Genome-Wide Linkage Analysis to Identify Chromosomal Regions Affecting Phenotypic Traits in the Chicken. I. Growth and Average Daily Gain

Two informative chicken F2 populations based on crosses between a broiler breeder male line and dams from genetically distinct, highly inbred (\u3e99%) chicken lines, the Leghorn G-B2 and Fayoumi M15.2, have been used for genome-wide linkage and QTL analysis. Phenotypic data on 12 body composition traits (breast muscle weight, breast muscle weight percentage, abdominal fat weight, abdominal fat weight percentage, heart weight, heart weight percentage, liver weight, liver weight percentage, spleen weight, spleen weight percentage, and drumstick weight, and drumstick weight percentage) were collected. Birds were genotyped for 269 microsatellite markers across the genome. The QTL Express program was used to detect QTL for body composition traits. Significant levels were obtained using the permutation test. For the twelve traits, a total of 61 (Gga 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 15, 18, 24, and Z) and 45 (Gga 1, 2, 3, 4, 6, 7, 8, 9, 10, 12, 15, 17, and E46) significant QTL were detected at the 5% chromosome-wise significance level, of which 19 and 11 were significant at the 5% genome-wise level for the broiler-Leghorn cross and broiler-Fayoumi cross, respectively. Phenotypic variation for each trait explained by all QTL across the genome ranged from 3.22 to 33.31% in the broiler-Leghorn cross and 4.83 to 47.12% in broiler-Fayoumi cross. Distinct QTL profiles between the 2 crosses were observed for most traits. Cryptic alleles were detected for each trait. Potential candidate genes within the QTL region for body composition traits at the 1% chromosome-wise significance level were identified from databases for future association study. The results of the current study will increase the knowledge of genetic markers associated with body composition traits and aid the process of identifying causative genes. Knowledge of beneficial genetic variation can be incorporated in breeding programs to enhance genetic improvement through marker-assisted selection in chickens

Genome-Wide Linkage Analysis to Identify Chromosomal Regions Affecting Phenotypic Traits in the Chicken. II. Body Composition

The current study is a comprehensive genome analysis to detect QTL affecting metabolic traits in chickens. Two unique F2 crosses generated from a commercial broiler male line and 2 genetically distinct inbred lines (Leghorn and Fayoumi) were used in the present study. The plasma glucagon, insulin, lactate, glucose, tri-iodothyronine, thyroxine, insulin-like growth factor I, and insulin-like growth factor II concentrations at 8 wk were measured in the 2 F2 crosses. Birds were genotyped for 269 microsatellite markers across the entire genome. The program QTL Express was used for QTL detection. Significance levels were obtained using the permutation test. For the 10 traits, a total of 6 and 9 significant QTL were detected at a 1% chromosome-wise significance level, of which 1 and 6 were significant at the 5% genome-wise level for the broiler-Leghorn cross and broiler-Fayoumi cross, respectively. Most QTL for metabolic traits in the present study were detected in Gga 2, 6, 8, 9, 13, and Z for the broiler-Leghorn cross and Gga 1, 2, 4, 7, 8, 13, 17, and E47 for the broiler-Fayoumi cross. Phenotypic variation for each trait explained by all QTL across genome ranged from 2.73 to 14.08% in the broiler-Leghorn cross and from 6.93 to 21.15% in the broiler-Fayoumi cross. Several positional candidate genes within the QTL region for metabolic traits at the 1% chromosome-wise significance level are biologically associated with the regulation of metabolic pathways of insulin, triiodothyronine, and thyroxine

Genome-Wide Linkage Analysis to Identify Chromosomal Regions Affecting Phenotypic Traits in the Chicken. III. Skeletal Integrity

Two unique chicken F2 populations generated from a broiler breeder male line and 2 genetically distinct inbred (\u3e99%) chicken lines (Leghorn and Fayoumi) were used for whole genome QTL analysis. Twelve phenotypic skeletal integrity traits (6 absolute and 6 relative traits) were measured or calculated, including bone mineral content, bone mineral density, tibia length, shank length, shank weight, and shank length:shank weight. All traits were also expressed as a percentage of BW at 8 wk of age. Birds were genotyped for 269 microsatellite markers across the entire genome. The QTL affecting bone traits in chickens were detected by the QTL express program. Significance levels were obtained using the permutation test. For the 12 traits, a total of 56 significant QTL were detected at the 5% chromosome-wise significance level, of which 14 and 10 were significant at the 5% genome-wise level for the broiler-Leghorn cross and broiler-Fayoumi cross, respectively. Phenotypic variation for each trait explained by all detected QTL across the genome ranged from 12.0 to 35.6% in the broiler-Leghorn cross and 2.9 to 31.3% in the broiler-Fayoumi cross. Different QTL profiles identified between the 2 related F2 crosses for most traits suggested that genetic background is an important factor for QTL analysis. Study of associations of biological candidate genes with skeletal integrity traits in chickens will reveal new knowledge of understanding biological process of skeletal homeostasis. The results of the current study have identified markers for bone strength traits, which may be used to genetically improve skeletal integrity in chickens by MAS, and to identify the causal genes for these traits

