QTL analysis of body weight and carcass body length traits in an F2 intercross between Landrace and Korean native pigs

Growth traits, such as body weight and carcass body length, directly affect productivity and economic efficiency in the livestock industry. We performed a genome‐wide linkage analysis to detect the quantitative trait loci (QTL) that affect body weight, growth curve parameters and carcass body length in an F₂ intercross between Landrace and Korean native pigs. Eight phenotypes related to growth were measured in approximately 1000 F₂ progeny. All experimental animals were subjected to genotypic analysis using 173 microsatellite markers located throughout the pig genome. The least squares regression approach was used to conduct the QTL analysis. For body weight traits, we mapped 16 genome‐wide significant QTL on SSC1, 3, 5, 6, 8, 9 and 12 as well as 22 suggestive QTL on SSC2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 16 and 17. On SSC12, we identified a major QTL affecting body weight at 140 days of age that accounted for 4.3% of the phenotypic variance, which was the highest test statistic (F‐ratio = 45.6 under the additive model, nominal P = 2.4 × 10^?11) observed in this study. We also showed that there were significant QTL on SSC2, 5, 7, 8, 9 and 12 affecting carcass body length and growth curve parameters. Interestingly, the QTL on SSC2, 3, 5, 6, 8, 9, 10, 12 and 17 influencing the growth‐related traits showed an obvious trend for co‐localization. In conclusion, the identified QTL may play an important role in investigating the genetic structure underlying the phenotypic variation of growth in pigs.     

Genome-wide QTL analysis of meat quality-related traits in a large F2 intercross between Landrace and Korean native pigs

Background

We conducted a genome-wide linkage analysis to identify quantitative trait loci (QTL) that influence meat quality-related traits in a large F₂ intercross between Landrace and Korean native pigs. Thirteen meat quality-related traits of the m. longissimus lumborum et thoracis were measured in more than 830 F₂ progeny. All these animals were genotyped with 173 microsatellite markers located throughout the pig genome, and the GridQTL program based on the least squares regression model was used to perform the QTL analysis.

Results

We identified 23 genome-wide significant QTL in eight chromosome regions (SSC1, 2, 6, 7, 9, 12, 13, and 16) (SSC for Sus Scrofa) and detected 51 suggestive QTL in the 17 chromosome regions. QTL that affect 10 meat quality traits were detected on SSC12 and were highly significant at the genome-wide level. In particular, the QTL with the largest effect affected crude fat percentage and explained 22.5% of the phenotypic variance (F-ratio = 278.0 under the additive model, nominal P = 5.5 × 10⁻⁵⁵). Interestingly, the QTL on SSC12 that influenced meat quality traits showed an obvious trend for co-localization.

Conclusions

Our results confirm several previously reported QTL. In addition, we identified novel QTL for meat quality traits, which together with the associated positional candidate genes improve the knowledge on the genetic structure that underlies genetic variation for meat quality traits in pigs.

Electronic supplementary material

The online version of this article (doi:10.1186/s12711-014-0080-6) contains supplementary material, which is available to authorized users.     

QTL analysis of white blood cell, platelet and red blood cell-related traits in an F2 intercross between Landrace and Korean native pigs

Haematological traits play important roles in disease resistance and defence functions. The objective of this study was to locate quantitative trait loci (QTL) and the associated positional candidate genes influencing haematological traits in an F₂ intercross between Landrace and Korean native pigs. Eight blood‐related traits (six erythrocyte traits, one leucocyte trait and one platelet trait) were measured in 816 F₂ progeny. All experimental animals were genotyped with 173 informative microsatellite markers located throughout the pig genome. We report that nine chromosomes harboured QTL for the baseline blood parameters: genomic regions on SSC 1, 4, 5, 6, 8, 9, 11, 13 and 17. Eight of twenty identified QTL reached genome‐wide significance. In addition, we evaluated the KIT locus, an obvious candidate gene locus affecting variation in blood‐related traits. Using dense single nucleotide polymorphism marker data on SSC 8 and the marker‐assisted association test, the strong association of the KIT locus with blood phenotypes was confirmed. In conclusion, our study identified both previously reported and novel QTL affecting baseline haematological parameters in pigs. Additionally, the positional candidate genes identified here could play an important role in elucidating the genetic architecture of haematological phenotype variation in swine and in humans.     

