The Genom of Homo sapiens.pdf

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GENETICS OF COMMON DISEASES 399SELECTING AND EVALUATINGTAGGING SNPSA typical way to currently apply tSNPs is to definethem on the basis of incomplete genotype data (perhaps 1SNP every 5 kb or so) available in a relatively small populationsample (up to about 60 unrelated individuals, ethnicallymatched to case/control cohort), and then to applythem in usually much larger phenotyped populations. Increasingthe number of tags increases performance, butalso increases expense. A consensus seems to be emergingthat the coefficient of determination should be at least0.80, which means that the increased sample size isn/0.85, in comparison with exhaustive typing. It is a strikingdemonstration of how fast the field has advanced tonote that only 4 years ago a coefficient of determinationof 0.2 was seen as a reasonable goal (Kruglyak 1999). Itis necessary, however, to test whether the selected tSNPswill (1) represent other SNPs not yet known and (2) tag asefficiently in a new sample of individuals from the samepopulation.To address the first point, we introduced a SNP droppingprocedure (Goldstein et al. 2003; Weale et al. 2003).The basic approach is to take the set of known SNPs andfor each SNP i drop it from the analysis in turn. For eachreduced set of N–1 SNPs, new tags are selected, and theirability to represent (predict) the dropped SNP i is assessed.In this way, a statistical estimate is obtained ofhow well the tSNPs can represent SNPs that are not observed(for example, SNPs that are not yet discovered) inthe region.Goldstein et al. (2003) carried out analysis along theselines on the Gabriel et al. data set with SNP densities ofup to 4 SNPs per 10 kb using SNPs with a minor allelefrequency above 8%. Averaged over the regions considered,we found that performance increased up to a SNPdensity of 1.5 SNPs per 10 kb, after which no further improvementwas noted (Goldstein et al. 2003). We estimatedusing this preliminary evidence that an averagemarker density of somewhere between 1 and 2 SNPs per10 kb would be sufficient to identify tSNPs that capturemost of the common allelic variation in the humangenome (Goldstein et al. 2003).The regions Goldstein et al. (2003) studied from theoriginal Gabriel et al. (2002) paper, however, did notcover a sufficiently broad range of densities, and the averageSNP density was 1 SNP approximately every 7 kb.The question still remains, therefore, of how tSNP performancechanges as density of the original genotype dataset increases to densities higher than 1 every 7 kb. A directway to address this is by assessing the performanceof tSNPs in data sets with manually adjusted densities,similar to the way in which Ke et al. (2004) have approachedthe question of block boundary identifications.If the performance of tSNPs improves (for example, assessedby SNP dropping and testing in independent samples,as described below) only modestly when densitiesare adjusted from 1 SNP every 5 kb to higher densities, asis implied by the asymptotic performance for the lessdenseregions studied in Goldstein et al. (2003), thiswould imply that higher densities may not be justified formost genomic regions. These experiments, however,have not yet been reported, although the HapMap projectwill shortly make available data sets ideal for this purpose:that is, data sets in which all SNPs have been genotyped(International HapMap Consortium 2003).One shortcoming of our original implementation of theSNP dropping procedure was that it ignored LD whichmay have been generated by the sampling procedure itself.More recently, we have carried out a similar experimentbut extended it by selecting the tSNPs in one populationsample, in which SNP i has been dropped, but thenevaluating the performance of the tSNP set in predictingthe state of SNP i in a second, independent sampled populationof the same size (K.R. Ahmadi et al., unpubl.).This approach more closely mimics the real situation. Wefound that the results of this experiment and the earlierversion of the SNP dropping procedure are similar forSNPs with MAF greater than about 6% and a sample sizeof 32 individuals. For SNPs with lower MAF, however,the performance of the tSNPs in one sample is not a goodguide to their performance in a new sample (K.R. Ahmadiet al., unpubl.).Some critics of haplotype mapping have argued thatrare variants may be the main genetic factor influencingdisease, and that these will be difficult to document usinghaplotype mapping. This raises the question of how welltags can capture rare variation. Most analyses to datehave simply discarded SNPs with low MAF. Althoughrare alleles are often young, and thus resident on relativelylong haplotypes, this does not mean that a set oftSNPs provides high power of detection when used in theordinary way. Although we consider it well establishedthat tSNPs can be used to adequately represent commonvariation, we share the concerns of some critics that it isunclear how well rare variants can be represented, althoughadjustments in tSNP selection and use may help.Considerably more work will be required to resolve theissues of how well SNPs with low MAF can be representedby tSNPs.Finally, although tagging rare variants may be possible inone population, the tSNP sets that tag such variants are expectedto behave largely as private rather than cosmopolitan,and therefore, tagging rare variation in multiple populationsof close ancestry may not be possible, with eachpopulation requiring a unique set of tSNPs (see below).GENOME-WIDE TAGA map-based approach incorporating tags can be appliedeither at a local (tagging variation across a gene/region) or genomic (tagging variation across the entiregenome) level. Various estimates of the number of tagsrequired to cover the genome have been made (Judson etal. 2002). More recently, using the same data set, Gabrielet al. (2002) and Goldstein et al. (2003) reached differentconclusions regarding the number of tSNPs required totag the human genome; Gabriel et al. predicted that approximately300,000 are needed and Goldstein et al. predictedthat only about 170,000 tags are sufficient for tagginga European population sample.
