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* Animal Improvement Programs Laboratory, ARS, USDA, Beltsville, MD 20705-2350
University of Maryland School of Medicine, Baltimore 21201
Bovine Functional Genomics Laboratory, ARS, USDA, Beltsville, MD 20705-2350
Division of Animal Sciences, University of Missouri, Columbia 65201
1 Corresponding author: john.cole{at}ars.usda.gov
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONSACKNOWLEDGMENTSREFERENCES |
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Key Words: calving trait • genomic selection • single nucleotide polymorphism • quantitative trait loci
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONSACKNOWLEDGMENTSREFERENCES |
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Genetic effects must exist somewhere on the chromosomes for any trait with a nonzero heritability. Marker locations and estimates of effects can be used to assess alternative hypotheses and prior assumptions about the distribution of genetic effects, and to identify chromosomal segments of interest for functional genomic study. Markers with large effects on traits of economic importance may be used to identify regions of the genome that merit further study to identify causative mutations.
Several studies have reported on QTL affecting conformation, calving traits, fertility, and longevity on Bos taurus autosome (BTA) 18. A QTL affecting longevity, maternal dystocia, and maternal stillbirth in the German Holstein population was described by Kühn et al. (2003), Holmberg and Andersson-Eklund (2006) identified a QTL affecting maternal calving ease in a resource population of Swedish Reds and Swedish Holsteins, and Thomasen et al. (2008) located a QTL with pleiotropic effects on dystocia, stillbirth, and calf size in a population of Danish Holsteins. A QTL for conformation in US Holsteins was reported by Schnabel et al. (2005), and a similar marker was identified in Canadian Holstein bulls (Kolbehdari et al., 2008). These results suggest that there may be a QTL on BTA18 affecting conformation and calving traits, although no previous study has demonstrated that a single QTL is responsible for these observations or proposed a biological mode of action for the marker effect.
The objectives of this paper were to 1) describe the effect of different priors on the reliability of genomic predictions; 2) demonstrate the value of markers with large effects to identify regions of the genome that merit further study; 3) report the location and size of effects of a marker on BTA18 that is associated with dystocia, conformation, net merit, and productive life; and 4) propose a model of inheritance for the dystocia locus that is consistent with the marker data.
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MATERIALS AND METHODS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONSACKNOWLEDGMENTSREFERENCES |
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10 daughters in their evaluations by April 2008. The main source of extracted DNA was from semen held in the Cooperative Dairy DNA Repository (Ashwell and Van Tassell, 1999). Semen from significant ancestor bulls was purchased independently or was provided by the National Center for Genetic Resources Preservation, ARS, USDA (Fort Collins, CO).
Genotypes for 38,416 SNP scored in 5,360 Holstein bulls were examined. The selected SNP were from the Illumina Bovine SNP50 BeadChip (Illumina Inc., San Diego, CA; Matukumalli et al., accepted) and had minor allele frequencies greater than 5% in Holsteins. Extraction of DNA and genotyping were performed at 6 locations: Bovine Functional Genomics Laboratory (Beltsville, MD); GeneSeek (Lincoln, NE); Genetics & IVF Institute (Fairfax, VA); Illumina (San Diego, CA); Department of Agricultural, Food and Nutritional Science, University of Alberta (Edmonton, Alberta, Canada); and Division of Animal Sciences, University of Missouri (Columbia, MO). Scoring of marker genotypes was performed using Illumina’s Beadstudio software (v3.2.23).
Allele frequencies in the base (founder) population were estimated using the algorithm of Gengler et al. (2007) that solves for gene content of nongenotyped ancestors and descendants using pedigrees. The pedigree file with all known ancestors of the 5,335 bulls included 41,414 cows and bulls. The genotype file included 205 million known and 2.0 million (1%) unknown genotypes. Allele counts from relatives were used to impute 974,961 (49%) of the 2 million missing genotypes.
The data included multitrait across-country evaluations from the Interbull Centre (Uppsala, Sweden) for bulls that had been progeny-tested in Canada and were expressed on the US scale. Dependent variables for analysis were daughter deviations for 27 traits related to dairy production: milk, fat, and protein yields; fat and protein percentages; productive life; SCS; daughter pregnancy rate; sire (SCE) and daughter (DCE) calving ease; final score; stature; strength; body depth; dairy form; foot angle; rear legs (side and rear views); rump angle and width; fore udder; rear udder height; udder depth and cleft; front teat placement; teat length; and net merit. Daughter deviations were weighted by reliability from daughters, which was computed from total daughter equivalents minus daughter equivalents from parent average (PA).
