M C Ledur

São Paulo State University, São Paulo, Estado de Sao Paulo, Brazil

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Publications (43)38.03 Total impact

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    ABSTRACT: MicroRNAs (miRNAs, miRs) encompass a class of small non-coding RNAs that often negatively regulate gene expression. miRNAs play an essential role in skeletal muscle, determining the proper development and maintenance of this tissue. In comparison to other organs and tissues, the full set of muscle miRNAs and its expression patterns are still poorly understood. In this report, a chicken skeletal muscle miRNA library was constructed, and the expression of selected miRNAs was further characterized during muscle development in chicken lines with distinct muscling phenotypes. Clone library sequence analysis revealed 40 small RNAs with similarities to previously described chicken miRNAs, seven miRNAs that were never identified before in chicken, and some sequence clusters representing other possible novel miRNAs. Temporal expression profiles of three miRNAs associated with cell proliferation and differentiation (miR-125b, miR-221, and miR-206) in two chicken lines (broiler and layer) revealed the differential steady-state levels of these miRs during skeletal muscle growth and suggests that miR-206 is involved in the muscling phenotype that is observed in growth-selected chicken lines.
    Genetics and molecular research: GMR 01/2014; 13(1):1465-79. · 0.99 Impact Factor
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    ABSTRACT: Interval mapping (IM) implemented in QTL Express or GridQTL is widely used, but presents some limitations, such as restriction to a fixed model, risk of mapping two QTL when there may be only one and no discrimination of two or more QTL using both cofactors located on the same and other chromosomes. These limitations were overcome with composite interval mapping (CIM). We reported QTL associated with performance and carcass traits on chicken chromosomes 1, 3, and 4 through implementation of CIM and analysis of phenotypic data using mixed models. Thirty-four microsatellite markers were used to genotype 360 F2 chickens from crosses between males from a layer line and females from a broiler line. Sixteen QTL were mapped using CIM and 14 QTL with IM. Furthermore, of those 30 QTL, six were mapped only when CIM was used: for body weight at 35 days (first and third peaks on GGA4), body weight at 41 days (GGA1B and second peak on GGA4), and weights of back and legs (both on GGA4). Three new regions had evidence for QTL presence: one on GGA1B associated with feed intake 35-41 d at 404 cM (LEI0107-ADL0183) and two on GGA4 associated with weight of back at 163 cM (LEI0076-MCW0240) and weight gain 35-41 d, feed efficiency 35-41 d and weight of legs at 241 cM (LEI0085-MCW0174). We dissected one more linked QTL on GGA4, where three QTL for BW35 and two QTL for BW41 were mapped. Therefore, these new regions mapped here need further investigations using high-density SNP to confirm these QTL and identify candidate genes associated with those traits.
    Journal of applied genetics 11/2013; · 1.85 Impact Factor
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    ABSTRACT: The objectives of this study were to estimate genetic parameters for accumulated egg production over 3-wk periods and for total egg production over 54 wk of egg-laying, and using principal component analysis (PCA), to explore the relationships among the breeding values of these traits to identify the possible genetic relationships present among them and hence to observe which of them could be used as selection criteria for improving egg production. Egg production was measured among 1,512 females of a line of White Leghorn laying hens. The traits analyzed were the number of eggs produced over partial periods of 3 wk, thus totaling 18 partial periods (P1 to P18), and the total number of eggs produced over the period between the 17 and 70 wk of age (PTOT), thus totaling 54 wk of egg production. Estimates of genetic parameters were obtained by means of the restricted maximum likelihood method, using 2-trait animal models. The PCA was done using the breeding values of partial and total egg production. The heritability estimates ranged from 0.05 ± 0.03 (P1 and P8) to 0.27 ± 0.06 (P4) in the 2-trait analysis. The genetic correlations between PTOT and partial periods ranged from 0.19 ± 0.31 (P1) to 1.00 ± 0.05 (P10, P11, and P12). Despite the high genetic correlation, selection of birds based on P10, P11, and P12 did not result in an increase in PTOT because of the low heritability estimates for these periods (0.06 ± 0.03, 0.12 ± 0.04, and 0.10 ± 0.04, respectively). The PCA showed that egg production can be divided genetically into 4 periods, and that P1 and P2 are independent and have little genetic association with the other periods.
