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ANCILLARY SCIENTISTS SYMPOSIUM |
* Department of Microbiology & Molecular Genetics, Michigan State University, East Lansing 48824
2 Corresponding author: dodgson{at}msu.edu
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONTHE GOOD NEWS: SYMPOSIUM...NOT SUCH GOOD NEWS?WHAT NEXT?REFERENCES |
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Key Words: genome sequence • linkage disequilibrium • quantitative trait locus • single nucleotide polymorphism
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONTHE GOOD NEWS: SYMPOSIUM...NOT SUCH GOOD NEWS?WHAT NEXT?REFERENCES |
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THE GOOD NEWS: SYMPOSIUM PRESENTATIONS |
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TOPABSTRACTINTRODUCTIONTHE GOOD NEWS: SYMPOSIUM...NOT SUCH GOOD NEWS?WHAT NEXT?REFERENCES |
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The Model Chicken
The keynote speaker, Dave Burt, began the symposium by providing a comprehensive overview of the use of the chicken as a model organism for contemporary biological research. He gave an introduction to the recently completed second build of the chicken genome sequence and described a variety of bioinformatic tools, illustrating their use in extracting value from this sequence. Burt described the growing common ground between those who study the chicken as a biomedical model and those primarily interested in the chicken as an agricultural species (Burt, 2005). As examples, he described 2 biomedical applications in some detail: the identification of a GNB3 mutation as the likely candidate for an inherited retinal globe enlargement eye defect and the identification of KIAA0586 as the gene encoding the talpid developmental mutation. This led to the characterization of a novel protein that participates in the sonic hedgehog signaling pathway.
Transcriptional Profiling: Array of Hope
The first, and still the most widespread, approach to functional genomics has been transcriptional profiling: high throughput measurement of mRNA levels in various tissues at various developmental stages and in response to a variety of genetic and environmental manipulations. This approach was enabled by the early collection of cDNA sequences (reviewed in Hubbard et al., 2005) that could be used to manufacture various sorts of microarrays for the measurement of mRNA abundance, and thus it began prior to the acquisition of a full genome sequence. It remains to be seen to what extent mRNA levels will directly correlate with, and inform us about, the cellular, let alone the organismal, phenotype. However, microarray data provide an extremely powerful hypothesis-generator and a window into systems biology.
Parker Antin began this session and spoke of a different type of transcriptional profiling, using in situ hybridization rather than microarrays. Although the throughput of in situ hybridization is lower, it provides exceptional detail on the sites of gene expression, making it ideal for the analysis of early development, given the small size and anatomical complexity of the early embryo. In choosing among the 20,000+ chicken genes now available, Antin is focusing on those encoding transcription factors, growth factors, and receptors and noncoding RNA, in particular microRNA (miRNA), that are likely to play key roles in developmental regulation. He also has developed an online in situ hybridization reference database (GEISHA, http://geisha.arizona.edu/geisha/; Bell et al., 2004) that provides options to query and share data for those interested in unraveling the complexities of the chick embryo.