Genomic Predictions
Predictions were computed using linear and nonlinear genomic models (VanRaden, 2007, 2008). For linear predictions, the traditional additive genetic relationship matrix is replaced by a genomic relationship matrix and is equivalent to assigning equal genetic variance to all markers. Three different nonlinear models were considered: an infinitesimal alleles model with a heavy-tailed prior in which smaller effects are regressed further toward 0 and markers with larger effects are regressed less to account for a nonnormal prior distribution of marker effects (model A), a finite locus model with a normal distribution of marker effects (model B), and a finite locus model with heavy tails (model AB). Infinitesimal allele models assume that all loci have nonzero effects, and finite loci models assume that only a fixed number of alleles have effects. Models A and AB are analogous to the BayesA and BayesB methods of Meuwissen et al. (2001), respectively.
The linear model assumed a normal prior distribution, and model B assumed a normal prior for a proportion of the effects and no effect for the others. The prior used for models A and AB was a heavy-tailed distribution generated by dividing a normal variable by 
where h determines departure from normality and s is the size of the estimated marker effect in standard deviations (VanRaden, 2008). Marker effects are normally distributed with no additional weight in the tails when h is 1, and variance in the tails grows with increasing values of h. In addition, the proportion of markers allowed to have effects substantially different from 0 was needed for models B and AB. Optimal values of these parameters for each model were determined by evaluating 3 values of h (1.08, 1.12, and 1.25) and 4 values of marker proportion (6.25, 12.5, 25, and 50%) and comparing the squared correlations of daughter deviations with genomic predictions. A value of 1.25 was used in simulation (VanRaden, 2008), but the optimal value of h using real data was 1.12 for model A and 1.08 for models B and AB. The best value for the proportion of markers was 25% for model B but was 50% for model AB. Model A with a parameter of 1.12 is used for the routine genomic evaluations.
Genomic predictions and PA calculated from August 2003 data of older animals were compared for ability to predict April 2008 evaluations for younger bulls for each of the 27 traits analyzed. Predictive ability was determined by calculating squared correlations between daughter deviations and PA or genomic predictions for the linear and nonlinear models. The experimental design provided an independent, realistic test by separating early daughter information of ancestors used to compute predictions from later daughter information of descendants used to assess prediction accuracy.
The location of causative mutations for simple recessive conditions also can be mapped using the genomic prediction machinery. Recessive codes were used to define phenotypes for red coat color (RED; Joerg et al., 1996), complex vertebral malformation (CVM; Agerholm et al., 2001), and bovine leukocyte adhesion deficiency (BLAD; Shuster et al., 1992). Numbers of genotyped carriers were 141 for CVM and 41 for BLAD, which were binary traits because no homozygous recessive animals were genotyped. All 3 genotypes were present for RED including 41 carriers and 50 homozygous recessives. Heritabilities for all 3 traits were set to 0.99 because reported test results were assumed to contain little error.
Because 2003 PA had not been stored for type traits or for calving ease, 2003 pedigree indices (PI) constructed as 0.5 (sire PTA) + 0.25 (maternal grandsire PTA) + 0.25 (birth year mean PTA) were substituted for PA for those traits. Reliability of PI is lower than that of PA, especially for highly heritable traits, because records for the dam are excluded. The 2008 PA was not substituted for the 2003 PA because then the son’s information would have added to his dam’s reliability.
Final genomic predictions combined 3 terms by selection index: 1) direct genomic prediction, 2) PA computed from the subset of genotyped ancestors using traditional relationships, and 3) published PA or PI. For each animal, a matrix (3 x 3) is set up with reliabilities for the 3 terms on the diagonals and functions of those 3 reliabilities on the off-diagonals (VanRaden et al., 2009). Reliabilities of direct genomic predictions are calculated by inversion of the mixed model equations for the linear model, and the theoretical reliability from the linear model is also used for the nonlinear models. Regressions and correlations were used to test predictions. A bull’s published PTA is a weighted mean of his daughter deviation and his PA, and the use of deregressed evaluations or daughter deviations as dependent variables helps to avoid part-whole correlations with PA. Because daughter deviations as defined by VanRaden and Wiggans (1991) were not available for all traits, daughter deviations were computed as deregressed evaluations. The regression coefficient was calculated from daughter equivalents from progeny, which was obtained by subtracting daughter equivalents from parents from the bull’s total daughter equivalents.
