- Email: [email protected]
Conflicting candidates for cattle QTLs
Update
TRENDS in Genetics Vol.22 No.6 June 2006
8 Mitelman, F. et al. (2004) Fusion genes and rearranged genes as a linear function of chromosome aberrations in cancer. Nat. Genet. 36, 331–334 9 Sankoff, D. et al. (2002) Chromosomal distributions of breakpoints in cancer, infertility, and evolution. Theor. Popul. Biol. 61, 497–501 10 Schwartz, M. et al. (2006) The molecular basis of common and rare fragile sites. Cancer Lett. 232, 13–26 11 Samonte, R.V. and Eichler, E.E. (2002) Segmental duplications and the evolution of the primate genome. Nat. Rev. Genet. 3, 65–72 12 Armengol, L. et al. (2003) Enrichment of segmental duplications in regions of breaks of synteny between the human and mouse genomes suggest their involvement in evolutionary rearrangements. Hum. Mol. Genet. 12, 2201–2208 13 Warburton, P.E. (2004) Chromosomal dynamics of human neocentromere formation. Chromosome Res. 12, 617–626
301
14 Amor, D.J. and Choo, K.H. (2002) Neocentromeres: role in human disease, evolution, and centromere study. Am. J. Hum. Genet. 71, 695–714 15 Bourque, G. et al. (2005) Comparative architectures of mammalian and chicken genomes reveal highly variable rates of genomic rearrangements across different lineages. Genome Res. 15, 98–110 16 Froenicke, L. et al. (2006) Are molecular cytogenetics and bioinformatics suggesting contradictory models of ancestral mammalian genomes? Genome Res. 16, 306–310 17 Glover, T.W. (2006) Common fragile sites. Cancer Lett. 232, 4–12 18 Chowdhary, B.P. et al. (1998) Emerging patterns of comparative genome organization in some mammalian species as revealed by ZooFISH. Genome Res 8, 577–589
0168-9525/$ - see front matter Q 2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.tig.2006.04.002
Conflicting candidates for cattle QTLs Dirk-Jan de Koning Genetics and Genomics, The Roslin Institute, Roslin, Midlothian, UK, EH25 9PS
Genome scans have identified quantitative trait loci (QTLs) affecting milk yield and composition in dairy cattle. For one QTL on bovine chromosome 6 (BTA6), previously fine-mapped to a 420-Kb region, mutations in two different genes (OPN and ABCG2) have been proposed as the underlying functional mutation. Comparing the arguments for each gene suggests that both mutations are equally probable. However, functional studies and/or additional populations are required to provide a definite answer.
transgenic models to demonstrate the functionality of candidate mutations is not an option for most livestock species. Hence evidence for functional loci needs to be provided by combining multiple sources of evidence that individually would not be convincing but jointly point to a single candidate mutation [5]. For a QTL on bovine chromosome 6, which affects milk yield, similar lines of investigation have lead to distinct conclusions regarding the candidate genes (OPN and ABCG2) [1,6] and here I compare the evidence that has been put forward for both candidates.
Dissection of complex traits in livestock Most genetic research in humans focuses on the genetic aspects of well-being, whereas genetic research in livestock has been driven by an interest to increase the efficiency of food production with the aim to use functional genetic polymorphisms in marker assisted selection (MAS). A commonality between human and livestock genetic research is that the identification of point mutations underlying variation in complex traits remains a formidable task. This observation is underlined by the few functional mutations that have been identified compared with the many quantitative trait loci (QTLs) for complex traits that have been described in livestock and humans. In livestock, the successful identification of functional mutations underlying a QTL is limited to a few genes including insulin-like growth factor 2 (IGF2) in pigs (Sus scrofa) [2], Callipyge (CLPG) in sheep (Ovis aries) [3] and DGAT1 in cattle (Bos taurus), which encodes acyl-CoA diacylglycerol acyltransferase 1 [4]. Constructing
Dairy cattle QTL and chromosome 6 Although livestock offers the opportunity of controlled breeding, it is often too expensive and time-consuming, particularly in cattle, to breed experimental populations such as crosses between extreme lines. However, because of large-scale recording of traits and the extensive use of artificial insemination (AI) with carefully controlled
Corresponding author: de Koning, D.-J. ([email protected]).
www.sciencedirect.com
Glossary Linkage disequilibrium: describes a situation in which some combinations of alleles or genetic markers occur more or less frequently in a population than would be expected from a random formation of haplotypes from alleles based on their frequencies Marker assisted selection: describes the process where DNA polymorphisms are used to improve the prediction of genetic merit in animal and plant breeding. Quantitative trait locus: genetic loci or chromosomal regions that contribute to variability in complex quantitative traits, as identified by statistical analysis. Quantitative traits are typically affected by several genes and by the environment. Statistical power: a statistic that describes how effective a given experiment is to detect a certain effect. Statistical power is expressed as the proportion of tests that are expected to be significant given a certain experiment and a certain effect.
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Update
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pedigrees in dairy cattle breeding worldwide, QTL analysis can be performed using existing family structures within the population. The methods that have been employed for detection and fine-mapping of QTLs in dairy cattle (and other livestock) are described in Box 1. Box 1. Methods used in cattle QTL (fine) mapping Numerous statistical methods have been developed for QTL analysis in livestock species, each of which incorporate the basic components of pedigree, phenotype and genotype differently. Here I give an overview of the methods that have been applied to map QTL in dairy cattle populations. Each QTL mapping approach comes in many different ‘flavours’ and the aim of this list is to outline the principles underlying the methodology rather than provide a detailed description.
Half-sib analyses These approaches exploit the paternal half-sib structure that exists because of extensive use of AI. In so called ‘daughter designs’, marker genotypes are measured on AI bulls and large groups of their daughters, whereas production traits are measured on the daughters [18]. In ‘granddaughter designs’ the genotypes are gathered on older AI bulls and moderate groups of their sons (50–200), who are also AI bulls. The information on production traits is provided by potentially large number of daughters from the sons (100–10 000) [18]. These analyses only model a paternal QTL component and ignore information from the maternal alleles or any genetic links between half-sib families.