400 GOLDSTEIN, CAVALLERI, AND AHMADIAt least part of the discrepancy between the results isdue to the way in which blocks were used in the identificationof the tSNPs. Gabriel et al. estimated the totalnumber of common haplotypes residing in blocks of highLD using their 51 regions and extrapolated the resultsacross the genome, then calculated the total number oftags needed to tag these block-derived haplotypes usingthe diversity-based haplotype-tagging method (D. ClaytonWeb site: http://www-gene.cimr.cam.ac.uk/clayton/).Goldstein et al., however, chose to ignore the underlyingblock structure of LD and selected tSNPs across each ofthe entire regions they studied, thus capitalizing on longrangeassociations that would be ignored in the blockbasedidentification of tags. They also used the haplotyper 2 criterion (Weale et al. 2003), which appears to increasetagging efficiency (see above). It would seem likely thatmuch of the difference between the two estimates has todo with whether tSNPs are selected within blocks, butmore detailed comparisons of different methods of selectingtSNPs are still required.COSMOPOLITAN TAGSA major goal of the HapMap project is to provide themeans by which a minimum set of tSNPs can be ascertainedand used in future association studies. However, thequestion remains as to how well tSNPs from one populationcan represent variation in others. It is already clear thatselecting tags in a population from one geographic region(e.g., Europe) and applying them to another (e.g., EastAsia) can result in a substantial decrease in performance(Weale et al. 2003). It is possible, however, that tSNPs canbe selected which are specifically designed to be cosmopolitanwithout requiring nearly as many tSNPs as thesum of those required for each constituent population.For example, a recent study of 105 genes identified thehaplotype-tagging SNPs (htSNPs) necessary to representall the observed haplotypes in population samples fromAfrica and Europe (Sebastiani et al. 2003). They foundthat the htSNPs identified in Europe were largely a subsetof those identified in Africa—a total of 538 and 379htSNPs were found to represent all the observed haplotypesin the African and European samples, respectively,and 360 of the European htSNPs (95%) were also observedin the Africans. Recent analyses from our lab alsoshow that only a 10–15% increase in the number of tSNPsfrom a northern European population (CEPH) is sufficientto tag the common genetic variation in the Japanese(K.R. Ahmadi et al., unpubl.). Overall, these results indicatethat although the patterns and magnitude of LD canbe markedly different across diverse populations, largelydue to population history, it may be possible to identify aset of tags that work adequately in multiple human populationgroups without excessive increases in the numberof markers that must be typed.CONCLUSIONSMap-based studies incorporating tagging offer an economical,yet powerful, tool to identify common variationcontributing to complex disease. With the inevitable publicationof dense SNP maps covering large genomic regions,typed in large cohorts from different global populations,tagging SNP design will become more refined asthe issues raised here and elsewhere are addressed withempirical data. Although considerable work remains tobe done to optimize the selection and use of tSNPs, weexpect relatively efficient and standardized approaches tobecome available soon. We are therefore already enteringan era in which the constraints for genetic associationstudies relate to the phenotype. The complexity of the geneticcontrol of common disease and responses to theirtreatment means that genetic association studies will needto be carried out in very large population cohorts with detailedinformation not only about disease outcome, butalso about intermediate phenotypes.REFERENCESBotstein D. and Risch N. 2003. Discovering genotypes underlyinghuman phenotypes: Past successes for Mendelian disease,future approaches for complex disease. Nat. Genet. (suppl.)33: 228.Chakravarti A., Buetow K.H., Antonarakis S.E., Waber P.G.,Boehm C.D., and Kazazian H.H. 1984. Nonuniform recombinationwithin the human β-globin gene cluster. Am. J. Hum.Genet. 36: 1239.Chapman J.M., Cooper J.D., Todd J.A., and Clayton D.G. 2003.Detecting disease associations due to linkage disequilibrium:A class of tests and the determinants of statistical power.Hum. Hered. 56: 18.Daly M.J., Rioux J.D., Schaffner S.F., Hudson T.J., and LanderE.S. 2001. High-resolution haplotype structure in the humangenome. Nat. Genet. 