The Genom of Homo sapiens.pdf

More documents

Recommendations

Info

GENETIC EPIDEMIOLOGY 361lytic epidemiology is to identify risk and protective factorsand their causal links to disease, with the ultimategoal of disease prevention. Genetic epidemiology employstraditional epidemiologic study designs to identifyexplanatory factors for aggregation in groups of relativesranging from twins to migrant cohorts. In general, studydesigns in genetic epidemiology either control for geneticbackground while letting the environment vary (e.g., migrantstudies, half siblings, separated twins) or control forthe environment while allowing variance in the geneticbackground (e.g., siblings, twins, adoptees– nonbiologicsiblings). Since epidemiology has developed sophisticateddesigns and analytic methods for identifying diseaserisk factors, these methods can now be extended toinclude both genes and environmental factors as geneidentification proceeds (Yang and Khoury 1997). Thetools of genetic epidemiology will be employed in the eraof genomics to derive estimates of the population distributionof disease genes, to test modes of disease transmissionin systematic samples that are representative of thepopulation, and to identify sources of gene–environmentinteractions for diseases.Despite the critical role of the traditional case-controlfamily studies that served as the core of human geneticsduring the earlier part of this century, systematically ascertainedfamily studies have largely been abandoned infavor of studies designed solely for gene finding. Althoughthe latter approaches do play an important role instudying Mendelian diseases, they are far less informativefor complex diseases, since it is impossible to incorporatesources of heterogeneity into the study design, particularlybecause most of the factors that lead to geneticcomplexity are still unknown.Although these sampling approaches do increase thepower for detecting genes, they have diminished the generalizabilityof the study findings, and contribute littleelse to the knowledge base if genes are not discovered. Itmay be advisable in the future to collect both families andcontrols from representative samples of the population inorder to enable estimation of population risk parameters,enhance generalizability, refine definitions of complexphenotypes, and examine the specificity of endophenotypictransmission. The nested case-control study built onan established cohort is likely to be a key study design inidentifying the joint contribution of genetic and environmentalrisk factors. Prospective cohort studies are alsovaluable sources of diagnostic stability, causal associationsbetween risk factors and disease, and developmentalaspects of complex disorders. Langholz et al. (1999)describe some of the world’s prospective cohort studiesthat may serve as a basis for studies of gene–disease associationsor gene–environment interactions. Finally, thehalf-sibling approach may eventually replace the adoptionparadigm to investigate genetic and environmentaleffects because of the recent trends toward selectiveadoption and the diminishing frequency of adoptions inthe US and in numerous other countries.Population-based studies. There are several reasonsthat population-based studies will be critical to the futureof genetics. First, the prevalence of newly identified polymorphisms,whether single nucleotide polymorphisms(SNPs) or other variants, especially in particular populationsubgroups, is not known. Second, current knowledgeof genes as risk factors is based nearly exclusively onclinical and nonsystematic samples. The significance ofthe susceptibility alleles that have been identified for cancer,heart disease, diabetes, and so forth is relatively unknownin the population at large. To provide accurate riskestimates, the next stage of research needs to move beyondsamples identified through affected individuals tothe general population in order to obtain estimates of therisk of specific