General pedigree linkage analysis Methods in this category use all genetic links that are provided in the pedigree and follow the principle that individuals that are identical by descent (IBD) for a relevant chromosome segment and will also be more similar for traits that are affected by that chromosome segment [19]. These methods are applicable to any pedigree with sufficient genetic links and are acclaimed to have greater statistical power and precision than family-based methods.
Combined linkage and linkage disequilibrium analysis (LDLA) Assuming that the QTL effect is a result of a mutation in a single ancestor of the current population, individuals that carry the mutation can be expected to share alleles identical by descent (IBD) at, and around, the mutation. The LDLA analysis follows the same principle as the general pedigree linkage analysis with the addition of IBD estimates between founder animals that are not related through the provided pedigree but are assumed to be related through a common, unknown, ancestor in whom the mutation arose [20]. These estimates are based on haplotypes in the founder animals of the experimental population and assumption about population history such as effective population size and time since mutation.
Haplotype analysis From the QTL analyses, the QTL genotypes of the (grand)parents of the QTL population can be inferred as heterozygous (Qq) and homozygous (QQ or qq). The holy grail of fine mapping is to find a haplotype or preferably a single mutation for which the genotypes in the QTL parents are consistent with the inferred QTL genotypes. This type of analysis was first demonstrated in the fine mapping of a QTL on BTA14 [21] and is often complementary to LDLA analysis.
Association study If the haplotype analysis is successful in identifying a conserved haplotype surrounding the functional mutation or proposing a candidate mutation, the effect of the haplotype or mutation can be evaluated at the population level without the need for extensive family structures. This type of analysis is also referred to as linkage disequilibrium mapping (LD mapping). www.sciencedirect.com
Bovine chromosome 6 (BTA6) has received considerable early interest from the breeding community because it harbours the casein locus, which contains four closely linked milk protein genes. Initial studies focused on associations between casein polymorphisms and production traits [7] and, subsequently, many QTL scans in dairy cattle started with BTA6 [8,9]. A recent review of QTL studies in dairy cattle confirmed that BTA6 is one of the most studied chromosomes and significant QTLs for milk production traits on this chromosome are found in several breeds [10]. Unlike BTA14, where many studies identified QTLs in the region of DGAT1 affecting mainly fat yield and fat percentage but also other milk production traits, on BTA6 there is evidence for multiple linked QTLs affecting milk production traits from analysis across [10] and within breeds [11]. One of these QTLs is located in the centre of the chromosome and seems to have its main effect on protein and fat content (%) of milk with a lesser effect on the yields (Kg) of fat and protein. Fine mapping of the QTL Using data on Norwegian dairy cattle, the QTL was fine mapped to an interval of 7.5 cM using combined linkage and linkage disequilibrium mapping (LDLA, Box 1) [12]. In a study of Israeli dairy cattle, a QTL with similar effects and location was mapped with a precision of 4 cM [11], which overlapped with that of the Norwegian study. Given the co-location of QTL and the similar effect on production traits, it could be postulated that both studies detected the same QTL. Subsequently, the Norwegian group increased the marker density in the QTL region by typing additional single nucleotide polymorphisms (SNPs) followed by LDLA analyses [13]. The haplotype analysis (Box 1) revealed six haplotypes that had a significant effect on the protein content of milk [13]. The most common of these haplotypes also had the most extreme effect. Further characterization of these haplotypes using sequence information, radiation hybrid mapping and comparative mapping with the human genome sequence resolved the QTL to a 420-Kb haplotype region (Figure 1) [13]. Using information from the comparative map with the human genome, only six known genes were identified within the QTL region (Figure 1) [14]. From haplotype to functional mutation On the basis of the 420-Kb interval and the assumption that the same functional mutation would be segregating in their own experimental population, two groups set out to identify the functional mutation underlying the QTL: Schnabel et al. [6] used USA dairy cattle, whereas CohenZinder et al. [1] used Israeli and USA dairy cattle. The overlap between the two studies with regard to the USA dairy population and the similar QTL effects and location strongly suggest that both studies are chasing the same QTL (see Table 1 for a comparison between the two studies). Both groups used sequence analysis to identify genetic variation within the critical region. The ‘holy grail’ of fine mapping is to find a haplotype or preferably a single mutation for which the genotypes in the QTL parents are consistent with the inferred QTL genotypes. Schnabel et al. singled out OPN, which encodes osteopontin, as the prime
Update
BTA6
TRENDS in Genetics Vol.22 No.6 June 2006
QTL region (x10)
ILSTS093
BMS3 INRA133 ILSTS090
FAM13A1
BM1329 BMS2508 . BM143 BMS382 BM3026 BMS1242 BMS690 OarJMF36 BL1099 BMS518 BM4322 BMS470 BMS483 BMS360 ILSTS097 BM4528 RM127 BM4621 RM028 BM415 1518
HERC3 HERC5 HERC6 PPM1K ABCG2 PKD2 SPP1(OPN) MEPE IBSP LAP3 MED28 KIAA1276 HCAP-G MLR1 BM143
ILSTS035 OarEL03 2220 CSN1S1 CSN3 ILSTS087 642 BMSB4049 ALBGC BM1236 BMS2460 BM4311 BMS511 ILSTS018 OarJMP4 AFR227 BP7 BM8124 ETH8 BMC4203 OarJMP8 BMS739 BM9257 BM9047 BM2320 BL1038 OarJMP12
TRENDS in Genetics
Figure 1. The linkage map of bovine chromosome 6 (BTA6, left) and a schematic overview of the genes in the QTL region (green region, right). The 420-Kb region is represented in red. Bovine linkage maps (MARC97 used in this figure) can be viewed at http://www.thearkdb.org/browser?speciesZcow. The maps were drawn www.sciencedirect.com