Expected and Realized Genetic Effects
Genetic effects were computed for each chromosome as sums of individual allele effects (SA), additive genetic variances calculated using base population allele frequencies (VB), additive genetic variances calculated using current population allele frequencies (VC), and EBV on a within-bull basis (VWB). Expected contributions were estimated by multiplying sums or variances by the proportional length of individual chromosomes, with the proportions summing to 1. Ratios of individual allele effects were calculated as:
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c| = the absolute value of the effect of the cth allele on the ith chromosome; ng = the number of markers in the genome; g is the effect of the gth allele in the genome; and lengthi and lengthg = the lengths of the ith chromosome and the genome, respectively, in base pairs. The allele effects are equivalent to average allele substitution effects (Falconer and MacKay, 1996).
The additive genetic variance for a trait may be calculated as a function of allele frequencies and squared allele substitution effects as 
where Va = the additive genetic variance; n = the number of alleles affecting the trait; pi = the frequency of the ith alelle affecting the trait; and i = the substitution effect of the ith allele (Falconer and MacKay, 1996). Ratios of additive genetic variances were calculated using allele frequencies from either the base (VB) or current (VC) populations:
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c = the substitution effect of the cth allele on the ith chromosome; ng = the number of alleles in the genome; pg = the frequency of the gth allele in the genome; g = the substitution effect of the gth allele in the genome; and lengthi and lengthg = the lengths of the ith chromosome and the genome, respectively, in base pairs. The values of VC are calculated in the same manner, although the gene frequencies pc and pg are replaced with estimates from the current population rather than the base population (VanRaden et al., 2009).
The ratios of additive genetic variances were also calculated on VWB to account for possible linkage disequilibrium. This was done by processing the chromosomal breeding values for each animal receiving an evaluation:
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c = the effect of the cth allele on the ith chromosome; ng = the number of alleles in the genome; g = the effect of the gth allele in the genome; and lengthi and lengthg are the lengths of the ith chromosome and the genome, respectively, in base pairs. All of these models are assumed to be purely additive; that is, dominance and epistatic variances are 0. |
RESULTS AND DISCUSSION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONSACKNOWLEDGMENTSREFERENCES |
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2 distribution. However, models B and AB differ from the BayesB model in that small marker effects are regressed toward, rather than are fixed at, zero. Model A had little advantage in squared correlation over the linear model except for fat and protein percentages, with increases of 8 and 6%, respectively (Table 1). Gains obtained in simulation averaged 3% but were mostly smaller with real data, indicating that most traits are influenced by more loci than the 100 QTL used in the simulation (VanRaden, 2008). Model B provided similar or poorer fits than did model A for all traits, including fat and protein percentage, for which there are known genes of large effect (Grisart et al., 2004; Cohen-Zinder et al., 2005). Model AB produced fits that were similar to, but slightly poorer than, those for models A and B. The assumption that relatively few loci have nonzero genetic variance is not consistent with these findings, and we did not observe the higher accuracies predicted by Meuwissen et al. (2001) with the BayesB method. Models that assumed that all markers have some effect rather than that most have no effect provided better squared correlations. Slight decreases in squared correlations were noted for most traits in model A when the amount of variance in the tails beyond 2 standard deviations was increased (data not shown).