polymorphisms for the population as awhole. Third, identification of risk profiles will requirevery large samples to assess the significance of vulnerabilitygenes with relatively low expected population frequencies.Fourth, similar to the role of epidemiology inquantifying risk associated with traditional disease riskfactors, applications of human genome epidemiology canprovide information on the specificity, sensitivity, andimpact of genetic tests to inform science and the individual(Yang et al. 2000).Several types of risk estimates are used in public health.The most common is relative risk, defined as the magnitudeof the association between an exposure and disease.It is independent of the prevalence of the exposure. Theabsolute risk is the overall probability of developing a diseasein an individual or in a particular population (Gordis2000). The attributable risk is the difference in the risk ofthe disease in those exposed to a particular risk factorcompared to the background risk of a disease in a population(i.e., in the unexposed). The population-attributablerisk relates to the risk of a disease in a total population (exposedand unexposed) and indicates the amount the diseasecan be reduced in a population if an exposure is eliminated.The population-attributable risk depends on theprevalence of the exposure, or in the case of genes, thegene frequency. Genetic attributable risk would indicatethe proportion of a particular disease that would be eliminatedif a particular gene or genes were not involved.Because genetic polymorphisms involved in complexdiseases are likely to be nondeterministic (i.e., the markerpredicts neither disease nor non-disease with certainty),traditional epidemiologic risk factor designs can be usedto estimate their impact (Ellsworth and Manolio 1999b).As epidemiologists add genes to their risk equations, it islikely that the contradictory findings from studies thathave generally employed solely environmental risk factors,such as diet, smoking, and alcohol use, will be resolved.Likewise, the studies that seek solely to identifygenes will also continue to be inconsistent without consideringthe effects of nongenetic biologic parameters aswell as environmental factors that contribute to the diseasesof interest.Identification of environmental factors. The identificationof gene–environment interactions is one of themost important future goals of genetic epidemiology.Study designs and statistical methods should focus increasinglyon gene–environment interaction (Smith and
362 MERIKANGASDay 1984; Ottman 1990; Hwang et al. 1994; Foppa andSpiegelman 1997; Yang and Khoury 1997; Khoury 1998;Garcia-Closas and Lubin 1999). There is accumulatingevidence that gene–environment interaction will underliemany of the complex human diseases. Some examplesinclude inborn errors of metabolism, individual variationin response to drugs (Nebert et al. 1999), substance usedisorders (Dick et al. 2001; Heath et al. 2001), and theprotective influence of a deletion in the CC-chemokinereceptor gene on exposure to HIV (Michael 1999;Guttmacher and Collins 2002). Over the next decades, itwill be important to identify and evaluate the effects ofspecific environmental factors on disease outcomes andto refine measurement of environmental exposures toevaluate specificity of effects. Once susceptibility geneshave been identified, case-control studies defined by vulnerabilitygenotypes can be employed to identify environmentalfactors that lead to either the potentiation orsuppression of particular genotypes.Evidence for environmental factors can be gleanedfrom migration studies, a particularly powerful tool toidentify environmental exposures that may modify geneexpression. Migration studies have shown that the ratesof breast cancer, heart disease, and type II diabetes haveincreased substantially in several subgroups who migratedto new cultural settings (Shaper and Elford 1991;Hanley et al. 1995; Trevisan et al. 1998).Intervention