The Genom of Homo sapiens.pdf

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HUMAN GENE EXPRESSION VARIATION 405Figure 2. Genomic locations of 50 most variable genes. Chromosomes that do not contain any of these genes are not included in thefigure.
406 CHEUNG ET AL.Figure 3. The expression levels measured by microarrays foreight highly variable and two relatively nonvariable genes in 50individuals. Each point represents the expression level for an individual.of expression levels among the individuals studied wasfrom 14- to 48-fold. The expression levels of 8 of thehighly variable genes and 2 relatively nonvariable genesfor the 50 unrelated subjects are shown in Figure 3.EVIDENCE FOR HERITABILITYWe assessed the heritability of expression variation intwo ways. In the first method, we used data from sets ofindividuals with different degrees of relatedness, and inthe second, we used the resemblance between offspringand parent. Figure 4 shows the results from the first approachand compares the variance in expression levelamong three groups of subjects: 50 unrelated members ofCEPH families (parents of the large CEPH sibships), 10sets of siblings (also CEPH sibships), and 10 pairs of MZtwins. We found that the variance is largest among the unrelatedindividuals (a sample from the European population)and smallest between members of the MZ twin pairs.For all 5 genes shown in Figure 4 (and for 10 others thatwe have tested), we found the following pattern: as the degreeof relatedness increases, the variance in expressionlevels decreases, suggesting that genetic (germ-line) differencescontribute to variation in gene expression.Although this kind of comparison of variances providesa valid initial assessment of evidence for heritabilityin some sense, other, more standard methods have theadvantage of providing numerical estimates of the conventionalmeasure, the so-called “narrow-sense” heritability.In addition to data for the CEPH parents, we haveobtained gene expression data for their parents (CEPHgrandparents). It is known that the regression of offspringon mid-parent value (in standard linear regression) providesan estimate of the desired heritability (Falconer andMacKay 1996), so we used the expression data for the unrelatedCEPH parents and their parents to estimate this regressioncoefficient. A striking example is shown for glutathioneS-transferase M2, GSTM2, in Figure 5. We haveestimated the regression of parent on mid-grandparentthis way for all ~3800 genes on the microarray that wereexpressed in the lymphoblastoid cells. Approximately50% of these values are negative, and thus give no evidencefor a heritable component. Viewing the collectionas a whole, if no genes showed evidence for heritability,we would expect that the distribution of regression estimateswould have as many negative as positive values.Instead, we find an excess of large positive values. Theregression coefficient (b) is greater than 0.5 for 94 genes,but b < – 0.5 for only 48. Similarly, we find b > 0.75 for9 genes, but b < – 0.75 for only 4 genes.Of course, we consider these estimates at best crude indicatorsof heritability, especially since we have looked atso many genes. However, our interest in the estimate ofheritability is not mainly as an end in itself; we use it inconjunction with the variance ratio to select genes for furtherstudy. In the follow-up studies, usually by RT-PCR,we will carry out group comparisons (sibships, twins,etc.) like those shown in Figure 4, tests of closely linkedFigure 4. Variance in expression level for five genes. QuantitativeRT-PCR data for 50 unrelated individuals, 89 offspring in10 CEPH sibships, and 10 sets of monozygotic twins.Figure 5. Regression plot of expression level of GSTM2 for 50offspring–midparent pairs. The slope (represented by solid line)for the regression is 0.75 with a standard error of 0.45.
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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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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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Page 366 and 367: Implications of Genomics for Public
Page 368 and 369: GENETIC EPIDEMIOLOGY 361lytic epide
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Page 372 and 373: A Model System for Identifying Gene
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Page 378: PTC TASTE GENETICS 371the emergence
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Page 390 and 391: Genetics of Schizophrenia and Bipol
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Page 402 and 403: The Genetics of Common Diseases: 10
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Page 411: 404 CHEUNG ET AL.netic analysis. Ex
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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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