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Luo, Lang; Yongcai, Mao; Xu, Shizhong

doi:10.1023/a:1026028928003

Cited by 16 publications

(14 citation statements)

References 10 publications

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“…A limited population size for identifying QTLs affects the accuracy of determining QTL locations and estimating QTL effects and, consequently, overestimates the phenotypic variances associated with QTLs [39]–[41]. To correct at least the small part of the overestimation due to sampling error, we applied the correction described in Montoya et al [27], as proposed by Luo et al [42] and Xu [43]. They suggest to multiply the explained variance by 1−1/(2*Ln(10)*LOD).…”

Section: Methodsmentioning

confidence: 99%

Genetic Architecture of Palm Oil Fatty Acid Composition in Cultivated Oil Palm (Elaeis guineensis Jacq.) Compared to Its Wild Relative E. oleifera (H.B.K) Cortés

et al. 2014

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We searched for quantitative trait loci (QTL) associated with the palm oil fatty acid composition of mature fruits of the oil palm E. guineensis Jacq. in comparison with its wild relative E. oleifera (H.B.K) Cortés. The oil palm cross LM2T x DA10D between two heterozygous parents was considered in our experiment as an intraspecific representative of E. guineensis. Its QTLs were compared to QTLs published for the same traits in an interspecific Elaeis pseudo-backcross used as an indirect representative of E. oleifera. Few correlations were found in E. guineensis between pulp fatty acid proportions and yield traits, allowing for the rather independent selection of both types of traits. Sixteen QTLs affecting palm oil fatty acid proportions and iodine value were identified in oil palm. The phenotypic variation explained by the detected QTLs was low to medium in E. guineensis, ranging between 10% and 36%. The explained cumulative variation was 29% for palmitic acid C16:0 (one QTL), 68% for stearic acid C18:0 (two QTLs), 50% for oleic acid C18:1 (three QTLs), 25% for linoleic acid C18:2 (one QTL), and 40% (two QTLs) for the iodine value. Good marker co-linearity was observed between the intraspecific and interspecific Simple Sequence Repeat (SSR) linkage maps. Specific QTL regions for several traits were found in each mapping population. Our comparative QTL results in both E. guineensis and interspecific materials strongly suggest that, apart from two common QTL zones, there are two specific QTL regions with major effects, which might be one in E. guineensis, the other in E. oleifera, which are independent of each other and harbor QTLs for several traits, indicating either pleiotropic effects or linkage. Using QTL maps connected by highly transferable SSR markers, our study established a good basis to decipher in the future such hypothesis at the Elaeis genus level.

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Section: Methodsmentioning

confidence: 99%

Genetic Architecture of Palm Oil Fatty Acid Composition in Cultivated Oil Palm (Elaeis guineensis Jacq.) Compared to Its Wild Relative E. oleifera (H.B.K) Cortés

et al. 2014

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“…A bias corrected estimate, denoted , can be derived using standard dispersion statistics: where is the estimated variance of γ a (the squared standard error of ), is the estimated variance of γ d and s ad is the sampling co-variance. This statistical issue has been addressed for a single locus [ 19 ], and we here generalize bias-correction for genetic variance predictions when there are interactions among loci. We extend the logic of Eq 5 to all eight genetic effect estimates associated with each QTL pair (see “ Linear model for estimation of genetic effects ” section above).…”

Section: Methodsmentioning

confidence: 99%

“…Standard equations used to calculate V A from genetic effects [ 11 ] assume that effects are estimated without error. Estimation error in genetic effects is often substantial even with large sample sizes, and failing to account for this error will result in an upward bias in variance predictions [ 19 ]. This is because genetic effects are squared and different effects are multiplied together when variances are calculated.…”

Section: Introductionmentioning

confidence: 99%

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Epistasis Is a Major Determinant of the Additive Genetic Variance in Mimulus guttatus

Monnahan

Kelly

2015

PLoS Genet

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The influence of genetic interactions (epistasis) on the genetic variance of quantitative traits is a major unresolved problem relevant to medical, agricultural, and evolutionary genetics. The additive genetic component is typically a high proportion of the total genetic variance in quantitative traits, despite that underlying genes must interact to determine phenotype. This study estimates direct and interaction effects for 11 pairs of Quantitative Trait Loci (QTLs) affecting floral traits within a single population of Mimulus guttatus. With estimates of all 9 genotypes for each QTL pair, we are able to map from QTL effects to variance components as a function of population allele frequencies, and thus predict changes in variance components as allele frequencies change. This mapping requires an analytical framework that properly accounts for bias introduced by estimation errors. We find that even with abundant interactions between QTLs, most of the genetic variance is likely to be additive. However, the strong dependency of allelic average effects on genetic background implies that epistasis is a major determinant of the additive genetic variance, and thus, the population’s ability to respond to selection.

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“…whereq is the maximum-likelihood estimate (MLE) for allele frequency,â is the MLE for the additive effect, and V P is the phenotypic variance. This estimated heritability is inflated by estimation error ofâ (Luo, Mao, & Xu, 2003;.…”

Section: Msg On Pcr Amplified Regionsmentioning

confidence: 99%

SNP‐skimming: A fast approach to map loci generating quantitative variation in natural populations

Wessinger

Kelly

Jiang

et al. 2018

Molecular Ecology Resources

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Genome-wide association mapping (GWAS) is a method to estimate the contribution of segregating genetic loci to trait variation. A major challenge for applying GWAS to non-model species has been generating dense genome-wide markers that satisfy the key requirement that marker data is error-free. Here we present an approach to map loci within natural populations using inexpensive shallow genome sequencing. This ‘SNP skimming’ approach involves two steps: an initial genome-wide scan to identify putative targets followed by deep sequencing for confirmation of targeted loci. We apply our method to a test dataset of floral dimension variation in the plant Penstemon virgatus, a member of a genus that has experienced dynamic floral adaptation that reflects repeated transitions in primary pollinator. The ability to detect SNPs that generate phenotypic variation depends on population genetic factors such as population allele frequency, effect size, and epistasis as well as sampling effects contingent on missing data and genotype uncertainty. However, both simulations and the Penstemon data suggest that the most significant tests from the initial SNP skim are likely to be true positives – loci with subtle but significant quantitative effects on phenotype. We discuss the promise and limitations of this method and consider optimal experimental design for a given sequencing effort. Simulations demonstrate that sampling a larger number of individual at the expense of average read depth per individual maximizes the power to detect loci.

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Untitled

Cited by 16 publications

References 10 publications

Genetic Architecture of Palm Oil Fatty Acid Composition in Cultivated Oil Palm (Elaeis guineensis Jacq.) Compared to Its Wild Relative E. oleifera (H.B.K) Cortés

Genetic Architecture of Palm Oil Fatty Acid Composition in Cultivated Oil Palm (Elaeis guineensis Jacq.) Compared to Its Wild Relative E. oleifera (H.B.K) Cortés

Epistasis Is a Major Determinant of the Additive Genetic Variance in Mimulus guttatus

SNP‐skimming: A fast approach to map loci generating quantitative variation in natural populations

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