Journal of Heredity 2003:94(6)
© 2003 The American Genetic Association 94:496-506
Mapping of QTL for Body Conformation and Behavior in Cattle
From the Institut für Tierzucht und Haustiergenetik der Justus-Liebig-Universität, D-35390 Giessen, Germany (Hiendleder, Leyhe-Horn, and Erhardt), Institut für Tierzucht und Tierhaltung der Christian-Albrechts-Universität, D-24118 Kiel, Germany (Thomsen, Reinsch, Bennewitz, Looft, Xu, and Kalm), Institut für Tierzucht der Ludwig-Maximilians-Universität, D-80539 München, Germany (Medjugorac, Russ, Förster), Forschungsinstitut für die Biologie Landwirtschaftlicher Nutztiere, Forschungsbereich Molekularbiologie, D-18196 Dummerstorf, Germany (Kühn, Brockmann, and Schwerin), Institut für die Fortpflanzung landwirtschaftlicher Nutztiere, D-16321 Schoenow, Germany (Blümel), Tierärztliches Institut der Georg-August-Universität, D-37073 Göttingen, Germany (Brenig), Vereinigte Informationssysteme Tierhaltung w.V., D-27283 Verden/Aller, Germany (Reinhardt and Reents), and Bayerische Landesanstalt für Tierzucht, D-85586 Grub, Germany (Averdunk).
Address correspondence to Stefan Hiendleder, Department of Molecular Animal Breeding and Biotechnology, Ludwig-Maximilian University Munich, Hackerstraße 27, D-85764 Oberschleißheim, Germany, or email: S.Hiendleder{at}gen.vetmed.uni-muenchen.de.
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Abstract |
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Genome scans for quantitative trait loci (QTL) in farm animals have concentrated on primary production and health traits, and information on QTL for other important traits is rare. We performed a whole genome scan in a granddaughter design to detect QTL affecting body conformation and behavior in dairy cattle. The analysis included 16 paternal half-sib families of the Holstein breed with 872 sons and 264 genetic markers. The markers were distributed across all 29 autosomes and the pseudoautosomal region of the sex chromosomes with average intervals of 13.9 cM and covering an estimated 3155.5 cM. All families were analyzed jointly for 22 traits using multimarker regression and significance thresholds determined empirically by permutation. QTL that exceeded the experiment-wise significance threshold (5% level) were detected on chromosome 6 for foot angle, teat placement, and udder depth, and on chromosome 29 for temperament. QTL approaching experiment-wise significance (10% level) were located on chromosome 6 for general quality of feet and legs and general quality of udder, on chromosome 13 for teat length, on chromosome 23 for general quality of feet and legs, and on chromosome 29 for milking speed. An additional 51 QTL significant at the 5% chromosome-wise level were distributed over 21 chromosomes. This study provides the first evidence for QTL involved in behavior of dairy cattle and identifies QTL for udder conformation on chromosome 6 that could form the basis of recently reported QTL for clinical mastitis.
Genome-wide screening with molecular markers in farm animal populations has successfully focused on the detection of quantitative trait loci (QTL) for primary production and health traits such as milk yield and clinical mastitis in dairy cattle (Georges et al. 1995; Heyen et al. 1999; Klungland et al. 2001; Zhang et al. 1999). After fine mapping and identification of causative mutations in positional candidate genes, these QTL have the potential to achieve additional genetic and economic gains by incorporating marker-assisted selection into breeding programs (Farnir et al. 2002; Grisart et al. 2002; Winter et al. 2002).
Breeding programs in cattle and other livestock species aim at selecting animals with the highest combined economic value for the next generation. Therefore most breeding programs select for a combination of production and nonproduction traits, for example, body conformation and behavior. Nonproduction traits are either correlated with traits of economic importance or have a direct economic value. In cattle, body conformation traits such as stature and body depth affect feed intake and thus milk production (Veerkamp 1998), while udder traits correlate with the incidence of clinical mastitis (Nash et al. 2000) and the length of productive life (Larroque and Ducrocq 2001). Temperament, defined as the animal's behavioral responses to handling by humans (Burrow 1997), is of special importance, as it determines the amount of time spent in handling animals. In addition, nervous or aggressive animals have a decreased milk flow and yield as a result of reduced oxytocin secretion during stress (Rushen et al. 1999, 2001).
