Quantitative trait loci with sex-specific effects for internal organs weights and hematocrit value in a broiler-layer cross.

Rapid growth in broilers is associated with susceptibility to metabolic disorders such as pulmonary hypertension syndrome (ascites) and sudden death. This study describes a genome search for QTL associated with relative weight of cardio respiratory and metabolically important organs (heart, lungs, liver and gizzard), and hematocrit value in a Brazilian broiler-layer cross. QTL with similar or different effects across sexes were investigated. At 42 days of age after fasted for 6 h, the F2 chickens were weighed and slaughtered. Weights and percentages of the weight relative to BW42 of gizzard, heart, lungs, liver and hematocrit were used in the QTL search. Parental, F1 and F2 individuals were genotyped with 128 genetic markers (127 microsatellites and 1 SNP) covering 22 linkage groups. QTL mapping analyses were carried out using mixed models. A total of 11 genome-wide significant QTL and five suggestive linkages were mapped. Thus, genome-wide significant QTL with similar effects across sexes were mapped to GGA2, 4 and 14 for heart weight, and to GGA2, 8 and 12 for gizzard %. Additionally, five genome-wide significant QTL with different effects across sexes were mapped to GGA 8, 19 and 26 for heart weight; GGA26 for heart % and GGA3 for hematocrit value. Five QTL were detected in chromosomal regions where QTL for similar traits were previously mapped in other F2 chicken populations. Seven novel genome-wide significant QTL are reported here, and 21 positional candidate genes in QTL regions were identified.

SNP and INDEL detection in a QTL region on chicken chromosome 2 associated with muscle deposition.

Genetic improvement is important for the poultry industry, contributing to increased efficiency of meat production and quality. Because breast muscle is the most valuable part of the chicken carcass, knowledge of polymorphisms influencing this trait can help breeding programs. Therefore, the complete genome of 18 chickens from two different experimental lines (broiler and layer) from EMBRAPA was sequenced, and SNPs and INDELs were detected in a QTL region for breast muscle deposition on chicken chromosome 2 between microsatellite markers MCW0185 and MCW0264 (105,849-112,649 kb). Initially, 94,674 unique SNPs and 10,448 unique INDELs were identified in the target region. After quality filtration, 77% of the SNPs (85,765) and 60% of the INDELs (7828) were retained. The studied region contains 66 genes, and functional annotation of the filtered variants identified 517 SNPs and three INDELs in exonic regions. Of these, 357 SNPs were classified as synonymous, 153 as non-synonymous, three as stopgain, four INDELs as frameshift and three INDELs as non-frameshift. These exonic mutations were identified in 37 of the 66 genes from the target region, three of which are related to muscle development (DTNA, RB1CC1 and MOS). Fifteen non-tolerated SNPs were detected in several genes (MEP1B, PRKDC, NSMAF, TRAPPC8, SDR16C5, CHD7, ST18 and RB1CC1). These loss-of-function and exonic variants present in genes related to muscle development can be considered candidate variants for further studies in chickens. Further association studies should be performed with these candidate mutations as should validation in commercial populations to allow a better explanation of QTL effects.

Variant discovery in a QTL region on chromosome 3 associated with fatness in chickens.

Abdominal fat content is an economically important trait in commercially bred chickens. Although many quantitative trait loci (QTL) related to fat deposition have been detected, the resolution for these regions is low and functional variants are still unknown. The current study was conducted aiming at increasing resolution for a region previously shown to have a QTL associated with fat deposition, to detect novel variants from this region and to annotate those variants to delineate potentially functional ones as candidates for future studies. To achieve this, 18 chickens from a parental generation used in a reciprocal cross between broiler and layer lines were sequenced using the Illumina next-generation platform with an initial coverage of 18X/chicken. The discovery of genetic variants was performed in a QTL region located on chromosome 3 between microsatellite markers LEI0161 and ADL0371 (33,595,706-42,632,651 bp). A total of 136,054 unique SNPs and 15,496 unique INDELs were detected in this region, and after quality filtering, 123,985 SNPs and 11,298 INDELs were retained. Of these variants, 386 SNPs and 15 INDELs were located in coding regions of genes related to important metabolic pathways. Loss-of-function variants were identified in several genes, and six of those, namely LOC771163, EGLN1, GNPAT, FAM120B, THBS2 and GGPS1, were related to fat deposition. Therefore, these loss-of-function variants are candidate mutations for conducting further studies on this important trait in chickens.

