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.     

Quantitative trait loci segregating in crosses between New Hampshire and White Leghorn chicken lines: III. Fat deposition and intramuscular fat content

In this study, a genome scan was performed to detect genomic loci that affect fat deposition in white adipose tissues and muscles in 278 F ₂ males of reciprocal crosses between the genetically and phenotypically extreme inbred chicken lines New Hampshire (NHI) and White Leghorn (WL77). Genome‐wide highly significant quantitative trait loci (QTL) influencing fat deposition in white adipose tissues were found on GGA2 and 4. The peak QTL positions for different visceral and subcutaneous white adipose tissues were located between 41.4 and 112.4 Mb on GGA2 and between 76.2 and 78.7 Mb on GGA4, which explained 4.2–10.4% and 4.3–11.6% respectively of the phenotypic F ₂ variances. Contrary to our expectations, the QTL allele descending from the lean line WL77 on GGA4 led to increased fat deposition. We suggest a transgressive action of the obesity allele only if it is not in the genetic background of the line WL77. Additional highly significant loci for subcutaneous adipose tissue mass were identified on GGA12 and 15. For intramuscular fat content, a suggestive QTL was located on GGA14. The analysed crosses provide a valuable resource for further fine mapping of fatness genes and subsequent gene discovery.     

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.     

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.     

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.     

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.     

Fine mapping of a distal chromosome 4 QTL affecting growth and muscle mass in a chicken advanced intercross line

In our previous research, QTL analysis in an F₂ cross between the inbred New Hampshire (NHI) and White Leghorn (WL77) lines revealed a growth QTL in the distal part of chromosome 4. To physically reduce the chromosomal interval and the number of potential candidate genes, we performed fine mapping using individuals of generations F₁₀, F₁₁ and F₁₂ in an advanced intercross line that had been established from the initial F₂ mapping population. Using nine single nucleotide polymorphism (SNP) markers within the QTL region for an association analysis with several growth traits from hatch to 20 weeks and body composition traits at 20 weeks, we could reduce the confidence interval from 26.9 to 3.4 Mb. Within the fine mapped region, markers rs14490774, rs314961352 and rs318175270 were in full linkage disequilibrium (D′ = 1.0) and showed the strongest effect on growth and muscle mass (LOD ≥ 4.00). This reduced region contains 30 genes, compared to 292 genes in the original region. Chicken 60 K and 600 K SNP chips combined with DNA sequencing of the parental lines were used to call mutations in the reduced region. In the narrowed‐down region, 489 sequence variants were detected between NHI and WL77. The most deleterious variants are a missense variant in ADGRA3 (SIFT = 0.02) and a frameshift deletion in the functional unknown gene ENSGALG00000014401 in NHI chicken. In addition, five synonymous variants were discovered in genes PPARGC1A, ADGRA3, PACRGL, SLIT2 and FAM184B. In our study, the confidence interval and the number of potential genes could be reduced 8‐ and 10‐ fold respectively. Further research will focus on functional effects of mutant genes.     

Genome-wide identification of quantitative trait loci for carcass composition and meat quality in a large-scale White Duroc × Chinese Erhualian resource population

Carcass and meat quality traits are economically important in pigs. In this study, 17 carcass composition traits and 23 meat quality traits were recorded in 1028 F₂ animals from a White Duroc × Erhualian resource population. All pigs in this experimental population were genotyped for 194 informative markers covering the entire porcine genome. Seventy-seven genome-wide significant quantitative trait loci (QTL) for carcass traits and 68 for meat quality were mapped to 34 genomic regions. These results not only confirmed many previously reported QTL but also revealed novel regions associated with the measured traits. For carcass traits, the most prominent QTL was identified for carcass length and head weight at 57 cM on SSC7, which explained up to 50% of the phenotypic variance and had a 95% confidence interval of only 3 cM. Moreover, QTL for kidney and spleen weight and lengths of cervical vertebrae were reported for the first time in pigs. For meat quality traits, two significant QTL on SSC5 and X were identified for both intramuscular fat content and marbling score in the longissimus muscle, while three significant QTL on SSC1 and SSC9 were found exclusively for IMF. Both LM and the semimembranous muscle showed common QTL for colour score on SSC4, 5, 7, 8, 13 and X and discordant QTL on other chromosomes. White Duroc alleles at a majority of QTL detected were favourable for carcass composition, while favourable QTL alleles for meat quality originated from both White Duroc and Erhualian.     