Genome-wide association analysis to identify SNP markers affecting teat numbers in an F2 intercross population between Landrace and Korean native pigs

Most reproductive traits have low heritability and are greatly affected by environmental factors. Teat number and litter size are traits related to the reproduction ability of pigs. To identify quantitative trait loci (QTLs) for teat number traits, a genome-wide association study (GWAS) was conducted using an F₂ intercross between Landrace and Korean native pigs. Genotype analysis was performed using the porcine SNP 60 K beadchip. The GWAS was performed using a mixed-effects model and linear regression approach. When a genome-wide threshold was determined using the Bonferroni method (P = 1.61 × 10^?6), 38 single nucleotide polymorphism (SNP) markers in pig chromosome 7 (SSC7) were significantly associated with three teat number traits (total teat number, left teat number, and right teat number). Among these, SNPs in 5 genes (HDDC3, LOC100156276, LOC100155863, ANPEP, SCAMP2) were selected for further study based primarily on their statistical significance. A significant association was detected in SCAMP2 g.25280 G>A for total teat number (P = 2.0 × 10^?12), HDDC3 g.1319 G>A SNP for left teat number (P = 2.3 × 10^?7), and SCAMP2 g.14198 G>A for right teat number (P = 4.7 × 10^?12). These results provide valuable information about the selective breeding for desirable teat numbers in pigs.     

Genome‐wide association study identifies quantitative trait loci affecting hematological traits in an F2 intercross between Landrace and Korean native pigs

Changes affecting the status of health and robustness can bring about physiological alterations including hematological parameters in swine. To identify quantitative trait loci (QTL) associated with eight hematological traits (one leukocyte trait, six erythrocyte traits and one platelet trait), we conducted a genome‐wide association study using the PorcineSNP60K BeadChip in a resource population derived from an intercross between Landrace and Korean native pigs. A total of 36 740 SNPs from 816 F₂ progeny were analyzed for each blood‐related trait after filtering for quality control. Data were analyzed by the genome‐wide rapid association using mixed model and regression (GRAMMAR) approach. A total of 257 significant SNPs (P < 1.36 × 10^?6) on SSC3, 6, 8, 13 and 17 were identified for blood‐related traits in this study. Interestingly, the genomic region between 17.9 and 130 Mb on SSC8 was found to be significantly associated with red blood cell, mean corpuscular volume and mean corpuscular hemoglobin. Our results include the identification of five significant SNPs within five candidate genes (KIT, IL15, TXK, ARAP2 and ERG) for hematopoiesis. Further validation of these identified SNPs could give valuable information for understanding the variation of hematological traits in pigs.     

QTL mapping for growth and carcass traits in an Iberian by Landrace pig intercross: additive,dominant and epistatic effects

Results from a QTL experiment on growth and carcass traits in an experimental F2 cross between Iberian and Landrace pigs are reported. Phenotypic data for growth, length of carcass and muscle mass, fat deposition and carcass composition traits from 321 individuals corresponding to 58 families were recorded. Animals were genotyped for 92 markers covering the 18 porcine autosomes (SSC). The results from the genomic scan show genomewide significant QTL in SSC2 (longissimus muscle area and backfat thickness), SSC4 (length of carcass, backfat thickness, loin, shoulder and belly bacon weights) and SSC6 (longissimus muscle area, backfat thickness, loin, shoulder and belly bacon weights). Suggestive QTL were also found on SSC1, SSC5, SSC7, SSC8, SSC9, SSC13, SCC14, SSC16 and SSC17. A bidimensional genomic scan every 10 cM was performed to detect interaction between QTL. The joint action of two suggestive QTL in SSC2 and SSC17 led to a genome-wide significant effect in live weight. The results of the bidimensional genomic scan showed that the genetic architecture was mainly additive or the experimental set-up did not have enough power to detect epistatic interactions.     