Tom Porter described the development and application of chicken microarray measurements for the chicken neuroendocrine system (Ellestad et al., 2006). The anatomical complexity and behavioral plasticity of the brain present unique challenges and provide exciting opportunities for future research. This provided another example where biomedical and agricultural interests overlap. In addition, other birds have been models for unique behaviors (song, sexual displays, etc.) and their associated hormonal changes, and chicken genomics has provided an excellent comparative model in support of such research. Porter also introduced the use of divergently selected broiler lines for growth and fat content with an examination of differences in pituitary gland gene expression between the high and low growth lines, the fat and lean lines, or both. Larry Cogburn also used these lines and similar arrays to examine differences in gene expression in the embryonic liver (Lagarrigue et al., 2006). His lab has focused on transcriptional changes at the embryo-hatchling transition, responses to fasting and refeeding, and the effects of acute hormone infusion. Array technology allows for the identification of candidate genes, differences in whose expression may control the selected phenotypic differences. As Cogburn described, the interpretation and application of microarray data require that one go beyond one-gene-at-a-time thinking and begin to assemble networks of interacting gene products that influence one another through a variety of biochemical feedback mechanisms. Cogburn’s results also pointed to a relationship between the lipid metabolism network and that of the inflammatory/innate immune system. The growth and metabolism theme continued in the presentation of Mark Richards, who spoke on the relationship between energy balance and feed intake/efficiency. He divided this between short-term mechanisms that primarily sense satiety and long-term mechanisms that sense energy balance. He focused on the role of ghrelin and obestatin, 2 peptides cleaved from the same precursor that, however, have opposing effects on feeding in the chicken. He also described the critical role of adenosine monophosphate-activated protein kinase in sensing adenosine triphosphate/adenosine monophosphate ratio and controlling metabolism. Because of their economic importance, food conversion and nutrition are probably better understood in the chicken than in humans, and thus, here again the chicken serves as a biomedical and agricultural model. Hyun Lillehoj changed the topic slightly to transcriptional profiling of immune system cells and the gut in relation to disease resistance, with a particular focus on coccidiosis. She used a macrophage array to analyze differences in cytokine and chemokine patterns in response to infection by 3 different Eimeria species, each of which has its own unique pathology. Her laboratory has also generated and used an intestinal tissue microarray to elucidate additional host response patterns to the parasite. This approach assumes that major changes in expression will identify those genes that are important in host-pathogen interactions. A major challenge has been the complexity of infection processes, especially in vivo. The genetic program differs not only from organ to organ, but from cell to cell depending on whether and when it has been infected and on endocrine and paracrine signals that may be received from neighboring infected cells.
QTL: Dissecting the Phenome
The next session in the symposium explored the detection and characterization of QTL, the genes encoding that part of the phenome of special interest to agriculture. The genome sequence aids this effort in 2 major ways. First, it generates mapping tools, in particular, a high density single nucleotide polymorphism (SNP) map, and these markers are then used to define (or refine) the QTL location. The International Chicken Polymorphism Map Consortium (2004) detected about 2.8 million SNP (
1 every 400 base pairs), and subsets of these are presently in use by several labs. Second, the sequence assists the process of attributing QTL to the causative DNA polymorphisms (termed quantitative trait nucleotides; QTN). The genes within the QTL interval become a limited set of "positional" candidate genes, and functional or comparative information about these genes can be used further to narrow the list of candidates to be examined for a putative QTN.
Sue Lamont began this session and continued the theme of disease resistance and host-pathogen interactions begun by Hyun Lillehoj. She pointed out that disease resistance is indeed a quantitative trait, although the statistical methodology of dealing with traits like survival can be challenging. She first described crosses between outbred broiler sires to highly inbred Leghorn, Spanish, or Fayoumi dams, an approach that allowed dissection of broiler QTL for Salmonella enteriditis vaccine response and bacterial burden in the F1 offspring. She then explained the value of additional generations of breeding that reduce the linkage disequilibrium (LD) in the population using the example of her own advanced intercross lines (F8 generation, broiler x Leghorn and broiler x Fayoumi; Deeb and Lamont, 2002). The high-density SNP map, referred to above, allows for a more rapid and more precise mapping of the QTL in question. Lamont’s lab is also applying microarray analysis to these populations, and she emphasized the need for integrating multiple approaches in the search for the genetic components of disease resistance.
