The Human Genomes Gil McVean, Department of Statistics, Oxford
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The Human Genomes
Gil McVean, Department of Statistics, Oxford
Genetic variation among humans
http://www.ncbi.nlm.nih.gov/genome/guide/human/
How do we differ? – Let me count the ways
• Single nucleotide polymorphisms– 1 every few hundred bp, mutation rate* ≈ 10-9
• Short indels (=insertion/deletion)– 1 every few kb, mutation rate v. variable
• Microsatellite (STR) repeat number– 1 every few kb, mutation rate ≤ 10-3
• Minisatellites– 1 every few kb, mutation rate ≤ 10-1
• Repeated genes– rRNA, histones
• Large inversions, deletions– Rare, e.g. Y chromosome
TGCATTGCGTAGGCTGCATTCCGTAGGC
TGCATT---TAGGCTGCATTCCGTAGGC
TGCTCATCATCATCAGCTGCTCATCA------GC
≤100bp
1-5kb
*per generation
Y chromosome variation
• Non-pathological rearrangements of the AZFc region on the Y chromosome
Tyler-Smith and McVean (2003)
Serological techniques for detecting variation
Human
Rabbit
A
A B AB O
Blood group systems in humans
• 28 known systems– 39 genes, 643 alleles
System Genes Alleles
ABO ABO 102
Colton C4A, C4B 7+
Chido-rodgers AQP1 7
Colton DAF 10
Diego SLC4A1 78
Dombrock DO 9
Duffy FY 9
Gerbich GYPC 9
GIL AQP3 2
H/h FUT1, FUT2 27/22
I GCNT2 7
Indian CD44 2
Kell KEL, XK 33/30
Kidd SLC14A1 8
Knops CR1 24+
Landsteiner-Wiener
ICAM4 3
Lewis FUT3, FUT6 14/20
Lutheran LU 16
MNS GYPA,GYPB,GYPE
43
OK BSG 2
P-related A4GALT, B3GALT3
14/5
RAPH-MER2 CD151 3
Rh RHCE, RHD, RHAG
129
Scianna ERMAP 4
Xg XG, CD99 -
YT ACHE 4
http://www.bioc.aecom.yu.edu/bgmut/summary.htm
HLA diversity at the MHC locus
DP DQ DR C4 C2 TNFa,b HLA-B HLA-C HLA-A
HLA-D
Class II Class III Class I
6p21.34 Mbp c. 127 genes
(18 genes)
European Caucasoids
African Blacks
HLA-A
Protein electrophoresis
+++
--- - -
-
++--- - -
-- - +
Starch or agar gel
Direction of travel
Lewontin and Hubby (1966)
Harris (1966)
The rise of DNA sequence analysis
• RFLPs– Cann et al 1987
• Sequencing of small regions– Vigilant et al 1991
• Whole genome sequencing– Ingman et al 2000
Different, but not that different
• Humans are one of the least diverse organisms (excepting cheetahs)
Species Diversity (percent)
Humans 0.08 - 0.1
Chimpanzees 0.12 - 0.17
Drosophila simulans 2
E. coli 5
HIV1 30
Photos from UN photo gallery www.un.org/av/photo
The biological significance of genetic variation
• Genetic variation must underlie both pathological and non-pathological traits that show significant heritability
– How do we locate these variants, and what use is finding them?
• Genetic variation has been influenced by several million years of human existence.
– How have human populations evolved over pre-historical times?
• The distribution of variation is influenced by fundamental evolutionary processes
– How has mutation, selection and recombination shaped the human genome?
Differences between autosomes, sex chromosomes, mtDNA
• Under very simple models of populations, average pairwise differences is predicted by the formula
• If ≈ 1.5x10-9 per site per generation, this implies that the human population is < 15,000– Population geneticists refer to this number as the effective population size
Genome Average pairwise differences / kb Relative copy number ()
Autosomes 0.5 – 0.85 1
X chromosome 0.47 3/4
Y chromosome 0.15 1/4
mtDNA 2.8 1/4
TISMWG (2001) Jobling, Hurles, Tyler-Smith (2004)
N4
Demographic factors affecting diversity
• Diversity is influenced by demographic factors such as– Variance in reproductive success– Differences in variance of success between males and females– Heritability of reproductive success– Changes in population size (growth, bottlenecks, natural fluctuations)
• Which effects are most important?– Iceland: faster drift in matrilines due to shorter generation interval, but no differences
between the sexes (Helgason et al 2003)– Quebec: heritability of reproductive success reduces diversity by more than an order in
magnitude (Austerlitz and Heyer 1998)
• The effective population size (Ne) is an approximation that allows simple mathematical models of populations to be applied to real data
• Ne<< N
Diversity is not randomly distributed across the genome
TISMWG (2001)
Chromosome 6
HLA
Correlates and determinants of diversity
• There is systematic variation in the mutation rate along chromosomes– Wolfe and Sharp (1987), Lercher et al (2001)
• Levels of diversity correlate with recombination rates– Nachman et al (1998)
• Diversity and the allele frequency spectrum of SNPs are influenced by the local GC content (above CpG frequency)
– Eyre-Walker (1999), Smith and Eyre-Walker (2001) Lercher et al (2002)
• Recombination rates are correlated (to some degree) with GC content– Eyre-Walker (1993), Fullerton et al (2001), Kong et al (2002)
Lercher and Hurst 2002Lercher et al (2001)
What is the link between recombination and diversity?
