Quantitative trait loci segregating in crosses between New Hampshire and White Leghorn chicken lines: II. Muscle weight and carcass composition

In order to identify genetic factors influencing muscle weight and carcass composition in chicken, a linkage analysis was performed with 278 F₂ males of reciprocal crosses between the extremely different inbred lines New Hampshire (NHI) and White Leghorn (WL77). The NHI line had been selected for high meat yield and the WL77 for low egg weight before inbreeding. Highly significant quantitative trait loci (QTL) controlling body weight and the weights of carcass, breast muscle, drumsticks–thighs and wings were identified on GGA4 between 151.5 and 160.5 cM and on GGA27 between 4 and 52 cM. These genomic regions explained 13.7–40.2% and 5.3–13.8% of the phenotypic F₂ variances of the corresponding traits respectively. Additional genome‐wide highly significant QTL for the weight of drumsticks–thighs were mapped on GGA1, 5 and 7. Moreover, significant QTL controlling body weight were found on GGA2 and 11. The data obtained in this study can be used for increasing the mapping resolution and subsequent gene targeting on GGA4 and 27 by combining data with other crosses where the same QTL were found.     

Quantitative trait loci segregating in crosses between New Hampshire and White Leghorn chicken lines: IV. Growth performance

Reciprocal crosses between the inbred lines New Hampshire (NHI) and White Leghorn (WL77) comprising 579 F₂ individuals were used to map QTL for body weight and composition. Here, we examine the growth performance until 20 weeks of age. Linkage analysis provided evidence for highly significant QTL on GGA1, 2, 4, 10 and 27 which had specific effects on early or late growth. The highest QTL effects, accounting for 4.6–25.6% of the phenotypic F₂ variance, were found on the distal region of GGA4 between 142 and 170 cM (F ≥ 13.68). The NHI QTL allele increased body mass by 141.86 g at 20 weeks. Using body weight as a covariate in the analysis of body composition traits provided evidence for genes in the GGA4 QTL region affecting fat mass independently of body mass. The QTL effect size differed between sexes and depended on the direction of cross. TBC1D1, CCKAR and PPARGC1A are functional candidate genes in the QTL peak region. Our study confirmed the importance of the distal GGA4 region for chicken growth performance. The strong effect of the GGA4 QTL makes fine mapping and gene discovery feasible.     

Fatness QTL on chicken chromosome 5 and interaction with sex

Quantitative trait loci (QTL) affecting fatness in male chickens were previously identified on chromosome 5 (GGA5) in a three-generation design derived from two experimental chicken lines divergently selected for abdominal fat weight. A new design, established from the same pure lines, produced 407 F2 progenies (males and females) from 4 F1-sire families. Body weight and abdominal fat were measured on the F₂ at 9 wk of age. In each sire family, selective genotyping was carried out for 48 extreme individuals for abdominal fat using seven microsatellite markers from GGA5. QTL analyses confirmed the presence of QTL for fatness on GGA5 and identified a QTL by sex interaction. By crossing one F1 sire heterozygous at the QTL with lean line dams, three recombinant backcross 1 (BC1) males were produced and their QTL genotypes were assessed in backcross 2 (BC2) progenies. These results confirmed the QTL by sex interaction identified in the F2 generation and they allow mapping of the female QTL to less than 8 Mb at the distal part of the GGA5. They also indicate that fat QTL alleles were segregating in both fat and lean lines.     

Reducing the interval of a growth QTL on chromosome 4 in laying hens

In our previous research, we identified a QTL with an interval of 3.4 Mb for growth on chicken chromosome (GGA) 4 in an advanced intercross population of an initial cross between the New Hampshire inbred line (NHI) and the White Leghorn inbred line (WL77). In the current study, an association analysis was performed in a population of purebred white layers (WLA) with White Leghorn origin. Genotypic data of 130 SNPs within the previously identified 3.4‐Mb region were obtained using a 60K SNP chip. In total, 24 significant SNPs (LOD ≥ 4.44) on GGA4 were detected for daily weigh gain from 8 to 14 weeks and two SNPs (LOD ≥ 4.80) for body weight at 14 weeks. The QTL interval was reduced by 1.9 Mb to an interval of 1.5 Mb (74.6–76.1 Mb) that harbors 15 genes. Furthermore, to identify additional loci for chicken growth, a genome‐wide association study (GWAS) was carried out in a WLA population. The GWAS identified an additional QTL on GGA6 for body weight at six weeks (19.8–21.2 Mb). Our findings showed that by using a WLA population we were able to further reduce the QTL confidence interval previously detected using a NHI × WL77 advanced intercross population.     