    Poultry Science 09/2013; 92(9):2283-9. · 1.52 Impact Factor
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    ABSTRACT: In the past, the focus of broiler breeding programs on yield and carcass traits improvement led to problems related to meat quality. Awareness of public concern for quality resulted in inclusion of meat quality traits in the evaluation process. Nevertheless, few genes associated with meat quality attributes are known. Previous studies mapped quantitative trait loci for weight at 35 and 42 days in a region of GGA4 flanked by the microsatellite markers, MCW0240 and LEI0063. In this region, there are 2 fibroblast growth factor binding protein (FGFBP) genes that play an important role in embryogenesis, cellular differentiation, and proliferation in chickens. The objective of this study was to identify and associate single nucleotide polymorphisms (SNPs) in FGFBP1 and FGFBP2 with performance, carcass, and meat quality in experimental and commercial chicken populations. In the commercial population, SNP g.2014G>A in FGFBP1 was associated with decreased carcass weight (P < 0.05), and SNP g.651G>A in FGFBP2 was associated with thawing loss and meat redness content (P < 0.05). Four haplotypes were constructed based on 2 SNPs and were associated with breast weight, thawing loss, and meat redness content. The diplotypes were associated with thawing loss, lightness, and redness content. The SNPs evaluated in the present study may be used as markers in poultry breeding programs to aid in improving growth and meat quality traits.
    Genetics and molecular research: GMR 01/2013; 12(1):208-22. · 0.99 Impact Factor
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    ABSTRACT: Meat quality is an important feature for the poultry industry and is associated with consumer satisfaction. The calpain 1 (CAPN1) gene is related to the tenderness process of meat post- mortem, and the calpain 3 (CAPN3) gene plays an important role in myofibrillar organization and growth. The objective of the present study was to identify polymorphisms in these genes and to determine the association between these polymorphisms and traits of economic interest in poultry. Eleven animals (F(1)) from an experimental poultry population at Embrapa Swine and Poultry were used to identify the polymorphisms. Four single nucleotide polymorphisms (SNPs) were found in the CAPN1 gene, and one SNP was found in the CAPN3 gene. A polymorphism from each gene was selected for genotyping in 152 chickens from the Embrapa F(2) experimental population and 311 chickens from a commercial population. Polymorphism g.2554T>C (CAPN1) was associated with body weight at 35 to 42 days, thigh weight, breast weight, carcass weight, and meat lightness content. SNP g.15486C>T (CAPN3) was associated with thigh yield, thawing-cooking loss, and shear force. Results suggest the possibility of using molecular markers in CAPN1 and CAPN3 genes as a tool for performance and meat quality traits in poultry breeding programs.
    Genetics and molecular research: GMR 01/2013; 12(1):472-82. · 0.99 Impact Factor
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    ABSTRACT: Two functional and positional candidate genes were selected in a region of chicken chromosome 1 (GGA1), based on their biological roles, and also where several quantitative trait loci (QTL) have been mapped and associated with performance, fatness and carcass traits in chickens. The insulin-like growth factor 1 (IGF1) gene has been associated with several physiological functions related to growth. The lysine (K)-specific demethylase 5A (KDM5A) gene participates in the epigenetic regulation of genes involved with the cell cycle. Our objective was to find associations of selected single-nucleotide polymorphisms (SNPs) in these genes with performance, fatness and carcass traits in 165 F(2) chickens from a resource population. In the IGF1 gene, 17 SNPs were detected, and in the KDM5A gene, nine SNPs were detected. IGF1 SNP c.47673G > A was associated with body weight and haematocrit percentage, and also with feed intake and percentages of abdominal fat and gizzard genotype × sex interactions. KDM5A SNP c.34208C > T genotype × sex interaction affected body weight, feed intake, percentages of abdominal fat (p = 0.0001), carcass, gizzard and haematocrit. A strong association of the diplotype × sex interaction (p < 0.0001) with abdominal fat was observed, and also associations with body weight, feed intake, percentages of carcass, drums and thighs, gizzard and haematocrit. Our findings suggest that the KDM5A gene might play an important role in the abdominal fat deposition in chickens. The IGF1 and KDM5A genes are strong candidates to explain the QTL mapped in this region of GGA1.
