evolution of human diversity• the 3a4 allele is more frequent in african populations...
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Evolution of human diversity
Genetics of human uniqueness
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Human and chimp genome
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Evolutionary differences
} Novel genes } Gene loss } Copy number variation } Mutation } Expression differences
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Human and chimp gene repertoire
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} Most human genes present in chimp and vice versa } Some genes lost in humans (e.g. many olfactory
receptors), rare gene gain (Morpheus – unknown function, miR-941 - miRNA)
} Human/chimp differences are not due to the presence or absence of a single “humanity gene”
Sequence variation
} 1.6% nucleotide differences: } 35 mln substitutions } 5 mln deletions, insertions, duplications
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Protein sequence differences} An average human protein differs from chimpanzee by 2
amino acids
} 29% proteins identical
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Gene differences - how to study
} Mutations accumulate since the split of lineages
} Looking for genes where the rate of evolution in one lineage is significantly different (violating the molecular clock)
} Looking for traces of positive selection (deviations in Ka/Ks (ω); McDonald-Kreitman test, likelihood models)
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The fundamental question of molecular evolution
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} What is the contribution of drift and selection to sequence diversity } population (polymorphisms) } species
} This is about quantitative differences! } Adaptations are always a result of selection!
Selection or drift?
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} Selectionism } most fixed mutations were selected for } most polymorphisms maintained by selection
} balancing selection, overdominance, frequency-dependent selection
} Neutralism (Kimura, 1968) } most fixed mutations are a result of drift } most polymorphisms are a result of drift } selected mutations are rare, do not affect the quantitative
analysis of diversity
Selectionism and neutralism
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} Selectionism: } most mutations are deleterious } most fixed mutations are beneficial } neutral alleles are rare
} Neutralism } most mutations are deleterious or neutral } most fixed mutations are neutral } beneficial mutations are rare (less frequent than neutral)
Gene differences
} In ~ 500-600 genes – significant deviations in evolutionary rates - accelerated substitutions in human lineage
} ~ 200 noncoding Human Accelerated Regions (HARs) – often regulatory
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The speech gene
Verbal dyspraxia:
Rare disorder - affected individuals incapable of speech, anatomy of larynx and vocal cords is normal
Gene: FOXP212
FOXP2 – rapid evolution
Enard et al. (2002) Nature 418, 869-7213
Not just in humans} FOXP2 expression levels correlate with the
complexity of vocalizations in birds } FOXP2 mutations affect vocal communication
in mice
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MYH16
} One of the myosin heavy chains
} Mutation ~ 2.5 MYA – evolution of skull (weaker facial skeleton, allowing for the larger neurocranium)
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Microcephalin
Affected 13 years Normal 11 years
Accelerated evolution in human lineage
Microcephaly
Kouprina et al., PLoS Biology, 2004, 5:E126
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Regulatory sequences
} HACNS1 enhancer (Human Accelerated Conserved Noncoding Sequence 1) } 546bp sequence conserved in terrestrial vertebrates } 16 human-chimp differences (vs. 4 expected under neutral model,
p=1,3×10-6)
Prabhakar et al., 2008, Science 321:1346-5017
HACNS1 function} Reporter gene controlled by human, chimp and rhesus HACNS1
● Only human HACNS1 expressed in developing limbs
● 13 of 16 human-specific nucleotides are responsible
● Human-specific hand morphology (opposable thumbs) – Homo faber
● Human-specific foot morphology (short toes, inflexible) - bipedality
Prabhakar et al., 2008, Science 321:1346-5018
miR-941
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miR-941
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} A human-specific miRNA gene } one of about 10 such miRNAs } the only one with strong expression in CNS
} Appeared between 6-7 and 1 million years ago } found in Denisovians, but not chimps
} Variable copy number in humans (2-11) } decreasing copy number after migration from
Africa
miR-941 – copy number evolution
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miR-941 - function
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} downregulation of the hedgehog and insulin pathways
} could influence lifespan
Summary} There is no single “humanity gene” } The differences between humans and other primates are
a result of many small differences in hundreds of genes } A small sequence difference could have a strong
phenotypic effect } Regulatory differences - hard to compare expression
between species
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Human population variability
Medical consequences - polymorphisms and associations
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Human genetic variability
} Sequence differences (genes, noncoding regions)
} Copy number variation
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Human genetic variability} “The 1000 genomes project” – NGS sequencing of
individual genomes (2500 persons) } Initial data (2010) – 15 million nucleotide variable sites } Is that a lot
} 0.5-1% of genome } More nucleotides than the entire yeast genome } But remember that...