Mapping quantitative trait loci affecting chicken body size traits via genome scanning

Body size traits reflect the condition of body development, are always mentioned when a breed is described, and are also targets in breeding programmes. In chicken, there are several reports focused on body size traits, such as shank length, tibia length or bone traits. However, no study was carried out on chest width (CW), chest depth (CD), body slope length (BL) and head width (HW) traits. In this study, genome scans were conducted on an F₂ resource population (238 F₂ individuals from 15 full‐sib families derived from an intercross of the White Plymouth Rock with the Silkies Fowl) to identify quantitative trait loci (QTL) associated with CW, CD, BL and HW from 7 to 12 weeks of age. In total, 21 significant or suggestive QTL were found that affected four body size traits. Four QTL reached 1% genome‐wide significance level: at 297 cM on GGA3 (associated with CW at 9 weeks of age), between 155 and 184 cM on GGA1 (affecting BL traits at 9 and 10 weeks of age), at 22 cM on GGA2 (related with BL traits at 12 weeks of age) and at 36 cM on GGA1 (for HW trait at 8 weeks of age).     

Quantitative trait loci analysis of a Duroc commercial population highlights differences in the genetic determination of meat quality traits at two different muscles

We performed a whole‐genome scan with 110 informative microsatellites in a commercial Duroc population for which growth, fatness, carcass and meat quality phenotypes were available. Importantly, meat quality traits were recorded in two different muscles, that is, gluteus medius (GM) and longissimus thoracis et lumborum (LTL), to find out whether these traits are determined by muscle‐specific genetic factors. At the whole‐population level, three genome‐wide QTL were identified for carcass weight (SSC7, 60 cM), meat redness (SSC13, 84 cM) and yellowness (SSC15, 108 cM). Within‐family analyses allowed us to detect genome‐wide significant QTL for muscle loin depth between the 3rd and 4th ribs (SSC15, 54 cM), backfat thickness (BFT) in vivo (SSC10, 58 cM), ham weight (SSC9, 69 cM), carcass weight (SSC7, 60 cM; SSC9, 68 cM), BFT on the last rib (SSC11, 48 cM) and GM redness (SSC8, 85 cM; SSC13, 84 cM). Interestingly, there was low positional concordance between meat quality QTL maps obtained for GM and LTL. As a matter of fact, the three genome‐wide significant QTL for colour traits (SSC8, SSC13 and SSC15) that we detected in our study were all GM specific. This result suggests that QTL effects might be modulated to a certain extent by genetic and environmental factors linked to muscle function and anatomical location.     

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.     

A molecular genome scan analysis to identify chromosomal regions influencing economic traits in the pig. I. Growth and body composition

Genome scans can be employed to identify chromosomal regions and eventually genes (quantitative trait loci or QTL) that control quantitative traits of economic importance. A three-generation resource family was developed by using two Berkshire grand sires and nine Yorkshire grand dams to detect QTL for growth and body composition traits in pigs. A total of 525 F₂ progeny were produced from 65 matings. All F₂ animals were phenotyped for birth weight, 16-day weight, growth rate, carcass weight, carcass length, back fat thickness, and loin eye area. Animals were genotyped for 125 microsatellite markers covering the genome. Least squares regression interval mapping was used for QTL detection. All carcass traits were adjusted for live weight at slaughter. A total of 16 significant QTL, as determined by a permutation test, were detected at the 5% chromosome-wise level for growth traits on Chromosomes (Chrs) 1, 2, 3, 4, 6, 7, 8, 9, 11, 13, 14, and X, of which two were significant at the 5% genome-wise level and two at the 1% genome-wise level (on Chrs 1, 2, and 4). For composition traits, 20 QTL were significant at the 5% chromosome-wise level (on Chrs 1, 4, 5, 6, 7, 12, 13, 14, 18), of which one was significant at the 5% genome-wise level and three were significant at the 1% genome-wise level (on Chrs 1, 5, and 7). For several QTL the favorable allele originated from the breed with the lower trait mean. Received: 29 November 2000 / Accepted: 27 March 2001     

Joint QTL analysis of three connected F2-crosses in pigs

Background

Numerous QTL mapping resource populations are available in livestock species. Usually they are analysed separately, although the same founder breeds are often used. The aim of the present study was to show the strength of analysing F₂-crosses jointly in pig breeding when the founder breeds of several F₂-crosses are the same.

Methods

Three porcine F₂-crosses were generated from three founder breeds (i.e. Meishan, Pietrain and wild boar). The crosses were analysed jointly, using a flexible genetic model that estimated an additive QTL effect for each founder breed allele and a dominant QTL effect for each combination of alleles derived from different founder breeds. The following traits were analysed: daily gain, back fat and carcass weight. Substantial phenotypic variation was observed within and between crosses. Multiple QTL, multiple QTL alleles and imprinting effects were considered. The results were compared to those obtained when each cross was analysed separately.

