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Genetics, Vol. 172, 425-436, January 2006, Copyright © 2006
doi:10.1534/genetics.105.046169
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,1
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* Laboratoire de Génétique Biochimique et Cytogénétique and Station de Génétique Quantitative et Appliquée, INRA 78352 Jouy-en-Josas, France, Department of Animal and Aquacultural Sciences and ** Centre for Integrative Genetics, Agricultural University of Norway, N-1432 Aas, Norway and Union Nationale des Coopératives d'Elevage et d'Insémination Animale, 75595 Paris, France
1 Corresponding author: Laboratoire de Génétique Biochimique et de Cytogénétique, Département de Génétique Animale, INRA, Domaine de Vilvert, 78352 Jouy-en-Josas, France.
E-mail: mathieu.gautier{at}jouy.inra.fr
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ABSTRACT |
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 TOP ABSTRACT MATERIALS AND METHODSRESULTSDISCUSSIONACKNOWLEDGEMENTSLITERATURE CITED |
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29 Mb. The presence of a QTL affecting protein yield was confirmed but its position was found to be more telomeric than the two QTLunderlying fat yield. Each identified QTL affecting milk fat yield was physically mapped within a segment estimated to be <500 kb. Two strong functional candidate genes involved, respectively, in fatty acid metabolism and membrane permeability were found to be localized within this segment while other functional candidate genes were discarded. A haplotype comprising the favorable allele at each QTL position appears to be overrepresented in the artificial insemination bull population.
In parallel, knowledge on the bovine genome has benefited greatly from structural genetics studies. Detailed human/bovine comparative maps have been developed (HAYES 1995; BAND et al. 2000; HAYES et al. 2003), increasing the possibility of exploiting the genome sequence and the growing functional characterization of reference species such as man (LANDER et al. 2001), rat (GIBBS et al. 2004), or mouse (WATERSTON et al. 2002). These data provide new insights to unravel the genetic determinism involved in the variation of some traits of breeding interest (ANDERSSON and GEORGES 2004). More recently, a first-generation physical map of the bovine genome was released (SCHIBLER et al. 2004), opening the way toward the whole bovine genome sequence planned to be assembled at the beginning of 2006.
In this study, most of the available positional cloning tools in cattle were applied for the fine mapping and the physical characterization of a highly significant QTL affecting milk fat yield on bovine chromosome 26 (BTA26). This QTL was originally described to segregate in the French Holstein dairy population (BOICHARD et al. 2003) and was confirmed later in a combined analysis associating French and German families (BENNEWITZ et al. 2003). According to the results from a recently published high-resolution comparative map between BTA26 and human chromosome 10 (HSA10) (GAUTIER et al. 2003), 14 publicly available microsatellite markers and 11 newly developed microsatellite markers were genotyped in the families of the original design extended by the addition of new families originating from the marker-assisted selection (MAS) program initiated in 2000 in France (BOICHARD et al. 2002). These new data were then analyzed using a methodology combining LA and LD to identify the most likely QTL marker interval. A detailed physical and comparative map of the corresponding interval was then constructed.
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MATERIALS AND METHODS |
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TOPABSTRACTMATERIALS AND METHODSRESULTSDISCUSSIONACKNOWLEDGEMENTSLITERATURE CITED |
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Genotyping data:
Previously available genotyping data:
Before the beginning of the study, genotypes for four microsatellite markers (ABS12, BMS907, INRA081, and IDVGA59) were available (BOICHARD et al. 2003). Nevertheless, ABS12 and IDVGA59 were genotyped only on the sons from the nine families of the original QTL program, which explains the lower number of genotyped sons (see Table 2).