Quantitative gene expression analysis on chromosome 6 between Korean native pigs and Yorkshire breeds for fat deposition

Pig chromosome 6 (SSC6) has been reported to have QTL affecting backfat thickness (BFT) and intramuscular fat (IMF). A human-pig comparative map covering 18 autosomes with the highest resolution has been constructed and based on this map SSC6 has conserved syntenicgroups with human chromosome (HSA) 16, 19, 1, and 18. In this study, the pig Affy elements mapped to the SSC6 were analyzed, and the differentially expressed genes in three tissues (liver, backfat and loin muscle) between Yorkshire and Korean Native Pigs (KNP) were collected, in particular those genes located in the internal between markers SW1355 and SW1823 where a quantitative trait loci (QTL) affecting the intramuscular fat content (IMF) have been detected in multiple pig populations. The genes listed here may offer information for further study the candidate genes affecting these QTL on the expression level.     

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.     

Genome-wide interval mapping using SNPs identifies new QTL for growth,body composition and several physiological variables in an F2 intercross between fat and lean chicken lines

Background

For decades, genetic improvement based on measuring growth and body composition traits has been successfully applied in the production of meat-type chickens. However, this conventional approach is hindered by antagonistic genetic correlations between some traits and the high cost of measuring body composition traits. Marker-assisted selection should overcome these problems by selecting loci that have effects on either one trait only or on more than one trait but with a favorable genetic correlation. In the present study, identification of such loci was done by genotyping an F₂ intercross between fat and lean lines divergently selected for abdominal fatness genotyped with a medium-density genetic map (120 microsatellites and 1302 single nucleotide polymorphisms). Genome scan linkage analyses were performed for growth (body weight at 1, 3, 5, and 7 weeks, and shank length and diameter at 9 weeks), body composition at 9 weeks (abdominal fat weight and percentage, breast muscle weight and percentage, and thigh weight and percentage), and for several physiological measurements at 7 weeks in the fasting state, i.e. body temperature and plasma levels of IGF-I, NEFA and glucose. Interval mapping analyses were performed with the QTLMap software, including single-trait analyses with single and multiple QTL on the same chromosome.

Results

Sixty-seven QTL were detected, most of which had never been described before. Of these 67 QTL, 47 were detected by single-QTL analyses and 20 by multiple-QTL analyses, which underlines the importance of using different statistical models. Close analysis of the genes located in the defined intervals identified several relevant functional candidates, such as ACACA for abdominal fatness, GHSR and GAS1 for breast muscle weight, DCRX and ASPSCR1 for plasma glucose content, and ChEBP for shank diameter.

Conclusions

The medium-density genetic map enabled us to genotype new regions of the chicken genome (including micro-chromosomes) that influenced the traits investigated. With this marker density, confidence intervals were sufficiently small (14 cM on average) to search for candidate genes. Altogether, this new information provides a valuable starting point for the identification of causative genes responsible for important QTL controlling growth, body composition and metabolic traits in the broiler chicken.     

Identification of genetic polymorphisms in FABP3 and FABP4 and putative association with back fat thickness in Korean native cattle

The aim of this study was to determine whether single nucleotide polymorphisms (SNP) in the beef cattle adipocyte fatty-acid binding protein 3 and 4 (FABP3 and FABP4) genes are associated with carcass weight (CW) and back fat thickness (BF) of beef cattle. By direct DNA sequencing in 24 unrelated Korean native cattle, we identified 20 SNPs in FABP3 and FABP4. Among them, 10 polymorphic sites were selected for genotyping in our beef cattle. We performed SNP, haplotype and linkage disequilibrium studies on 419 Korean native cattle with the 10 SNPs in the FABP genes. Statistical analysis revealed that 220AG (I74V) and 348+303TC polymorphisms in FABP4 showed putative associations with BF traits (P=0.02 and 0.01, respectively). Our findings suggest that the polymorphisms in FABP4 may play a role in determining one of the important genetic factors that influence BF in beef cattle.     

