Mapping of QTL for chicken body weight, carcass composition, and meat quality traits in a slow-growing line.

Slow-growing chicken lines are valuable genetic resources for the development of well-perceived alternative free-range production. While there is no constraint on increasing growth rate, breeding programs have to evolve in order to include new traits improving the positioning of such lines in the growing market for parts and processed products. In this study, we used dense genotyping to fine map QTL for chicken growth, body composition, and meat quality traits in view of developing new tools for selection of a slow-growing line. The dataset included a total of 836 birds (10 sires, 87 dams, 739 descendants) and 40,203 SNP. QTL for the 15 traits analyzed were detected by 3 different methods, i.e., linkage and linkage disequilibrium haplotype-based analysis (LDLA), family-based single marker association (FASTA), and Bayesian multi-marker regression (Bayes Cπ). After filtering for QTL redundancy, we found 16, 16, and 9 QTL when using the FASTA, LDLA, and Bayes Cπ methods, respectively, with a threshold of 2.49 × 10-5 for FASTA and LDLA, and a Bayes factor of 150 for the Bayes Cπ analysis. They comprised 17 QTL for body weight, 9 QTL for body composition, and 15 QTL for breast meat quality or behavior at slaughter. The 3 methods agreed in the detection of highly significant QTL such as that detected on GGA24 for body weight at 3, 6, and 9 wk, and the 2 QTL detected on GGA17 and GGA18 for breast meat yield. Several significant QTL were also detected for the different components of breast meat quality. This study provided new locations for investigation in order to improve our understanding of the genetic architecture of growth, carcass composition, and meat quality in the chicken and to develop molecular tools for the selection of these traits in a slow-growing line.

Large-scale investigation of the parameters in response to Eimeria maxima challenge in broilers.

Coccidiosis, a parasitic disease of the intestinal tract caused by members of the genera Eimeria and Isospora, is one of the most common and costly diseases in chicken. The aims of this study were to assess the effect of the challenge and level of variability of measured parameters in chickens during the challenge with Eimeria maxima. Furthermore, this study aimed to investigate which parameters are the most relevant indicators of the health status. Finally, the study also aimed to estimate accuracy of prediction for traits that cannot be measured on large scale (such as intestinal lesion score and fecal oocyst count) using parameters that can easily be measured on all animals. The study was performed in 2 parts: a pilot challenge on 240 animals followed by a large-scale challenge on 2,024 animals. In both experiments, animals were challenged with 50,000 Eimeria maxima oocysts at 16 d of age. In the pilot challenge, all animals were measured for BW gain, plasma coloration, hematocrit, and rectal temperature and, in addition, a subset of 48 animals was measured for oocyst count and the intestinal lesion score. All animals from the second challenge were measured for BW gain, plasma coloration, and hematocrit whereas a subset of 184 animals was measured for intestinal lesion score, fecal oocyst count, blood parameters, and plasma protein content and composition. Most of the parameters measured were significantly affected by the challenge. Lesion scores for duodenum and jejunum (P < 0.001), oocyst count (P < 0.05), plasma coloration for the optical density values between 450 and 490 nm (P < 0.001), albumin (P < 0.001), α1-globulin (P < 0.01), α2-globulin (P < 0.001), α3-globulin (P < 0.01), and ß2-globulin (P < 0.001) were the most strongly affected parameters and expressed the greatest levels of variation. Plasma protein profiles proved to be a new, reliable parameter for measuring response to Eimeria maxima. Prediction of intestinal lesion score and fecal oocyst count using the other parameters measured was not very precise (R2 < 0.7). The study was successfully performed in real raising conditions on a large scale. Finally, we observed a high variability in response to the challenge, suggesting that broilers' response to Eimeria maxima has a strong genetic determinism, which may be improved by genetic selection.

Comparison of whole-genome (13X) and capture (87X) resequencing methods for SNP and genotype callings.