    Journal of applied genetics 12/2012; · 1.85 Impact Factor
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    ABSTRACT: Egg production curves describe the laying patterns of hen populations over time. The objectives of this study were to fit the weekly egg production rate of selected and nonselected lines of a White Leghorn hen population, using nonlinear and segmented polynomial models, and to study how the selection process changed the egg-laying patterns between these 2 lines. Weekly egg production rates over 54 wk of egg production (from 17 to 70 wk of age) were measured from 1,693 and 282 laying hens from one selected and one nonselected (control) genetic line, respectively. Six nonlinear and one segmented polynomial models were gathered from the literature to investigate whether they could be used to fit curves for the weekly egg production rate. The goodness of fit of the models was measured using Akaike's information criterion, mean square error, coefficient of determination, graphical analysis of the fitted curves, and the deviations of the fitted curves. The Logistic, Yang, Segmented Polynomial, and Grossman models presented the best goodness of fit. In this population, there were significant differences between the parameter estimates of the curves fitted for the selected and nonselected lines, thus indicating that the effect of selection changed the shape of the egg production curves. The selection for egg production was efficient in modifying the birds' egg production curve in this population, thus resulting in genetic gain from the 5th to the 54th week of egg laying and improved the peak egg production and the persistence of egg laying.
    Poultry Science 11/2012; 91(11):2977-87. · 1.52 Impact Factor
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    ABSTRACT: Major objectives of the poultry industry are to increase meat production and to reduce carcass fatness, mainly abdominal fat. Information on growth performance and carcass composition are important for the selection of leaner meat chickens. To enhance our understanding of the genetic architecture underlying the chemical composition of chicken carcasses, an F(2) population developed from a broiler × layer cross was used to map quantitative trait loci (QTL) affecting protein, fat, water and ash contents in chicken carcasses. Two genetic models were applied in the QTL analysis: the line-cross and the half-sib models, both using the regression interval mapping method. Six significant and five suggestive QTL were mapped in the line-cross analysis, and four significant and six suggestive QTL were mapped in the half-sib analysis. A total of eleven QTL were mapped for fat (ether extract), five for protein, four for ash and one for water contents in the carcass using both analyses. No study to date has reported QTL for carcass chemical composition in chickens. Some QTL mapped here for carcass fat content match, as expected, QTL regions previously associated with abdominal fat in the same or in different populations, and novel QTL for protein, ash and water contents in the carcass are presented here. The results described here also reinforce the need for fine mapping and to perform multi-trait analyses to better understand the genetic architecture of these traits.
    Animal Genetics 02/2012; 43(5):570-6. · 2.58 Impact Factor
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    ABSTRACT: We estimated genetic parameters for egg production in different periods by means of random regression models, aiming at selection based on partial egg production from a generation of layers. The production was evaluated for each individual by recording the number of eggs produced from 20 to 70 weeks of age, with partial records taken every three weeks for a total of 17 periods. The covariance functions were estimated with a random regression model by the restricted maximum likelihood method. A model composed of third-order polynomials for the additive effect, ninth-order polynomials for the permanent environment, and a residual variance structure with five distinct classes, was found to be most suitable for adjusting the egg production data for laying hens. The heritability estimates varied from 0.04 to 0.14. The genetic correlations were all positive, varying from 0.10 to 0.99. Selection applied in partial egg production periods will result in greater genetic profit for the adjacent periods. However, as the distance in time between periods increases, selection becomes less efficient. Selection based on the second period (23 to 25 weeks of age), where greater heritability was estimated, would note benefit the final egg-laying cycle periods.