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candidate among the six genes in the 420-Kb region (Figure 1) and sequenced 12.3 Kb spanning the entire OPN gene in four heterozygous and four homozygous sires to detect SNPs that would be consistent with the QTL genotypes. By contrast, Cohen-Zinder et al. targeted parts of ten genes in and around the 420-Kb region (Figure 1), and sequenced two heterozygous sires to identify SNPs that were subsequently evaluated in a number of experimental populations (Table 1). Following these analyses, Schnabel et al. proposed that an indel (insertion and deletion) in the poly-T tract w1240 bp upstream of the OPN transcription initiation site was the causative polymorphism. The authors propose that the motif might represent a novel regulatory region. However, CohenZinder et al. proposed that a missense mutation (tyrosine to serine substitution) in the fifth extracellular region of the ABCG2 protein is the causal mutation underlying the QTL. OPN is a secreted glycoprotein involved in cell-matrix interactions, whereas ABCG2 is a half-transporter that has been shown to transport cytostatic drugs across the plasma membrane. The evidence that is presented to support the involvement of these mutations is remarkably similar. (i) Among all the SNPs that were evaluated, the genotypes of the putative mutation are in unique agreement with the inferred QTL genotypes of the (grand)parents of the halfsib families that were used in each study. (ii) The mutation has a significant association at the population level, which implies that the mutation either represents the functional mutation or is in strong linkage disequlibrium (LD) with the functional mutation. (iii) Including the mutation as a fixed effect in QTL or association analysis (Box 1), accounts for the entire QTL effect. (iv) OPN and ABCG2 are the only two genes (among the six in the 420-Kb region) that are differentially expressed between different stages of lactation in the bovine mammary gland [1]. (v) Both genes have been shown to have a role in mammary gland processes in mouse models. (vi) Both genes have a reasonable level of sequence (OPN) or amino acid (ABCG2) conservation among mammals in the region of the proposed functional mutation. Will the real functional mutation please stand up? The most notable shortcoming of both studies is that they are incomplete. The approach taken by Schnabel et al. could identify the causative mutation if the correct gene is chosen, whereas that taken by Cohen-Zinder et al. could lead to the identification the causative mutation, if it is a protein altering mutation but it might miss causal mutations in regulatory regions. Both studies leave a large area of the QTL interval unexplored. Schnabel et al. acknowledge that they looked at only a fraction of the 420Kb interval and estimate that the probability that there will be no other SNP with the same segregation pattern within the 420-Kb region is w0.40. This suggests that the probability of finding at least one additional SNP with the same segregation pattern within the 420-Kb interval would be w0.60, which is fairly high and a relevant discussion point. Cohen-Zinder et al. state that the using MapChart. The schematic overview of the QTL region was drawn using the results from Cohen-Zinder et al. [1].
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Table 1. Overview of two studies that proposed a mutation for a QTL on chromosome 6 Population
Evidence from mapping
Approach to detect functional mutation
Schnabel et al. [6] 3147 Holstein bulls divided over 45 half-sib families; 167 sires that represent the lines that are present in an American cattle DNA repository
QTL mapping (half-sib analysis, general pedigree linkage analysis and LDLA, Box 1) for five milk production traits using 38 markers and three different statistical methods resulted in consistent QTL near marker BM143; 95% confidence interval of 7.2 cM Complete sequencing of 12.3 Kb spanning OPN in four heterozygous bulls and four homozygous bulls; any candidate SNP should be concordant with inferred QTL genotype of eight sequenced sires
Cohen-Zinder et al. [1] Nine bulls from Israel with known QTL genotype (two heterozygous and seven homozygous); nine bulls from USA with known QTL genotype (one heterozygous and eight homozygous); 670 daughters of heterozygous sires (Israel); 411 Israeli bulls with progeny testing data QTL mapping (half-sib analyses and haplotype sharing, Box 1) for loci affecting five milk production traits described in previous papers, resulting in a confidence interval of 4 cM spanning the QTL region [11] Partial sequencing of ten known genes in and around the 420-Kb interval in two heterozygous sires for polymorphism detection (total of 31665 bp); subsequent evaluation of these polymorphisms in the various experimental populations ABCG2 ABCG2 is a half-transporter that has been shown to transport xenobiotics and cytostatic drugs across the plasma membrane; the authors propose that ABCG2 can transport cholesterol into milk Missense mutation: tyrosine to serine substitution in the fifth extracellular region of ABCG2
Proposed gene Function of proposed gene
OPN, SPP1, Eta-1 OPN is a secreted glycoprotein involved in cell matrix interactions and cellular signalling
Nature of mutation
Indel (OPN3907) in poly-T tract at w1240 bp upstream of the OPN transcription initiation site; possibly novel regulatory element? Strongest population-wide association among all Analysis of four OPN SNPs in 167 sires shows that only polymorphisms that were tested; Furthermore, all OPN3907 is significant; analysis of maternally inherited other associations were no longer significant when OPN3907 allele shows significant effect on fat and accounting for the ABCG2 effect; allele frequency over protein percentage; including OPN3907 genotype as a time is in agreement with changes in selection goals; separate effect accounts for all the variation due to the segregation status (QTL genotype) of 18 sires agrees QTL; probability that none of the undetected SNPs in with genotype of candidate mutation; probability of the 420-Kb interval will be concordant with segregation chance association !0.0001 status w0.40 OPN and ABCG2 are the only genes within the 420-Kb region that are differentially expressed in bovine mammary gland [1] Expression in murine mammary tissue is dependant on ABGC2 has demonstrated to be responsible for post-natal developmental stage; OPN is necessary for secreting important substrate into milk in a mouse normal mammary development (mouse model) model
Additional statistical evidence
Functional evidence from cattle studies Additional evidence from other studies