Like primary production traits, body conformation and behavior traits are of a quantitative nature and display varying amounts of genetic variation. Heritability estimates in dairy cattle range from 0.25 to 0.60 for body conformation traits and from 0.15 to 0.25 for behavior (i.e., temperament) (Schrooten et al. 2000; Visscher and Goddard 1995). Selection for these traits has relied solely on phenotypic and pedigree data, using statistical methods for partitioning the phenotypic performances of individuals into their additive genetic values plus environmental contributions.
Previous attempts to detect nonproduction trait QTL in cattle were limited in power by relatively small family size (Spelman et al. 1999) or a limited number of markers (Ashwell et al. 2001). So far only one large genome-wide search for QTL affecting body conformation and behavior has been completed with a sufficiently dense marker map (Schrooten et al. 2000). This study, however, excluded the sex chromosomes, and "chromosome-wise" significance thresholds were derived from a single chromosome, which was also used to calculate "genome-wise" significance levels. In the present study we performed a comprehensive whole-genome scan with empirically determined chromosome-wise and experiment-wise significance thresholds to identify bovine chromosome regions harboring QTL for economically important body conformation and behavior traits as a first step toward dissecting the quantitative genetic nature of these traits and to identify candidate genes for further studies.
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Materials and Methods |
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Resource Population and Recorded Traits
The resource population was established within the bovine genome mapping project of the German Cattle Breeders Association (ADR, Bonn, Germany). German artificial insemination (A.I.) organizations contributed 872 semen samples from 16 paternal half-sib families of the German Holstein breed. The number of sons per sire ranged from 19 to 127 (average 54.5). In addition, semen from three great-grandsires was available. A detailed description of this granddaughter design is given in Thomsen et al. (2001). Phenotypic data were obtained from the United Data Systems for Animal Production (VIT, Verden, Germany) for 22 body conformation and behavior traits (Table 1 and Figure 1). Trained inspectors or dairy farmers scored the traits subjectively, the majority (body conformation and behavior linear traits) on a linear scale from 1 to 9, divided into nine classes, where 1 and 9 represent biological extremes and 5 the population mean. Four traits (conformation general characteristics) were graded using a point system with a maximum of 100. Estimated breeding values (EBVs) for body conformation and behavior traits of sons were calculated by using a Best Linear Unbiased Prediction (BLUP) animal model. Genetic parameters and breeding values for QTL analyses were estimated from data of the same population. All data were taken from the national breeding value evaluation (November 1999). Detailed information on traits, data standardization, and the estimation of heritability and breeding values can be found at http://www.vit.de/English/Homepage.html. The number of daughters per sire with trait values for body conformation traits and for behavior traits was 123 on average. Dere-gressed EBVs were used in QTL analysis as a unit of phenotypic measurement. The breeding values were standardized to 100 with a standard deviation of 12. Deregression of the EBVs was carried out according to Lien et al. (1995). The measurement represents the average daughter deviation of each son from the population mean after accounting for the fixed effects in the model. Although not all sons in the evaluation run were provided for genotyping, they were retained in the QTL analyses to contribute to the calculation of the fixed effect of the grandsire in the regression model.
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The different number of daughters contributing to the calculation of deregressed breeding values was accounted for by a weighting factor, W,
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Marker Genotyping and Linkage Mapping
A whole-genome scan covering all autosomes (chromosomes 1–29) and the pseudoautosomal region of the sex chromosomes (X/Yps) was performed with 264 genetic markers. The marker panel consisted of 247 microsatellite markers, 8 single-strand conformation polymorphisms (SSCP), 4 protein polymorphisms, and 5 erythrocyte antigen loci from published marker maps (Barendse et al. 1994, 1997; Bishop et al. 1994; Kappes et al. 1997; Thomsen et al. 2000; Weikard et al. 1997). Microsatellite and SSCP genotypes were determined by automated fragment analysis (A.L.F. express, Amersham-Pharmacia; ABI377, Perkin-Elmer). Commercial blood typing laboratories determined erythrocyte antigen genotypes and protein polymorphisms according to standard procedures. All marker data were transferred to the ADR database and checked for typing errors by using software that analyzed the scored genotypes for Mendelian segregation (Reinsch 1999). Marker maps were calculated using the multipoint option of CRIMAP (Green et al. 1990). Markers, typing procedures, linkage analysis, and maps have previously been reported by Thomsen et al. (2000).