    Genetics and molecular research: GMR 01/2012; 11(3):1819-29. · 0.99 Impact Factor
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    ABSTRACT: The objectives of this paper were to identify the phenotypic egg-laying patterns in a White Leghorn line mainly selected for egg production, to estimate genetic parameters of traits related to egg production and to evaluate the genetic association between these by principal components analysis to identify trait(s) that could be used as selection criteria to improve egg production. Records of 54 wk of egg production from a White Leghorn population were used. The data set contained records of the length:width ratio of eggs at 32, 37, and 40 wk of age; egg weight at 32, 37, and 40 wk of age; BW at 54 and 62 wk of age; age at first egg; early partial egg production rate from 17 to 30 wk and from 17 to 40 wk of age; late partial egg production rate from 30 to 70 wk and from 40 to 70 wk of age; and total egg production rate (TEP). The estimates of genetic parameters between these traits were estimated by the restricted maximum likelihood method. Multivariate analyses were performed: a hierarchical cluster analysis, a nonhierarchical clustering analysis by the k-means method of weekly egg production rate to describe the egg-laying patterns of hens, and a principal components analysis using the breeding values of all traits. The highest heritability estimates were obtained for BW at 54 wk of age (0.68 ± 0.07) and age at first egg (0.53 ± 0.07). It is recommended that a preliminary clustering analysis be performed to obtain the population structure that takes into account the pattern of egg production, rather than the TEP, because hens may have the same final egg production with different patterns of egg laying. Early partial production periods were not good indicators for use in improving total egg production because these traits presented an overestimated genetic correlation with TEP because of the part-whole genetic correlation component. Egg production might be improved by selecting individuals based on TEP.
    Poultry Science 10/2011; 90(10):2174-88. · 1.52 Impact Factor
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    ABSTRACT: The objective of this study was to estimate genetic and phenotypic correlations of body weight at 6 weeks of age (BW6), as well as final carcass yield, and moisture, protein, fat and ash contents, using data from 3,422 F2 chickens originated from reciprocal cross between a broiler and a layer line. Variance components were estimated by the REML method, using animal models for evaluating random additive genetic and fixed contemporary group (sex, hatch and genetic group) effects. The heritability estimates (h(2)) for BW6, carcass yield and percentage of carcass moisture were 0.31 ± 0.07, 0.20 ± 0.05 and 0.33 ± 0.07, respectively. The h(2) for the percentages of protein, fat and ash on a dry matter basis were 0.48 ± 0.09, 0.55 ± 0.10 and 0.36 ± 0.08, respectively. BW6 had a positive genetic correlation with fat percentage in the carcass, but a negative one with protein and ash contents. Carcass yield, thus, appears to have only low genetic association with carcass composition traits. The genetic correlations observed between traits, measured on a dry matter basis, indicated that selection for carcass protein content may favor higher ash content and a lower percentage of carcass fat.
    Genetics and Molecular Biology 07/2011; 34(3):429-34. · 0.74 Impact Factor
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    ABSTRACT: Neural networks are capable of modeling any complex function and can be used in the poultry and animal production areas. The aim of this study was to investigate the possibility of using neural networks on an egg production data set and fitting models to the egg production curve by applying 2 approaches, one using a nonlinear logistic model and the other using 2 artificial neural network models [multilayer perceptron (MLP) and radial basis function]. Two data sets from 2 generations of a White Leghorn strain that had been selected mainly for egg production were used. In the first data set, the mean weekly egg-laying rate was ascertained over a 54-wk egg production period. This data set was used to adjust and test the logistic model and to train and test the neural networks. The second data set, covering 52 wk of egg production, was used to validate the models. The mean absolute deviation, mean square error, and R(2) were used to evaluate the fit of the models. The MLP neural network had the best fit in the test and validation phases. The advantage of using neural networks is that they can be fitted to any kind of data set and do not require model assumptions such as those required in the nonlinear methodology. The results confirm that MLP neural networks can be used as an alternative tool to fit to egg production. The benefits of the MLP are the great flexibility and their lack of a priori assumptions when estimating a noisy nonlinear model.