probability that the SNP and the QTL co-segregate by chance is 0.00008. However, this figure ignores the previous evidence for a QTL in this region and the level of LD that will be present within the 420-Kb region. Both analyses leave sufficient room for one or more, as yet undetected, SNPs in the 420-Kb region that might be in near-complete LD with either or both putative mutations. Arguments in favour of ABCG2 being the gene underlying the QTL are (i) the mutation had a significant effect in multiple sub samples of two populations; and (ii) the candidate mutation had functional implications. The case for OPN could have been stronger if the segregation status of the mutation in OPN in relation to the QTL genotype was confirmed by Schnabel et al. for all 45 bulls in the experimental population. The authors provide details of the segregation status of OPN3490 among all 45 bulls (demonstrating discrepancy between QTL genotype and SNP genotype) but curiously not for OPN3907. However, the results of Schabel et al. were available to Cohen-Zinder et al. and the latter acknowledge that they included sequence data on the proposed OPN effect. Because OPN was proposed first, this put the onus on Cohen-Zinder et al. to demonstrate that OPN was not the functional gene. However, they state that the region surrounding the OPN mutation is hyper variable with at least four SNPs in a 20-bp region surrounding the poly-T sequence, and that therefore this mutation cannot be the underlying www.sciencedirect.com
functional mutation. They could have demonstrated that the segregation of the OPN mutation is not concordant with the QTL genotypes in their population, but they do not make any specific statements about the segregation of OPN3907. Given the close links (as a result of AI) between Holstein cattle populations in the USA (and worldwide), it is surprising that Schabel et al. describe a limited number of polymorphisms in the eight US bulls they analysed, whereas Cohen-Zinder et al. report that the region is hypervariable in 18 Israeli and US bulls with known QTL genotype. This discrepancy between their results might result from the difficulty in obtaining a good sequence read of a region with an indel and the risk of finding spurious heterozygotes (R. Schnabel, personal communication). Currently, the arguments against OPN are not convincing and the failure to dismiss the OPN mutation suggests that, in the population evaluated by Cohen-Zinder et al., there might be perfect LD between the putative OPN allele and the ABCG2 allele. Therefore, based on the current, mostly statistical, evidence, the case for either OPN or ABCG2 has not been conclusively proved nor dismissed. Although both studies use the reasoning of MacKay [5] to make a joint case for their functional mutation, the statistical arguments are not all from ‘independent sources’ and the circumstantial evidence (expression in mammary tissue, conservation among mammals, possible function) is of more or less of equal merit for both studies.
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Concluding remarks Although both articles present a coherent case for a putative functional mutation underlying a QTL on BTA6, there is no clear ‘winner’. It seems likely that, in the populations that were studied, the two candidate mutations are in strong or even complete LD. It is possible that the QTL is due to a polymorphism that is yet to be detected. A third functional mutation on BTA6 (in PPARGC1A) [15] has been described, but it might underlie a different QTL than the one analyzed in these studies. Both studies depend heavily on the assumption that the 420-Kb interval that was described in Norwegian cattle [13] will harbour the causative mutation underlying their QTL, but widening the search beyond this interval should be considered until this can be corroborated in another population that is segregating for the same QTL. An important lesson from these studies is that one should not ignore the extensive disequilibrium in such populations. Both studies present high levels of LD that are characteristic for dairy cattle [16]. As a result, several candidate mutations can be in complete LD and hence have the same statistical evidence for being the functional mutation. For definite proof, functional evidence, such as an effect of the mutation on expression or activity of the locus and its product, should be provided [17], which has been acknowledged by both groups. Alternatively, one can search for populations where the candidate mutations are not in complete LD. Given the large number of QTL studies that included BTA6 [10], there is ample data to test both candidate mutations. The wide variety of breeds in which a QTL in this region has been described increases the opportunity to identify a population in which the two candidate loci are not in complete LD. Both groups have laid the ground work for future studies to determine whether OPN, ABCG2 or an, as yet, unknown mutation is responsible for the QTL effect.
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Acknowledgements I acknowledge support from the BBSRC. Chris Haley, Robert Schnabel and two anonymous referees are gratefully acknowledged for their comments on this article.
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References 1 Cohen-Zinder, M. et al. (2005) Identification of a missense mutation in the bovine ABCG2 gene with a major effect on the QTL on chromosome 6 affecting milk yield and composition in Holstein cattle. Genome Res. 15, 936–944 2 Van Laere, A.S. et al. (2003) A regulatory mutation in IGF2 causes a major QTL effect on muscle growth in the pig. Nature 425, 832–836 3 Freking, B.A. et al. (2002) Identification of the single base change
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causing the callipyge muscle hypertrophy phenotype, the only known example of polar overdominance in mammals. Genome Res. 12, 1496–1506 Grisart, B. et al. (2002) Positional candidate cloning of a QTL in dairy cattle: identification of a missense mutation in the bovine DGAT1 gene with major effect on milk yield and composition. Genome Res. 12, 222–231 Mackay, T.F. (2001) The genetic architecture of quantitative traits. Annu. Rev. Genet. 35, 303–339 Schnabel, R.D. et al. (2005) Fine-mapping milk production quantitative trait loci on BTA6: analysis of the bovine osteopontin gene. Proc. Natl. Acad. Sci. U. S. A. 102, 6896–6901 Bovenhuis, H. and Weller, J.I. (1994) Mapping and analysis of dairy cattle quantitative trait loci by maximum likelihood methodology using milk protein genes as genetic markers. Genetics 137, 267–280 Spelman, R.J. et al. (1996) Quantitative trait loci analysis for five milk production traits on chromosome six in the Dutch Holstein-Friesian population. Genetics 144, 1799–1808 Velmala, R.J. et al. (1999) A search for quantitative trait loci for milk production traits on chromosome 6 in Finnish Ayrshire cattle. Anim. Genet. 