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Information about individual SNP with large associated effects was obtained from the National Center for Biotechnology Information’s Single Nucleotide Polymorphism Database, and all results are based on version 4.0 of the Bos taurus assembly (Table 2). The SNP ss86324977 is located at 57,125,868 bp on BTA18 and has the largest effects of all tested SNP on SCE and DCE, strength, stature, body depth, and rump width. A BLAST analysis of the 5' flanking sequence indicates that the SNP is located in an intron of the sialic acid binding Ig-like lectin (Siglec)-5 gene. A related molecule, Siglec-6, is expressed in the human placenta and may be involved in the regulation of signals related to the initiation of parturition (Brinkman-Van der Linden et al., 2007). It has been found that Siglec-5 and 6 both bind to leptin, with the latter exhibiting tighter binding (Patel et al., 1999). Human Siglec-9 also is expressed at high levels in many tissues, including the placenta and fetal liver (Foussias et al., 2000). Siglec-6 sequesters leptin (Brinkman-Van der Linden et al., 2007), which is secreted by adipocytes and circulates in the bloodstream in a protein-bound form (Houseknecht et al., 1998), and leptin-deficient mice show a delayed onset of parturition (Mounzih et al., 1998). High levels of bound leptin also signal that bodily energy reserves are high, resulting in decreased feed intake and increased rates of weight loss. Animals homozygous for the rare SNP may express high levels of Siglec-5, resulting in a leptin deficiency leading to delayed parturition. The association of ss86324977 with SCE, DCE, strength, stature, body depth, and rump width may be due to increased calf size associated with longer gestation lengths.
Hansen et al. (2004) estimated genetic correlations among direct and maternal gestation length, calving difficulty, and calf size, which showed that calves with longer gestation lengths are larger at birth and have higher rates of dystocia. Similarly, cows that carry their calves longer more frequently bear larger calves with a greater risk of calving difficulty. Genes within the Siglec family show a high rate of evolution within species; therefore, human and bovine physiology may differ. Significant additional work is needed to characterize the bovine Siglecs and their effects on metabolism.
The SNP ss105250374 is located within an intron of the zinc finger protein 577 gene at 57,539,664 bp on BTA18 and has the largest association with productive life of all tested SNP. Zinc finger protein 577 is a member of the superfamily of Krüppel-associated box proteins, which are well-known transcriptional repressors (Bellefroid et al., 1991; Margolin et al., 1994). Ganss and Jheon (2004) also reported in a review of literature that several zinc finger transcription factors play important roles in mammalian bone and skeletal development. Additional research is needed to better understand how Krüppel-associated box proteins are involved in metabolic processes that affect productive life.
Location of Causative Mutations
The availability of low-cost, high-density genotyping arrays may allow faster identification of causal mutations associated with harmful recessives by identifying SNP in strong linkage disequilibrium with the recessive locus. The SNP with the strongest association may not necessarily be located in the underlying gene but should identify a region of a specific chromosome that can more closely be investigated. This approach was tested using recessive codes for 3 well-known phenotypes in Holsteins, BLAD, CVM, and RED, for which the causative mutations are known. Statistical models for binomial responses (Tempelman, 1998) were not used because our goal was to determine the suitability of the existing genomic prediction system for this task.
The locations of the SNP with the strongest associations with each trait are presented in Table 3, as are the names and locations of the genes in which the causal mutations are known to lie. The National Center for Biotechnology Information’s Single Nucleotide Polymorphism Database was used to determine if the most strongly associated SNP were located in known genes. None of these SNP were located in the protein in which the causal mutations lie, and the SNP associated with CVM and RED were not located in known genes. The BLAD-associated SNP is located in the pituitary tumor-transforming 1 interacting protein gene, and the RED-associated SNP lies in an intron of vacuolar sorting protein 35. There are 2, 42, and 17 SNP closer to the causal mutations within the genes involved in BLAD, CVM, and RED, respectively, than the SNP most strongly associated with the respective phenotype. The SNP with the strongest associations with RED and BLAD lie within about 100 kbp of the causal mutation, which is expected given the resolution of the assay. However, the SNP strongly associated with CVM is about 1 Mb away from the causal mutation. Given that very little linkage disequilibrium (LD) exists in cattle at the 1 Mb range (McKay et al., 2007; Sargolzaei et al., 2008), and that this association was detected despite the 42 putatively closer SNP, it is likely that the SNP are not correctly ordered in this region of the genome.
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The distribution of marker effects (Figure 2) shows that most SNP have small effects (contribute random noise), whereas markers in regions in which causative mutations lie have much larger effects. Although the markers with the largest associated effects from a genomic analysis may not track perfectly the causative mutations, they are potentially useful tools for identifying chromosomal regions associated with recessive Mendelian phenotypes. Animals exhibiting a previously unknown recessive condition, such as brachyspina syndrome (Agerholm et al., 2006), could be genotyped and the data processed using the existing genomic evaluation system. The resulting marker effects would identify specific chromosomal regions in which the causative mutation may be located.