studies are another method to test environmentaletiology. Systematic trials of weight loss and exerciseamong high-risk offspring of parents with type II diabeteshave shown a significant decrease in incidenceamong those in the weight loss group (Knoller et al. 2002).The increased incidence of type II diabetes among offspringexposed prenatally to maternal diabetes also suggeststhat intrauterine exposure should be incorporatedinto genetic studies of this disorder (Sobngwi et al. 2003).ISSUES IN THE TRANSLATION OFGENOMICS TO THE PUBLICScientists, health care providers, ethicists, regulators,patient groups, and the pharmaceutical industry are challengedby the rapid changes in the science and technologyof genetic research. Researchers and policymakers arecontinually updating and ensuring that they keep pacewith the rapidly evolving field by instilling ethicallysound, workable guidelines for both the research andclinical applications of human genetics. As a result of theincreased education, discussion, and understanding of therelevant issues, one area that will be particularly importantin genetic epidemiology research is informed consent.It is important to address consent issues in a reasoned,practical, and consistent way, including input frompatients and their families, health care providers, ethicists,scientists, regulatory bodies, research sponsors, andthe lay community. Responsibility for assessing issues relatedto family consent for research should remain withlocal investigators, ethics boards, and study sponsors. A“one-size-fits-all” perspective in the form of new regulations,for example, would likely be a disservice to all(Renegar et al. 2001). Specific issues that need to be consideredin applying genomics in the general populationinclude public comprehension of genetic information,confidentiality, and accurate risk estimation for complexdisorders.ConfidentialityThere has been substantial attention to ethical issues aspart of the activities involved in mapping the humangenome (Human Genome Project Information 2003:http://www.ornl.gov/sci/techresources/Human_Genome/elsi/elsi.shtml). Principles of ethical research, confidentiality,and communication have been well-established.There are several aspects of human subject protection thatdiffer when collecting DNA from samples of individualsrecruited from the general population compared with fromthe disease-based samples for which genetic counselinghas been most sought. With respect to complex disorders,and mental disorders in particular, several ethical issuesrequire attention including (1) protection of the privacy ofthe proband in the ascertainment process; (2) permissionto contact other family members; (3) specific problems associatedwith informed consent such as the ability of an individualin acute stages of illness to provide informed consent;and (4) therapeutic intervention (Alexander 2003).Successful and unsuccessful approaches that have beenapplied in ongoing population-based studies may ultimatelybe used to develop standards for the ethical applicationof genomics in general population samples.ComprehensionOne major impediment to the application of the tools ofgenomics to public health is a lack of public understandingof the implications of genetic markers. Althoughthere has been a dramatic increase in awareness of therole of genes in human behavior stemming from the accomplishmentsin genomics, some evidence suggests thatthere has not been a concomitant increase in public comprehensionof the role of genes in disease etiology. Forexample, comparison of the results of a telephone surveyon basic principles of genetics for some common humandiseases administered in 1990 and 2000 showed no differencein accuracy between administration in 1990 and2000 (Institute for Social Research, University of Michigan)(Weiss 2003). Moreover, the public tends to attributefar more of the causality of human behavior andcomplex diseases to genes than warranted by empiricalevidence. As knowledge regarding specific roles of genesin complex diseases evolves, public education regardingrisk estimation will be critical in the translation of genomicsto public health.Risk Estimation for Complex DiseasesThe lack of knowledge regarding genes underlyingcomplex diseases reduces our ability to communicate ac-