    Poultry Science 03/2011; 90(3):705-11. · 1.52 Impact Factor
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    ABSTRACT: Studies estimating genetic parameters for reproductive traits in chickens can be useful for understanding and improvement of their genetic architecture. A total of 1276 observations of fertility (FERT), hatchability of fertile eggs (HFE) and hatchability of total eggs (HTE) were used to estimate the genetic and phenotypic parameters of 467 females from an F2 population generated by reciprocal crossing between a broiler line and a layer line, which were developed through a poultry genetics breeding program, maintained by Embrapa Swine and Poultry, Concordia, Santa Catarina, Brazil. Estimates of heritability and genetic and phenotypic correlations were obtained using restricted maximum likelihood calculations under the two-trait animal model, including the fixed effect of group (hatching of birds from the same genetic group) and the random additive genetic and residual effects. The mean percentages for FERT, HFE and HTE were 87.91 ± 19.77, 80.07 ± 26.81 and 70.67 ± 28.55%, respectively. The highest heritability estimate (h(2)) was 0.28 ± 0.04 for HTE. Genetic correlations for FERT with HFE (0.43 ± 0.17), HFE with HTE (0.98 ± 0.02) and FERT with HTE (0.69 ± 0.10) were positive and significant. Individuals with high breeding value for HTE would have high breeding values for HFE and FERT because of the high genetic association between them. These results suggest that HTE should be included as a selection criterion in genetic breeding programs to improve the reproductive performance of chickens, because HTE had the highest heritability estimate and high genetic correlation with FERT and HFE, and it is the easiest to measure.
    Genetics and molecular research: GMR 01/2011; 10(3):1337-44. · 0.99 Impact Factor
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    ABSTRACT: An F(2) experimental population, developed from a broiler layer cross, was used in a genome scan of QTL for percentage of carcass, carcass parts, shank and head. Up to 649 F(2) chickens from four paternal half-sib families were genotyped with 128 genetic markers covering 22 linkage groups. Total map length was 2630 cM, covering approximately 63% of the genome. QTL interval mapping using regression methods was applied to line-cross and half-sib models. Under the line-cross model, 12 genome-wide significant QTL and 17 suggestive linkages for percentages of carcass parts, shank and head were mapped to 13 linkage groups (GGA1, 2, 3, 4, 5, 7, 8, 9, 11, 12, 14, 18 and 27). Under the paternal half-sib model, six genome-wide significant QTL and 18 suggestive linkages for percentages of carcass parts, shank and head were detected on nine chicken linkage groups (GGA1, 2, 3, 4, 5, 12, 14, 15 and 27), seven of which seemed to corroborate positions revealed by the previous model. Overall, three novel QTL of importance to the broiler industry were mapped (one significant for shank% on GGA3 and two suggestive for carcass and breast percentages on GGA14 and drums and thighs percentage on GGA15). One novel QTL for wings% was mapped to GGA3, six novel QTL (GGA1, 3, 7, 8, 9 and 27) and suggestive linkages (GGA2, 4, and 5) were mapped for head%, and suggestive linkages were identified for back% on GGA2, 11 and 12. In addition, many of the QTL mapped in this study confirmed QTL previously reported in other populations.