Quantitative trait loci segregating in crosses between New Hampshire and White Leghorn chicken lines: I. egg production traits

A genome scan was performed to detect chromosomal regions that affect egg production traits in reciprocal crosses between two genetically and phenotypically extreme chicken lines: the partially inbred line New Hampshire (NHI) and the inbred line White Leghorn (WL77). The NHI line had been selected for high growth and WL77 for low egg weight before inbreeding. The result showed a highly significant region on chromosome 4 with multiple QTL for egg production traits between 19.2 and 82.1 Mb. This QTL region explained 4.3 and 16.1% of the phenotypic variance for number of eggs and egg weight in the F₂ population, respectively. The egg weight QTL effects are dependent on the direction of the cross. In addition, genome‐wide suggestive QTL for egg weight were found on chromosomes 1, 5, and 9, and for number of eggs on chromosomes 5 and 7. A genome‐wide significant QTL affecting age at first egg was mapped on chromosome 1. The difference between the parental lines and the highly significant QTL effects on chromosome 4 will further support fine mapping and candidate gene identification for egg production traits in chicken.     

Identification of genetic determinants of IGF‐1 levels and longevity among mouse inbred strains

The IGF‐1 signaling pathway plays an important role in regulating longevity. To identify the genetic loci and genes that regulate plasma IGF‐1 levels, we intercrossed MRL/MpJ and SM/J, inbred mouse strains that differ in IGF‐1 levels. Quantitative trait loci (QTL) analysis of IGF‐1 levels of these F2 mice detected four QTL on chromosomes (Chrs) 9 (48 Mb), 10 (86 Mb), 15 (18 Mb), and 17 (85 Mb). Haplotype association mapping of IGF‐1 levels in 28 domesticated inbred strains identified three suggestive loci in females on Chrs 2 (13 Mb), 10 (88 Mb), and 17 (28 Mb) and in four males on Chrs 1 (159 Mb), 3 (52 and 58 Mb), and 16 (74 Mb). Except for the QTL on Chr 9 and 16, all loci co‐localized with IGF‐1 QTL previously identified in other mouse crosses. The most significant locus was the QTL on Chr 10, which contains the Igf1 gene and which had a LOD score of 31.8. Haplotype analysis among 28 domesticated inbred strains revealed a major QTL on Chr 10 overlapping with the QTL identified in the F2 mice. This locus showed three major haplotypes; strains with haplotype 1 had significantly lower plasma IGF‐1 and extended longevity (P < 0.05) than strains with haplotype 2 or 3. Bioinformatic analysis, combined with sequencing and expression studies, showed that Igf1 is the most likely QTL gene, but that other genes may also play a role in this strong QTL.     

Genome-Wide Linkage Analysis Identifies Loci for Testicle and Ovary Traits in Chickens

Development of testes or ovaries is critical to chicken breeders. Understanding the genetic mechanisms influencing the development of the testes and ovaries could enhance selection efforts which target reproductive traits. The linkage analysis was conducted within an F2 population derived from Beijing-You chickens and a commercial broiler line. The results have identified one quantitative trait loci (QTL, designated T1) for bilateral testicular weight (TW) and the percentage of TW to carcass weight, and five QTLs (designated O1–O5) for ovary weight (follicle-free, OW) and the percentage of OW to carcass weight. For the testes traits, QTL T1 is located between 6.55 and 8.56 Mb on GGA13. Especially, the gene gamma-amino butyric acid A receptor, alpha 1 (GABRA1) located near the T1 peak. For ovarian traits, QTL O2 was located at 29.31 Mb on GGA7. G protein-coupled receptor 39 (GPR39) present at the O2 peak was expressed at higher levels within the reproductive tract. It is also involved in the regulation of several reproductive functions. Other QTL peaks and the genes’ function in the ovary and testes need to be evaluated. The QTLs and the genes identified in this study could provide valuable information for establishing reproductive traits in chickens, and need further investigation.     