• A positive correlation between recombination rate and diversity could mean– Recombination is mutagenic– Diversity promotes recombination– Recombination and mutation are linked by a third factor (chromatin accessibility,
transcription, Hill-Robertson effects)
Hellmann et al 2003Recombination rate (cM/Mb)
Div
ers
ity ()
Hitch-hiking
Mutation
Diversity is not evenly distributed across genes I
• Adaptive evolution ‘wipes out’ diversity nearby due to the hitch-hiking effects of a selective sweep
– e.g. Duffy-null locus in sub-Saharn africa, protects against P. vivax– Hamblin and Di Rienzo (2000)
Pop1
Pop2
0 0 0 0 0 0 0 0 0 0 0 0 0 0 C 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 C 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 C 0 0 0 0 0 0 0 0C 0 0 0 T 0 0 0 0 0 0 0 0 0 C 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 C 0 0 0 0 0 0 0 00 0 0 0 0 0 0 0 0 0 0 0 0 0 C 0 0 0 T 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 C 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 G 0 0 -1 -1 C C 0 0 0 0 0 0 00 0 0 0 0 0 0 0 0 G 0 0 -1 -1 C C 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 G 0 0 -1 -1 C C 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 C 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 C 0 0 C 0 0 0 0 00 0 0 0 0 0 0 0 0 0 0 0 0 0 C 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 C 0 0 0 0 0 0 0 00 0 0 0 0 0 0 0 0 0 0 0 0 0 C 0 C 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 C 0 0 0 0 0 0 0 00 0 0 0 0 0 0 0 0 0 0 0 0 0 C 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 C 0 0 0 0 0 0 0 0
C 0 0 0 T 0 0 0 0 0 0 0 0 0 C 0 0 0 0 0 0 0 0C 0 0 0 T 0 0 0 0 0 0 0 0 0 C 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 C 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 C 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 C 0 0 0 0 0 0 0 00 0 0 0 0 0 0 0 0 0 0 0 0 0 C 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 G 0 0 -1 -1 C C 0 0 0 0 0 0 00 0 0 0 0 0 0 0 0 G 0 0 -1 -1 C C 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 G 0 0 -1 -1 C C 0 0 0 0 0 0 00 0 0 0 0 0 0 0 0 G 0 0 -1 -1 C C 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 G 0 0 -1 -1 C C 0 T 0 0 0 0 0
0 0 0 0 0 0 0 0 0 G 0 0 -1 -1 C C 0 T 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 C 0 0 0 0 0 0 0 00 0 0 0 0 0 0 0 0 0 0 0 0 0 C 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 C 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 C 0 0 0 0 0 0 0 00 0 0 0 0 0 0 0 0 0 0 0 0 0 C 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 C 0 0 0 0 0 0 0 00 0 0 0 0 0 0 0 0 G 0 0 -1 -1 C C 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 G 0 0 -1 -1 C C 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 C 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 C 0 0 0 0 0 0 0 00 0 0 0 0 0 0 0 0 0 0 0 0 0 C 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 C 0 0 0 0 0 0 0 00 0 0 0 0 0 0 0 0 0 0 0 0 0 C 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 C 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 C 0 0 0 0 0 0 0 00 0 0 0 0 0 0 0 T 0 0 0 0 0 C 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 C 0 0 0 0 0 0 0 00 0 G 0 0 0 0 0 0 0 0 0 0 0 C 0 0 0 0 0 0 0 0