    Animal Genetics 09/2010; · 2.58 Impact Factor
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    ABSTRACT: Macro- and microarrays are well-established technologies to determine gene functions through repeated measurements of transcript abundance. We constructed a chicken skeletal muscle-associated array based on a muscle-specific EST database, which was used to generate a tissue expression dataset of ~4500 chicken genes across 5 adult tissues (skeletal muscle, heart, liver, brain, and skin). Only a small number of ESTs were sufficiently well characterized by BLAST searches to determine their probable cellular functions. Evidence of a particular tissue-characteristic expression can be considered an indication that the transcript is likely to be functionally significant. The skeletal muscle macroarray platform was first used to search for evidence of tissue-specific expression, focusing on the biological function of genes/transcripts, since gene expression profiles generated across tissues were found to be reliable and consistent. Hierarchical clustering analysis revealed consistent clustering among genes assigned to 'developmental growth', such as the ontology genes and germ layers. Accuracy of the expression data was supported by comparing information from known transcripts and tissue from which the transcript was derived with macroarray data. Hybridization assays resulted in consistent tissue expression profile, which will be useful to dissect tissue-regulatory networks and to predict functions of novel genes identified after extensive sequencing of the genomes of model organisms. Screening our skeletal-muscle platform using 5 chicken adult tissues allowed us identifying 43 'tissue-specific' transcripts, and 112 co-expressed uncharacterized transcripts with 62 putative motifs. This platform also represents an important tool for functional investigation of novel genes; to determine expression pattern according to developmental stages; to evaluate differences in muscular growth potential between chicken lines, and to identify tissue-specific genes.
    Genetics and molecular research: GMR 01/2010; 9(1):188-207. · 0.99 Impact Factor
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    ABSTRACT: Some factors complicate comparisons between linkage maps from different studies. This problem can be resolved if measures of precision, such as confidence intervals and frequency distributions, are associated with markers. We examined the precision of distances and ordering of microsatellite markers in the consensus linkage maps of chromosomes 1, 3 and 4 from two F(2) reciprocal Brazilian chicken populations, using bootstrap sampling. Single and consensus maps were constructed. The consensus map was compared with the International Consensus Linkage Map and with the whole genome sequence. Some loci showed segregation distortion and missing data, but this did not affect the analyses negatively. Several inversions and position shifts were detected, based on 95% confidence intervals and frequency distributions of loci. Some discrepancies in distances between loci and in ordering were due to chance, whereas others could be attributed to other effects, including reciprocal crosses, sampling error of the founder animals from the two populations, F(2) population structure, number of and distance between microsatellite markers, number of informative meioses, loci segregation patterns, and sex. In the Brazilian consensus GGA1, locus LEI1038 was in a position closer to the true genome sequence than in the International Consensus Map, whereas for GGA3 and GGA4, no such differences were found. Extending these analyses to the remaining chromosomes should facilitate comparisons and the integration of several available genetic maps, allowing meta-analyses for map construction and quantitative trait loci (QTL) mapping. The precision of the estimates of QTL positions and their effects would be increased with such information.
    Genetics and molecular research: GMR 01/2010; 9(3):1357-76. · 0.99 Impact Factor
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    ABSTRACT: Although genome-wide association (GWA) studies are not worth the effort in crosses between inbred lines, many crosses are actually made up of divergent yet outbred populations. Despite its relevance, however, this experimental setting has not been studied at a time when SNP microarrays are available in many species. To assess whether GWA can be useful in this setting, we performed combined coalescence--gene dropping simulations. We studied the influence of marker density, QTL effect and QTL allele frequency on power, false discovery rate (FDR) and accuracy. Our results suggest that GWA in outbred F(2) crosses is useful, especially in large populations. Under these circumstances, accuracy increased and FDR decreased as compared with classical linkage analysis. However, current SNP densities (in the order of 30-60 K SNPs/genome or equivalent to 10-20 SNPs per cM) may not be much better than linkage analysis and higher SNP densities may be required. SNP ascertainment had an important effect; the best option was to select SNPs as uniformly as possible without setting any restriction on allele frequency. Using only SNPs with fixed alternative alleles in each breed controlled false positive rate but was not useful to detect variability within lines. Finally, the most significant SNP was not necessarily the closest to the causal SNP, although the closest SNPs were usually above the significance threshold; thus, it is prudent to follow-up significant signals located in regions of interest even if they do not correspond to absolute maxima.