Genetic analysis of an F2 intercross between two strains of Japanese quail provided evidence for quantitative trait loci affecting carcass composition and internal organs

The purpose of this study was to identify genomic regions, quantitative trait loci (QTL), affecting carcass traits on chromosome 1 in an F2 population of Japanese quail. For this purpose, two white and wild strains of Japanese quail (16 birds) were crossed reciprocally and F1 generation (34 birds) was created. The F2 generation was produced by intercrossing of the F1 birds. Phenotypic data including carcass weight, internal organs and carcass parts were collected on F2 animals (422 birds). The total mapping population (472 birds) was genotyped for 8 microsatellite markers on chromosome 1. QTL analysis was performed with interval mapping method applying the line-cross model. Significant QTL were identified for breast weight at 0 (P < 0.01), 172 (P < 0.05) and 206 (P < 0.01), carcass weight at 91 (P < 0.05), carcass fatness at 0 (P < 0.01), pre-stomach weight at 206 (P < 0.01) and uropygial weight gland at 197 (P < 0.01) cM on chromosome 1. There was also evidence for imprinted QTL affecting breast weight (P < 0.01) on chromosome 1. The proportion of the F2 phenotypic variation explained by the significant additive, dominance and imprinted QTL effects ranged from 1.0 to 7.3 %, 1.2 to 3.3 % and 1.4 to 2.2 %, respectively.     

Fine mapping of porcine chromosome 6 QTL and LEPR effects on body composition in multiple generations of an Iberian by Landrace intercross

The leptin receptor gene (LEPR) is a candidate for traits related to growth and body composition, and is located on SSC6 in a region where fatness and meat composition quantitative trait loci (QTL) have previously been detected in several F2 experimental designs. The aims of this work were: (i) to fine map these QTL on a larger sample of animals and generations (F3 and backcross) of an Iberian x Landrace intercross and (ii) to examine the effects of LEPR alleles on body composition traits. Eleven single nucleotide polymorphisms (SNPs) were detected by sequencing LEPR coding regions in Iberian and Landrace pig samples. Three missense polymorphisms were genotyped by pyrosequencing in 33 F0, 70 F1, 418 F2, 86 F3 and 128 individuals coming from the backcross of four F2 males with 24 Landrace females. Thirteen microsatellites and one SNP were also genotyped. Traits analysed were: backfat thickness at different locations (BF(T)), intramuscular fat percentage (IMF(P)), eye muscle area (EM(A)), loin depth (LO(D)), weight of shoulder (SH(W)), weight of ribs (RIB(W)) and weight of belly bacon (BB(W)). Different statistical models were applied in order to evaluate the number and effects of QTL on chromosome 6 and the possible causality of the LEPR gene variants with respect to the QTL. The results support the presence of two QTL on SSC6. One, at position 60-100 cM, affects BF(T) and RIB(W). The other and more significant maps in a narrow region (130-132 cM) and affects BF(T), IMF(P), EM(A), LO(D), SH(W), RIB(W) and BB(W). Results also support the association between LEPR alleles and BF(T) traits. The possible functional implications of the analysed polymorphisms are considered.     

Whole-genome association study for the roan coat color in an intercrossed pig population between Landrace and Korean native pig

The roan coat color is characterized by white hairs intermingled with colored hairs. Candidate genes based on comparative phenotypes in horses and cattle involve the KIT and KIT ligand (MGF) genes. Here, we report the result of the whole genome scanning to detect genomic regions responsible for the roan coat color, using a three-generation pedigree of 62 pigs in an intercross between Landrace and Korean native pig. These pigs were genotyped using the PorcineSNP 60 BeadChip (Illumina, USA). The whole genome scan indicated that three genomic regions, 35～36 Mb, 38～39 Mb, and 58～59 Mb on SSC8, were commonly and highly associated/linked with the roan phenotype in the case/control, sib-pair, and linkage test, respectively. The porcine KIT was selected as a candidate gene, because it is located in one of the three significant regions and its function is related to coat color formation. SNPs and Indels within coding sequence (CDS), promoter, and 3′-UTR of KIT were surveyed. Twenty-two SNPs in the CDS reported previously, as well as nine variations in promoter (2 SNPs) and 3′-UTR (5 SNPs and 2 Indels) were detected. Although no causative mutations were identified, these results will help to elucidate the genetic mechanisms involved in the expression of the roan phenotype and will aid in identifying key mutations responsible for the roan phenotype in further studies.     

