The Genom of Homo sapiens.pdf

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DNA UNDER SELECTION FROM HUMAN–MOUSE 253served) selected distribution of the score, which makesthem hard to separate. As we have seen, this strong overlapis also present in conservation scores computed onwindows when those windows are small, e.g., only 30 bp.This casts doubt on the stringency of lower bounds obtainedfrom very small windows: Technically, they arevalid lower bounds, but they might yield significant underestimatesof the share under selection.Using larger windows (instead of single-base positionsor very small windows) to estimate the share under selectioncarries other limitations. We can produce a morestringent lower bound, and thus a better estimate for thefraction of windows that appear to be under selection andthe fraction of the human genome that is contained insuch windows. However, because the score applies toeach window as a whole, we need to restrict attention towell-aligned windows, and recall that our share estimatedoes not automatically equate to an estimate of the fractionof individual bases under selection. This may not bea severe limitation, because bases are not selected entirelyindependently from their neighbors; altogether, itmay make more sense to consider small regions under selectionthan individual bases under selection.From a certain point onward, increasing the windowsize appears to cause an inflation in the total estimatedshare of the genome under selection beyond what we canattribute to better separation of the neutral and selectedscore distributions; see Table 1. However, this is a misinterpretationof the results. For instance, in attempting tocompare “on a base-by-base level” the estimated fractionof the genome in 50-bp windows under selection to theestimated fraction in 100-bp windows, we are implicitlyconverting the probability that a window is selected,Pr(w selected | S(w) = S) defined above, into the expectednumber of bases in the window w that are under selection,which is not legitimate. In fact, the estimates for 50-bpwindows and for 100-bp windows are not directly comparablein this fashion: They are estimates of two differentunderlying quantities, one measuring evolution ofsmaller (50 bp) segments and the other larger (100 bp)segments.From a biological perspective, we would like to reliablydetect the effects of purifying selection on as small aunit as makes sense; ideally at most a few tens of bases.Given the limitations posed by employing only the human–mousealignment, the best we can do at this point isto use 50-bp WA-windows: About 5% of the humangenome is contained in 50-bp WA-windows that are moreconserved than neighboring neutral windows (modeledby ancestral repeats) and thus appear to be under selection.As discussed above, we cannot eliminate the possibilitythat mechanisms other than purifying selection explainthe data we see. In particular, some unidentifiedspecialized types of molecular evolution within ancestralrepeats could be causing some kinds of neutral windowsto be significantly more conserved than neighboring neutralwindows from ancestral repeats, which would artificiallyinflate our estimate of the share under selection.Tests with alternate score functions that compensate forcompositional effects (Roskin et al. 2002, 2003) and testswith different subsets of ancestral repeats as neutral modelsprovide some evidence against the existence of an extremebias of this type, but cannot eliminate this possibility.Additional evidence will be required to positivelyprove that the effect we are seeing is due to selection.The estimate, if valid, leads to the question of whatfunction these elements under selection may possess. 5%is considerably more than can be accounted for by the estimatedfraction of the genome that is coding, which isabout 1.5%. Note that including all 50-bp windows thatcontain any coding bases typically adds only about 25 bpon either end of a 200-bp coding exon, increasing the 1.5coding percentage by a factor of only 5/4. Moreover, thisis a considerable overassessment of the effect of codingbases on our estimate of the share under selection because,as we have seen, only about 70% of fully coding50-bp WA-windows (WAC-windows) are contributing tothe estimate as it is, and we expect the fraction to be lessfor partially coding 50-bp WA-windows. Hence, the bulkof the “selection signal” we are detecting is likely to becoming from noncoding bases, possibly performing regulatoryor other important functions.With multiple alignments to several mammals, itshould be possible to develop better score functions basedon more accurate models of molecular evolution. Thesewill allow us to separate neutral and selected windowsmore effectively, and thus to further investigate the propertiesof small regions of the human genome that are underselection.ACKNOWLEDGMENTSWe thank all researchers involved in the