Quantitative trait loci affecting body weight and fatness from a mouse line selected for extreme high growth.

Quantitative trait loci (QTL) influencing body weight were mapped by linkage analysis in crosses between a high body weight selected line (DU6) and a control line (DUKs). The two mouse lines differ in body weight by 106% and in abdominal fat weight by 100% at 42 days. They were generated from the same base population and maintained as outbred colonies. Determination of line-specific allele frequencies at microsatellite markers spanning the genome indicated significant changes between the lines on 15 autosomes and the X chromosome. To confirm these effects, a QTL analysis was performed using structured F2 pedigrees derived from crosses of a single male from DU6 with a female from DUKs. QTL significant at the genome-wide level were mapped for body weight on chromosome 11; for abdominal fat weight on chromosomes 4, 11, and 13; for abdominal fat percentage on chromosomes 3 and 4; and for the weights of liver on chromosomes 4 and 11, of kidney on chromosomes 2 and 9, and of spleen on chromosome 11. The strong effect on body weight of the QTL on chromosome 11 was confirmed in three independent pedigrees. The effect was additive and independent of sex, accounting for 21-35% of the phenotypic variance of body weight within the corresponding F2 populations. The test for multiple QTL on chromosome 11 with combined data from all pedigrees indicated the segregation of two loci separated by 36 cM influencing body weight.     

Genetic variation in the β, β‐carotene‐9′, 10′‐dioxygenase gene and association with fat colour in bovine adipose tissue and milk

β, β‐carotene‐9′, 10′‐dioxygenase (BCO2) plays a role in cleaving β‐carotene eccentrically, and may be involved in the control of adipose and milk colour in cattle. The bovine BCO2 gene was sequenced as a potential candidate gene for a beef fat colour QTL on chromosome (BTA) 15. A single nucleotide base change located in exon 3 causes the substitution of a stop codon (encoded by the A allele) for tryptophan⁸⁰ (encoded by the G allele) (c. 240G>A, p.Trp80stop, referred to herein as SNP W80X). Association analysis showed significant differences in subcutaneous fat colour and beta‐carotene concentration amongst cattle with different BCO2 genotypes. Animals with the BCO2 AA genotype had more yellow beef fat and a higher beta‐carotene concentration in adipose tissues than those with the GA or GG genotype. QTL mapping analysis with the BCO2 SNP W80X fitted as a fixed effect confirmed that this SNP is likely to represent the quantitative trait nucleotide (QTN) for the fat colour‐related traits on BTA 15. Moreover, animals with the AA genotype had yellower milk colour and a higher concentration of beta‐carotene in the milk.     

A genome‐wide association study for a proxy of intermuscular fat level in the Italian Large White breed identifies genomic regions affecting an important quality parameter for dry‐cured hams

Intermuscular fat content in protected designations of origin dry‐cured hams is a very important meat quality trait that affects the acceptability of the product by the consumers. An excess in intermuscular fat (defined as the level of fat deposition between leg muscles) is a defect that depreciates the final product. In this study we carried out a genome‐wide association study for visible intermuscular fat (VIF) of hams in the Italian Large White pig breed. This trait was evaluated on the exposed muscles of green legs in 1122 performance‐tested gilts by trained personnel, according to a classification scale useful for routine and cheap evaluation. All animals were genotyped with the Illumina PorcineSNP60 BeadChip. The genome‐wide association study identified three QTL regions on porcine chromosome 1 (SSC1; accounting for ~79% of the SNPs below the 5.0E?04 threshold) and SSC2, two on SSC7 and one each on SSC3, SSC6, SSC9, SSC11, SSC13, SSC15, SSC16 and SSC17. The most significant SNP (ALGA0004143 on SSC1 at 77.3 Mb; P_FDR < 0.05), included in the largest QTL region which spanned about 6.8 Mb on SSC1, is located within the glutamate ionotropic receptor kainate type subunit 2 (GRIK2) gene. Functional annotation of all genes included in QTL regions for VIF suggested that intermuscular fat in the Italian Large White breed is a complex trait apparently influenced by complex biological mechanisms also involving obesity‐related processes. These QTL target mainly chromosome regions different from those affecting subcutaneous and intramuscular fat deposition.     