Page 6 and 7:
ForewordIn 2001, as we considered t
Page 8:
The Finished Genome Sequence of Hom
Page 11 and 12:
4 ROGERSFigure 2. Accumulation of h
Page 13 and 14:
6 ROGERSFigure 4. Sequencing center
Page 15 and 16:
8 ROGERSabcFigure 5. Ensembl view o
Page 17 and 18:
10 ROGERS2000. Analysis of vertebra
Page 20 and 21:
The Human Genome: Genes, Pseudogene
Page 22 and 23:
VARIATION ON CHROMOSOME 7 15rived f
Page 24 and 25:
VARIATION ON CHROMOSOME 7 17DNAs an
Page 26 and 27:
VARIATION ON CHROMOSOME 7 19expecte
Page 28 and 29:
VARIATION ON CHROMOSOME 7 21Drosoph
Page 30 and 31:
Mutational Profiling in the Human G
Page 32 and 33:
HUMAN MUTATIONAL PROFILING 25Anothe
Page 34 and 35:
HUMAN MUTATIONAL PROFILING 27Figure
Page 36:
HUMAN MUTATIONAL PROFILING 29Rieder
Page 39 and 40:
32 SCHMUTZ ET AL.algorithm itself,
Page 41 and 42:
34 SCHMUTZ ET AL.Figure 2. Genomic
Page 43 and 44:
36 SCHMUTZ ET AL.compared. Some of
Page 46 and 47:
Human Subtelomeric DNAH. RIETHMAN,
Page 48 and 49:
HUMAN SUBTELOMERIC SEQUENCES 41The
Page 50 and 51:
HUMAN SUBTELOMERIC SEQUENCES 43cate
Page 52 and 53:
HUMAN SUBTELOMERIC SEQUENCES 45Figu
Page 54:
HUMAN SUBTELOMERIC SEQUENCES 47pres
Page 57 and 58:
50 COLLINSand expand the genomics r
Page 59 and 60:
52 COLLINSFigure 2. A public-sector
Page 61 and 62:
54 COLLINSdefine all the parts of t
Page 63 and 64:
56 BENTLEYmon over many generations
Page 65 and 66:
58 BENTLEYTable 1. Genetic Disease
Page 67 and 68:
60 BENTLEY(Clark et al. 1998; Reich
Page 69 and 70:
62 BENTLEYACKNOWLEDGMENTSThe author
Page 72 and 73:
SNP Genotyping and Molecular Haplot
Page 74:
GENETIC ANALYSIS OF DNA POOLS 67gen
Page 77 and 78:
70 FAN ET AL.matrix is then mated t
Page 79 and 80:
72 FAN ET AL.Figure 3. Views of gen
Page 81 and 82:
74 FAN ET AL.including 32 duplicate
Page 83 and 84:
76 FAN ET AL.Figure 7. Allele-speci
Page 85 and 86:
78 FAN ET AL.microsphere-based assa
Page 87 and 88:
80 BERTRANPETIT ET AL.function, may
Page 89 and 90:
82 BERTRANPETIT ET AL.diversity in
Page 91 and 92:
84 BERTRANPETIT ET AL.gree of block
Page 93 and 94:
86 BERTRANPETIT ET AL.Figure 1. Dec
Page 95 and 96:
88 BERTRANPETIT ET AL.1999. Populat
Page 97 and 98:
90 WINDEMUTH ET AL.Expression data.
Page 99 and 100:
92 WINDEMUTH ET AL.Table 1. A Summa
Page 101 and 102:
94 WINDEMUTH ET AL.Table 2. Signifi
Page 103 and 104:
96 WINDEMUTH ET AL.Table 3. Summary
Page 105 and 106:
98 WINDEMUTH ET AL.Table 6. List of
Page 107 and 108:
100 WINDEMUTH ET AL.Table 6. (Conti
Page 109 and 110:
102 WINDEMUTH ET AL.Table 6. (Conti
Page 111 and 112:
104 WINDEMUTH ET AL.much of a surpr
Page 113 and 114:
106 WINDEMUTH ET AL.Given our resul
Page 116 and 117:
Genetic Variation and the Control o
Page 118 and 119:
GENETIC CONTROL OF TRANSCRIPTION 11
Page 120 and 121:
GENETIC CONTROL OF TRANSCRIPTION 11
Page 122 and 123:
Genome-wide Detection and Analysis
Page 124 and 125:
RECENT SEGMENTAL DUPLICATIONS 117Fi
Page 126 and 127:
RECENT SEGMENTAL DUPLICATIONS 119St
Page 128 and 129:
RECENT SEGMENTAL DUPLICATIONS 121Co
Page 130 and 131:
RECENT SEGMENTAL DUPLICATIONS 123Ho
Page 132 and 133:
The Effects of Evolutionary Distanc
Page 134 and 135:
EVOLUTIONARY DISTANCE AND GENE PRED
Page 136 and 137:
EVOLUTIONARY DISTANCE AND GENE PRED
Page 138 and 139:
Lineage-specific Expansion of KRAB
Page 140 and 141:
EVOLUTION OF ZNF GENES 133Figure 2.
Page 142 and 143:
EVOLUTION OF ZNF GENES 135Figure 4.
Page 144 and 145:
EVOLUTION OF ZNF GENES 137get gene,
Page 146 and 147:
EVOLUTION OF ZNF GENES 139Y., Goodw
Page 148 and 149:
Sequence Organization and Functiona
Page 150 and 151:
CENTROMERE ANNOTATION 143THE CENTRO
Page 152 and 153:
CENTROMERE ANNOTATION 145Figure 4.