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Microsatellite sequence isolation from these 39 bovine BAC clones was performed according to standard procedures (VAIMAN et al. 1994). The protocol was slightly improved to increase the yield. Briefly, BAC clone DNA was extracted by mini-preparation in 96-well plates using a modified alkaline lysis procedure (SCHIBLER et al. 2004). Pools of three to four BAC DNA (300–500 ng each) were mixed, digested to completion with Sau3A (Promega, Madison, WI), and cloned in a dephosphorylated pGEM4Z vector (Promega). Sublibraries were then organized in 96-well plates and individual clones were spotted onto a 22 x 22-cm membrane at a medium density (Amersham, Arlington Heights, IL). Arrays were then screened using (TG)10 and (TC)10 32P-radiolabeled oligonucleotides and DNA of positive clones was extracted for sequencing with an ABI377 sequencer (ABI Prism) according to standard procedures. Of the 32 microsatellite sequences obtained, 16 either contained repetitive sequences or were too short to design suitable primers; they were thus discarded from further analysis (our unpublished data). PCR primers were designed for the 16 remaining and first tested to screen the BAC library (EGGEN et al. 2001) to identify or confirm the BAC clone(s) from which the microsatellite markers were isolated.
Three additional microsatellite markers (BZ840628, BES26_1, and CC471573) were produced in silico from BAC end sequences available in the public domain. The chosen BACs were selected according to the first draft of the physical map of the bovine genome (SCHIBLER et al. 2004) and their expected positions were checked before genotyping by radiation hybrid mapping on the BTA26 RH map using standard mapping procedures (GAUTIER et al. 2003).
Finally, 42 microsatellite markers were considered in this study (23 publicly available and 19 newly developed), of which 13 were not included in the analysis (see below). The remaining 29 markers are described in Table 2.
Genotyping procedure:
The genotyping procedure consisted of a multiplex fluorescent PCR amplification with one fluorescent end-labeled primer (MWG-Biotech). According to the fluorochrome dye and the PCR product length, the 38 microsatellites were assembled into five groups. For each group, one or two multiplex PCRs were performed and resulting PCR products pooled before migration. Multiplex PCR conditions were set up by adjusting the final concentration of marker primers after several successive testing experiments performed on calibrated bovine DNA (20 ng/µl) as template. PCR reactions were performed using the Multiplex PCR kit (QIAGEN, Valencia, CA) and according to the QIAGEN recommendations on a PTC-100 thermocycler (MJ Research, Watertown, MA) in a 10-µl final volume. Samples were preheated for 5 min at 94° and subjected to 35 cycles of 94° for 20 sec, 55° for 30 sec, and 72° for 30 sec and then to a final extension step of 5 min at 72°. During the setup process, PCR products were run on a 377 ABI sequencer and raw data were analyzed with the Genotyper software (ABI Prism). PCR products from further genotyping were first purified on Sephadex G50 before running on a MegaBACE 96 capillaries sequencer (Molecular Dynamics, Sunnyvale, CA). Raw data were then analyzed using Genetic Profiler v1.5 (Molecular Dynamics). Three markers, ARO25, INRA320 (AY609072), and INRA325 (AY609075), were discarded during the setting up of the multiplex PCR conditions for technical reasons (nonspecific cross products in PCR amplification). Final conditions for the multiplex genotyping of the 35 remaining markers are available upon request.
Linkage map construction:
Marker order and map distances were estimated using the CRIMAP 2.4 software (GREEN et al. 1990). First, the marker order was challenged against that of the comprehensive RH map of BTA26 (GAUTIER et al. 2003), using the FLIPS option with a five-marker window to obtain the most likely order given our data set. The CHROMPIC option was subsequently used to identify unlikely double crossovers, which were considered as missing genotypes (0.2% of the genotypes). Final map distances were computed on the basis of Haldane's mapping function.