30, 136–143 Khatkar, M.S. et al. (2004) Quantitative trait loci mapping in dairy cattle: review and meta-analysis. Genet. Sel. Evol. 36, 163–190 Ron, M. et al. (2001) Multiple quantitative trait locus analysis of bovine chromosome 6 in the Israeli Holstein population by a daughter design. Genetics 159, 727–735 Olsen, H.G. et al. (2004) Fine mapping of milk production QTL on BTA6 by combined linkage and linkage disequilibrium analysis. J. Dairy Sci. 87, 690–698 Olsen, H.G. et al. (2005) Mapping of a milk production quantitative trait locus to a 420-kb region on bovine chromosome 6. Genetics 169, 275–283 Everts-van der Wind, A. et al. (2004) A 1463 gene cattle-human comparative map with anchor points defined by human genome sequence coordinates. Genome Res. 14, 1424–1437 Weikard, R. et al. (2005) The bovine PPARGC1A gene: molecular characterization and association of an SNP with variation of milk fat synthesis. Physiol. Genomics 21, 1–13 Farnir, F. et al. (2000) Extensive genome-wide linkage disequilibrium in cattle. Genome Res. 10, 220–227 Mehrabian, M. et al. (2005) Integrating genotypic and expression data in a segregating mouse population to identify 5-lipoxygenase as a susceptibility gene for obesity and bone traits. Nat. Genet. 37, 1224–1233 Weller, J.I. et al. (1990) Power of daughter and granddaughter designs for determining linkage between marker loci and quantitative trait loci in dairy cattle. J. Dairy Sci. 73, 2525–2537 Heath, S.C. (1997) Markov Chain Monte Carlo segregation and linkage analysis for oligogenic models. Am. J. Hum. Genet. 61, 748–760 Meuwissen, T.H. et al. (2002) Fine mapping of a quantitative trait locus for twinning rate using combined linkage and linkage disequilibrium mapping. Genetics 161, 373–379 Riquet, J. et al. (1999) Fine-mapping of quantitative trait loci by identity by descent in outbred populations: application to milk production in dairy cattle. Proc. Natl. Acad. Sci. U. S. A. 96, 9252–9257
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8 Mitelman, F. et al. (2004) Fusion genes and rearranged genes as a linear function of chromosome aberrations in cancer. Nat. Genet. 36, 331–334 9 Sankoff, D. et al. (2002) Chromosomal distributions of breakpoints in cancer, infertility, and evolution. Theor. Popul. Biol. 61, 497–501 10 Schwartz, M. et al. (2006) The molecular basis of common and rare fragile sites. Cancer Lett. 232, 13–26 11 Samonte, R.V. and Eichler, E.E. (2002) Segmental duplications and the evolution of the primate genome. Nat. Rev. Genet. 3, 65–72 12 Armengol, L. et al. (2003) Enrichment of segmental duplications in regions of breaks of synteny between the human and mouse genomes suggest their involvement in evolutionary rearrangements. Hum. Mol. Genet. 12, 2201–2208 13 Warburton, P.E. (2004) Chromosomal dynamics of human neocentromere formation. Chromosome Res. 12, 617–626
301
14 Amor, D.J. and Choo, K.H. (2002) Neocentromeres: role in human disease, evolution, and centromere study. Am. J. Hum. Genet. 71, 695–714 15 Bourque, G. et al. (2005) Comparative architectures of mammalian and chicken genomes reveal highly variable rates of genomic rearrangements across different lineages. Genome Res. 15, 98–110 16 Froenicke, L. et al. (2006) Are molecular cytogenetics and bioinformatics suggesting contradictory models of ancestral mammalian genomes? Genome Res. 16, 306–310 17 Glover, T.W. (2006) Common fragile sites. Cancer Lett. 232, 4–12 18 Chowdhary, B.P. et al. (1998) Emerging patterns of comparative genome organization in some mammalian species as revealed by ZooFISH. Genome Res 8, 577–589
0168-9525/$ - see front matter Q 2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.tig.2006.04.002
Conflicting candidates for cattle QTLs Dirk-Jan de Koning Genetics and Genomics, The Roslin Institute, Roslin, Midlothian, UK, EH25 9PS
Genome scans have identified quantitative trait loci (QTLs) affecting milk yield and composition in dairy cattle. For one QTL on bovine chromosome 6 (BTA6), previously fine-mapped to a 420-Kb region, mutations in two different genes (OPN and ABCG2) have been proposed as the underlying functional mutation. Comparing the arguments for each gene suggests that both mutations are equally probable. However, functional studies and/or additional populations are required to provide a definite answer.
transgenic models to demonstrate the functionality of candidate mutations is not an option for most livestock species. Hence evidence for functional loci needs to be provided by combining multiple sources of evidence that individually would not be convincing but jointly point to a single candidate mutation [5]. For a QTL on bovine chromosome 6, which affects milk yield, similar lines of investigation have lead to distinct conclusions regarding the candidate genes (OPN and ABCG2) [1,6] and here I compare the evidence that has been put forward for both candidates.
Dissection of complex traits in livestock Most genetic research in humans focuses on the genetic aspects of well-being, whereas genetic research in livestock has been driven by an interest to increase the efficiency of food production with the aim to use functional genetic polymorphisms in marker assisted selection (MAS). A commonality between human and livestock genetic research is that the identification of point mutations underlying variation in complex traits remains a formidable task. This observation is underlined by the few functional mutations that have been identified compared with the many quantitative trait loci (QTLs) for complex traits that have been described in livestock and humans. In livestock, the successful identification of functional mutations underlying a QTL is limited to a few genes including insulin-like growth factor 2 (IGF2) in pigs (Sus scrofa) [2], Callipyge (CLPG) in sheep (Ovis aries) [3] and DGAT1 in cattle (Bos taurus), which encodes acyl-CoA diacylglycerol acyltransferase 1 [4]. Constructing
Dairy cattle QTL and chromosome 6 Although livestock offers the opportunity of controlled breeding, it is often too expensive and time-consuming, particularly in cattle, to breed experimental populations such as crosses between extreme lines. However, because of large-scale recording of traits and the extensive use of artificial insemination (AI) with carefully controlled
Corresponding author: de Koning, D.-J. ([email protected]).
www.sciencedirect.com
Glossary Linkage disequilibrium: describes a situation in which some combinations of alleles or genetic markers occur more or less frequently in a population than would be expected from a random formation of haplotypes from alleles based on their frequencies Marker assisted selection: describes the process where DNA polymorphisms are used to improve the prediction of genetic merit in animal and plant breeding. Quantitative trait locus: genetic loci or chromosomal regions that contribute to variability in complex quantitative traits, as identified by statistical analysis. Quantitative traits are typically affected by several genes and by the environment. Statistical power: a statistic that describes how effective a given experiment is to detect a certain effect. Statistical power is expressed as the proportion of tests that are expected to be significant given a certain experiment and a certain effect.