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Variance ratios calculated on a within-sire basis may be preferred to VB or VC as a measure of the magnitude of genetic effects because they account for LD among adjacent loci. The VB and VC methods assume that adjacent loci are statistically independent, implying that covariances among all loci are 0, which is not the case for markers that are in LD. In the VWB approach, CEBV and GEBV were calculated for each bull and then averaged over all genotyped bulls to calculate ratios for each chromosome. In some cases, the VWB ratio for a known QTL is larger than for VB or VC, and in some cases, it is smaller. Values of VWB substantially different from VB or VC may identify regions in which there are multiple loci contributing to variation in a trait and the transmission of LD blocks is being tracked, rather than the individual causative mutations.
Many association studies have reported upward biases in estimates of marker effects when the marker location and effect size are jointly estimated. Göring et al. (2001) first described this problem in detail, showing that the bias is a function of several parameters, including the true effect size and the sample size. They also noted that in many common situations, estimates of marker effects are essentially independent of the true marker effects. Biased marker effects would have an effect on all 4 calculated ratios (SA, VB, VC, and VWB), with the variances being most affected, and this may partially explain the differences among VB, VC, and VWB discussed above. Several approaches for minimizing the problem have been proposed in the literature, but no particularly satisfying solutions have been developed. Simultaneous estimation of all marker effects, such as performed in this study, may reduce but not eliminate upward biases. Allison et al. (2002) also suggested that the use of empirical Bayes approaches may help reduce bias. Given the large sample size in this study and the high correlation between marker effects from the linear and nonlinear models, it is tempting to assume that the marker effects are unbiased, but this may not be the case. The magnitude of marker effects should be interpreted cautiously until confirmed by follow-up studies.
The expected proportions of genetic variance for each trait accounted for by SNP on a chromosome were calculated based on chromosome lengths assuming that all markers had equal effects. Based on its length, BTA18 was expected to account for 2.2% of the additive genetic variance for each trait, but it actually accounted for 2.9% (economic merit) to 7.6% (SCE) of the additive genetic variation. Markers on BTA1 accounted for considerably less variance than expected based on its length, although it is unclear why this would be the case.
The active X chromosome in female humans and mice, and the sole X chromosome in males, is upregulated by a doubling of global gene expression (Nguyen and Disteche, 2006). As a result, X-linked loci are expected to explain as much variance as autosomal loci. However, the X chromosome accounts for much less variance than expected when using VC but is closer to expectation when VWB is used, suggesting that there may be large LD blocks on that chromosome. Vicoso and Charlesworth (2006) reported that DNA sequences of X-linked genes under positive selection change faster than those under negative selection, which may lead to higher rates of fixation and lower-than-expected variance for genes on the X chromosome.
Marker association effects for most other traits were evenly distributed across all chromosomes, with only a few regions having larger effects. This may explain why the infinitesimal model and standard quantitative genetic theories have worked well. The distribution of marker effects indicates that favorable alleles will not become quickly fixed within a population, and genetic variation will remain even after intense selection. Thus, dairy cattle breeders may expect genetic progress to continue for many generations.
Calving Complex
Bulls that sire large calves transmit genes to their daughters, which, in turn, support high fetal growth rates. McDermott et al. (1992) reported that calf birth weight is the single most important factor in predicting dystocia, and Johanson and Berger (2003) showed that birth weight is also a valuable predictor of the probability of stillbirth. Comparison of genome-wide and BTA18-specific correlations among SCE, DCE, longevity, net merit, stature, strength, body depth, and rump width suggests that the putative QTL on BTA18 is associated with calf growth rate.
Correlations among BTA18-specific EBV (Table 7, upper diagonal) reveal favorable correlations among longevity and economic merit (r = 0.88), undesirable correlations among conformation and calving ease traits (r = 0.78 to 0.95), and unfavorable correlations of calving ease with net merit, longevity, and conformation (r = –0.44 to –0.72). Comparison of BTA18 specific to genome-wide correlations (Table 7, lower diagonal) strongly suggests that the QTL on BTA18 affects calf size and that selection for extreme conformation (larger body size) has resulted in larger calves and increased rates of dystocia, which are largely attributable to feto-pelvic incompatibility (Meijering, 1984).