Sam Aggrey switched the topic from disease-related to growth and body composition QTL, focusing on data derived from crossing the selected high and low growth lines discussed earlier by Porter and Cogburn. He showed that growth was best modeled as a 2-phase process in which early growth (ca. 0 to 2 wk) was treated separately from later growth (ca. 2 to 9 wk). Aggrey made a very important point in emphasizing that great caution should be exercised before inferring global QTL, those hypothesized to exist in 2 or more unrelated populations. He also echoed the theme of this session in noting that integration of QTL mapping and expression data can be a powerful approach. The next speaker, Dirk de Koning, expanded on that theme by introducing the area of genetical genomics (Jansen and Nap, 2001). Genetical genomics takes advantage of the fact that mRNA expression is itself a quantitative trait whose hereditary influences can be mapped like any other QTL. Relevant gene expression QTL (eQTL) can map "in cis" (at, or very close to, the gene whose expression is being considered, e.g., a promoter mutation) or "in trans" (distant from the gene under consideration, e.g., an alteration in a relevant transcription factor). As de Koning emphasized, if one looks only for expression changes in candidate genes that map within a classical QTL interval, one will miss a trans-acting gene, whereas such QTL are detected when treating expression itself as a quantitative trait. At present, the major limitations for genetical genomics (de Koning and Haley, 2005) are the cost and accuracy of transcriptional profiling and the need to control the error rate when making so many tests (e.g., correlating 10,000 gene expression measurements with segregation of 1,000 markers is the rough equivalent of testing 10 million hypotheses). de Koning concluded that most eQTL studies done to date are underpowered, and he offered suggestions for targeted eQTL mapping methods to reduce cost and lower the false discovery rate. He noted that the chicken provides an excellent target for eQTL studies and described his own pilot eQTL study and the candidate genes so far identified.
More "-Omics": Emerging Technologies and Relevance to Industry
One presumes that most, though certainly not all, polymorphic chicken phenotypes result, in some way, from altered proteins or altered protein expression levels (this does not imply that the causative genetic change is within the gene encoding those proteins, as noted above). Why not assay these proteins directly? This is the primary justification for proteomics, as discussed by Hsiao-Ching Liu. She described her use of mass spectrometry to catalog expressed proteins in a high throughput manner and, in particular, the response of protein expression profiles to infection with Marek’s disease virus (Liu et al., 2006). This technology requires a full genome sequence (or at minimum near-complete cDNA sequence collections) to predict the molecular weight of all possible peptide fragment patterns for comparison to the mass spectrometry data obtained. Liu described the nonredundant database of chicken proteins that she has generated from cDNA and genomic sequences and also her use of mass spectrometry to detect protein phosphorylation that occurs in response to viral infection. She also speculated that miRNA were playing an important role in chicken cells and reviewed her work in cloning and sequencing a large panel of chicken miRNA.
So, what good are all these "-omics"? Are they within reach of poultry breeders, and would the answers they give justify the investment they require? Dave Harry addressed the impact of the genome sequence on the industry. He noted that the 3- to 4-yr timeline to product that exists in the breeding industry requires that breeders think ahead in preparing for future opportunities and challenges. He also emphasized the extent to which the breeders must consider public opinion in planning for the future. Harry indicated that industry is already disseminating genome sequence-based information and making personnel changes to respond to new opportunities that may develop. He reviewed genetic tests already being used in the industry and emphasized the fact that at least some companies already have taken the leap into high throughput SNP genotyping. There also is considerable interest in genome-wide marker-assisted selection (discussed further below) within the breeding companies. The scale of industry investment in genomics to date is truly impressive. The extent to which transcriptional and proteomic profiles are of direct value to the industry remains to be determined. What is not in doubt, however, is that the chicken genome sequence and related technologies will surely infiltrate all aspects of chicken biology in the near future and thereby provide at least indirect benefits to all aspects of poultry production. As noted previously (Dodgson and Cheng, 1999), "it is as hard to imagine that genomics will fail to generate major advances in poultry science as it is to imagine that it will not have enormous consequences in medicine".
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NOT SUCH GOOD NEWS? |
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TOPABSTRACTINTRODUCTIONTHE GOOD NEWS: SYMPOSIUM...NOT SUCH GOOD NEWS?WHAT NEXT?REFERENCES |
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The genome sequence also allows us to appreciate (and be forewarned by) the remarkable genetic diversity of the chicken. Of course, the phenotypic diversity of so-called fancy breeds is well-known, as are the enormous phenotypic differences between broilers and layers (Delany, 2006). However, given their intensive selection over the last several decades, we might have expected commercial birds to have a rather narrow genetic base. In fact, the genetic diversity (SNP density) within broilers, especially, is only marginally less than that between all chickens from Red Jungle Fowl to Chinese Silkies (International Chicken Polymorphism Map Consortium, 2004). We have yet to fully appreciate the reasons for, or the implications of, this observation, but it suggests that modern commercial populations will exhibit a potentially bewildering array of allelic combinations and interactions—a major challenge to breeders and poultry biologists.