302
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pedigrees in dairy cattle breeding worldwide, QTL analysis can be performed using existing family structures within the population. The methods that have been employed for detection and fine-mapping of QTLs in dairy cattle (and other livestock) are described in Box 1. Box 1. Methods used in cattle QTL (fine) mapping Numerous statistical methods have been developed for QTL analysis in livestock species, each of which incorporate the basic components of pedigree, phenotype and genotype differently. Here I give an overview of the methods that have been applied to map QTL in dairy cattle populations. Each QTL mapping approach comes in many different ‘flavours’ and the aim of this list is to outline the principles underlying the methodology rather than provide a detailed description.
Half-sib analyses These approaches exploit the paternal half-sib structure that exists because of extensive use of AI. In so called ‘daughter designs’, marker genotypes are measured on AI bulls and large groups of their daughters, whereas production traits are measured on the daughters [18]. In ‘granddaughter designs’ the genotypes are gathered on older AI bulls and moderate groups of their sons (50–200), who are also AI bulls. The information on production traits is provided by potentially large number of daughters from the sons (100–10 000) [18]. These analyses only model a paternal QTL component and ignore information from the maternal alleles or any genetic links between half-sib families.
General pedigree linkage analysis Methods in this category use all genetic links that are provided in the pedigree and follow the principle that individuals that are identical by descent (IBD) for a relevant chromosome segment and will also be more similar for traits that are affected by that chromosome segment [19]. These methods are applicable to any pedigree with sufficient genetic links and are acclaimed to have greater statistical power and precision than family-based methods.
Combined linkage and linkage disequilibrium analysis (LDLA) Assuming that the QTL effect is a result of a mutation in a single ancestor of the current population, individuals that carry the mutation can be expected to share alleles identical by descent (IBD) at, and around, the mutation. The LDLA analysis follows the same principle as the general pedigree linkage analysis with the addition of IBD estimates between founder animals that are not related through the provided pedigree but are assumed to be related through a common, unknown, ancestor in whom the mutation arose [20]. These estimates are based on haplotypes in the founder animals of the experimental population and assumption about population history such as effective population size and time since mutation.
Haplotype analysis From the QTL analyses, the QTL genotypes of the (grand)parents of the QTL population can be inferred as heterozygous (Qq) and homozygous (QQ or qq). The holy grail of fine mapping is to find a haplotype or preferably a single mutation for which the genotypes in the QTL parents are consistent with the inferred QTL genotypes. This type of analysis was first demonstrated in the fine mapping of a QTL on BTA14 [21] and is often complementary to LDLA analysis.
Association study If the haplotype analysis is successful in identifying a conserved haplotype surrounding the functional mutation or proposing a candidate mutation, the effect of the haplotype or mutation can be evaluated at the population level without the need for extensive family structures. This type of analysis is also referred to as linkage disequilibrium mapping (LD mapping). www.sciencedirect.com
Bovine chromosome 6 (BTA6) has received considerable early interest from the breeding community because it harbours the casein locus, which contains four closely linked milk protein genes. Initial studies focused on associations between casein polymorphisms and production traits [7] and, subsequently, many QTL scans in dairy cattle started with BTA6 [8,9]. A recent review of QTL studies in dairy cattle confirmed that BTA6 is one of the most studied chromosomes and significant QTLs for milk production traits on this chromosome are found in several breeds [10]. Unlike BTA14, where many studies identified QTLs in the region of DGAT1 affecting mainly fat yield and fat percentage but also other milk production traits, on BTA6 there is evidence for multiple linked QTLs affecting milk production traits from analysis across [10] and within breeds [11]. One of these QTLs is located in the centre of the chromosome and seems to have its main effect on protein and fat content (%) of milk with a lesser effect on the yields (Kg) of fat and protein. Fine mapping of the QTL Using data on Norwegian dairy cattle, the QTL was fine mapped to an interval of 7.5 cM using combined linkage and linkage disequilibrium mapping (LDLA, Box 1) [12]. In a study of Israeli dairy cattle, a QTL with similar effects and location was mapped with a precision of 4 cM [11], which overlapped with that of the Norwegian study. Given the co-location of QTL and the similar effect on production traits, it could be postulated that both studies detected the same QTL. Subsequently, the Norwegian group increased the marker density in the QTL region by typing additional single nucleotide polymorphisms (SNPs) followed by LDLA analyses [13]. The haplotype analysis (Box 1) revealed six haplotypes that had a significant effect on the protein content of milk [13]. The most common of these haplotypes also had the