Detection of QTL for immune response to sheep red blood cells in laying hens

The aim of this study is to detect quantitative trait loci (QTL) involved in the regulation of the primary and the secondary immune response to sheep red blood cells (SRBC) in a resource population using microsatellite DNA markers. The F2 resource population originates from a cross of two divergently selected lines for either high (H line) or low (L line) primary antibody response to SRBC. The F2 population consisted of six half-sib families, three families per each of reciprocal crosses. Total antibody titres to SRBC were determined by agglutination in serum from all birds. F2, F1 and F0 generations were genotyped for 170 microsatellite markers, using a whole-genome scan approach. The half-sib and the line-cross analyses were performed to determine QTL regions associated with regulation of the immune response. In the half-sib analysis, four QTL for SRBC primary response have been identified: on GGA3, GGA5, GGA16 and GGA23. No QTL was identified for SRBC secondary response under the half-sib model. In the line-cross analysis, three QTL were identified on GGA10, GGA16 and GGA27 for SRBC primary response and five QTL were identified on GGA6, GGA9, GGA15, GGA16 and GGA27 for SRBC secondary response. Subsequently, the family contribution of individual families to the QTL was analysed. The family with the largest contribution was genotyped with additional microsatellite markers in the QTL region on GGA5. The extended half-sib analysis with additional genotype information results in narrowing down the QTL region on GGA5.     

Genome-wide mapping of Quantitative Trait Loci for fatness,fat cell characteristics and fat metabolism in three porcine F2 crosses

Background

QTL affecting fat deposition related performance traits have been considered in several studies and mapped on numerous porcine chromosomes. However, activity of specific enzymes, protein content and cell structure in fat tissue probably depend on a smaller number of genes than traits related to fat content in carcass. Thus, in this work traits related to metabolic and cytological features of back fat tissue and fat related performance traits were investigated in a genome-wide QTL analysis. QTL similarities and differences were examined between three F₂ crosses, and between male and female animals.

Methods

A total of 966 F₂ animals originating from crosses between Meishan (M), Pietrain (P) and European wild boar (W) were analysed for traits related to fat performance (11), enzymatic activity (9) and number and volume of fat cells (20). Per cross, 216 (M × P), 169 (W × P) and 195 (W × M) genome-wide distributed marker loci were genotyped. QTL mapping was performed separately for each cross in steps of 1 cM and steps were reduced when the distance between loci was shorter. The additive and dominant components of QTL positions were detected stepwise by using a multiple position model.

Results

A total of 147 genome-wide significant QTL (76 at P < 0.05 and 71 at P < 0.01) were detected for the three crosses. Most of the QTL were identified on SSC1 (between 76-78 and 87-90 cM), SSC7 (predominantly in the MHC region) and SSCX (in the vicinity of the gene CAPN6). Additional genome-wide significant QTL were found on SSC8, 12, 13, 14, 16, and 18. In many cases, the QTL are mainly additive and differ between F₂ crosses. Many of the QTL profiles possess multiple peaks especially in regions with a high marker density. Sex specific analyses, performed for example on SSC6, SSC7 and SSCX, show that for some traits the positions differ between male and female animals. For the selected traits, the additive and dominant components that were analysed for QTL positions on different chromosomes, explain in combination up to 23% of the total trait variance.