Genome-Wide Linkage Analysis to Identify Chromosomal Regions Affecting Phenotypic Traits in the Chicken. I. Growth and Average Daily Gain

Two informative chicken F2 populations based on crosses between a broiler breeder male line and dams from genetically distinct, highly inbred (>99%) chicken lines, the Leghorn G-B2 and Fayoumi M15.2, have been used for genome-wide linkage and QTL analysis. Phenotypic data on 12 body composition traits (breast muscle weight, breast muscle weight percentage, abdominal fat weight, abdominal fat weight percentage, heart weight, heart weight percentage, liver weight, liver weight percentage, spleen weight, spleen weight percentage, and drumstick weight, and drumstick weight percentage) were collected. Birds were genotyped for 269 microsatellite markers across the genome. The QTL Express program was used to detect QTL for body composition traits. Significant levels were obtained using the permutation test. For the twelve traits, a total of 61 (Gga 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 15, 18, 24, and Z) and 45 (Gga 1, 2, 3, 4, 6, 7, 8, 9, 10, 12, 15, 17, and E46) significant QTL were detected at the 5% chromosome-wise significance level, of which 19 and 11 were significant at the 5% genome-wise level for the broiler-Leghorn cross and broiler-Fayoumi cross, respectively. Phenotypic variation for each trait explained by all QTL across the genome ranged from 3.22 to 33.31% in the broiler-Leghorn cross and 4.83 to 47.12% in broiler-Fayoumi cross. Distinct QTL profiles between the 2 crosses were observed for most traits. Cryptic alleles were detected for each trait. Potential candidate genes within the QTL region for body composition traits at the 1% chromosome-wise significance level were identified from databases for future association study. The results of the current study will increase the knowledge of genetic markers associated with body composition traits and aid the process of identifying causative genes. Knowledge of beneficial genetic variation can be incorporated in breeding programs to enhance genetic improvement through marker-assisted selection in chickens.This article is from Poultry Science 85 (2006): 1712, doi:10.1093/ps/85.10.1712.</p

Genome-Wide Linkage Analysis to Identify Chromosomal Regions Affecting Phenotypic Traits in the Chicken. II. Body Composition

The current study is a comprehensive genome analysis to detect QTL affecting metabolic traits in chickens. Two unique F2 crosses generated from a commercial broiler male line and 2 genetically distinct inbred lines (Leghorn and Fayoumi) were used in the present study. The plasma glucagon, insulin, lactate, glucose, tri-iodothyronine, thyroxine, insulin-like growth factor I, and insulin-like growth factor II concentrations at 8 wk were measured in the 2 F2 crosses. Birds were genotyped for 269 microsatellite markers across the entire genome. The program QTL Express was used for QTL detection. Significance levels were obtained using the permutation test. For the 10 traits, a total of 6 and 9 significant QTL were detected at a 1% chromosome-wise significance level, of which 1 and 6 were significant at the 5% genome-wise level for the broiler-Leghorn cross and broiler-Fayoumi cross, respectively. Most QTL for metabolic traits in the present study were detected in Gga 2, 6, 8, 9, 13, and Z for the broiler-Leghorn cross and Gga 1, 2, 4, 7, 8, 13, 17, and E47 for the broiler-Fayoumi cross. Phenotypic variation for each trait explained by all QTL across genome ranged from 2.73 to 14.08% in the broiler-Leghorn cross and from 6.93 to 21.15% in the broiler-Fayoumi cross. Several positional candidate genes within the QTL region for metabolic traits at the 1% chromosome-wise significance level are biologically associated with the regulation of metabolic pathways of insulin, triiodothyronine, and thyroxine.This article is from Poultry Science 86 (2007): 267, doi:10.1093/ps/86.2.267.</p

Genome-Wide Linkage Analysis to Identify Chromosomal Regions Affecting Phenotypic Traits in the Chicken. IV. Metabolic Traits