InternationalMouse Genome Sequencing Consortium for help anddata sharing. In particular, we are grateful to E. Lander,M. Zody, R. Waterston, F. Collins, P. Green, N. Goldman,A. Smit, and W. Miller for their suggestions andcomments. We also thank T. Pringle for comments on theresearch. F.C. was supported by National HumanGenome Research Institute grant HG-02238. R.J.W.,K.M.R., M.D., W.J.K., and D.H. were supported byNHGRI grant 1P41HG-02371. D.H. was also supportedby the Howard Hughes Medical Institute.REFERENCESBernardi G. 1993. The isochore organization of the humangenome and its evolutionary history: A review. Gene 135: 57.Efron B., Tibshirani R., Storey J., and Tusher V. 2001. EmpiricalBayes analysis of a microarray experiment. J. Am. Stat.Assoc. 96: 1151.Elnitski L., Hardison R., Li J., Yang S., Kolbe D., Eswara P.,O’Connor M., Schwartz S., Miller W., and Chiaromonte F.2003. Distinguishing regulatory DNA from neutral sites.Genome Res. 13: 64.Hardison R., Roskin K., Yang S., Diekhans M., Kent W., WeberR., Elnitski L., Li J., O’Connor M., Kolbe D., Schwartz S.,Furey T.S., Whelan S., Goldman N., Smit A., Miller W.,Chiaromonte F., and Haussler D. 2003. Co-variation in frequenciesof substitution, deletion, transposition and recombinationduring eutherian evolution. Genome Res. 13: 13.Holmes I. and Durbin R. 1998. Dynamic programming alignmentaccuracy. J. Comput. Biol. 5: 493.
254 CHIAROMONTE ET AL.Ihaka R. and Gentlman R. 1996. R: A language for data analysisand graphics. J. Comput. Graph. Stat. 5: 299.Kent W.J. 2002. BLAT: The BLAST-like alignment tool.Genome Res. 12: 656.Kent W.J., Baertsch R., Hinrichs A., Miller W., and Haussler D.2003. Evolution’s cauldron: Duplication, deletion, and rearrangementin the mouse and human genomes. Proc. Natl.Acad. Sci. 100: 11484.Kent W.J., Sugnet C., Furey T., Roskin K., Pringle T., Zahler A.,and Haussler D. 2002. The human genome browser at UCSC.Genome Res. 12: 996.Lander E.S., Linton L.M., Birren B., Nusbaum C., Zody M.C.,Baldwin J., Devon K., Dewar K., Doyle M., FitzHugh W.,Funke R., Gage D., Harris K., Heaford A., Howland J., KannL., Lehoczky J., LeVine R., McEwan P., McKernan K.,Meldrim J., Mesirov J.P., Miranda C., Morris W., and NaylorJ., et al. (International Human Genome Sequencing Consortium).2001. Initial sequencing and analysis of the humangenome. Nature 409: 860.Lindsay B.G. 1995. Mixture models: Theory, geometry and applications(NFS-CBMS Regional Conference Series in Probabilityand Statistics), vol. 5. American Statistical Association,Alexandria, Virginia.McLachlan G.J. and Peel D. 2000. Finite mixture models. Wiley,New York.Pruitt K. and Maglott D. 2001. RefSeq and LocusLink: NCBIgene-centered resources. Nucleic Acids Res. 29: 137.Roskin K.M., Diekhans M., and Haussler D. 2003. Scoring twospecieslocal alignments to try to statistically separate neutrallyevolving from selected DNA segments. In Proceedingsof the 7th Annual International Conference on Research inComputational Molecular Biology (RECOMB 2003), p. 257.Roskin K., Diekhans M., Kent W., and Haussler D. 2002. Scorefunctions for assessing conservation in locally aligned regionsof DNA from two species. UCSC Technical Report CRL-02-30, September 14, 2002. Center for Biomolecular Science andEngineering, Baskin Engineering, University of California,Santa Cruz.Schwartz S., Kent W., Smit A., Zhang Z., Baertsch R., HardisonR., Haussler D., and Miller W. 2003. Human-mouse alignmentswith Blastz. Genome Res. 13:103.Silverman B. 1986. Density estimation for statistics and dataanalysis. Chapman and Hall, London, United Kingdom.Venter J.C., Adams M.D., Myers E.W., Li P.W., Mural R.J., SuttonG.G., Smith H.O., Yandell M., Evans C.A., Holt R.A.,Gocayne J.D., Amanatides P., Ballew R.M., Huson D.H.,Wortman J.R., Zhang Q., Kodira C.D., Zheng X.H., Chen L.,Skupski M., Subramanian G., Thomas P.D., Zhang J., GaborMiklos G.L., and Nelson C., et al. 2001. The sequence of thehuman genome. Science 291: 1304.Waterston R.H., Lindblad-Toh K., Birney E., Rogers J., AbrilJ.F., Agarwal P., Agarwala R., Ainscough R., AlexanderssonM., An P., Antonarakis S.E., Attwood J., Baertsch R., BaileyJ., Barlow K., Beck S., Berry E., Birren B., Bloom T., BorkP., Botcherby M., Bray N., Brent M.R., Brown D.G., andBrown S.D., et al. (Mouse Genome Sequencing Consortium).2002. Initial sequencing and comparative analysis of themouse genome. Nature 420: 520.Wegman E. 1972. Nonparametric probability density estimation.Technometrics 14: 533.WEB SITE REFERENCESmit A.F. and Green P. 1999. RepeatMasker. (http://ftp.genome.washington.edu/RM/RepeatMasker.html)
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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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Page 210 and 211: MOUSE GENOME ENCYCLOPEDIA PROJECT 2
Page 212 and 213: DNA Sequence Assembly and Multiple
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Page 231 and 232: 224 ZHANGWe are waiting for experim