Conclusions

Our results reveal specific and partly new QTL positions across genetically diverse pig crosses. For some of the traits associated with specific enzymes, protein content and cell structure in fat tissue, it is the first time that they are included in a QTL analysis. They provide large-scale information to analyse causative genes and useful data for the pig industry.     

Composite interval mapping and mixed models reveal QTL associated with performance and carcass traits on chicken chromosomes 1, 3, and 4

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.     

Mapping quantitative trait loci affecting fatness and breast muscle weight in meat-type chicken lines divergently selected on abdominal fatness

Quantitative trait loci (QTL) for abdominal fatness and breast muscle weight were investigated in a three-generation design performed by inter-crossing two experimental meat-type chicken lines that were divergently selected on abdominal fatness. A total of 585 F₂ male offspring from 5 F₁ sires and 38 F₁ dams were recorded at 8 weeks of age for live body, abdominal fat and breast muscle weights. One hundred-twenty nine microsatellite markers, evenly located throughout the genome and heterozygous for most of the F₁ sires, were used for genotyping the F₂ birds. In each sire family, those offspring exhibiting the most extreme values for each trait were genotyped. Multipoint QTL analyses using maximum likelihood methods were performed for abdominal fat and breast muscle weights, which were corrected for the effects of 8-week body weight, dam and hatching group. Isolated markers were assessed by analyses of variance. Two significant QTL were identified on chromosomes 1 and 5 with effects of about one within-family residual standard deviation. One breast muscle QTL was identified on GGA1 with an effect of 2.0 within-family residual standard deviation.     

基于CSSL的高密度物理图谱定位水稻分蘖角度QTL

对以籼稻9311为遗传背景携带粳稻日本晴基因组的染色体片段置换系（CSSL）的遗传图谱进行分子标记加密,构建了含250个多态标记的高密度物理图谱。以119个CSSLs为材料,P≤0.001为阈值,筛选到分蘖角度与受体亲本9311差异极显著的10个系。结合物理图谱和代换作图方法,共鉴定出5个分蘖角度QTL,其中qTA11的加性效应表现为增效作用,来源于9311的等位基因;其余4个QTL的加性效应为减效作用,均来源于日本晴的等位基因。qTA6-1和qTA6-2分别被定位于第6染色体RM253–RM527之间的3.55Mb区段和RM3139–RM494的1.65Mb区间;qTA9被定位于第9染色体RM257–RM189之间的3.40Mb区段;qTA10被定位在第10染色体RM222–S10-1之间的2.10Mb区段;qTA11被定位于第11染色体RM1761–RM4504之间的3.30Mb区间。以上研究结果为水稻分蘖角度QTL的精细定位和株型育种提供了依据。     

Mapping QTL for growth and shank traits in chickens divergently selected for high or low body weight

An F₂ population (695 individuals) was established from broiler chickens divergently selected for either high (HG) or low (LG) growth, and used to localize QTL for developmental changes in body weight (BW), shank length (SL9) and shank diameter (SD9) at 9 weeks. QTL mapping revealed three genome‐wide QTL on chromosomes (GGA) 2, 4 and 26 and three suggestive QTL on GGA 1, 3 and 5. Most of the BW QTL individually explained 2–5% of the phenotypic variance. The BW QTL on GGA2 explained about 7% of BW from 3 to 7 weeks of age, while that on GGA4 explained 15% of BW from 5 to 9 weeks. The BW QTL on GGA2 and GGA4 could be associated with early and late growth respectively. The GGA4 QTL also had the largest effect on SL9 and SD9 and explained 7% and 10% of their phenotypic variances respectively. However, when SL9 and SD9 were corrected with BW9, a shank length percent QTL was identified on GGA2. We identified novel QTL and also confirmed previously identified loci in other chicken populations. As the foundation population was established from commercial broiler strains, it is possible that QTL identified in this study could still be segregating in commercial strains.     