QTL mapping statistical analysis:
LA:
LA was performed using a classical regression interval analysis (HALEY and KNOTT 1992), using the web-based version of the software QTL express (SEATON et al. 2002), with both a one-QTL and a two-QTL model computed every centimorgan along the chromosome. During this step, marker informativity at each position along the chromosome was also computed as the mean (1 – 2pij)2, where pij is the probability for son j of inheriting from sire i one arbitrarily defined chromosome segment at the position considered (SEATON et al. 2002). Analyzed data correspond for each trait of interest to twice the so-called DYD of the bulls (BOICHARD et al. 2003) weighted by their respective reliabilities. A 95% chromosomewise significance threshold was computed on the basis of 10,000 permutations and a confidence interval was estimated using the bootstrapping option on 10,000 iterations. The heterozygous status of the different sires was established on the basis of the t-test value at the position considered. To evaluate the status at positions other than the maximum peak one, a subset of the data set surrounding the position of interest was used.
The variance component-based LA was performed using a similar model to that detailed in the next section except that base haplotypes were considered unrelated (MEUWISSEN et al. 2002). The fraction of the total additive genetic variance explained by the QTL was estimated as 2
h2/(2h2 + u2), where h2 and u2 correspond, respectively, to the variance component associated with the haplotype effect and the additive polygenic effect (see below).
Combined linkage disequilibrium and linkage analysis:
Fine mapping of the QTL was performed using the same approach previously described (MEUWISSEN et al. 2002). Briefly, it consists of a variance component mapping method (HOESCHELE et al. 1997), which is extended to take into account information provided by residual LD in the population. The procedure consists of three successive steps:
u2 with A being the additive genetic relationship matrix based on the pedigree of the bulls, Var(h) = Gph2, and Var(e) = Re2, where R is a diagonal matrix with nj–1 on the diagonals (nj being the effective number of daughters of bull j). The variance components of the random effects u2, h2, and e2 and the likelihood Lp of the above model were estimated by the ASREML package (GILMOUR et al. 2000) at each position p. The log-likelihood ratio test LRT = –2(log(L0) – log(Lp)) is then computed, where L0 corresponds to the likelihood of the null hypothesis model that assumes Var(h) = 0. This test statistic is approximately chi-square distributed with 1 d.f. (OLSEN et al. 2004). As above, the fraction of the total genetic variance explained by the QTL was estimated as 2h2/(2h2 + u2), where h2 and u2 correspond, respectively, to the variance component associated with the haplotype effect and the additive polygenic effect.
A two-QTL model linkage disequilibrium and linkage analysis:
To confirm and test the presence of two QTL affecting the trait of interest, a two-QTL model was used to perform linkage disequilibrium and linkage analysis (LDLA). The records were thus modeled by: y = µ1 + Zq1h1 + Zq2h2 + Zau + e, where y, µ, u, e, and 1 are as defined above, and h1 and h2 are vectors of random QTL effects for the two brackets (1 and 2) analyzed simultaneously. Zq1 and Zq2 are the corresponding incidence matrices. As defined previously, Var(u) = Au2, Var(h1) = G1h12, Var(h2) = G2h22, and Var(e) = Re2. The variance components of the random effects u2, h12, h22, and e2 and the likelihood Lpq of the above model were estimated by the ASREML package (GILMOUR et al. 2000) at each P x (P – 1)/2 pair of positions p and q (P being the total number of brackets). Two kinds of log-likelihood-ratio tests were then computed:
The fraction of the total genetic variance explained by the QTL at position p was estimated as 2h12/(2h12 + 2h22 + u2), where h12, h22, and u2 correspond to the variance component associated with the two haplotype effects at marker bracket positions p and q and the additive polygenic effect, respectively. The heterozygous status of an individual at marker bracket position p was estimated by testing the difference between its paternal and maternal alleles. The test was thus performed using the two-effect estimates difference (Dh = hmat – hpat) and its prediction error variance [PEV = Cii + Cjj – 2Cij, where Ckl corresponds to the element (k, l) of the inverse of the mixed-model equation of the haplotype effect], each of the terms of the equation being provided by ASREML. Under the null hypothesis, the statistic N = Dh/PEV0.5 follows a T-distribution with n = npat + nmat – 2 d.f. (npat and nmat correspond to the number of times the paternally or maternally inherited haplotype is observed, respectively). For most sires (npat + nmat) is large enough to assume that N is approximately standard normally distributed under the null hypothesis.