0 G 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 G 00 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 T 0 G 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 T 0 G 00 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 T 0 G 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 T 0 G 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 T 0 G 00 0 0 T 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 T 0 G 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 T 0 G 00 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 T 0 G 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 T 0 G 00 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 T 0 G 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 T 0 0 0 0 G 0
C 0 0 T 0 0 0 0 0 0 0 0 0 0 0 0 T 0 0 0 0 G 00 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 T 0 0 0 0 G 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 T 0 0 0 0 0 00 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 T 0 0 0 0 0 0
0 0 0 T 0 0 0 0 0 0 0 0 0 0 0 0 T 0 0 0 0 0 0
0 0 0 T 0 0 0 0 0 0 0 0 0 0 0 0 T 0 0 0 0 0 00 0 0 T 0 0 0 0 0 0 0 0 0 0 0 0 T 0 0 0 0 0 A
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 T 0 0 0 0 0 A0 0 0 T 0 0 0 0 0 0 0 0 0 0 0 0 T 0 0 0 0 0 A
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 T 0 0 0 0 0 AC 0 0 0 T 0 -1 0 0 0 T 0 0 0 0 0 T 0 0 0 0 0 0
C 0 0 0 T 0 -1 0 0 0 T 0 0 0 0 0 T 0 0 0 0 0 0
C 0 0 0 T 0 -1 0 0 0 T 0 0 0 0 0 T 0 0 0 0 0 0C 0 0 0 T 0 -1 0 0 0 T 0 0 0 0 0 T 0 0 0 0 0 0
C 0 0 0 T 0 -1 0 0 0 T 0 0 0 0 0 T 0 0 0 0 0 0C 0 0 0 T 0 -1 0 0 0 T 0 0 0 0 0 T 0 0 0 0 0 0
C 0 0 0 T 0 -1 0 0 0 T 0 0 0 0 0 T 0 0 0 0 0 0
C 0 0 0 T T -1 G 0 0 T T 0 0 0 C 0 0 0 0 0 0 0C 0 0 0 T T -1 G 0 0 T T 0 0 0 C 0 0 0 0 0 0 0
C 0 0 0 T T -1 G 0 0 T T 0 0 0 C 0 0 0 0 0 0 00 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 C 0 0 0 0 G 0 0
0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 C 0 0 0 0 G 0 0
European
African
FY*O mutation
Ancestral alleleDerived alleleMissing data
Diversity is not evenly distributed across genes II
• Purifying selection eliminates deleterious mutations and reduces diversity in regions of strong functional constraint
0123456789
Intergenic Intronic Exonic UTR
SN
Ps
pe
r 1
0k
b
Zhao et al (2003)
Diversity is not evenly distributed across genes III
• Some genes are under balancing or diversifying selection, where diversity is actively selected for
– MHC complex: heterozygote advantage and frequency-dependent selection driven by recognition of pathogens
Horton et al (1998)
Diversity is not evenly distributed across populations I
• African populations are more diverse than non-African populations– More polymorphisms– Polymorphisms at less skewed frequencies
• Why?– Out-of-Africa event associated with a bottleneck?– Selection on genome in adaptation to novel habitats?
Population Segregating sites per kb (n = 30)
Diversity per kb
Tajima D statistic
Hausa (African)
4.8 0.11 -0.33
Italian 3.2 0.10 1.18
Chinese 3.0 0.07 1.19
Frisse et al (2001)
The Tajima D statistic
• Measures departure from neutral coalescent expectations in allele frequency distribution
– +ve values indicate excess of intermediate frequency variants– -ve values indicate excess of low-frequency variants– E.g. human mtDNA
0
20
40
60
80
100
120
140
160
180
200
1 11 21
Rare allele frequency
No. sites
Observed
Expected23.2D
Data from Ingman et al (2000)
Diversity is not evenly distributed across populations II
• Small, isolated populations often have skewed allele frequencies (+ve Tajima D) due to founder effects and high degree of genetic drift
– Marginal populations (Evenki, Saami)– Island populations (Iceland, Sardinia)
Finns
Swedes
Saami
Evenki
Minor allele frequencies at 50 SNPs (Kaessmann et al 2002)
The second dimension of human diversity!