In sum, we appear to be entering a new era of chicken genetics. As children grow to realize that their parents are not the all-powerful, all-knowing entities they once believed, we are growing up and being forced to deal with real, rather than hypothetical, QTL. Both parents and QTL can be frightening in their complexity and variability. It may be inevitable, but it’s not always fun to grow up. In the future, molecular biologists will need to be mathematically sophisticated, and quantitative geneticists will have to understand and appreciate biochemical realities. Thankfully, I am too old to be retrained but young enough to still appreciate being along for at least the first part of this wild ride.
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WHAT NEXT? |
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TOPABSTRACTINTRODUCTIONTHE GOOD NEWS: SYMPOSIUM...NOT SUCH GOOD NEWS?WHAT NEXT?REFERENCES |
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One Sequence Is Not Enough
It’s increasingly clear that one sequence, even accompanied by extensive SNP typing of many lines and individuals, fails to reasonably encompass the genetic diversity of a species. Fortunately, "needs have a way of inducing their fulfillments" (Soller and Medjugorac, 1999), and new sequencing technologies are now being realized (e.g., Margulies et al., 2005) that should provide cost-effective means to sequence several selected broilers and layers. As the existing chicken genome sequence is refined to ever-higher accuracy and coverage (Warren et al., 2005), it will provide a template that allows cost-effective sequence acquisition and assembly of additional chicken genomes. If possible, it would be particularly useful to identify the major foundation stocks that have contributed to modern layer and broiler chickens and choose representatives of these lines for additional sequencing. Other than new mutations, one expects that the continued genetic progress that commercial breeders have demonstrated relies on obtaining better and better combinations of the genetic diversity already present in these foundation lines.
GMAS and Molecular Breeding Values
Genome-wide MAS (GMAS, Meuwissen et al., 2001) is the potential use of large genotype data sets (most likely SNP at sub-cM spacing) to generate a molecular breeding value on which to make selection decisions, once the correlation between desired phenotypes and genome-wide genotype is established. Although GMAS could be used as an adjunct to phenotypic selection, it is likely to be most powerful where phenotypic selection is impossible (egg traits in males) or expensive (disease resistance traits). Will GMAS ever become cost-effective, such that selection coefficients can be based heavily, if not solely, on high throughput multilocus genotypes? We now have exceeded the target cited by Soller and Medjugorac (1999)—"What this country needs is a good 5 cent data point!"—and will likely approach the 1 cent/genotype level in the not-too-distant future. At this rate, 10,000 SNP could provide 0.4-cM resolution coverage of the genome for $100/bird, less than the cost of some types of phenotype analysis (e.g., progeny testing), although still a substantial investment. Genome-wide MAS also requires effective training programs that generate the needed correlations between genotype and phenotype, as well as retraining at regular intervals to respond to fixation of major QTL alleles and possible new mutations. Working out if and how this can be done will require statistical prowess and a thorough understanding of the needs and contingencies of the breeding industry. Opportunities also will present themselves to use high throughput genomics data other than genotypes (transcriptional profiles, proteomic profiles, etc.) to generate molecular breeding values, as long as sufficiently high correlations exist between these molecular phenotypes and the commercially desirable outcomes. As noted in the symposium by Dave Harry, it remains to be seen to what extent this might become feasible.
Converting QTL to QTN
Understanding the molecular explanation for the link between genotype and phenotype is not necessarily required for breeding purposes, but it remains the Holy Grail for most animal geneticists. To date, most QTL have been mapped to an interval (10 cM) in pedigreed populations. However, going from 10 cM resolution (3 million base pairs) to a unique QTN (if one exists) only rarely has been possible. For example, Flint et al. (2005) estimated that less than 1% of mouse QTL had been converted to QTN despite the many advantages of working with this species. Although several reasons exist, perhaps the principal obstacle is the high cost/effort required to obtain enough meiotic recombinants to narrow the QTL interval and the fact that QTL of low effect will disappear (lose adequate statistical significance) during this process. Thus, there is great interest in (as well as some controversy about) the use of association mapping that essentially takes advantage of all the historical recombinants that have occurred in a population since the origin (presumably by mutation) of the QTN polymorphism in question and that therefore obviates the need to do any additional breeding. Among other things, association mapping requires that the QTN remain in LD with nearby polymorphisms that existed in the genome in which it arose. Thus, the level of LD in a population determines the power of association mapping. However, LD is a mixed blessing. Minimal LD implies that a very dense SNP map is required to identify the QTN-containing interval (and the subsequent problem of multiple hypothesis testing with such large data sets), whereas long LD segments (haplotypes) imply that the QTN-containing interval, once identified, will also be long, and it may be difficult (or even impossible) to distinguish which polymorphism(s) within the interval are causative.