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Human genetic diversity is low
Reason – rapid population expansion
Analiza mtDNA
Analiza nDNA
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Medicine and the genome} Looking for mutations causing the genetic disorders
} Mendelian disorders are rare
} Looking for the genetic component of multifactorial traits } Common disorders are multifactorial
} Looking for genetic changes in cancer } personalized oncology
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Multifactorial traits
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Mendelian and multifactorial traits
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Mendelian Multifactorial
Simple genetics: single gene mutations, high penetrance
Complex genetics: many contributing factors, each of them with a low penetrance
Mutation causes the disease Gene polymorphisms influence risk (increase or decrease)
Studied using: parametric linkage analysis, sequencing
Studied using: multiple statistical methods, mostly nonparametric
Rare disorders Many common disorders and traits
Heritability
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} Each phenotype is the product of the interaction between genotype and environment
} Heritability: the proportion of phenotypic variation due to genotypic variation } twin studies
} Monozygotic (MZ) vs. dizygotic (DZ)
} adoption studies } familial aggregation
} are the 1st degree relatives affected more often than nonrelated individuals?
Misconceptions about heritability
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} It is not a proportion of phenotype that is genetic } it’s a proportion of phenotype variation that is due to
genetic factors
} It is not the similarity of parents to offspring } typical autosomal recessive traits have 100% heritability,
but normal parents have affected children
} It is not an absolute property of a trait } depends on the population structure (allele frequencies),
environment etc.
Familial aggregation
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} Affective disorders (mood disorders) } unipolar (depression) } bipolar (manic-depressive disorder)
} Unipolar } population risk ~3 % (men) ~5-9% (women) } 1st degree relatives of an affected person – risk~10% } relatively low heritability
} Bipolar } population risk ~1% } 1st degree relatives of an affected person – risk ~ 20% } significant heritability
Familial aggregation
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Familial aggregation
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λS = population risk
risk for 1st degree relatives of affected individuals
E.g.
population 0.8
1st degree relatives 8.5
λS = 10,6
λS – recurrence risk ratio
Familial aggregation
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} Example λS values
} Mendelian traits: } cystic fibrosis: 0.25/0.0004 = 500 } Huntington’s: 0.5/0.0001 = 5000
} Multifactorial traits } autism: ~ 110 } sclerosis multiplex: ~25 } schizophrenia: ~10 } type I diabetes (IDDM): ~15 } type II diabetes (NIDDM): ~3.5
Association
} Nonrandom correlation of alleles and phenotypes in a population
} Is it always a causal relationship? } Does it always have a diagnostic/predictive value? } Does it reveal “genes for ....”?
Associations can be misleading
} the 3A4 allele of cytochrome P450 (CYP3A) and prostate cancer (worse prognosis, more advanced at diagnosis)
} CYP3A can influence the rate of testosterone hydroxylation - a causal relationship?
• The 3A4 allele does not influence testosterone metabolic rates • The 3A4 allele is more frequent in African populations (African-
American), than Eurasian • Similar correlations were observed for other alleles that are more
frequent in Africans • Worse prognosis of prostate cancer in African-Americans (socio-
economical causes - health care quality dependent on income)?
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Association} Functional dependence
} often involving MHC (HLA) alleles - immune function, autoimmune disorders
} looking for associations for common SNP alleles } pharmacogenetics and pharmacogenomics – polymorphisms
and drugs } CRHR1 (corticotropin receptor) - response of asthma to
corticosteroid treatment } HLA-B27 - sensitivity to Abcavir side effects } HTR2A (serotonin 2A) - reaction to antidepressants
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Ankylosing spondylitis
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Strong association - example} HLA-B27 and autoimmune disease - ankylosing
spondylitis (90% of patients are positive)
Affected Healthy
HLA-B27 + 90 1000
HLA-B27 - 10 9000
Fisher exact test:
p≈2·10-76
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Risk 8%
Risk 0.11%
Population risk ~1%
Genes for …?
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In fact ...