Results

For daily gain, back fat and carcass weight, 13, 15 and 16 QTL were found, respectively. For back fat, daily gain and carcass weight, respectively three, four, and five loci showed significant imprinting effects. The number of QTL mapped was much higher than when each design was analysed individually. Additionally, the test statistic plot along the chromosomes was much sharper leading to smaller QTL confidence intervals. In many cases, three QTL alleles were observed.

Conclusions

The present study showed the strength of analysing three connected F₂-crosses jointly. In this experiment, statistical power was high because of the reduced number of estimated parameters and the large number of individuals. The applied model was flexible and was computationally fast.     

QTL alleles on chromosome 7 from fatty Meishan pigs reduce fat deposition

For detecting QTL in the whole swine genome, 1068 pigs from three F₂ populations constructed by crossing European Wild boar and Pietrain (W×P), Meishan and Pietrain (M×P), and Wild Boar and Meishan (W × M) were genotyped for genetic markers evenly spaced at approximately 20 cM intervals. AQTL analysis was performed using a least-squares method. Here the results of the QTL analysis on the porcine chromosome 7 are presented. QTL for carcass composition (e.g. head weight, carcass length, backfat depth, abdominal fat and bacon meat) were mapped in the chromosomal region CYPA/CYPD-TNFB-S0102 in M×P and W×M, but not in W×P. The QTL explained 5.3%–27.2% of the F₂ phenotypic variance in the two F₂ populations. Most traits affected by the mapped QTL were related to carcass fatness. The mode of gene action of QTL was additive. Surprisingly, in contrast to the parental phenotype, the QTL alleles from fatty Meishan were associated with thinner backfat than Pietrain and Wild Boar alleles, suggesting that the genome of the fatty Meishan pig contains genes which can reduce fat content of carcass substantially.     

猪部分内脏器官性状和乳头数的QTL检测

对内脏器官重量性状的QTL定位研究,所见报道不多;对于猪的繁殖性状,尚需做进一步的探讨。本研究在总共214头(180头F2个体)组成的资源家系中,在猪的SSC4、SSC6、SSC7、SSC8 和 SSC13上共选取39个微卫星标记,检测了8种内脏器官的重量性状:心重 (HW)、肺重 (LW)、肝 胆重 (LGW)、脾重 (SPW)、胃重 (STW)、小肠重(SIW)、大肠重(LIW) 和肾重(KW);其他一些胴体性状:胴体长性状1(自第一颈椎,CL1)、胴体长性状2(自第一胸椎,CL2)、肋骨数(RNS)和繁殖性状乳头数(TNS)的QTL定位。结果表明,检测到3个染色体极显著水平的QTL(P≤0.01),它们是HW QTL定位在SSC6上30 cM处,RNS QTL定位在SSC7上115 cM处和TNS QTL定位在SSC7上 110 cM处;另外6个染色体显著水平的QTL(P≤0.05)是:LW(SSC13上119 cM处)、LGW(SSC6上94 cM处)、SPW(SSC8上106 cM处)、SIW(SSC 4上0 cM处)、LIW(SSC 4上170 cM 处)和TNS(SSC 6上95 cM处)。上述QTL解释的表型变异从 0.04% 到 14.06%,有些位点的 QTL 可以解释表型变异的 10%以上,如 HW 的 QTL 解释表型变异的9.52%、SIW的QTL解释表型变异的13.47%、定位在SSC6上的TNS QTL解释表型变异的14.06%,而定位在 SSC7上的TNS QTL解释表型变异的11.30%。多数内脏器官重量性状的QTL定位结果未见报道。胴体长未见显著水平的QTL,而在SSC7上定位染色体极显著水平的肋骨数QTL。     

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.     