• The distribution of alleles at different loci are not independent
• Correlations between SNPs are particularly strong for those <50kb
• These correlations indicate shared evolutionary history
Lipoprotein Lipase: 10kb48 African Americans
Chromosome 22: 1Mb57 Europeans
Xq13: 10kb69 worldwide
1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 11 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 11 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 11 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 0 1 1 1 1 1 1 1 1 11 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 11 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 11 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 11 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 11 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 0 0 1 0 1 0 1 1 1 1 1 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1 1 1 0 1 1 1 0 1 1 1 1 1 1 0 1 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 11 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 11 0 1 1 1 0 0 0 1 1 1 1 1 0 0 1 0 1 1 1 0 1 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 11 1 1 1 0 0 0 0 1 1 1 0 0 0 1 0 0 1 1 1 1 1 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 0 1 1 1 11 1 1 1 0 0 0 0 1 1 1 0 0 0 1 0 0 1 1 1 1 1 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 11 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 0 1 0 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 1 1 1 1 1 11 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 0 1 0 1 1 1 2 2 1 2 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 0 1 1 0 2 1 1 11 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 0 1 1 1 11 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 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1 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 1 0 0 1 0 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 01 0 1 1 1 1 1 1 1 1 0 1 1 0 0 0 0 ? 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 ? 00 0 0 0 0 0 1 1 1 1 0 1 1 0 0 0 1 ? 1 1 0 0 0 0 1 1 1 1 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 1 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 ? 10 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 ? ? 0 1 0 0 0 0 ? 1 1 1 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 ? 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 ? 00 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 ? ? 0 0 0 0 0 0 ? 1 1 1 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 0 0 1 0 1 0 0 0 0 0 0 1 1 0 ? 1 1 1 0 0 0 0 0 0 1 0 0 0 0 0 ? 01 0 1 1 1 0 0 1 1 0 1 0 0 1 1 0 0 0 0 1 1 1 0 1 0 1 0 1 0 1 1 1 1 0 1 1 1 0 0 1 0 0 1 0 1 1 1 0 1 1 0 0 1 0 1 1 0 0 1 0 1 1 0 1 1 1 0 1 1 1 0 0 1 1 1 1 1 1 0
Ch
rom
oso
me
s
Sites
Correlations between SNPs are measured by linkage disequilibrium
Linkage equilibrium Linkage disequilibrium
AB Ab
aB ab
AB
Ab
aBab
Af
af
Bf bf
Af
af
Bf bf
Why are SNPs correlated?
The mutation arises on a particular genetic background
If the mutation increases in frequency by drift (or selection) the associated haplotype will also increase in frequency
Over time the association between the new mutation and linked mutations will decay by recombination
... ... ...
What generates and destroys LD?
• Genetic drift– Stochastic sampling process in finite population
• Population structure and admixture– Correlations between mutations arising through shared population history
• Natural selection– Combinations of favoured/unfavoured alleles (weak force)
• Recombination is the ONLY force which breaks down LD– LD is a balance between recombination and other forces
Empirical patterns of LD
• Large-scale surveys of LD in humans– e.g. Huttley et al. (1999), Abecasis et al. (2001), Reich et al. (2001)
– LD extends over considerable distance (>>10kb) in most populations
Reich et al. (2001)
Distance (kb)
|D’|
Kruglyak prediction
1 5 10 20 40 80 160 unlinked
Differences between populations
• African populations show less LD than European populations (e.g. Frisse et al. 2001)
• Small, isolated populations (e.g. Saami, Evenki) show increased LD (Kaessmann et al 2002)
• Founder populations (e.g. Finland, Sardinia) do not always show increased LD (e.g. Eaves et al. 2000)
Mean r2 (MAF > 10%)
0
0.1
0.2
0.3
0.4
0.5
0.6
0 20 40 60 80 100 120 140 160 180 200
Distance (kb)
African American
Asian
W.Eur
r2
Assessing the contribution of structure to LD
• Rosenberg et al. (2002)
• Population differences in allele frequency exist, but many markers/loci are required in order to estimate ethnic origin with accuracy
• Admixture between populations has played an important historical role
AmericaOceania
AsiaMiddle eastEuropeAfrica
Differences between genomic regions
• Evidence for heterogeneity in LD along/between chromosomes– Taillon-Miller et al (2000), Jeffreys et al (2001), Daly et al (2001), Patil et al (2001), Reich
et al (2001), Reich et al (2002), Gabriel et al (2002), Dawson et al (2002), Phillips et al (2002)
Reich et al (2001)
0.00
0.25
0.50
0.75
1.00
0 5 10 15 20 25 30
D'
Avera
ge |
D’|
Dawson et al (2002)
Differences within genomic regions
Jeffreys et al (2001)
Recombination hot-spots in the MHC region
• Other genes with recombination hot-spots– B-globin– PAR/SHOX– MS32– (Chi sequences)
Jeffreys et al (2001)
In an ideal block world...
Pääbo (2003)
• Blocks extend many (>100) kbs.• All alleles within blocks are in strong associations.• There are no associations between blocks.• In each block, only a few (4-5) haplotypes account for the majority (>90%) of variation.• In each block, only a few SNPs are required to map the majority of haplotype variation.• Blocks correspond to recombination hot-spots.