29: 229.Dean M., Carrington M., Winkler C., Huttley G.A., Smith M.W.,Allikmets R., Goedert J.J., Buchbinder S.P., Vittinghoff E.,Gompert E., Donfield S., Vlahov D., Kaslow R., Saah A, RinaldoC., Detels R., and O’Brien S.J. 1996. Genetic restrictionof HIV-1 infection and progresssion to AIDS by a deletionallele of the CKR5 structural gene Hemophilia Growth andDevelopment Study, Multicenter AIDS Cohort Study, MulticenterHemophila Cohort Study, San Francisco City Cohort,ALIVE Study (erraturm in Science [1996] 274: 1069). Science273: 1856.Gabriel S.B., Schaffner S.F., Nguyen H., Moore J.M., Roy J.,Blumenstiel B., Higgins J., DeFelice M., Lochner A., FaggartM., Liu-Cordero S.N., Rotimi C., Adeyemo A., Cooper R.,Ward R., Lander E.S., Daly M.J., and Altshuler D. 2002. Thestructure of haplotype blocks in the human genome. Science296: 2225.Glazier A.M., Nadeau J.H., and Aitman T.J. 2002. Finding genesthat underlie complex traits. Science 298: 2345.Goldstein D.B., Ahmadi K.R., Weale M.E., and Wood N.W.2003. Genome scans and candidate gene approaches in thestudy of common diseases and variable drug responses.Trends Genet. 19: 615.Hirschhorn J.N., Lohmueller K., Byrne E., and Hirschhorn K.2002. A comprehensive review of genetic association studies.Genet. Med. 4: 45.International HapMap Consortium. 2003. The InternationalHapMap Project. Nature 426: 789.Jeffreys A.J., Kauppi L., and Neumann R. 2001. Intensely punctatemeiotic recombination in the class II region of the majorhistocompatibility complex. Nat. Genet. 29: 217.Jeffreys A.J., Ritchie A., and Neumann R. 2000. High resolutionanalysis of haplotype diversity and meiotic crossover in thehuman TAP2 recombination hotspot. Hum. Mol. Genet. 9:725.Johnson G.C., Esposito L., Barratt B.J., Smith A.N., Heward J.,Di Genova G., Ueda H., Cordell H.J., Eaves I.A., Dudbridge
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ForewordIn 2001, as we considered t
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The Finished Genome Sequence of Hom
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4 ROGERSFigure 2. Accumulation of h
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6 ROGERSFigure 4. Sequencing center
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8 ROGERSabcFigure 5. Ensembl view o
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10 ROGERS2000. Analysis of vertebra
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The Human Genome: Genes, Pseudogene
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VARIATION ON CHROMOSOME 7 15rived f
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VARIATION ON CHROMOSOME 7 17DNAs an
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VARIATION ON CHROMOSOME 7 19expecte
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VARIATION ON CHROMOSOME 7 21Drosoph
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Mutational Profiling in the Human G
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HUMAN MUTATIONAL PROFILING 25Anothe
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HUMAN MUTATIONAL PROFILING 27Figure
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HUMAN MUTATIONAL PROFILING 29Rieder
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32 SCHMUTZ ET AL.algorithm itself,
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34 SCHMUTZ ET AL.Figure 2. Genomic
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36 SCHMUTZ ET AL.compared. Some of
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Human Subtelomeric DNAH. RIETHMAN,
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HUMAN SUBTELOMERIC SEQUENCES 41The
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HUMAN SUBTELOMERIC SEQUENCES 43cate
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HUMAN SUBTELOMERIC SEQUENCES 45Figu
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HUMAN SUBTELOMERIC SEQUENCES 47pres
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50 COLLINSand expand the genomics r
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52 COLLINSFigure 2. A public-sector
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54 COLLINSdefine all the parts of t
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56 BENTLEYmon over many generations
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58 BENTLEYTable 1. Genetic Disease
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60 BENTLEY(Clark et al. 1998; Reich
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62 BENTLEYACKNOWLEDGMENTSThe author
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SNP Genotyping and Molecular Haplot
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GENETIC ANALYSIS OF DNA POOLS 67gen
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70 FAN ET AL.matrix is then mated t
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72 FAN ET AL.Figure 3. Views of gen
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74 FAN ET AL.including 32 duplicate
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76 FAN ET AL.Figure 7. Allele-speci
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78 FAN ET AL.microsphere-based assa
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80 BERTRANPETIT ET AL.function, may