The number of polymorphisms identified with next-generation sequencing approaches depends directly on the sequencing depth and therefore on the experimental cost. Although higher levels of depth ensure more sensitive and more specific SNP calls, economic constraints limit the increase of depth for whole-genome resequencing (WGS). For this reason, capture resequencing is used for studies focusing on only some specific regions of the genome. However, several biases in capture resequencing are known to have a negative impact on the sensitivity of SNP detection. Within this framework, the aim of this study was to compare the accuracy of WGS and capture resequencing on SNP detection and genotype calling, which differ in terms of both sequencing depth and biases. Indeed, we have evaluated the SNP calling and genotyping accuracy in a WGS dataset (13X) and in a capture resequencing dataset (87X) performed on 11 individuals. The percentage of SNPs not identified due to a sevenfold sequencing depth decrease was estimated at 7.8% using a down-sampling procedure on the capture sequencing dataset. A comparison of the 87X capture sequencing dataset with the WGS dataset revealed that capture-related biases were leading with the loss of 5.2% of SNPs detected with WGS. Nevertheless, when considering the SNPs detected by both approaches, capture sequencing appears to achieve far better SNP genotyping, with about 4.4% of the WGS genotypes that can be considered as erroneous and even 10% focusing on heterozygous genotypes. In conclusion, WGS and capture deep sequencing can be considered equivalent strategies for SNP detection, as the rate of SNPs not identified because of a low sequencing depth in the former is quite similar to SNPs missed because of method biases of the latter. On the other hand, capture deep sequencing clearly appears more adapted for studies requiring great accuracy in genotyping.

AnnotQTL: a new tool to gather functional and comparative information on a genomic region.

AnnotQTL is a web tool designed to aggregate functional annotations from different prominent web sites by minimizing the redundancy of information. Although thousands of QTL regions have been identified in livestock species, most of them are large and contain many genes. This tool was therefore designed to assist the characterization of genes in a QTL interval region as a step towards selecting the best candidate genes. It localizes the gene to a specific region (using NCBI and Ensembl data) and adds the functional annotations available from other databases (Gene Ontology, Mammalian Phenotype, HGNC and Pubmed). Both human genome and mouse genome can be aligned with the studied region to detect synteny and segment conservation, which is useful for running inter-species comparisons of QTL locations. Finally, custom marker lists can be included in the results display to select the genes that are closest to your most significant markers. We use examples to demonstrate that in just a couple of hours, AnnotQTL is able to identify all the genes located in regions identified by a full genome scan, with some highlighted based on both location and function, thus considerably increasing the chances of finding good candidate genes. AnnotQTL is available at http://annotqtl.genouest.org.

Regulation of LPCAT3 by LXR.

In this work we analyzed the transcriptome profiles of chicken hepatoma cells (LMH) in response to T0901317, a pharmacological agonist of the liver X receptor (LXR). Through an in silico search for LXRE (LXR response element) consensus sequences in the promoter of genes whose expression was shown to be sensitive to TO901317, we identified a LXRE in the promoter of the LPCAT3 (lysophosphatidylcholine acyltransferase 3). This motif is highly conserved between species. We further investigated the regulation of this gene and showed that the expression of LPCAT3 was induced both in chicken and human hepatoma cells (LMH and HuH-7, respectively) in response to T0901317. Transactivation and electrophoretic mobility shift assays allowed us to locate a functional LXRE in the chicken LPCAT3 promoter. Altogether these data evidence for the first time that the chicken LPCAT3 gene is a direct target of LXR and therefore suggest a new role for LXR in phospholipid homeostasis.

Liver X receptor alpha regulates fatty acid synthase expression in chicken.

Liver X receptor alpha (LXRalpha), also referred to as nuclear receptor subfamily 1, group H, member 3 is a member of the nuclear hormone receptor superfamily, and has recently been shown to act as a master transcription factor governing hepatic lipogenesis in mammals. Liver X receptor alpha directly regulates both the expression of other lipogenic transcription factors and the expression of lipogenic enzymes, thereby enhancing hepatic fatty acid synthesis (FASN). In birds, like in humans, fatty acid synthesis primarily occurs in the liver. Whether LXRalpha is involved in hepatic regulation of lipogenic genes remained to be investigated in this species. Here we show that fatty acid synthase and the expression of other lipogenic genes (sterol regulatory element binding protein 1 and steroyl coenzyme A desaturase 1) are induced in chicken hepatoma cells in response to a pharmacological liver X receptor agonist, T0901317. A detailed analysis of the chicken FASN promoter revealed a functional liver X response element. These data define the chicken FASN gene as a direct target of LXRalpha and further expand the role of LXRalpha as a regulator of lipid metabolism in this species.

A comprehensive analysis of QTL for abdominal fat and breast muscle weights on chicken chromosome 5 using a multivariate approach.