Physical map construction:
As mentioned previously, all the microsatellite markers considered in this study were screened for in the INRA BAC library (EGGEN et al. 2001). Together with previous physical and comparative map information for BTA26 (GAUTIER et al. 2003; SCHIBLER et al. 2004), these data allowed us to anchor each marker into the INRA first-generation bovine physical map (http://locus.jouy.inra.fr/fpc/cattle). This map is further anchored on the international physical map, which serves as a basis for the genome sequencing consortium (http://www.bcgsc.ca/lab/mapping/bovine), using international BAC clones included in both maps and a precise estimate of the location of the INRA contig and of some of their markers on the international physical map. To refine and confirm the bovine-human comparative map, some of the bovine BAC end sequences (BES) from international (LARKIN et al. 2003) and INRA BAC clones (out of the 30,000 recently produced and submitted to GenBank) in the region were aligned against the HSA10 Build35 sequence assembly (http://genome.ucsc.edu/), using the BLAST alignment tool (ALTSCHUL et al. 1997). Repetitive sequences were masked and only those alignments producing hits >80 bp long with at least 85% of overall sequence similarity were considered as significant.
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RESULTS |
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TOPABSTRACTMATERIALS AND METHODSRESULTSDISCUSSIONACKNOWLEDGEMENTSLITERATURE CITED |
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Finally, 29 markers were considered to build the genetic map, displaying on average 645 informative meioses, from 72 for FASMC2 to 1300 for RME040 (Table 2). Respectively, 6 and 16 markers (Figure 1A, underlined ) were in common with the 8-marker IBRP97 map (BARENDSE et al. 1997) and the updated 63-marker MARC map (IHARA et al. 2004). Distances and orders were in perfect agreement especially when comparing with the high-density MARC map. In addition, as shown below, the order is supported by independent results from both RH and physical maps.
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70% of the total length of the chromosome. As shown in Figure 1B, the 29 microsatellite markers of the linkage map belong to 13 different INRA contigs (each containing from 1 to 5 markers). Further details for each of these contigs are accessible online (http://locus.jouy.inra.fr/fpc/cattle/). For the four contigs containing >2 markers, physical data confirm the linkage map marker order. Similarly, five (674, 4967, 676, 670, and 151), three (679, 1617, and 680), and two (153 and 684) consecutive INRA contigs are respectively anchored to contig 13420, contig 1154, and contig 10397 from the international physical map (http://www.bcgsc.ca/lab/mapping/bovine).
Integration of linkage and improved comparative map (Figure 1C):
Two blocks of conserved synteny between BTA26 and HSA10 have been previously described (GAUTIER et al. 2003). Inside each group, only a few discrepancies in the conservation of the gene order remained but they can be attributed to the resolution limit of the RH map (SCHIBLER et al. 2004). In this study, each of the 11 contigs from the integrated BTA26 linkage and physical map were precisely anchored to HSA10, using significant BLAST alignments of bovine BES (Figure 1C and http://locus.jouy.inra.fr/fpc/cattle/), consequently confirming previous results. Additionally, assignment of PRKG1 to BTA26 by linkage mapping of INRA310 allowed us to refine the boundary between the two blocks of conserved synteny to a region <100 kb formed by two overlapping BAC clones inside INRA contig 676 (Figure 1C). As a result, the integrated map strongly supports an overall gene content and order conservation inside each of the two blocks of conserved synteny between BTA26 and HSA10. This provides additional support for the marker order in our BTA26 linkage map and its coverage appears to provide a good marker density with no gap exceeding 6 Mb as estimated on the HSA10 genome map (Figure 1C).