Page 154 and 155:
CENTROMERE ANNOTATION 147CONCLUSION
Page 156:
CENTROMERE ANNOTATION 149Schueler M
Page 159 and 160:
152 PARKHILL AND THOMSONFigure 1. T
Page 161 and 162:
154 PARKHILL AND THOMSONshow very h
Page 163 and 164:
156 PARKHILL AND THOMSONGene Loss a
Page 165 and 166:
158 PARKHILL AND THOMSONYersinia ad
Page 167 and 168:
160 MCKAY ET AL.Choosing Candidate
Page 169 and 170:
162 MCKAY ET AL.new comparative too
Page 171 and 172:
164 MCKAY ET AL.rich. Based on a th
Page 173 and 174:
166 MCKAY ET AL.Embryonic Muscle an
Page 175 and 176:
168 MCKAY ET AL.native polyadenylat
Page 178 and 179:
Building Comparative Maps Using 1.5
Page 180 and 181:
HUMAN CHROMOSOME 1p IN THE DOG 1731
Page 182 and 183:
HUMAN CHROMOSOME 1p IN THE DOG 175(
Page 184:
HUMAN CHROMOSOME 1p IN THE DOG 177l
Page 187 and 188:
180 GEORGES AND ANDERSSON5. There i
Page 189 and 190:
182 GEORGES AND ANDERSSONplied to r
Page 191 and 192:
184 GEORGES AND ANDERSSONbe common
Page 193 and 194:
186 GEORGES AND ANDERSSONin humans
Page 196 and 197:
Evolving Methods for the Assembly o
Page 198 and 199:
ASSEMBLING LARGE GENOMES 191Figure
Page 200 and 201:
ASSEMBLING LARGE GENOMES 193tant ad
Page 202 and 203:
Mouse Genome Encyclopedia ProjectY.
Page 204 and 205:
MOUSE GENOME ENCYCLOPEDIA PROJECT 1
Page 206 and 207:
Page 208 and 209:
Page 210 and 211:
Page 212 and 213:
DNA Sequence Assembly and Multiple
Page 214 and 215:
EULERIAN ASSEMBLY AND MULTIPLE ALIG
Page 216 and 217:
Page 218 and 219:
Page 220 and 221:
Ensembl: A Genome InfrastructureE.
Page 222:
ENSEMBL 215projects often submit th
Page 225 and 226:
218 ZHANGthe majority of these are
Page 227 and 228:
220 ZHANGFigure 2. Demonstration of
Page 229 and 230:
222 ZHANG(G.X. Chen et al., in prep
Page 231 and 232:
224 ZHANGWe are waiting for experim
Page 234 and 235:
Ontologies for Biologists: A Commun
Page 236 and 237:
ONTOLOGIES FOR BIOLOGISTS 229al. 20
Page 238 and 239:
ONTOLOGIES FOR BIOLOGISTS 231TOPIC
Page 240 and 241:
ONTOLOGIES FOR BIOLOGISTS 233a.b.Fi
Page 242:
ONTOLOGIES FOR BIOLOGISTS 2352003.