Quantitative trait loci (QTL) influencing the weight of abdominal fat (AF) and of breast muscle (BM) were detected on chicken chromosome 5 (GGA5) using two successive F(2) crosses between two divergently selected 'Fat' and 'Lean' INRA broiler lines. Based on these results, the aim of the present study was to identify the number, location and effects of these putative QTL by performing multitrait and multi-QTL analyses of the whole available data set. Data concerned 1186 F(2) offspring produced by 10 F(1) sires and 85 F(1) dams. AF and BM traits were measured on F(2) animals at slaughter, at 8 (first cross) or 9 (second cross) weeks of age. The F(0), F(1) and F(2) birds were genotyped for 11 microsatellite markers evenly spaced along GGA5. Before QTL detection, phenotypes were adjusted for the fixed effects of sex, F(2) design, hatching group within the design, and for body weight as a covariable. Univariate analyses confirmed the QTL segregation for AF and BM on GGA5 in male offspring, but not in female offspring. Analyses of male offspring data using multitrait and linked-QTL models led us to conclude the presence of two QTL on the distal part of GGA5, each controlling one trait. Linked QTL models were applied after correction of phenotypic values for the effects of these distal QTL. Several QTL for AF and BM were then discovered in the central region of GGA5, splitting one large QTL region for AF into several distinct QTL. Neither the 'Fat' nor the 'Lean' line appeared to be fixed for any QTL genotype. These results have important implications for prospective fine mapping studies and for the identification of underlying genes and causal mutations.

Effects of quantitative trait loci on chromosomes 1, 2, 4, and 7 on growth, carcass, and meat quality traits in backcross Meishan x Large White pigs.

The aim of this work was to estimate whether genetic dissection of QTL on chromosomes 1, 2, 4, and 7, detected in an F2 Meishan x Large White population, can be achieved with a recombinant back-cross progeny test approach. For this purpose, a first generation of backcross (BC1) was produced by using frozen semen of F1 Large White x Meishan boars with Large White females. Four BC1 boars were selected because of their heterozygosity for at least 1 of the 4 regions. The BC1 boars were crossed with Large White sows, and the resulting BC2 offspring were measured for several growth and body composition traits. Contrary to the F2 animals, BC2 animals were also measured for meat quality traits in adductor, gluteus superficialis (GS), longissimus dorsi, and biceps femoris (BF) muscles. Each BC1 boar was tested for a total of 39 traits and for the 4 regions with statistical interval mapping analyses. The QTL effects obtained in BC1 families showed some differences compared with those described in F1 families. However, we confirmed QTL effects for growth in the SW1301-SW2512 markers interval on chromosome 1 and also for body composition in the SW1828-SW2512 markers interval on chromosome 1, in the SW2443-SWR783 markers interval on chromosome 2, and in the SW1369-SW632 markers interval on chromosome 7. In addition, we detected new QTL for growth traits on chromosome 2 and for meat quality traits on chromosomes 1 and 2. Growth of animals from weaning to the end of the test was influenced by the IGF2 gene region on chromosome 2. Concerning meat quality, ultimate pH of adductor, longissimus dorsi, and BF were affected by the interval delimited by UMNP3000 and SW2512 markers on chromosome 1, and a* of GS, L* of BF, and water-holding capacity of GS were affected by QTL located between marker loci SW2443 and SWR783 on chromosome 2. Recombinant progeny testing appeared to be a suitable strategy for the genetic dissection of the QTL investigated.

Mapping of 443 porcine EST improves the comparative maps for SSC1 and SSC7 with the human genome.

Numerous mapping studies of complex traits in the pig have resulted in quantitative trait loci (QTL) intervals of 10-20 cM. To improve the chances to identify the genes located in such intervals, increased expressed sequence tags (EST)-based marker density, coupled with comparative mapping with species whose genomes have been sequenced such as human and mouse, is the most efficient tool. In this study, we mapped 443 porcine EST with a radiation hybrid (RH) panel (384 had LOD > 6.0) and a somatic cell hybrid panel. Requiring no discrepancy between two-point and multipoint RH data allowed robust assignment of 309 EST, of which most were located on porcine chromosomes (SSC) 1, 4, 7, 8 and X. Moreover, we built framework maps for two chromosomes, SSC1 and SSC7, with mapped QTL in regions with known rearrangement between pig and human genomes. Using the Blast tool, we found orthologies between 407 of the 443 pig cDNA sequences and human genes, or to existing pig genes. Our porcine/human comparative mapping results reveal possible new homologies for SSC1, SSC3, SSC5, SSC6, SSC12 and SSC14 and add markers in synteny breakpoints for chromosome 7.

Exclusion of the swine leukocyte antigens as candidate region and reduction of the position interval for the Sus scrofa chromosome 7 QTL affecting growth and fatness.