Genome-wide Search for QTL
A multiple marker regression approach (Knott et al. 1996) was used for a QTL scan over 3155.5 cM. QTL mapping was performed for each trait separately, as described by Reinsch et al. (1999). The most likely marker haplotype for each chromosome of each grandsire was determined from marker genotypes of all typed animals in the granddaughter design, that is, all typed progeny of each grandsire and, where available, the marker genotypes of the great-grandsires. The number of genotyped progeny ranged from 19 to 127 per family. In all three-generation families, the use of great-grandsire marker information reduced the number of possible grandsire haplotypes considerably. Using these haplotypes, the probability that a sire inherited the first allele of a possible QTL was derived from the smallest informative marker interval available for each sire every centiMorgan on each chromosome. For the analysis of 872 progeny, about 2.7 million QTL transition probabilities were required. A joint analysis of all families was undertaken by fitting every centiMorgan the following regression model to the data:
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levels was previously stressed by others (e.g., Cassady et al. 2001). All computations were done with the BIGMAP and ADRQTL programs, which are directly connected to the ADR database (Reinsch 1999), on a SUN sparc SUNW, Ultra-1 workstation. The programs are available upon request (reinsch{at}fbn-dummerstorf.de). |
Results |
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The genetic marker map covered 3155.5 cM of the bovine genome across all autosomes and the pseudoautosomal region of the sex chromosomes. The average marker interval was 13.9 cM and ranged from 6.0 cM (chromosome 23) to 23.2 cM (chromosome 9) on individual chromosomes (Table 2). A total of 60 QTL with chromosome-wise significance (5% level) were identified on 20 autosomes and the sex chromosomes. Significant QTL were detected for all but one (rear leg set side view) of the 22 investigated body conformation and behavior traits (Table 3). The QTL for foot angle, teat placement, and udder depth on chromosome 6 and for temperament on chromosome 29 exceeded the experiment-wise significance threshold at the 5% level. QTL for general quality of the feet and legs and general quality of the udder on chromosome 6, for teat length on chromosome 13, for general quality of the feet and legs on chromosome 23, and for milking speed on chromosome 29 approached experiment-wise significance at the 10% significance level (Figure 2). Only nine chromosomes (3, 4, 12, 14, 15, 19, 24, 26, and 28) did not harbor any QTL, but four chromosomes (6, 5, 18, and X/Y) with 10, 7, 6, and 5 QTL, respectively, contained almost 50% of the total QTL detected. Chromosomes with and without QTL did not differ in average marker interval (14.1 ± 0.85 versus 13.4 ± 1.3, P =.662) or the average number of informative meioses per marker (492.4 ± 16.5 versus 446.1 ± 25.2, P =.135). This is also evident from comparisons between marker characteristics of individual chromosomes, for example, comparisons between the QTL-rich chromosomes 5 or 6 with the QTL-poor chromosomes 3 or 12 (Table 2). QTL detection was therefore not related to marker number, marker distribution, or marker information content. Chromosome 6 showed a remarkable QTL cluster for eight body conformation traits at positions 85–89 cM, five of them exceeding (5% level) or approaching (10% level) experiment-wise significance. The QTL cluster includes three very diverse groups of genetically correlated traits. These are suspensory ligament, teat placement, udder depth, and general quality of the udder (rG = 0.25–0.74), foot angle and general quality of the feet and legs (rG = 0.58), and body and rump width (rG = 0.22) (Table 3 and Figure 2). In addition to the QTL on chromosome 6, other chromosomes also showed QTL for correlated traits to be present in the same chromosome regions. An example are QTL for temperament and milking speed (rG = 0.53) on chromosome 5 at 136/136 cM, on chromosome 18 at 105/109 cM, on chromosome 29 at 20/20 cM, and on chromosome X/Y at 9/9 cM or QTL for hocks and rear leg set rear view (rG = 0.19) on chromosome 11 at 61/61 cM, on chromosome 13 at 53/54 cM, and on chromosome 21 at 12/12 cM (Table 3 and Figure 2).
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Discussion |
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We have detected 60 QTL significant at the 5% chromosome-wise level for 22 body conformation and behavior traits with estimated heritabilities ranging from 0.07–0.41 (Tables 1 and 3), demonstrating that a considerable number of loci with effects on body conformation and behavior are still segregating in the Holstein cattle population. These traits are of economic importance and have been under phenotypic selection with differing intensity for several generations. Therefore negative, albeit nonsignificant (P >.05), regression coefficients for the number of QTL per trait on heritability estimates of the population are expected (b = 2.66 for 60 QTL at the 5% chromosome level, and b = 3.29 when an additional 40 putative QTL at the 10% chromosome level are included) because of fixation of favorable alleles. The proportion of genetic variance explained by nine QTL with a genome-wise P value of less than 10% ranged from 1.6% to 2.5% (Table 4). It has to be considered, however, that in our experimental design these estimates are derived from a combined analysis of families with heterozygous and homozygous grandsires, and the number of heterozygous sires among the total of 16 grandsires (Table 3) was generally small (mean 2.8, range 1–6). Average allele substitution effects of these QTL ranged from 0.71 to 3.60 genetic standard deviations (Table 4) and are in line with allele substitution effects for nonproduction trait QTL reported by others (e.g., Boichard et al. 2003).
