both treatments. In 2015, 20 and 17 compound isoforms from eight unique compound classes were measured at harvest and after cold storage, respectively. In total, 68 quantitative trait loci (QTLs) were detected on 13 linkage groups (LG). Thirty two and 36 QTLs were detected for compounds measured at harvest and after cold storage, respectively. The apple chromosomes with the most QTLs were LG3, LG5, LG9 and LG17. The largest effect QTL was for trihydroxy-urs-12-ene-28-oic acid, located on LG5; this was measured in 2015 after storage, and was inherited from the ‘Royal Gala’ parent (24.9% of the phenotypic variation explained).

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