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For comparison} HLA-B27 and autoimmune disease - ankylosing
spondylitis
€
OR =
901000109000
= 81
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Affected Healthy
HLA-B27 + 90 1000
HLA-B27 - 10 9000
Fisher exact test:
p≈2·10-76
Important!
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} Association is not a “gene for...”! } Diagnostic value not certain, particularly for rare traits
} Could be useful in differential diagnosis
} Odds ratios are useless without a context of population risk
Causes of association
Need to study homogenous populations
Population structure (hidden variable)
Many artifacts
Simpson’s paradox: A trend that appears in different groups of data disappears when these groups are combined, and the reverse trend appears for the aggregate data
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Simpson’ paradox
} A simplified example: the university should not discriminate against women applying for graduate studies
Mężczyźni KobietyHistory 1/5 (20%) 2/8 (25%)
Geography 6/8 (75%) 4/5 (80%)
Total 7/13 (54%) 6/13 (46%)
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University of California, Berkeley, 1973
Dept. Men (admitted) Women (admitted)
A 825 62% 108 82% B 560 63% 25 68% C 325 37% 593 34% D 417 33% 375 35% E 191 28% 393 24% F 272 6% 341 7% Total 8442 44% 4321 35%
More women applied for more harder departments
Graduate school admissions
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A medical exampleTwo therapies for kidney stones compared
Therapy A Therapy B
78 % (273/350) 83% (289/350)
But:
Therapy A Therapy BSmall stones 93% (81/87) 87% (234/270)
Large stones 73% (192/263) 69% (55/80)
Total 78 % (273/350) 83% (289/350)
Therapy A used more frequently for larger stones (more difficult to cure)
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Double blind testing
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} In drug and treatment studies } treatment vs. placebo; new treatment vs. old, etc.
} Neither patient, nor the physician should know, which group an individual is in
Genetic risk factors} GWAS – genome wide association studies } Correlating genetic variation with disease risk } Many correlations found, but no major causal agents } The missing heritability problem
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Wellcome Trust study 2005-2007
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} GWA – genome-wide association } 7 common multifactorial disorders, 17 000
participants (affected and healthy), 200 researchers, 9 million UK pounds
} One of several recent large GWA studies
The results of the Wellcome Trust study
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} Bipolar disorder } Many correlations, none very significant
} Coronary heart disease } Several loci strongly influencing risk, one locus on chr. 9 by
50% in heterozygotes and twofold in homozygotes
} Crohn disease } Variants in 3 genes (RGM, NKX2-3 i PTPN2) increasing
risk and a new region containing 7 new genes
The results of the Wellcome Trust study
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} Hypertension } No strong causation - many polymorphism with small
individual effects
} Rheumatoid arthritis } New risk factors found } correlation with heart disease and type I diabetes
The results of the Wellcome Trust study
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} Type I diabetes } 4 new risk factors, including PTPN2 (Crohn disease) } At least 10 genes known
} Type II diabetes } Kilka nowych czynników ryzyka } FTO - indirect effect, obesity risk factor } CDKAL1, CDKN2A, IGF2BP2 - direct effect } Confirmed earlier known associations ~10 genes in total
Reproducibility of association studies
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Missing heritability
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} Many multifactorial traits show high heritability in twin and familial studies
} Genomic association studies show a slight increase in risk for any single polymorphism (e.g. any single polymorphism explains no more than 5% of differences in height)
} Heritability has to be explained by genetic interactions (epistatic)
Missing heritability} “dark matter” of the genome } known associations explain ~5% of heritability } possible explanations:
} other, rare variants } whole-genome sequencing, 1000 genome project
} genetic interactions } systems biology approach, interaction networks
} copy number variation
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Copy number variation
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} CNV is common and can include gene sequences } Associations found, e.g. with autism } Evolutionary studies
} e.g. higher copy number of amylase gene (AMY1) in populations of farming ancestry (high-starch diet) than hunter-gatherer ancestry (low-starch diet)
“One gene – one trait”?} Simpe one gene - one trait (Mendelian) relationship is rare } Most phenotypic traits are a result of interactions of many
genes (and environment) } Complex interaction networks – biological complexity is
built by evolution using combinatorics, not by increasing complexity of individual parts
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Example - human height} Variation in least 150 loci influence
this, relatively simple trait
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Perspectives
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} Go beyond studying single loci
} Genome-wide studies on multiple loci
Analysis using 1000 SNPs in skin cancer
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Evolutionary medicine
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