Page 245 and 246:
238 JOSHI-TOPE ET AL.Figure 1. The
Page 247 and 248:
240 JOSHI-TOPE ET AL.state of knowl
Page 249 and 250:
242 JOSHI-TOPE ET AL.and co-immunop
Page 252 and 253:
The Share of Human Genomic DNA unde
Page 254 and 255:
DNA UNDER SELECTION FROM HUMAN-MOUS
Page 256 and 257:
Page 258 and 259:
Page 260 and 261:
Page 262 and 263:
Detecting Highly Conserved Regions
Page 264 and 265:
DETECTING MULTISPECIES CONSERVED SE
Page 266 and 267:
Page 268 and 269:
Page 270:
Page 273 and 274:
266 ROE ET AL.noncoding regions. On
Page 275 and 276:
268 ROE ET AL.a48 hpf embryos in Mi
Page 277 and 278:
270 ROE ET AL.aNovel gene KIAA0819[
Page 279 and 280:
272 ROE ET AL.aMouseRatAP00354.2 Hu
Page 281 and 282:
274 ROE ET AL.Tautz D. and Pfeifle
Page 283 and 284:
276 JAILLON ET AL.Detection of Evol
Page 285 and 286:
278 JAILLON ET AL.Table 1. Distribu
Page 287 and 288:
280 JAILLON ET AL.Table 3. Distribu
Page 289 and 290:
282 JAILLON ET AL.ecotig is a resul
Page 291 and 292:
284 OVCHARENKO AND LOOTSdivergent r
Page 293 and 294:
286 OVCHARENKO AND LOOTSmodulation
Page 295 and 296:
288 OVCHARENKO AND LOOTSsequencing
Page 297 and 298:
290 OVCHARENKO AND LOOTSments of cl
Page 300 and 301:
Evolution of Eukaryotic Gene Repert
Page 302 and 303:
EVOLUTION OF EUKARYOTIC GENES AND I
Page 304 and 305:
Page 306 and 307:
Page 308:
Page 311 and 312:
304 PENNACCHIO, BAROUKH, AND RUBINA
Page 313 and 314:
306 PENNACCHIO, BAROUKH, AND RUBINh
Page 315 and 316:
308 PENNACCHIO, BAROUKH, AND RUBINA
Page 318: High-Throughput Mouse Knockouts Pro
Page 321 and 322: 314 FRIDDLE ET AL.screen to lines o
Page 324 and 325: Identification of Novel Functional
Page 326 and 327: 100 bp ladder68G1168G1168H1168H6100
Page 328 and 329: FUNCTIONAL ELEMENTS IN HUMAN DNA 32
Page 330 and 331: High-resolution Human Genome Scanni
Page 332 and 333: HUMAN GENOME SCANNING 325false-posi
Page 334 and 335: HUMAN GENOME SCANNING 327affecting
Page 336: HUMAN GENOME SCANNING 329methods fo
Page 339 and 340: 332 MALEK ET AL.Figure 1. The bacte
Page 341 and 342: 334 MALEK ET AL.J., Vincent S., and
Page 343 and 344: 336 HARDISON ET AL.reflect blocks o
Page 345 and 346: 338 HARDISON ET AL.plain the region
Page 347 and 348: 340 HARDISON ET AL.CALIBRATION OF T
Page 349 and 350: 342 HARDISON ET AL.PositionRP2.3noE
Page 351 and 352: 344 HARDISON ET AL.cific chromosoma
Page 353 and 354: 346 WESTON ET AL.these differences
Page 355 and 356: 348 WESTON ET AL.els controlled by
Page 357 and 358: 350 WESTON ET AL.ures prominently i
Page 359 and 360: 352 WESTON ET AL.nal and Bop, which
Page 361 and 362: 354 WESTON ET AL.ablp 1466 bopbcrtB
Page 363 and 364: 356 WESTON ET AL.like fold (Fig. 6)
Page 366 and 367: Implications of Genomics for Public
Page 370 and 371: GENETIC EPIDEMIOLOGY 363curate risk
Page 372 and 373: A Model System for Identifying Gene
Page 374 and 375: PTC TASTE GENETICS 367Figure 2. Hap
Page 376 and 377: PTC TASTE GENETICS 369Table 2. Hapl