The number of QTL for body conformation and behavior identified in previous studies (Ashwell et al. 2001; Schrooten et al. 2000; Spelman et al. 1999) is not directly comparable to the present study because of differences in the number and type of traits analyzed, differences in experimental design, and differences in significance thresholds employed. Using a similar spectrum of 17 traits in a study with less power because of a smaller number of families and a smaller number of sons per family analyzed, Spelman et al. (1999) reported a single QTL for stature on chromosome 14 at the 15% experiment-wise error level. The highly genetically correlated trait of body weight peaked at the same position, but did not reach the suggestive significance level. Other studies (Ashwell et al. 2001; Schrooten et al. 2000) and our study have not confirmed this QTL. Schrooten et al. (2000) reported a total of nine QTL exceeding an experiment-wise significance threshold of 10% and an additional 36 QTL exceeding a chromosome-wise threshold of 0.0345% for 20 traits scored very similar to the traits used in our study. However, the experiment-wise significance threshold was not derived empirically for each trait with all chromosomes, but was calculated from permutation data of a single "average" chromosome, which was then used to calculate "genome-wise" significance levels. Chromosome-wise thresholds were also derived from the single "average" chromosome permutation data. Nevertheless, our study confirmed a QTL for udder depth on chromosome 5 at 109/106 cM (position reported by Schrooten et al. (2000) presented first). A QTL for milking speed at 57/72 cM on chromosome 2 only approached chromosome-wise significance at P <.072 (data not shown, available upon request) in our study, stressing the need to report QTL at suggestive thresholds or make nonsignificant data available. A QTL for stature was mapped to chromosome 6 at 11 cM by Schrooten et al. (2000) and to 66 cM in the present study. The QTL for "chest width" reported by Schrooten et al. (2000) on chromosome 2 at 139 cM could be identical to the QTL for the trait "body depth" reported at 82 cM in the present study (chromosome-wise significance P <.094; data not shown, available upon request). The data presented by Ashwell et al. (2001) did not show any indication of QTL shared with our experiment, perhaps due to different traits or trait definitions.
While this manuscript was processed, Boichard et al. (2003) reported QTL for nonproduction traits in three dairy cattle breeds. This granddaughter design combined 1548 sires of the Holstein, Montbéliarde, and Normande breeds in 14 families and is similar to our study in the Holstein breed. Boichard et al. (2003) used an approximately 30% lower marker density than the present study, but nevertheless detected 86 QTL for 15 body conformation and one behavior trait at chromosome-wise and/or experiment-wise significance levels determined empirically by permutation. Although the overlap in traits and especially trait definitions used for the QTL searches by Boichard et al. (2003) and the present study is limited, a considerable number of QTL were identified in both studies and are therefore confirmed. A QTL involved in chest width/depth was mapped by Schrooten et al. (2000) to chromosome 2 at 139 cM, at 82 cM in the present study (see above), and at 40 cM by Boichard et al. (2003). The QTL for milking speed at 72 cM on chromosome 5 detected in our study was also identified by Boichard et al. (2003) at 97 cM. The QTL for stature detected by Schrooten et al. (2000) on chromosome 6 at 11 cM and at 66 cM in the present study was mapped to 54 cM by Boichard et al. (2003). On chromosome 6, four additional QTL reported by Boichard et al. (2003) were confirmed in our study. These are QTL for rump width at 62/87 cM, for stature at 54/66 cM, for teat placement at 87/88 cM, and for udder depth at 147/89 cM (position reported by Boichard et al. (2003) presented first). Further QTL reported by Boichard et al. (2003) that are likely to be identical and thus confirmed in the present study are found on chromosome 9 (overall quality of udder at 48/63 cM), chromosome 13 (teat length at 0/39 cM), chromosome 17 (rump angle at 8/34 cM), chromosome 18 (foot angle at 74/36 cM, rear udder height at 98/52 cM, teat placement at 115/43 cM), and chromosome 20 (suspensory ligament at 9/0 cM, overall quality of udder at 48/9 cM). Although three confirmed QTL (teat placement and udder depth on chromosome 6, teat length on chromosome 13) were significant at the genome-wise 10% threshold in our study, 7 of 11 additional QTL assumed to be confirmed reached only the suggestive chromosome-wise significance level (P <.10). This again stresses the need to report putative QTL at suggestive thresholds.