QTL mapping results:
Linkage analysis:
Regression LA on the extended design confirmed the existence of the QTL affecting fat yield (P < 0.001) and protein yield (P < 0.001) (Figure 2). No significant QTL was detected for the three other milk production traits considered: milk yield, fat percentage, and protein percentage, with an F-value profile never >1.5 along the chromosome. According to the t-test performed at the peak position (14 cM on the map), seven sires (2010, 3517, 3518, 3538, 3539, 3542, and 3544) were heterozygous for the QTL affecting fat yield. The F-value profile for the protein yield trait appears very different, with a very flat peak (position 64 cM) at the end of the chromosome where the informativity is weaker due to a lower marker density. Five sires were found to be heterozygous, among which only three (3518, 3538, and 3539) are in common with the previous one. Although the 95% bootstrap confidence intervals are overlapping for the two traits (covering almost the entire chromosome for protein yield), our results suggest the segregation of different QTL affecting protein and fat yield. LDLA results confirmed this hypothesis (see below). We then focused on fat yield and its QTL in the first part of the chromosome where we developed and used a dense marker map. In the following, only fat yield results are presented.
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We thus performed a two-QTL regression analysis. The maximum F-value statistics for the models of two QTL vs. no QTL (42 and 1510 – 42 = 1478 d.f.) and two QTL vs. one QTL (42 and 21 d.f.) = 2.27 (P2vs.0 < 8.3 x 10–6) and 1.75 (P2vs.1 < 0.084), respectively. On the two-QTL regression model F-value contour plot curve along the two-dimensional map surface, the surface harboring an F-value superior to an arbitrarily chosen threshold 2.1 was found to cover regions from position 10 to 14 cM for the first fitted QTL (corresponding to the peak value under a one-QTL model) and 33–39 cM and 44–79 cM for the second fitted QTL (the highest F-value being for two QTL located at positions 15 and 68 cM). However, position results should be considered with care because of lack of informativity at the end of the chromosome (see above) and modest resolution of the regression model. Thus, the hypothesis of at least two segregating QTL seems likely.
A variance component LA using linkage information alone (i.e., using LD within the known pedigree) was also performed (Figure 3) and provided an estimate of the QTL effect considered here as the proportion of the total additive genetic variance explained. The curve was found to be slightly different since the two-peak shape was less clear than previously, the first peak being smaller than the second one. Nevertheless, this analysis seems also to agree with the hypothesis of the segregation of two QTL. The QTL variance as estimated over the entire chromosome was found to explain on average 12.8% (from 8.9 to 17.3) of the total additive genetic variance.
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To test this hypothesis of two segregating QTL, we performed a two-QTL model LDLA (Figure 4). The maximum LRT (LRT = 26.22) value in two dimensions was obtained for the position fitting haplotype effects for both the [BMS907/INRA311] and [BES26_1/HAUT27] brackets. As shown in Figure 3, the likelihood-ratio test of the LDLA model fitting two QTL (at positions [BMS907/INRA311] and [BES26_1/HAUT27]) against the one-QTL model including only the effect of the [BMS907/INRA311] haplotype is 7.28 (P < 0.007). Similarly, against the one-QTL model fitting only the effect of the [BES26_1/HAUT27] haplotype, the LRT = 7.68 (P < 0.006).
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Haplotype effect:
Following LDLA results, the [BMS907/INRA311] and the [BES26_1/HAUT27] marker brackets were found to explain, respectively, 1.8 and 2.5% of the total fat yield additive genetic variance under the two-QTL model. Under the one-QTL LDLA, these estimates were 4.34 and 2.50%, which might indicate the cosegregation of a haplotype carrying alleles of similar effect at the two QTL positions (see below). For the [BMS907/INRA311] and the [BES26_1/HAUT27] marker brackets, respectively, 442 and 247 different identity-by-descent haplotypes were found to segregate in the pedigree. The two corresponding distributions appear approximately bimodal (Figure 5), thus suggesting a biallelic effect for each of the QTL positions, with one favorable allele (increasing fat yield). However, for the [BMS907/INRA311] marker bracket effect distribution, 10 IBD haplotypes seem to have a clear decreasing effect. Some clustering procedures might help to confirm this trend.