Genome scan linkage analysis identifies quantitative trait loci affecting serum clinical–chemical traits in Korean native chicken

Alterations in robustness- and health-related traits lead to physiological changes, such as changes in the serum clinical chemical parameters in individuals. Therefore, clinical–chemical traits can be used as biomarkers to examine the health status of chickens. The aim of the present study was to detect the quantitative trait loci (QTLs) influencing eight clinical–chemical traits (glucose, total protein, creatinine, high-density lipoprotein cholesterol, total cholesterol, glutamic oxaloacetic transaminase, glutamic pyruvic transaminase, and α-amylase) in an F₁ nuclear families comprising 83 F₀ founders and 585 F₁ progeny of Korean native chickens. Genotypic data on 135 DNA markers representing 26 autosomes have been generated for this resource pedigree. The total length of the map was 2729.4 cM. We used a multipoint variance component linkage approach to identify QTLs for the traits. A significant QTL affecting serum α-amylase levels was identified on chicken chromosome (GGA) 7 [logarithm of odds (LOD) = 3.02, P value = 1.92 × 10^?4]. Additionally, we detected several suggestive linkage signals for the levels of total cholesterol, glutamic oxaloacetic transaminase, glutamic pyruvic transaminase, and creatinine on GGA 4, 12, 13, and 15. In this study, serum α-amylase levels related significant QTL was mapped on GGA7 and cholesterol, glutamic oxaloacetic transaminase, glutamic pyruvic transaminase, and creatinine traits related suggestive QTLs were detected on GGA4, 12, 13 and 15, respectively. Further verification and fine mapping of these identified QTLs can provide valuable information for understanding the variations of clinical chemical trait in chickens.     

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.     

QTLs for cell membrane stability and flag leaf area under drought stress in a wheat RIL population

A population of 206 recombinant inbred lines (RILs F₉–F₁₀) derived from wheat cross WL711/C306 was phenotyped for morpho-physiological traits such as flag leaf area (FLA), flag leaf length (FLL), flag leaf width (FLW), and cell membrane stability (CMS) under water deficit stress (WDS) environment. High yielding cultivar, WL711 had higher FLA than the medium yielding cultivar C306 across trials under both environments. Parent cultivar C306 maintained membrane integrity while WL711 showed higher membrane damage under WDS. The RIL population showed considerable variation, normal distribution and transgressive segregation for FLA, FLL, FLW and CMS under WDS. The genetic linkage map of WL711/C306 RIL population was constructed comprising of 346 markers. The total map distance was 4526.8 cM with an averaged interval of 12.9 cM between adjacent markers. Major consistent QTL for FLA, FLL, FLW, and CMS were identified on chromosomes 2DS and 3BS respectively in the WL711/C306 RIL population under WDS. The major QTL for FLA, qFLAWD.2D.1 which expressed in multiple environments and for CMS, qCMSWD.3B.3 and qCMSWD.3B.4, accounted for a large proportion of phenotypic variance (PV) with positive allele being contributed by C306, a drought resistant (DR) parent. QTL qFLAWD.2D.1 for FLA co-located with QTL for grain number (GN) and days to flowering (DTF) while QTL qCMSWD.3B.3 and qCMS.3B.4 co-located with QTL for grain yield and its components, days to flowering, canopy temperature and coleoptiles length as reported in our previous publications on the WL711/C306 population (Shukla et al. in Euphytica 203:449–467, 2015; Singh et al. in J Plant Biochem Biotechnol 24:324–330, 2015). Two candidate genes Ghd7 for grain yield and heading date and OsCDK4 for calcium dependent protein kinases were identified in the 2DS and 3BS QTL regions respectively on comparison with gene content of rice chromosomes 7 and 1 respectively. Hence, QTLs qFLAWD.2D.1 and qCMSWD.3B.3 are potential target regions for fine mapping and marker assisted selection for FLA and CMS respectively in wheat under water deficit environments.

     

Identification of a 3.7-Mb region for a marbling QTL on bovine chromosome 4 by identical-by-descent and association analysis

QTL mapping for growth and carcass traits was performed using a paternal half-sib family composed of 325 Japanese Black cattle offspring. Nine QTL were detected at the 1% chromosome-wise significance level at a false discovery rate of less than 0.1. These included two QTL for marbling on BTA 4 and 18, two QTL for carcass weight on BTA 14 and 24, two QTL for longissimus muscle area on BTA 1 and 4, two QTL for subcutaneous fat thickness on BTA 1 and 15 and one QTL for rib thickness on BTA 6. Although the marbling QTL on BTA 4 has been replicated with significant linkages in two Japanese Black cattle sires, the three Q (more marbling) haplotypes, each inherited maternally, were apparently different. To compare the three Q haplotypes in more detail, high-density microsatellite markers for the overlapping regions were developed within the 95% CIs (65 markers in 44–78 cM). A detailed haplotype comparison indicated that a small region (<3.7 Mb) around 46 cM was shared between the Qs of the two sires, whose dams were related. An association of this region with marbling was shown by a regression analysis using the local population, in which the two sires were produced and this was confirmed by an association study using a population collected throughout Japan. These results strongly suggest that the marbling QTL on BTA 4 is located in the 3.7-Mb region at around 46 cM.