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.     

Mapping carcass and meat quality QTL on Sus Scrofa chromosome 2 in commercial finishing pigs

Quantitative trait loci (QTL) affecting carcass and meat quality located on SSC2 were identified using variance component methods. A large number of traits involved in meat and carcass quality was detected in a commercial crossbred population: 1855 pigs sired by 17 boars from a synthetic line, which where homozygous (A/A) for IGF2. Using combined linkage and linkage disequilibrium mapping (LDLA), several QTL significantly affecting loin muscle mass, ham weight and ham muscles (outer ham and knuckle ham) and meat quality traits, such as Minolta-L* and -b*, ultimate pH and Japanese colour score were detected. These results agreed well with previous QTL-studies involving SSC2. Since our study is carried out on crossbreds, different QTL may be segregating in the parental lines. To address this question, we compared models with a single QTL-variance component with models allowing for separate sire and dam QTL-variance components. The same QTL were identified using a single QTL variance component model compared to a model allowing for separate variances with minor differences with respect to QTL location. However, the variance component method made it possible to detect QTL segregating in the paternal line (e.g. HAMB), the maternal lines (e.g. Ham) or in both (e.g. pHu). Combining association and linkage information among haplotypes improved slightly the significance of the QTL compared to an analysis using linkage information only.     

QTL mapping of complex binary traits in an advanced intercross line

An advanced intercross line (AIL) is an easier and more cost-effective approach compared to recombinant inbred lines for fine mapping of quantitative trait loci (QTL) identified by F(2) designs. In an AIL, a complex binary trait can be mapped through analysis of either continuously distributed proxy traits for the liability of the binary trait or the liability itself, the latter presenting the greater statistical challenge. In another work, we successfully applied both approaches in an AIL to fine map previously identified QTL underlying anatomical parameters of the cardiac inter-atrial septum including patent foramen ovale. Here, we describe the statistical methods that we used to analyse complex binary traits in our AIL design. This is achieved using a likelihood-based method, with the expectation-maximisation algorithm allowing use of standard logistic regression methods for model fitting.     

Association of PIT1, GH and GHRH polymorphisms with performance and carcass traits in Landrace pigs

The study of candidate genes, based on physiological effects, is an important tool to identify genes to be used in marker-assisted selection programs. In this study, a group of halothane gene-free, non-castrated, male Landrace pigs was used to study the association between polymorphisms in the PIT1 (n = 218), GH (n = 213) and GHRH (n = 206) genes and fat thickness, average daily gain, and the EPD (expected progeny difference) for fat thickness, average daily gain, and litter size. These genes are potential candidate markers because of their important physiological effects. The pigs were genotyped by PCR-RFLP, and the statistical model used to analyze the association between genotypes and the traits measured included genotypes as a fixed effect and age and weight as covariates. PIT1 polymorphisms were associated with fat thickness (P = 0.0019), EPD for average daily gain (P = 0.0001) and EPD for fat thickness (P = 0.0001), whereas GH polymorphisms were associated with fat thickness (P = 0.0326) and average daily gain (P = 0.0127), and GHRH polymorphisms were associated with the average daily gain (P = 0.0001) and EPD for fat thickness (P = 0.0004). These results confirmed the potential usefulness of these genes in marker-assisted selection programs for pig breeding.     