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Haplotype analysis among the sires:
Interestingly, 5 sires (2010, 3518, 3538, 3542, and 3544) found to be heterozygous after regression LA (see above) at the two QTL positions share a common haplotype from marker NOR6 to RME040 (Figure 6). Among the 16 other sires, 8 (1200, 1351, 3519, 3532, 3534, 3539, 3545, and 3546) also share this haplotype. Nevertheless, 5 are homozygous at the two QTL positions (1351, 3532, 3534, 3545, and 3546), 2 (1200 and 3519) are heterozygous only at the second position, and 1 (3539) only at the first position according to the t-test (Figure 6). These sires may have inherited a favorable allele from their dam. For instance, sire 1351 has inherited at the first QTL position a haplotype identical by state to the haplotype of interest and containing in particular the allelic combination 1_2_1 (see above). However, some sires, such as 3532 and 3534, have inherited a maternal haplotype harboring an effect significantly different from the paternal one (respectively, P < 0.076 and P < 0.003) at the second position analyzed. Thus, their homozygous status declared after regression LA could originate from lack of power of the t-test (particularly because of their small family size). More generally, status at the two QTL positions appears to be broadly concordant from both regression LA and N-value test statistics. Differences may be attributed to a greater power of the LDLA (because the clustering process is equivalent to an increased family size). Moreover, their close location makes it difficult to discriminate status at the two QTL positions from the t-test derived from regression, whereas LDLA succeeds in a better disentangling of the two linked QTL by accounting for many more recombination events.
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A physical map of the two QTL:
As shown in Figure 1, we assigned the marker bracket [BMS907-INRA311] to the BAC contig 676. According to the anchorage of this contig on the HSA10 orthologous region and the overall conservation of the region (GAUTIER et al. 2003), the size of the interval was estimated to be 700 kb. This contig is also anchored to the international contig 13420, which has an estimated size of 3079 kb. On the basis of the relative positions of BACs mapped to both INRA and international contigs and flanking the marker of interest (see MATERIALS AND METHODS), the size of the bracket was found to be 250 kb on the bovine physical map.
Similarly, as shown in Figure 1, the INRA BAC contigs containing HAUT27 (680) and BES26_1 (1617) were anchored on the HSA10 orthologous region. They are also both integrated in the INRA contig 679 (containing BMS332) and the INRA contig 1909 anchored on a unique international contig (1730) with an estimated size of 1148 kb. According to the comparative map results, the marker bracket [BES26_1/HAUT27] was estimated to cover an orthologous region of <1 Mb on the HSA10 chromosome. As above, the size of the bracket was found to be 300 kb on the bovine physical map.
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DISCUSSION |
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TOPABSTRACTMATERIALS AND METHODSRESULTSDISCUSSIONACKNOWLEDGEMENTSLITERATURE CITED |
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We further focused mainly on the milk fat yield trait. To refine the position of the QTL and confirm the hypothesis of the segregation of two linked QTL, we used a combined LDLA approach that has already proved to be powerful by taking advantage of the structure of the livestock population (GRISART et al. 2002; MEUWISSEN et al. 2002; BLOTT et al. 2003). Two approaches (one-QTL and two-QTL models) were applied to our data set. First, the locations of the QTL affecting fat yield and the QTL affecting protein yield were clearly separated, disagreeing with the hypothesis of the existence of a QTL with a pleiotropic effect (BENNEWITZ et al. 2003). Second, the existence of two QTL affecting milk fat yield was confirmed by both the one-QTL and the two-QTL model LDLAs. The most likely location of these two QTL was refined to two marker brackets, [BMS907-INRA311] and [BES26_1/HAUT27], separated from each other by a distance of 20 cM. Their respective sizes were estimated at 700 and 300 kb. According to comparative map results, the physical distance separating these two QTL covers 29 Mb on the HSA10 conserved region.