“Association studies suddenly look much less difficult...” Goldstein (2001)
The international Hapmap project
• International partnership of scientists and funding agencies from Canada, China, Japan, Nigeria, the United Kingdom and the United States to develop a public resource that will help researchers find genes associated with human disease and response to pharmaceuticals
– Gibbs et al (2003)
• Aims to survey variation across entire human genome at 1 SNP per 5kb or less, in three populations (CEPH Europeans, Chinese/Japanese, Yoruban Africans). More than 600,000 SNPs with MAF>5%
– http://www.hapmap.org/
• All data is public access and available through the Data Coordination Center (DCC)
How are blocks defined?
• Incompatibility through the four-gamete test– Wang et al. (2002)
• Regions with consistently high pairwise LD measures– Gabriel et al. (2002)
• Dynamic programming solutions based on– Measures of pairwise LD structure - Zhang et al. (2002)– Minimum description length (information theoretic principles) – Koivisto et al.
(2002), Anderson et al (2003)
Empirical block pattern
Daly et al (2001)
Blocks
Length
% match
frequencies
Problems with blocks
• Block definitions depend on marker spacing, allele frequency and algorithm.• Blocks (as defined by some algorithms) may not reflect variation in the
recombination rate
0
20
40
60
80
100
120
140
0 10 20 30 40 50 60
Distance between adjacent markers (kb)
Med
ian
blo
ck l
eng
th (
kb)
Gabriel et al. [E(block) = 22 kb]
Patil et al [E(block) = 13.3 kb]
Dawson et al. [E(block) = 38 kb]
All reported mean block lengths consistent with uniform recombination (+ 1 SD)
Phillips et al (2003)
Do we need haplotype blocks?
• The key determinant of LD is recombination– True haplotype blocks are formed by regions of low recombination separated by
recombination hotspots
• If we knew the fine-scale (<<Mb) structure of recombination-rate variation, blocks would not be necessary
• Genetic maps estimated from pedigree studies show recombination rate variation
• BUT do not have the resolution to define recombination hotspots
Chromosome 3Kong et al (2000)
Rate estimates from sperm
(Jeffreys et al 2001)
Genes
n=50 unrelated European genotypes
0.01
0.1
1
10
100
1000
0 50 100 150 200
Learning about recombination from diversity
• We can estimate the fine-scale structure of recombination rates from patterns of genetic variation
Comparison with pedigree-based maps
• Summing fine-scale estimates over 2Mb intervals accurately recovers variation in recombination rate detected by pedigree studies
Position (kb)Position (kb)
Sex
-ave
rag
ed
reco
mb
inat
ion
rat
e (c
M/M
b)
Chromosome 19 Chromosome 22
Markers for pedigree-based map
PedigreePopulation genetic
0.0
1.0
2.0
3.0
4.0
5.0
35000 45000 55000 65000 75000
0.0
1.0
2.0
3.0
4.0
5.0
0 5000 10000 15000 20000 25000 30000 35000
A chromosomal view of recombination rate variation
• 10Mb of Chromosome 20, 96 CEPH genotypes, 4337 SNPs
0.001
0.01
0.1
1
10
100
39000 41000 43000 45000 47000 49000
Position
Sex
-ave
rage
d re
com
bina
tion
rate
(cM
/Mb)
deCODE rates for region
Population genetic estimate of rates 2.5 - 97.5 percentiles of sampling distribution
Genes on forward and reverse strands
NCOA3
• What is the probability that there exists a SNP in this region that is NOT in LD with currently observed SNPs?
?
SNP not in LD? SNP in LD
If recombination is high, the untyped SNP is unlikely to be in association
The answer depends on recombination
Recombination rate
If recombination is low, the untyped SNP is likely to be in association
Recombination rate
We can use population genetic methods to estimate the recombination rate and predict the distribution of the untyped
SNP
Hapmap challenges
• Prediction– Do the SNPs currently genotyped provide an accurate representation of variation
at linked SNPs in other samples from the same population?
• Selection of tagging SNPs– What is the smallest number of SNPs I need type in order to achieve a given
level of power?
• Demography– Are the results from one population transferable to other populations?
Suggested reading
• Jobling MA, Hurles ME and Tyler-Smith C. 2004. Human Evolutionary Genetics: Origins, Peoples & Disease. Garland Science
• Balding DJ, Bishop M and Cannings C. 2001. Handbook of Statistical Genetics. John Wiley and Sons Ltd.
• Li W-H. 2001. Molecular evolution. Sinauer.
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