Polymorphisms in FGFBP1 and FGFBP2 genes associated with carcass and meat quality traits in chickens.

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.

Identification and association of polymorphisms in CAPN1 and CAPN3 candidate genes related to performance and meat quality traits in chickens.

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 (F1) 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 F2 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.

Quantitative trait loci associated with chemical composition of the chicken carcass.

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.

QTL for percentage of carcass and carcass parts in a broiler x layer cross.

An F2 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 F2 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.

Precision of distances and ordering of microsatellite markers in consensus linkage maps of chromosomes 1, 3 and 4 from two reciprocal chicken populations using bootstrap sampling.

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.

Quantitative trait loci associated with fatness in a broiler-layer cross.

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.

Quantitative trait loci for performance traits in a broiler x layer cross.

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.

Partners of Rpb8p, a small subunit shared by yeast RNA polymerases I, II and III.

Rpb8p, a subunit common to the three yeast RNA polymerases, is conserved among eukaryotes and absent from noneukaryotes. Defective mutants were found at an invariant GGLLM motif and at two other highly conserved amino acids. With one exception, they are clustered on the Rpb8p structure. They all impair a two-hybrid interaction with a fragment conserved in the largest subunits of RNA polymerases I (Rpa190p), II (Rpb1p), and III (Rpc160p). This fragment corresponds to the pore 1 module of the RNA polymerase II crystal structure and bears a highly conserved motif (P.I.KP.LW.GKQ) facing the GGLLM motif of Rpb8p. An RNA polymerase I mutant (rpa190-G728D) at the invariant glycyl of P.I.KP.LW.GKQ provokes a temperature-sensitive defect. Increasing the gene dosage of another common subunit, Rpb6p, suppresses this phenotype. It also suppresses a conditional growth defect observed when replacing Rpb8p by its human counterpart. Hence, Rpb6p and Rpb8p functionally interact in vivo. These two subunits are spatially separated by the pore 1 module and may also be possibly connected by the disorganized N half of Rpb6p, not included in the present structure data. Human Rpb6p is phosphorylated at its N-terminal Ser2, but an alanyl replacement at this position still complements an rpb6-Delta null allele. A two-hybrid interaction also occurs between Rpb8p and the product of orphan gene YGR089w. A ygr089-Delta null mutant has no detectable growth defect but aggravates the conditional growth defect of rpb8 mutants, suggesting that the interaction with Rpb8p may be physiologically relevant.

A protein-protein interaction map of yeast RNA polymerase III.

The structure of the yeast RNA polymerase (pol) III was investigated by exhaustive two-hybrid screening using a library of random genomic fragments fused to the Gal4 activation domain. This procedure allowed us to identify contacts between individual polypeptides, localize the contact domains, and deduce a protein-protein interaction map of the multisubunit enzyme. In all but one case, pol III subunits were able to interact in vivo with one or sometimes two partner subunits of the enzyme or with subunits of TFIIIC. Four subunits that are common to pol I, II, and III (ABC27, ABC14.5, ABC10alpha, and ABC10beta), two that are common to pol I and III (AC40 and AC19), and one pol III-specific subunit (C11) can associate with defined regions of the two large subunits. These regions overlapped with highly conserved domains. C53, a pol III-specific subunit, interacted with a 37-kDa polypeptide that copurifies with the enzyme and therefore appears to be a unique pol III subunit (C37). Together with parallel interaction studies based on dosage-dependent suppression of conditional mutants, our data suggest a model of the pol III preinitiation complex.