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82 BERTRANPETIT ET AL.diversity in
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84 BERTRANPETIT ET AL.gree of block
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86 BERTRANPETIT ET AL.Figure 1. Dec
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88 BERTRANPETIT ET AL.1999. Populat
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90 WINDEMUTH ET AL.Expression data.
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92 WINDEMUTH ET AL.Table 1. A Summa
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94 WINDEMUTH ET AL.Table 2. Signifi
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96 WINDEMUTH ET AL.Table 3. Summary
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98 WINDEMUTH ET AL.Table 6. List of
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100 WINDEMUTH ET AL.Table 6. (Conti
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102 WINDEMUTH ET AL.Table 6. (Conti
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104 WINDEMUTH ET AL.much of a surpr
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106 WINDEMUTH ET AL.Given our resul
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Genetic Variation and the Control o
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GENETIC CONTROL OF TRANSCRIPTION 11
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GENETIC CONTROL OF TRANSCRIPTION 11
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Genome-wide Detection and Analysis
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RECENT SEGMENTAL DUPLICATIONS 117Fi
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RECENT SEGMENTAL DUPLICATIONS 119St
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RECENT SEGMENTAL DUPLICATIONS 121Co
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RECENT SEGMENTAL DUPLICATIONS 123Ho
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The Effects of Evolutionary Distanc
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EVOLUTIONARY DISTANCE AND GENE PRED
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EVOLUTIONARY DISTANCE AND GENE PRED
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Lineage-specific Expansion of KRAB
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EVOLUTION OF ZNF GENES 133Figure 2.
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EVOLUTION OF ZNF GENES 135Figure 4.
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EVOLUTION OF ZNF GENES 137get gene,
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EVOLUTION OF ZNF GENES 139Y., Goodw
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Sequence Organization and Functiona
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CENTROMERE ANNOTATION 143THE CENTRO
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CENTROMERE ANNOTATION 145Figure 4.
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CENTROMERE ANNOTATION 147CONCLUSION
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CENTROMERE ANNOTATION 149Schueler M
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152 PARKHILL AND THOMSONFigure 1. T
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154 PARKHILL AND THOMSONshow very h
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156 PARKHILL AND THOMSONGene Loss a
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158 PARKHILL AND THOMSONYersinia ad
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160 MCKAY ET AL.Choosing Candidate
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162 MCKAY ET AL.new comparative too
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164 MCKAY ET AL.rich. Based on a th
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166 MCKAY ET AL.Embryonic Muscle an
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168 MCKAY ET AL.native polyadenylat
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Building Comparative Maps Using 1.5
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HUMAN CHROMOSOME 1p IN THE DOG 1731
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HUMAN CHROMOSOME 1p IN THE DOG 175(
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HUMAN CHROMOSOME 1p IN THE DOG 177l
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180 GEORGES AND ANDERSSON5. There i
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182 GEORGES AND ANDERSSONplied to r
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184 GEORGES AND ANDERSSONbe common
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186 GEORGES AND ANDERSSONin humans
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Evolving Methods for the Assembly o
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ASSEMBLING LARGE GENOMES 191Figure
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ASSEMBLING LARGE GENOMES 193tant ad
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Mouse Genome Encyclopedia ProjectY.