Klungland et al. (2001) recently reported a QTL for clinical mastitis with experiment-wise significance on bovine chromosome 6 in the same approximate chromosome region where we identified several highly significant QTL affecting udder conformation traits (teat placement, udder depth, overall udder quality) and leg conformation traits (overall quality of the feet and legs, foot angle). In addition, we detected QTL for udder conformation traits at the chromosome-wise suggestive level (P <.10) on chromosomes 8 and 27 (data not shown), two chromosomes reported by Klungland et al. (2001) to harbor QTL for udder health at the chromosome-wise significance level. Recent heritability estimates for the incidence of clinical mastitis and the number of clinical episodes range from 0.11 to 0.25 and 0.15 to 0.45, respectively, and relationships between clinical mastitis and udder conformation traits have been well established (Nash et al. 2000). The QTL for udder conformation traits could therefore form the basis for the mastitis QTL on chromosomes 6, 8, and 27. QTL for leg conformation on chromosome 6, however, might also contribute to mastitis susceptibility, since animals with poor feet could be more prone to infections, perhaps because of longer bedding times and associated microbial udder contamination.
Previous studies attempting to localize QTL for temperament in dairy cattle (Schrooten et al. 2000; Spelman et al. 1999) were unsuccessful. We have detected one QTL for temperament significant at the experiment-wise level and two QTL significant at the chromosome-wise level. An additional three QTL (chromosomes 10, 20, and X/Y) were significant at the chromosome-suggestive level (P <.10, data not shown). This is the first evidence for QTL affecting behavior in dairy cattle. Schmutz et al. (2001) reported QTL for behavior traits in beef cattle on chromosomes 1, 5, 9, 11, 14, and 15. Although these data are not directly comparable because of differences in experimental design and a different trait definition for temperament (response to isolation), it is possible that the QTL for temperament on chromosome 5 in dairy cattle is identical to the QTL on chromosome 5 for temperament in beef cattle. Schrooten et al. (2000) reported two suggestive QTL for milking speed on chromosomes 3 and 23, but neither chromosome showed QTL evidence for milking speed or temperament in our study. In agreement with the high genetic correlation between both traits, we detected QTL for milking speed at an almost equal frequency and, when suggestive QTL with a chromosome-wise P value of less than.10 are included, always at identical or nearly identical chromosome positions. Milk secretion is directly dependent on oxytocin, a hormone sensitive to various types of emotional stress (Wellnitz and Bruckmaier 2001), and stressed or nervous animals have a delayed milk flow and therefore reduced milking speed as a result of reduced oxytocin secretion (Rushen et al. 1999, 2001).
Relatively few coding genes are yet mapped in cattle, and no compelling candidate genes are evident for the QTL affecting udder traits on chromosomes 6 and 13 or leg traits on chromosomes 6 and 23. However, new comparative maps, such as the high-resolution integrated and comparative map of bovine chromosome 6, which accesses information from orthologous human and mouse chromosomes (Weikard et al. 2002), should enable identification of candidate genes. On chromosome 29, the tyrosinase (TYR) gene maps to the QTL region for temperament and milking speed with the highest test statistic (Schmidtz et al. 2001; Schmutz and Moker 1999). Tyrosinase is a multifunctional enzyme involved in metabolism of the neurotransmitter dopamine. It catalyzes the conversion of tyrosine to dihydroxyphenylalanine (DOPA), the precursor of dopamine, and oxidizes DOPA to form DOPA quinone. Tyrosinase also oxidizes dopamine to form melanin via dopamine quinone, which has been shown to inactivate tyrosine hydroxylase, the rate-limiting enzyme for dopamine synthesis (Higashi et al. 2002; Xu et al. 1998). The tyrosinase gene is therefore a QTL candidate for temperament in cattle.
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2g) explained and average substitution effects ( |
Acknowledgments |
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This study was supported by the German Ministry of Education, Science, Research and Technology (BMBF) and the German Cattle Breeders Federation (ADR). The technical assistance of the staffs of all participating laboratories is gratefully acknowledged.
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Footnotes |
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Corresponding Editor: Bruce Weir
Received October 10, 2002
Accepted July 31, 2003
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References |
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