most extreme effect. Further characterization of these haplotypes using sequence information, radiation hybrid mapping and comparative mapping with the human genome sequence resolved the QTL to a 420-Kb haplotype region (Figure 1) [13]. Using information from the comparative map with the human genome, only six known genes were identified within the QTL region (Figure 1) [14]. From haplotype to functional mutation On the basis of the 420-Kb interval and the assumption that the same functional mutation would be segregating in their own experimental population, two groups set out to identify the functional mutation underlying the QTL: Schnabel et al. [6] used USA dairy cattle, whereas CohenZinder et al. [1] used Israeli and USA dairy cattle. The overlap between the two studies with regard to the USA dairy population and the similar QTL effects and location strongly suggest that both studies are chasing the same QTL (see Table 1 for a comparison between the two studies). Both groups used sequence analysis to identify genetic variation within the critical region. The ‘holy grail’ of fine mapping is to find a haplotype or preferably a single mutation for which the genotypes in the QTL parents are consistent with the inferred QTL genotypes. Schnabel et al. singled out OPN, which encodes osteopontin, as the prime
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QTL region (x10)
ILSTS093
BMS3 INRA133 ILSTS090
FAM13A1
BM1329 BMS2508 . BM143 BMS382 BM3026 BMS1242 BMS690 OarJMF36 BL1099 BMS518 BM4322 BMS470 BMS483 BMS360 ILSTS097 BM4528 RM127 BM4621 RM028 BM415 1518
HERC3 HERC5 HERC6 PPM1K ABCG2 PKD2 SPP1(OPN) MEPE IBSP LAP3 MED28 KIAA1276 HCAP-G MLR1 BM143
ILSTS035 OarEL03 2220 CSN1S1 CSN3 ILSTS087 642 BMSB4049 ALBGC BM1236 BMS2460 BM4311 BMS511 ILSTS018 OarJMP4 AFR227 BP7 BM8124 ETH8 BMC4203 OarJMP8 BMS739 BM9257 BM9047 BM2320 BL1038 OarJMP12
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Figure 1. The linkage map of bovine chromosome 6 (BTA6, left) and a schematic overview of the genes in the QTL region (green region, right). The 420-Kb region is represented in red. Bovine linkage maps (MARC97 used in this figure) can be viewed at http://www.thearkdb.org/browser?speciesZcow. The maps were drawn www.sciencedirect.com
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candidate among the six genes in the 420-Kb region (Figure 1) and sequenced 12.3 Kb spanning the entire OPN gene in four heterozygous and four homozygous sires to detect SNPs that would be consistent with the QTL genotypes. By contrast, Cohen-Zinder et al. targeted parts of ten genes in and around the 420-Kb region (Figure 1), and sequenced two heterozygous sires to identify SNPs that were subsequently evaluated in a number of experimental populations (Table 1). Following these analyses, Schnabel et al. proposed that an indel (insertion and deletion) in the poly-T tract w1240 bp upstream of the OPN transcription initiation site was the causative polymorphism. The authors propose that the motif might represent a novel regulatory region. However, CohenZinder et al. proposed that a missense mutation (tyrosine to serine substitution) in the fifth extracellular region of the ABCG2 protein is the causal mutation underlying the QTL. OPN is a secreted glycoprotein involved in cell-matrix interactions, whereas ABCG2 is a half-transporter that has been shown to transport cytostatic drugs across the plasma membrane. The evidence that is presented to support the involvement of these mutations is remarkably similar. (i) Among all the SNPs that were evaluated, the genotypes of the putative mutation are in unique agreement with the inferred QTL genotypes of the (grand)parents of the halfsib families that were used in each study. (ii) The mutation has a significant association at the population level, which implies that the mutation either represents the functional mutation or is in strong linkage disequlibrium (LD) with the functional mutation. (iii) Including the mutation as a fixed effect in QTL or association analysis (Box 1), accounts for the entire QTL effect. (iv) OPN and ABCG2 are the only two genes (among the six in the 420-Kb region) that are differentially expressed between different stages of lactation in the bovine mammary gland [1]. (v) Both genes have been shown to have a role in mammary gland processes in mouse models. (vi) Both genes have a reasonable level of sequence (OPN) or amino acid (ABCG2) conservation among mammals in the region of the proposed functional mutation. Will the real functional mutation please stand up? The most notable shortcoming of both studies is that they are incomplete. The approach taken by Schnabel et al. could identify the causative mutation if the correct gene is chosen, whereas that taken by Cohen-Zinder et al. could lead to the identification the causative mutation, if it is a protein altering mutation but it might miss causal mutations in regulatory regions. Both studies leave a large area of the QTL interval unexplored. Schnabel et al. acknowledge that they looked at only a fraction of the 420Kb interval and estimate that the probability that there will be no other SNP with the same segregation pattern within the 420-Kb region is w0.40. This suggests that the probability of finding at least one additional SNP with the same segregation pattern within the 420-Kb interval would be w0.60, which is fairly high and a relevant discussion point. Cohen-Zinder et al. state that the using MapChart. The schematic overview of the QTL region was drawn using the results from Cohen-Zinder et al. [1].