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The relationships among DCE and conformation follow a similar pattern to SCE. Genome-wide correlations among DCE and conformation are moderate and positive, ranging from 0.23 to 0.30, indicating that over time larger cows will themselves have more calving difficulty. The BTA18-specific correlations are large and positive, ranging from 0.69 to 0.79, and reflect the effect of dam genes on calf growth rate in utero. Dam genes can affect calf birth weight by their expression in the growing fetus as well as in the uterine environment.
The QTL on BTA18 had the largest effects genome-wide on both sire and daughter stillbirth. The effect was greatest on sire stillbirth and is consistent with previously reported relationships of dystocia with risk of stillbirth (Meyer et al., 2000). Larger calves are more likely to experience dystocia, and dystocia is genetically associated with stillbirths (Hansen et al., 2004). The effect of the QTL on daughter stillbirth was small, implying that it does not significantly increase the likelihood of a bull’s daughters delivering stillborn calves.
Several studies have reported QTL for conformation traits, calving traits, fertility, and longevity on BTA18. A QTL affecting longevity, maternal dystocia, and maternal stillbirth in the German Holstein population was described by Kühn et al. (2003). Holmberg and Andersson-Eklund (2006) identified a QTL affecting maternal calving ease in a resource population of Swedish Reds and Swedish Holsteins. Thomasen et al. (2008) located a QTL with pleiotropic effects on dystocia, stillbirth, and calf size in a population of Danish Holsteins.
Schnabel et al. (2005) performed a genome scan in 2 half-sib US Holstein families to identify QTL for yield and conformation and reported microsatellite associations with foot angle, fore udder attachment, and teat length. Their analysis included similar calving ease and conformation data to the current study but did not report significant QTL effects on BTA18, possibly due to the limited number of parental chromosomes screened in that study. Their results were similar to the earlier findings of Schrooten et al. (2000), who genotyped 949 young bulls in the Netherlands belonging to 22 half-sib families and reported no significant QTL effects for conformation or dystocia on BTA18.
More recently, Kolbehdari et al. (2008) genotyped 462 Canadian Holstein bulls and identified SNP rs41636734 at 53,743,293 bp on BTA18 to be associated with conformation, the mammary system, overall rump score, and direct (sire) calving ease. They reported no significant associations with dairy strength, stature, herd life, or fertility, possibly because of the limited number of genotyped bulls. This SNP is located in an intron of the cluster of differentiation 37 (CD37) gene, which is a member of the transmembrane 4 superfamily, whose members are cell-surface proteins that mediate signal transduction events (van Spriel et al., 2004). The SNP reported in this study (ss86324977) is located in an intron of the gene encoding the Siglec-5 protein at 57,125,868 bp, which is far enough from the CD37 gene that there should be no significant LD between them if the assembly is correct in this region of the genome.
These results support our findings that there is a QTL on BTA18 that has significant effects on body size, dystocia, longevity, and economic merit. The QTL results in high calf birth weights, which are in turn associated with greater risks of dystocia, reduced dam fertility, and reduced lifetime economic merit of dams (Meijering, 1984; Mee, 2008). Large cows, in turn, bear large calves with increased risks of dystocia. The large, positive genetic correlations among the conformation and dystocia traits imply that the QTL has a large effect on calf size, and the association of SNP ss86324977 with the Siglec-5 gene suggests a possible underlying mechanism. Although GEBV for stillbirth have yet to be calculated, and were unavailable for analysis, the literature is clear that high calf birth weights substantially increase the risk of stillbirth (Johanson and Berger, 2003).
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CONCLUSIONS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONSACKNOWLEDGMENTSREFERENCES |
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Marker associations on BTA18 were centered on SNP ss86324977, which had large effects on economic merit, longevity, calving ease, and conformation. Relationships among those traits may be attributable to a gene product or regulatory element associated with calf birth weight, and the SNP associated with the QTL is located in an intron of the Siglec-5 gene, which may regulate the onset of parturition through leptin sequestration. The presence of significant marker effects resulted in greater-than-expected proportions of explained genetic variance being assigned to BTA18. Analysis of known recessive genes suggests that marker solutions from genomic evaluations may be useful for identifying chromosomal regions that merit further study.
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ACKNOWLEDGMENTS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONSACKNOWLEDGMENTSREFERENCES |
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Received for publication September 29, 2008. Accepted for publication January 9, 2009.
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONSACKNOWLEDGMENTSREFERENCES |
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