Page 378: PTC TASTE GENETICS 371the emergence
Page 381 and 382: 374 MCCALLION ET AL.Figure 1. Schem
Page 383 and 384: 376 MCCALLION ET AL.lier (Carrasqui
Page 385 and 386: 378 MCCALLION ET AL.Table 3. HSCR A
Page 387 and 388: 380 MCCALLION ET AL.Figure 3. Trans
Page 390 and 391: Genetics of Schizophrenia and Bipol
Page 392 and 393: SCHIZOPHRENIA AND BIPOLAR AFFECTIVE
Page 402 and 403: The Genetics of Common Diseases: 10
Page 404 and 405: GENETICS OF COMMON DISEASES 397with
Page 406 and 407: GENETICS OF COMMON DISEASES 399SELE
Page 408: GENETICS OF COMMON DISEASES 401F.,
Page 411 and 412: 404 CHEUNG ET AL.netic analysis. Ex
Page 413 and 414: 406 CHEUNG ET AL.Figure 3. The expr
Page 416 and 417: Regulation of α-Synuclein Expressi
Page 418 and 419:
α-SYNUCLEIN EXPRESSION AND PD 411T
Page 420 and 421:
1. The levels of α-synuclein prote
Page 422:
α-SYNUCLEIN EXPRESSION AND PD 415g
Page 425 and 426:
418 BOTSTEINFigure 1. (A) Blectron
Page 427 and 428:
420 BOTSTEINFigure 3. Cluster diagr
Page 429 and 430:
422 BOTSTEINFigure 6. Kaplan-Meier
Page 431 and 432:
424 BOTSTEINGarber M.E., Troyanskay
Page 433 and 434:
426 ANTONARAKIS ET AL.1316192225283
Page 435 and 436:
428 ANTONARAKIS ET AL.Figure 5. Sam
Page 437 and 438:
430 ANTONARAKIS ET AL.POPULATION VA
Page 439 and 440:
432 JORGENSEN ET AL.tive small mole
Page 441 and 442:
434 JORGENSEN ET AL.FLAG-tagged pro
Page 443 and 444:
436 JORGENSEN ET AL.visualization t
Page 445 and 446:
438 JORGENSEN ET AL.AArp2/3 Complex
Page 447 and 448:
Pathway40S440 JORGENSEN ET AL.ANutr
Page 449 and 450:
442 JORGENSEN ET AL.Giaever G., Chu
Page 452 and 453:
Genomic Disorders: Genome Architect
Page 454 and 455:
GENOME ARCHITECTURE AND GENOMIC DIS
Page 456 and 457:
Page 458 and 459:
Page 460 and 461:
Page 462 and 463:
Human Versus Chimpanzee Chromosome-
Page 464 and 465:
HUMAN VS. CHIMP CHROMOSOME COMPARIS
Page 466 and 467:
HUMAN VS. CHIMP CHROMOSOME COMPARIS
Page 468 and 469:
Novel Transcriptional Units and Unc
Page 470 and 471:
TRANSCRIPTIONAL UNITS AND GENE PAIR
Page 472 and 473:
Page 474 and 475:
Page 476 and 477:
Page 478 and 479:
mtDNA Variation, Climatic Adaptatio
Page 480 and 481:
mtDNA VARIATION 473Figure 3. Region
Page 482 and 483:
ANALYSIS OF ADAPTIVE SELECTION FORR
Page 484 and 485:
mtDNA VARIATION 477Figure 8. Temper
Page 486 and 487:
Positive Selection in the Human Gen
Page 488 and 489:
HUMAN-SPECIFIC EVOLUTIONARY CHANGES
Page 490 and 491:
Page 492:
Page 495 and 496:
488 UNDERHILLorigin episodes, each
Page 497 and 498:
490 UNDERHILLhaplogroups C through
Page 499 and 500:
492 UNDERHILLO (Fig. 2e) that share
Page 502 and 503:
The New Quantitative BiologyM.V. OL
Page 504 and 505:
NEW QUANTITATIVE BIOLOGY 497alone.
Page 506 and 507:
NEW QUANTITATIVE BIOLOGY 499There w
Page 508 and 509:
NEW QUANTITATIVE BIOLOGY 501ceded,
show all

The Genom of Homo sapiens.pdf

Create successful ePaper yourself

Delete template?

Save as template?