    Heredity 10/2009; 105(2):173-82. · 4.11 Impact Factor
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    ABSTRACT: An F(2) population established by crossing a broiler male line and a layer line was used to map quantitative trait loci (QTL) affecting abdominal fat weight, abdominal fat percentage and serum cholesterol and triglyceride concentrations. Two genetic models, the line-cross and the half-sib, were applied in the QTL analysis, both using the regression interval method. Three significant QTL and four suggestive QTL were mapped in the line-cross analysis and four significant and four suggestive QTL were mapped in the half-sib analysis. A total of five QTL were mapped for abdominal fat weight, six for abdominal fat percentage and four for triglyceride concentration in both analyses. New QTL associated with serum triglyceride concentration were mapped on GGA5, GGA23 and GG27. QTL mapped between markers LEI0029 and ADL0371 on GGA3 for abdominal fat percentage and abdominal fat weight and a suggestive QTL on GGA12 for abdominal fat percentage showed significant parent-of-origin effects. Some QTL mapped here match QTL regions mapped in previous studies using different populations, suggesting good candidate regions for fine-mapping and candidate gene searches.
    Animal Genetics 06/2009; 40(5):729-36. · 2.58 Impact Factor
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    Mônica Corrêa Ledur, Nicolas Navarro, Miguel Pérez-Enciso
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    ABSTRACT: Genome-wide association studies have successfully identified several loci underlying complex diseases in humans. The development of high density SNP maps in domestic animal species should allow the detection of QTLs for economically important traits through association studies with much higher accuracy than traditional linkage analysis. Here we report the association analysis of the dataset simulated for the XII QTL-MAS meeting (Uppsala). We used two strategies, single marker association and haplotype-based association (Blossoc) that were applied to i) the raw data, and ii) the data corrected for infinitesimal, sex and generation effects. Both methods performed similarly in detecting the most strongly associated SNPs, about ten loci in total. The most significant ones were located in chromosomes 1, 4 and 5. Overall, the largest differences were found between corrected and raw data, rather than between single and multiple marker analysis. The use of raw data increased greatly the number of significant loci, but possibly also the rate of false positives. Bootstrap model aggregation removed most of discrepancies between adjusted and raw data when SMA was employed. Model choice should be carefully considered in genome-wide association studies.
    BMC proceedings 02/2009; 3 Suppl 1:S9.
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    ABSTRACT: An F(2) resource population, derived from a broiler x layer cross, was used to map quantitative trait loci (QTL) for body weights at days 1, 35 and 41, weight gain, feed intake, feed efficiency from 35 to 41 days and intestinal length. Up to 577 F(2) chickens were genotyped with 103 genetic markers covering 21 linkage groups. A preliminary QTL mapping report using this same population focused exclusively on GGA1. Regression methods were applied to line-cross and half-sib models for QTL interval mapping. Under the line-cross model, eight QTL were detected for body weight at 35 days (GGA2, 3 and 4), body weight at 41 days (GGA2, 3, 4 and 10) and intestine length (GGA4). Under the half-sib model, using sire as common parent, five QTL were detected for body weight at day 1 (GGA3 and 18), body weight at 35 days (GGA2 and 3) and body weight at 41 days (GGA3). When dam was used as common parent, seven QTL were mapped for body weight at day 1 (GGA2), body weight at day 35 (GGA2, 3 and 4) and body weight at day 41 (GGA2, 3 and 4). Growth differences in chicken lines appear to be controlled by a chronological change in a limited number of chromosomal regions.
    Animal Genetics 02/2009; 40(2):200-8. · 2.58 Impact Factor

Publication Stats

82 Citations
401 Downloads
2k Views
38.03 Total Impact Points

Institutions

  • 2011–2013
    • São Paulo State University
      • Departamento de Ciências Exatas
      São Paulo, Estado de Sao Paulo, Brazil
  • 2006–2013
    • University of São Paulo
      • Departamento de Zootecnia (ZAZ) (Pirassununga)
      Ribeirão Preto, Estado de Sao Paulo, Brazil
  • 2009
    • Brazilian Agricultural Research Corporation (EMBRAPA)
      • Embrapa Suínos e Aves
      Brasília, Distrito Federal, Brazil
    • Autonomous University of Barcelona
      Cerdanyola del Vallès, Catalonia, Spain