Overlapping mouse subcongenic strains successfully separate two linked body fat QTL on distal MMU 2

Background

Mouse chromosome 2 is linked to growth and body fat phenotypes in many mouse crosses. With the goal to identify the underlying genes regulating growth and body fat on mouse chromosome 2, we developed five overlapping subcongenic strains that contained CAST/EiJ donor regions in a C57BL/6J^hg/hg background (hg is a spontaneous deletion of 500 Kb on mouse chromosome 10). To fine map QTL on distal mouse chromosome 2 a total of 1,712 F2 mice from the five subcongenic strains, plus 278 F2 mice from the HG2D founder congenic strain were phenotyped and analyzed. Interval mapping (IM) and composite IM (CIM) were performed on body weight and body fat traits on a combination of SNP and microsatellite markers, which generated a high-density genotyping panel.

Results

Phenotypic analysis and interval mapping of total fat mass identified two QTL on distal mouse chromosome 2. One QTL between 150 and 161 Mb, Fatq2a, and the second between 173.3 and 175.6 Mb, Fatq2b. The two QTL reside in different congenic strains with significant total fat differences between homozygous cast/cast and b6/b6 littermates. Both of these QTL were previously identified only as a single QTL affecting body fat, Fatq2. Furthermore, through a novel approach referred here as replicated CIM, Fatq2b was mapped to the Gnas imprinted locus.

Conclusions

The integration of subcongenic strains, high-density genotyping, and CIM succesfully partitioned two previously linked QTL 20 Mb apart, and the strongest QTL, Fatq2b, was fine mapped to a ~2.3 Mb region interval encompassing the Gnas imprinted locus.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-014-1191-8) contains supplementary material, which is available to authorized users.     

Quantitative trait Loci for regional adiposity in mouse lines divergently selected for food intake

Objective: Obesity is thought to result from an interaction between genotype and environment. Excessive adiposity is associated with a number of important comorbidities; however, the risk of obesity‐related disease varies with the distribution of fat throughout the body. The aim of this study was to map quantitative trait loci (QTLs) associated with regional fat depots in mouse lines divergently selected for food intake corrected for body mass. Research Methods and Procedures: Using an F₂ intercross design (n = 457), the dry mass of regional white (subcutaneous, gonadal, retroperitoneal, and mesenteric) adipose tissue (WAT) and brown adipose tissue (BAT) depots were analyzed to map QTLs. Results: The total variance explained by the mapped QTL varied between 12% and 39% for BAT and gonadal fat depots, respectively. Using the genome‐wide significance threshold, nine QTLs were associated with multiple fat depots. Chromosomes 4 and 19 were associated with WAT and BAT and chromosome 9 with WAT depots. Significant sex × QTL interactions were identified for gonadal fat on chromosomes 9, 16, and 19. The pattern of QTLs identified for the regional deposits showed the most similarity between retroperitoneal and gonadal fat, whereas BAT showed the least similarity to the WAT depots. Analysis of total fat mass explained in excess of 40% of total variance. Discussion: There was limited concordance between the QTLs mapped in our study and those reported previously. This is likely to reflect the unique nature of the mouse lines used. Results provide an insight into the genetic basis of regional fat distribution.     

SNP mapping of QTL affecting growth and fatness on chicken GGA1

An F2 chicken population was established from a crossbreeding between a Xinghua line and a White Recessive Rock line. A total of 502 F2 chickens in 17 full-sib families from six hatches was obtained, and phenotypic data of 488 individuals were available for analysis. A total of 46 SNP on GGA1 was initially selected based on the average physical distance using the dbSNP database of NCBI. After the polymorphism levels in all F0 individuals (26 individuals) and part of the F1 individuals (22 individuals) were verified, 30 informative SNP were potentially available to genotype all F2 individuals. The linkage map was constructed using Cri-Map. Interval mapping QTL analyses were carried out. QTL for body weight (BW) of 35 d and 42 d, 49 d and 70 d were identified on GGA1 at 351–353 cM and 360 cM, respectively. QTL for abdominal fat weight was on GGA1 at 205 cM, and for abdominal fat rate at 221 cM. Two novel QTL for fat thickness under skin and fat width were detected at 265 cM and 72 cM, respectively.