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MOUSE GENOME ENCYCLOPEDIA PROJECT 1
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DNA Sequence Assembly and Multiple
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EULERIAN ASSEMBLY AND MULTIPLE ALIG
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Ensembl: A Genome InfrastructureE.
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ENSEMBL 215projects often submit th
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218 ZHANGthe majority of these are
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220 ZHANGFigure 2. Demonstration of
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222 ZHANG(G.X. Chen et al., in prep
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224 ZHANGWe are waiting for experim
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Ontologies for Biologists: A Commun
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ONTOLOGIES FOR BIOLOGISTS 229al. 20
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ONTOLOGIES FOR BIOLOGISTS 231TOPIC
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ONTOLOGIES FOR BIOLOGISTS 233a.b.Fi
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ONTOLOGIES FOR BIOLOGISTS 2352003.
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238 JOSHI-TOPE ET AL.Figure 1. The
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240 JOSHI-TOPE ET AL.state of knowl
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242 JOSHI-TOPE ET AL.and co-immunop
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The Share of Human Genomic DNA unde
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DNA UNDER SELECTION FROM HUMAN-MOUS
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Detecting Highly Conserved Regions
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DETECTING MULTISPECIES CONSERVED SE
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266 ROE ET AL.noncoding regions. On
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268 ROE ET AL.a48 hpf embryos in Mi
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270 ROE ET AL.aNovel gene KIAA0819[
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272 ROE ET AL.aMouseRatAP00354.2 Hu
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274 ROE ET AL.Tautz D. and Pfeifle
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276 JAILLON ET AL.Detection of Evol
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278 JAILLON ET AL.Table 1. Distribu
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280 JAILLON ET AL.Table 3. Distribu
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282 JAILLON ET AL.ecotig is a resul
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284 OVCHARENKO AND LOOTSdivergent r
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286 OVCHARENKO AND LOOTSmodulation
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288 OVCHARENKO AND LOOTSsequencing
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290 OVCHARENKO AND LOOTSments of cl
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Evolution of Eukaryotic Gene Repert
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EVOLUTION OF EUKARYOTIC GENES AND I
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304 PENNACCHIO, BAROUKH, AND RUBINA
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306 PENNACCHIO, BAROUKH, AND RUBINh
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308 PENNACCHIO, BAROUKH, AND RUBINA
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High-Throughput Mouse Knockouts Pro
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314 FRIDDLE ET AL.screen to lines o
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Identification of Novel Functional
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100 bp ladder68G1168G1168H1168H6100
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FUNCTIONAL ELEMENTS IN HUMAN DNA 32
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High-resolution Human Genome Scanni
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HUMAN GENOME SCANNING 325false-posi
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HUMAN GENOME SCANNING 327affecting
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HUMAN GENOME SCANNING 329methods fo
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332 MALEK ET AL.Figure 1. The bacte
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334 MALEK ET AL.J., Vincent S., and
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336 HARDISON ET AL.reflect blocks o
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338 HARDISON ET AL.plain the region
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340 HARDISON ET AL.CALIBRATION OF T
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342 HARDISON ET AL.PositionRP2.3noE
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344 HARDISON ET AL.cific chromosoma
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346 WESTON ET AL.these differences
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Human Versus Chimpanzee Chromosome-
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HUMAN VS. CHIMP CHROMOSOME COMPARIS
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HUMAN VS. CHIMP CHROMOSOME COMPARIS
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Novel Transcriptional Units and Unc
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TRANSCRIPTIONAL UNITS AND GENE PAIR
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mtDNA Variation, Climatic Adaptatio
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mtDNA VARIATION 473Figure 3. Region
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ANALYSIS OF ADAPTIVE SELECTION FORR
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mtDNA VARIATION 477Figure 8. Temper
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Positive Selection in the Human Gen
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HUMAN-SPECIFIC EVOLUTIONARY CHANGES
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488 UNDERHILLorigin episodes, each
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490 UNDERHILLhaplogroups C through
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492 UNDERHILLO (Fig. 2e) that share
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The New Quantitative BiologyM.V. OL
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NEW QUANTITATIVE BIOLOGY 497alone.
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NEW QUANTITATIVE BIOLOGY 499There w
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NEW QUANTITATIVE BIOLOGY 501ceded,
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