Pig chromosome 7 (SSC 7) has been shown to be rich in QTL affecting performance and quality traits. Most studies mapped the QTL close to the swine leukocyte antigens (SLA), which has a large effect on adaptability and natural selection. Previous comparative mapping studies suggested that the 15-cM region limited by markers LRA1 (mapped at 55 cM) and S0102 (mapped at 70 cM) contains hundreds of genes. To decrease the number of candidate genes, we improved the mapping resolution with a genetic chromosome dissection through a backcross recombinant progeny test program between Meishan (MS) and European (EU; i.e., Large White or Landrace) breeds. Three first-generation backcross--(EU x MS) x EU--and two second-generation backcross--([EU x MS] x EU) x EU--sires carrying a recombination in the QTL mapping interval were progeny-tested (i.e., measured for a total of 44 growth, fatness, carcass and meat quality traits). Progeny family size varied from 29 to 119 pigs. Animals were genotyped for markers covering the region of interest. Progeny-test results allowed the QTL interval to be decreased from 15 to 20 cM down to 10 cM, and even less than 6 cM if we assumed that the EU pigs used in this study share only one QTL allele. Except for a putative QTL affecting some carcass composition traits, the SLA is excluded as a candidate region, suggesting that it might be possible to apply a marker-assisted selection strategy for this QTL, while controlling SLA allele diversity. The strong QTL effects remaining in animals with only 12.5% (issued from first-generation backcross boars) and 6.25% (issued from second-generation back-cross boars) Meishan genetic background shows that epistatic interactions are likely to be limited. Finally, the QTL does not have strong effects on meat quality traits.

[Institutional violence against pregnant women in highly precarious situations]. / Violences institutionnelles faites aux femmes enceintes en situation de grande précarité

Pregnant women may sleep in the streets or find refuge in places not suited to their condition. These women often attend prenatal visits late, and rarely voluntarily. Appropriate maternity beds are not always available. After giving birth, these women must bring up their children in difficult conditions. There is a shortage of shelter structures. Clandestines are in an inextricable situation.

Determination of PRKAG1 coding sequence and mapping of PRKAG1 and PRKAG2 relatively to porcine back fat thickness QTL.

PRKAG1, PRKAG2 and PRKAG3 encode three isoforms of AMP-activated protein kinase gamma chain. A major effect on meat quality and a medium effect on back fat thickness of the RN- mutation in the PRKAG3 gene has previously been reported. We have now mapped PRKAG1 and PRKAG2 at expected locations on SSC5 and SSC18 by analysis of radiation hybrids (IMpRH panel). PRKAG2 has been mapped in a region where no quantitative trait loci (QTL) has been reported. PRKAG1 has been mapped close to (but probably outside) a region containing a QTL influencing fatness traits. We have determined the full coding sequence of PRKAG1. No missense mutation was identified when comparing the coding sequence of one Meishan and one Large White boars. Further work is, however, required to determine if a polymorphism in PRKAG1 could be responsible for a part of the variability observed on fatness traits.

Detection of quantitative trait loci for fat androstenone levels in pigs.

A QTL analysis of fat androstenone levels from a three-generation experimental cross between Large White and Meishan pig breeds was carried out. A total of 485 F2 males grouped in 24 full-sib families, their 29 parents and 12 grandparents were typed for 137 markers distributed over the entire porcine genome. The F2 male population was measured for fat androstenone levels at 100, 120, 140, and 160 d of age and at slaughter around 80 kg liveweight. Statistical analyses were performed using two interval mapping methods: a line-cross (LC) regression method, which assumes alternative alleles are fixed in founder lines, and a half- full-sib (HFS) maximum likelihood method, where allele substitution effects were estimated within each half- and full-sib family. Both methods revealed genomewide significant gene effects on chromosomes 3, 7, and 14. The QTL explained, respectively, 7 to 11%, 11 to 15%, and 6 to 8% of phenotypic variance. Three additional significant QTL explaining 4 to 7% of variance were detected on chromosomes 4 and 9 using LC method and on chromosome 6 using HFS method. Suggestive QTL were also obtained on chromosomes 2, 10, 11, 13, and 18. Meishan alleles were associated with higher androstenone levels, except on chromosomes 7, 10, and 13, although 10 and 13 additive effects were near zero. The QTL had essentially additive effects, except on chromosomes 4, 10, and 13. No evidence of linked QTL or imprinting effects on androstenone concentration could be found across the entire porcine genome. The steroid chromosome P450 21-hydroxylase (CYP21) and cytochrome P450 cholesterol side chain cleavage subfamily XIA (CYP11A) loci were investigated as possible candidate genes for the chromosome 7 QTL. No mutation of coding sequence has been found for CYP21. Involvement of a candidate regulatory mutation of CYP11A gene proposed by others can be excluded in our animals.