Interestingly, among the sires of the granddaughter design, most of them also used as AI bulls, a long haplotype containing the two favorable alleles at the two QTL positions was found to be overrepresented. This might be the consequence of selection acting positively on this favorable combination. This is particularly notable when observing the offspring of sire 3538. Historically, this bull is one of the most-used AI bulls in France. Among its 10 closely related descendants (eight sons, 1351, 2010, 3517, 3518, 3519, 3532, 3533, and 3534; and two grandsons, 1200, a son of 2010, and 3546 from the maternal side) included as sires in the granddaughter design, 8 carry the full haplotype, 1 a recombinant haplotype, and only 1 the other haplotype. This also holds true when observing the two sons of sire 3539, which inherited the same favorable haplotype. The close relationship among most families is inherent to the granddaughter design since it uses populations from production farms (WELLER et al. 1990). As a result, a bias is introduced in the regression LA, which assumed all families to be independent. Nevertheless, this bias seems relatively weak since variance components LA, which take into account relationships among sires, gave similar results.
One of the main advantages of LDLA over LA is that it uses historical recombinations, the information of which is carried mainly by maternal haplotypes. Since only one generation of recombination is analyzed in LA and since the two QTL are 20 cM apart, <20% of the sons are expected to be recombinants between the two positions (and <4% double recombinants). This explains the lack of resolution of the two-QTL regression model even if the meiosis could be traced almost with certainty (in our case 15 markers are mapped between the two positions). This results in lack of precision when estimating the status of the sires at the two QTL positions. This tendency is more pronounced as the family size decreases, thus making discrimination of the effect of the two QTL more difficult. LDLA, even with a one-QTL model, appeared to be far less sensitive to the segregation of linked QTL because of the large number of different and informative maternal haplotypes. The estimation of the QTL effect (respectively 1.8 and 2.5% of the total genetic variance) was still slightly overestimated using a one-QTL model compared to the two-QTL model for LDLA.
Several genes involved in lipid metabolism or catabolism pathways are located on BTA26, as revealed by direct mapping or suggested by comparative mapping (GAUTIER et al. 2003). They all were a priori strong functional candidate genes. Among these, from the centromere to the telomere we can quote LIPF (gastric lipase), LIPA (lipase A, cholesterol esterase), SCD (stearyl co-A desaturase), or GPAM (glycerol-3-phosphate acyltransferase, mitochondrial). For the last three, microsatellite markers close to each of these genes (respectively, INRA318, INRA272, and RME011), were included and genotyped and thus could be excluded as positional candidate genes. In contrast, INRA311 is a microsatellite marker developed from a BAC containing the LIPF gene. Whereas no clear effect of any of the INRA311 alleles on fat yield was established (data not shown), this gene appears to represent a strong positional and functional candidate gene. One of its alleles, if associated to a putative mutation inside this gene, could be frequent in the population. This is suggested by the high population frequency of allele "2" belonging to the allelic combination associated with the favorable IBD haplotype at the marker bracket.
The most likely position of the second QTL was found to be located in the marker bracket [BES26_1/HAUT27]. The comparative map showed that the orthologous regions on HSA10, RNO01, and MMU19 were strongly conserved among the three species and measured 1 Mb. As revealed by human genome sequence data (http://genome.ucsc.edu/), this region contains only one characterized gene (namely SORCS1), which does not represent a clear functional candidate gene. However, in both mouse and rat orthologous regions, an additional gene is described, the insulin 1 precursor (INS1). This gene is a strong functional candidate gene since insulin is known to accelerate glycolysis and glycogen synthesis in liver and additionally to increase cell permeability to fatty acids. However, the presence of a similar functional short sequence mapping to the corresponding bovine genome orthologous region needs to be confirmed and potential bovine polymorphisms need to be analyzed.
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ACKNOWLEDGEMENTS |
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TOPABSTRACTMATERIALS AND METHODSRESULTSDISCUSSIONACKNOWLEDGEMENTSLITERATURE CITED |
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FOOTNOTES |
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LITERATURE CITED |
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TOPABSTRACTMATERIALS AND METHODSRESULTSDISCUSSIONACKNOWLEDGEMENTSLITERATURE CITED |
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