The current study is a comprehensive genome analysis to detect QTL affecting metabolic traits in chickens. Two unique F2 crosses generated from a commercial broiler male line and 2 genetically distinct inbred lines (Leghorn and Fayoumi) were used in the present study. The plasma glucagon, insulin, lactate, glucose, tri-iodothyronine, thyroxine, insulin-like growth factor I, and insulin-like growth factor II concentrations at 8 wk were measured in the 2 F2 crosses. Birds were genotyped for 269 microsatellite markers across the entire genome. The program QTL Express was used for QTL detection. Significance levels were obtained using the permutation test. For the 10 traits, a total of 6 and 9 significant QTL were detected at a 1% chromosome-wise significance level, of which 1 and 6 were significant at the 5% genome-wise level for the broiler-Leghorn cross and broiler-Fayoumi cross, respectively. Most QTL for metabolic traits in the present study were detected in Gga 2, 6, 8, 9, 13, and Z for the broiler-Leghorn cross and Gga 1, 2, 4, 7, 8, 13, 17, and E47 for the broiler-Fayoumi cross. Phenotypic variation for each trait explained by all QTL across genome ranged from 2.73 to 14.08% in the broiler-Leghorn cross and from 6.93 to 21.15% in the broiler-Fayoumi cross. Several positional candidate genes within the QTL region for metabolic traits at the 1% chromosome-wise significance level are biologically associated with the regulation of metabolic pathways of insulin, triiodothyronine, and thyroxine.This article is from Poutlry Science 86 (2007): 267, doi:10.1093/ps/86.2.267.</p

Genome-Wide Linkage Analysis to Identify Chromosomal Regions Affecting Phenotypic Traits in the Chicken. III. Skeletal Integrity

Two unique chicken F2 populations generated from a broiler breeder male line and 2 genetically distinct inbred (>99%) chicken lines (Leghorn and Fayoumi) were used for whole genome QTL analysis. Twelve phenotypic skeletal integrity traits (6 absolute and 6 relative traits) were measured or calculated, including bone mineral content, bone mineral density, tibia length, shank length, shank weight, and shank length:shank weight. All traits were also expressed as a percentage of BW at 8 wk of age. Birds were genotyped for 269 microsatellite markers across the entire genome. The QTL affecting bone traits in chickens were detected by the QTL express program. Significance levels were obtained using the permutation test. For the 12 traits, a total of 56 significant QTL were detected at the 5% chromosome-wise significance level, of which 14 and 10 were significant at the 5% genome-wise level for the broiler-Leghorn cross and broiler-Fayoumi cross, respectively. Phenotypic variation for each trait explained by all detected QTL across the genome ranged from 12.0 to 35.6% in the broiler-Leghorn cross and 2.9 to 31.3% in the broiler-Fayoumi cross. Different QTL profiles identified between the 2 related F2 crosses for most traits suggested that genetic background is an important factor for QTL analysis. Study of associations of biological candidate genes with skeletal integrity traits in chickens will reveal new knowledge of understanding biological process of skeletal homeostasis. The results of the current study have identified markers for bone strength traits, which may be used to genetically improve skeletal integrity in chickens by MAS, and to identify the causal genes for these traits.This article is from Poultry Science 86 (2007): 255, doi:10.1093/ps/86.2.255.</p

Increased milk production by Holstein cows consuming endophyte-infected fescue seed during the dry period.

Ergot alkaloids in endophyte-infected grasses inhibit prolactin (PRL) secretion and may reduce milk production of cows consuming endophyte-infected grasses. We hypothesized that consumption of endophyte-infected fescue during the dry period inhibits mammary differentiation and subsequent milk production. Twenty-five multiparous Holstein cows were randomly assigned to 3 treatment groups. Starting at 90-d prepartum, cows were fed endophyte-free fescue seed (control, CON; n = 9), endophyte-free fescue seed and 3x/wk subcutaneous injections of bromocryptine (0.11 mg/kg BW; positive control, BROMO; n = 8), or endophyte-infected fescue seed as 10% of the as-fed diet (INF; n = 8). Although milk yield of groups did not differ at 1290 d prepartum, at dry-off ( 1260 d) INF and BROMO cows produced less milk (P < 0.05) than CON (averaging 20, 11 and 14 kg/d for CON, INF and BROMO cows). Throughout the treatment period, concentrations of PRL in the circulation were lower in INF and BROMO cows than CON cows (P < 0.05). Basal concentrations of PRL in venous plasma averaged 25.3, 2.8 and 3.7 ng/ml for CON, INF and BROMO cows, respectively. Prepartum release of PRL was also reduced by ergot alkaloids, averaging 19.5, 9.2 and 1.1 \u3bcg PRL/ml*h (area under curve) for CON, INF and BROMO cows, respectively. At 10 d of lactation, when treatments were terminated, basal concentrations of PRL in plasma averaged 22.5, 1.6 and 1.4 ng/ml for CON, INF and BROMO cows, respectively. Three wk after the end of treatment, circulating concentrations of PRL were equivalent across groups (P > 0.05). Gestation length did not differ between groups. Although treatment 4 wk before dry-off reduced milk yield in INF and BROMO cows, milk production in the ensuing lactation was increased 8% and 9% in INF and BROMO cows relative to CON (P < 0.05). We reject our initial hypothesis, as data show that consumption of ergot alkaloids during the dry period increases milk production in the ensuing lactation. We propose that this effect is due to a reduction in PRL during the dry period, analogous to the production effect realized by exposing cows to reduced photoperiod (low PRL) during the dry perio