A priori, it seemed likely that commercial chickens would exhibit rather long blocks of LD due to the recent development of separate meat-type and egg-type breeds and the intensive selection involved. Thus, one could hope that selective sweeps (LD blocks surrounding major QTN fixed by selection) would be identified, for example, by comparison of broiler and layer genomes. In fact, a surprisingly low level of LD has been detected in broilers, whereas much more LD (though still considerable genetic diversity) is found within commercial layers (International Chicken Polymorphism Map Consortium, 2004; Heifetz et al., 2005; H. Cheng, USDA-ARS Avian Disease and Oncology Laboratory, East Lansing, MI, personal communication). The full implications for QTN identification in chickens remain to be determined, but this suggests that QTN intervals will be hard to find but highly informative in broilers, although vice versa in layers.
As described elsewhere (Dodgson, 2003; Siegel et al., 2006), rapid progress in chicken genome mapping relied on a collaborative approach in which investigators worldwide shared 2 International Reference Mapping populations (Bumstead and Palyga, 1992; Crittenden et al., 1993). However, most chicken QTL research has been somewhat parochial, using matings of local lines or breeds. The mouse genetics community has begun to explore other options such as the Collaborative Cross (generation of 1,000 recombinant inbred lines from crosses of 8 parental strains; The Complex Trait Consortium, 2004) or focusing efforts on heterogeneous stocks derived from intercrosses among a limited number of known ancestral inbred strains (Flint et al., 2005). Might such approaches be warranted in chickens? Might there already be existing experimental or commercial lines derived from a diverse, but limited, number of still-existing inbred (or at least closed) lines? If so, high density SNP analysis (or even draft genome sequences as suggested above) of the parental lines might allow for fine structure QTL mapping by association in the heterogeneous stock in question. Unfortunately, the high cost of maintaining bird populations in adequate numbers and the limited resources generally available make such collaborative approaches toward QTN identification difficult, at best.
The Transgenic Bird
The painfully slow progress in development of a cost-effective and routine method with which to generate transgenic chickens has been discussed previously (Dodgson et al., 1997; Dodgson, 2003; Siegel et al., 2006). This technology is critical to the verification of putative QTN. Even the most powerful QTN mapping approaches will likely generate an interval containing several polymorphisms, among which the causative one(s) must be distinguished. Transgenic manipulation is the most obvious way in which to do this. In addition, valuable QTN will usually be identified in lines that no longer are at the cutting edge of commercial viability. Introgression of such QTN by standard breeding will be difficult, if not unrealistic. Introgression by transgenic techniques, were they efficient and reliable, might be done more quickly. Recent progress (van de Lavoir et al., 2006) suggests that genetic modification of primordial germ cells followed by generation of primordial germ cell chimeras may be more effective than the many other proven, but costly, transgenic approaches used to date. Advances continue to be made in the use of viral vectors, as well (Mozdziak et al., 2003; McGrew et al., 2004; Chapman et al., 2005). Despite a history of shattered dreams and failed promises, perhaps there really is light at the end of the transgenic chicken tunnel.
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ACKNOWLEDGMENTS |
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FOOTNOTES |
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Received for publication February 6, 2007. Accepted for publication February 10, 2007.
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REFERENCES |
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TOPABSTRACTINTRODUCTIONTHE GOOD NEWS: SYMPOSIUM...NOT SUCH GOOD NEWS?WHAT NEXT?REFERENCES |
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