Replication of long-bone length QTL in the F9-F10 LG,SM advanced intercross

Quantitative trait locus (QTL) mapping techniques are frequently used to identify genomic regions associated with variation in phenotypes of interest. However, the F₂ intercross and congenic strain populations usually employed have limited genetic resolution resulting in relatively large confidence intervals that greatly inhibit functional confirmation of statistical results. Here we use the increased resolution of the combined F₉ and F₁₀ generations (n = 1455) of the LG,SM advanced intercross to fine-map previously identified QTL associated with the lengths of the humerus, ulna, femur, and tibia. We detected 81 QTL affecting long-bone lengths. Of these, 49 were previously identified in the combined F₂-F₃ population of this intercross, while 32 represent novel contributors to trait variance. Pleiotropy analysis suggests that most QTL affect three to four long bones or serially homologous limb segments. We also identified 72 epistatic interactions involving 38 QTL and 88 novel regions. This analysis shows that using later generations of an advanced intercross greatly facilitates fine-mapping of confidence intervals, resolving three F₂-F₃ QTL into multiple linked loci and narrowing confidence intervals of other loci, as well as allowing identification of additional QTL. Further characterization of the biological bases of these QTL will help provide a better understanding of the genetics of small variations in long-bone length. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Fine mapping of QTL for prepulse inhibition in LG/J and SM/J mice using F2 and advanced intercross lines

Prepulse inhibition (PPI) of the startle response is a measure of sensorimotor gating, a process that filters out extraneous sensory, motor and cognitive information. Humans with neurological and psychiatric disorders, including schizophrenia, obsessive‐compulsive disorder and Huntington's disease, exhibit a reduction in PPI. Habituation of the startle response is also disrupted in schizophrenic patients. In order to elucidate the genes involved in sensorimotor gating, we phenotyped 472 mice from an F₂ cross between LG/J × SM/J for PPI and genotyped these mice genome‐wide using 162 single nucleotide polymorphism (SNP) markers. We used prepulse intensity levels that were 3, 6 and 12 dB above background (PPI3, PPI6 and PPI12, respectively). We identified a significant quantitative trait locus (QTL) on chromosome 12 for all three prepulse intensities as well as a significant QTL for both PPI6 and PPI12 on chromosome 11. We identified QTLs on chromosomes 7 and 17 for the startle response when sex was included as an interactive covariate and found a QTL for habituation of the startle response on chromosome 4. We also phenotyped 135 mice from an F₃₄ advanced intercross line (AIL) between LG/J × SM/J for PPI and genotyped them at more than 3000 SNP markers. Inclusions of data from the AIL mice reduced the size of several of these QTLs to less than 5 cM. These results will be useful for identifying genes that influence sensorimotor gaiting and show the power of AIL for fine mapping of QTLs.     

Association between ovocalyxin-32 gene haplotypes and eggshell quality traits in an F2 intercross between two chicken lines divergently selected for eggshell strength

Broken and cracked eggshells contribute significantly to economic losses in the egg production industry. We previously identified ovocalyxin-32 as a potential gene influencing eggshell traits, by analysing an intercross between two parent lines developed from the same founder population by a two-way selection for eggshell strength with non-destructive deformation (DEF) conducted over 14 generations. We determined the nucleotide sequences of six ovocalyxin-32 exons in the parent individuals and analysed the association between ovocalyxin-32 and eggshell traits in the F₂ individuals. We identified three haplotypes (W, M and S) of ovocalyxin-32 in the parent individuals. A mismatch amplification mutation assay was performed to distinguish six diplotype individuals (WW, MM, SS, WM, MS and WS) in the F₂ population. The egg weight (EW) of SS-diplotype individuals was significantly higher than that of WW-, WM- and WS-diplotypes. Short length of the egg (SLE) of SS-diplotype individuals was significantly higher than that of WW-, WM- and MS-diplotypes. Long length of the egg (LLE) of SS-diplotype individuals was significantly higher than that of WM- and WS-diplotypes. DEF of WW-diplotype individuals was significantly higher than that of SS-, WM, MS and WM-diplotypes. Haplotypic effect analyses showed significant differences between the W-haplotype and the S-haplotypes in the EW, SLE, LLE and DEF. The DEF of M-haplotype was significantly lower than that of W- and S-haplotypes. These results suggest that S- and M-haplotypes are critical for high quality of eggshells in the F₂ population. In conclusion, ovocalyxin-32 is a useful marker of eggshell traits and can be used to develop strategies for improving eggshell traits in commercial layer houses.