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Table 1. Overview of two studies that proposed a mutation for a QTL on chromosome 6 Population
Evidence from mapping
Approach to detect functional mutation
Schnabel et al. [6] 3147 Holstein bulls divided over 45 half-sib families; 167 sires that represent the lines that are present in an American cattle DNA repository
QTL mapping (half-sib analysis, general pedigree linkage analysis and LDLA, Box 1) for five milk production traits using 38 markers and three different statistical methods resulted in consistent QTL near marker BM143; 95% confidence interval of 7.2 cM Complete sequencing of 12.3 Kb spanning OPN in four heterozygous bulls and four homozygous bulls; any candidate SNP should be concordant with inferred QTL genotype of eight sequenced sires
Cohen-Zinder et al. [1] Nine bulls from Israel with known QTL genotype (two heterozygous and seven homozygous); nine bulls from USA with known QTL genotype (one heterozygous and eight homozygous); 670 daughters of heterozygous sires (Israel); 411 Israeli bulls with progeny testing data QTL mapping (half-sib analyses and haplotype sharing, Box 1) for loci affecting five milk production traits described in previous papers, resulting in a confidence interval of 4 cM spanning the QTL region [11] Partial sequencing of ten known genes in and around the 420-Kb interval in two heterozygous sires for polymorphism detection (total of 31665 bp); subsequent evaluation of these polymorphisms in the various experimental populations ABCG2 ABCG2 is a half-transporter that has been shown to transport xenobiotics and cytostatic drugs across the plasma membrane; the authors propose that ABCG2 can transport cholesterol into milk Missense mutation: tyrosine to serine substitution in the fifth extracellular region of ABCG2
Proposed gene Function of proposed gene
OPN, SPP1, Eta-1 OPN is a secreted glycoprotein involved in cell matrix interactions and cellular signalling
Nature of mutation
Indel (OPN3907) in poly-T tract at w1240 bp upstream of the OPN transcription initiation site; possibly novel regulatory element? Strongest population-wide association among all Analysis of four OPN SNPs in 167 sires shows that only polymorphisms that were tested; Furthermore, all OPN3907 is significant; analysis of maternally inherited other associations were no longer significant when OPN3907 allele shows significant effect on fat and accounting for the ABCG2 effect; allele frequency over protein percentage; including OPN3907 genotype as a time is in agreement with changes in selection goals; separate effect accounts for all the variation due to the segregation status (QTL genotype) of 18 sires agrees QTL; probability that none of the undetected SNPs in with genotype of candidate mutation; probability of the 420-Kb interval will be concordant with segregation chance association !0.0001 status w0.40 OPN and ABCG2 are the only genes within the 420-Kb region that are differentially expressed in bovine mammary gland [1] Expression in murine mammary tissue is dependant on ABGC2 has demonstrated to be responsible for post-natal developmental stage; OPN is necessary for secreting important substrate into milk in a mouse normal mammary development (mouse model) model
Additional statistical evidence
Functional evidence from cattle studies Additional evidence from other studies
probability that the SNP and the QTL co-segregate by chance is 0.00008. However, this figure ignores the previous evidence for a QTL in this region and the level of LD that will be present within the 420-Kb region. Both analyses leave sufficient room for one or more, as yet undetected, SNPs in the 420-Kb region that might be in near-complete LD with either or both putative mutations. Arguments in favour of ABCG2 being the gene underlying the QTL are (i) the mutation had a significant effect in multiple sub samples of two populations; and (ii) the candidate mutation had functional implications. The case for OPN could have been stronger if the segregation status of the mutation in OPN in relation to the QTL genotype was confirmed by Schnabel et al. for all 45 bulls in the experimental population. The authors provide details of the segregation status of OPN3490 among all 45 bulls (demonstrating discrepancy between QTL genotype and SNP genotype) but curiously not for OPN3907. However, the results of Schabel et al. were available to Cohen-Zinder et al. and the latter acknowledge that they included sequence data on the proposed OPN effect. Because OPN was proposed first, this put the onus on Cohen-Zinder et al. to demonstrate that OPN was not the functional gene. However, they state that the region surrounding the OPN mutation is hyper variable with at least four SNPs in a 20-bp region surrounding the poly-T sequence, and that therefore this mutation cannot be the underlying www.sciencedirect.com
functional mutation. They could have demonstrated that the segregation of the OPN mutation is not concordant with the QTL genotypes in their population, but they do not make any specific statements about the segregation of OPN3907. Given the close links (as a result of AI) between Holstein cattle populations in the USA (and worldwide), it is surprising that Schabel et al. describe a limited number of polymorphisms in the eight US bulls they analysed, whereas Cohen-Zinder et al. report that the region is hypervariable in 18 Israeli and US bulls with known QTL genotype. This discrepancy between their results might result from the difficulty in obtaining a good sequence read of a region with an indel and the risk of finding spurious heterozygotes (R. Schnabel, personal communication). Currently, the arguments against OPN are not convincing and the failure to dismiss the OPN mutation suggests that, in the population evaluated by Cohen-Zinder et al., there might be perfect LD between the putative OPN allele and the ABCG2 allele. Therefore, based on the current, mostly statistical, evidence, the case for either OPN or ABCG2 has not been conclusively proved nor dismissed. Although both studies use the reasoning of MacKay [5] to make a joint case for their functional mutation, the statistical arguments are not all from ‘independent sources’ and the circumstantial evidence (expression in mammary tissue, conservation among mammals, possible function) is of more or less of equal merit for both studies.
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Concluding remarks Although both articles present a coherent case for a putative functional mutation underlying a QTL on BTA6, there is no clear ‘winner’. It seems likely that, in the populations that were studied, the two candidate mutations are in strong or even complete LD. It is possible that the QTL is due to a polymorphism that is yet to be detected. A third functional mutation on BTA6 (in PPARGC1A) [15] has been described, but it might underlie a different QTL than the one analyzed in these studies. Both studies depend heavily on the assumption that the 420-Kb interval that was described in Norwegian cattle [13] will harbour the causative mutation underlying their QTL, but widening the search beyond this interval should be considered until this can be corroborated in another population that is segregating for the same QTL. An important lesson from these studies is that one should not ignore the extensive disequilibrium in such populations. Both studies present high levels of LD that are characteristic for dairy cattle [16]. As a result, several candidate mutations can be in complete LD and hence have the same statistical evidence for being the functional mutation. For definite proof, functional evidence, such as an effect of the mutation on expression or activity of the locus and its product, should be provided [17], which has been acknowledged by both groups. Alternatively, one can search for populations where the candidate mutations are not in complete LD. Given the large number of QTL studies that included BTA6 [10], there is ample data to test both candidate mutations. The wide variety of breeds in which a QTL in this region has been described increases the opportunity to identify a population in which the two candidate loci are not in complete LD. Both groups have laid the ground work for future studies to determine whether OPN, ABCG2 or an, as yet, unknown mutation is responsible for the QTL effect.
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Acknowledgements I acknowledge support from the BBSRC. Chris Haley, Robert Schnabel and two anonymous referees are gratefully acknowledged for their comments on this article.
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References 1 Cohen-Zinder, M. et al. (2005) Identification of a missense mutation in the bovine ABCG2 gene with a major effect on the QTL on chromosome 6 affecting milk yield and composition in Holstein cattle. Genome Res. 15, 936–944 2 Van Laere, A.S. et al. (2003) A regulatory mutation in IGF2 causes a major QTL effect on muscle growth in the pig. Nature 425, 832–836 3 Freking, B.A. et al. (2002) Identification of the single base change
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