Page 234 and 235: Ontologies for Biologists: A Commun
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Page 245 and 246: 238 JOSHI-TOPE ET AL.Figure 1. The
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Page 252 and 253: The Share of Human Genomic DNA unde
Page 254 and 255: DNA UNDER SELECTION FROM HUMAN-MOUS
Page 262 and 263: Detecting Highly Conserved Regions
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Page 273 and 274: 266 ROE ET AL.noncoding regions. On
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Page 283 and 284: 276 JAILLON ET AL.Detection of Evol
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Page 291 and 292: 284 OVCHARENKO AND LOOTSdivergent r
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Page 295 and 296: 288 OVCHARENKO AND LOOTSsequencing
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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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348 WESTON ET AL.els controlled by
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350 WESTON ET AL.ures prominently i
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352 WESTON ET AL.nal and Bop, which
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354 WESTON ET AL.ablp 1466 bopbcrtB
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356 WESTON ET AL.like fold (Fig. 6)
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Implications of Genomics for Public
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GENETIC EPIDEMIOLOGY 361lytic epide
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GENETIC EPIDEMIOLOGY 363curate risk
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A Model System for Identifying Gene
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PTC TASTE GENETICS 367Figure 2. Hap
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PTC TASTE GENETICS 369Table 2. Hapl
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PTC TASTE GENETICS 371the emergence
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374 MCCALLION ET AL.Figure 1. Schem
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376 MCCALLION ET AL.lier (Carrasqui
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378 MCCALLION ET AL.Table 3. HSCR A
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380 MCCALLION ET AL.Figure 3. Trans
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Genetics of Schizophrenia and Bipol
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SCHIZOPHRENIA AND BIPOLAR AFFECTIVE
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The Genetics of Common Diseases: 10
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GENETICS OF COMMON DISEASES 397with
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GENETICS OF COMMON DISEASES 399SELE
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GENETICS OF COMMON DISEASES 401F.,
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404 CHEUNG ET AL.netic analysis. Ex
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406 CHEUNG ET AL.Figure 3. The expr
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Regulation of α-Synuclein Expressi
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α-SYNUCLEIN EXPRESSION AND PD 411T
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1. The levels of α-synuclein prote
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α-SYNUCLEIN EXPRESSION AND PD 415g
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418 BOTSTEINFigure 1. (A) Blectron
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420 BOTSTEINFigure 3. Cluster diagr
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422 BOTSTEINFigure 6. Kaplan-Meier
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424 BOTSTEINGarber M.E., Troyanskay
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426 ANTONARAKIS ET AL.1316192225283
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428 ANTONARAKIS ET AL.Figure 5. Sam
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430 ANTONARAKIS ET AL.POPULATION VA
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432 JORGENSEN ET AL.tive small mole
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434 JORGENSEN ET AL.FLAG-tagged pro
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436 JORGENSEN ET AL.visualization t
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438 JORGENSEN ET AL.AArp2/3 Complex
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Pathway40S440 JORGENSEN ET AL.ANutr
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442 JORGENSEN ET AL.Giaever G., Chu
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Genomic Disorders: Genome Architect
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GENOME ARCHITECTURE AND GENOMIC DIS
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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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