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

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1 1 1 0 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 1 1 0 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 0 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 1 1 1 1 0 1 1 1 1 1 1 1 1

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1 1 1 0 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 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 0 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 1 1 1 1 0 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 1 1 1 1 0 1 1 1 1 1 1 1 1

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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

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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 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

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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 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 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 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 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 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 1 1 1 1 1 1 1 1 1 1 1 1

1 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 1 0 1 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 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 00 0 1 0 0 0 1 1 1 1 0 1 1 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 01 0 1 1 1 1 0 0 0 0 1 1 1 0 0 1 0 0 0 0 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 1 1 0 0 0 0 0 ? 0 0 0 1 0 0 0 0 1 0 1 0 0 1 1 0 0 0 0 0 0 0 1 0 1 1 0 1 1 0 1 0 0 01 0 1 0 0 0 0 0 0 0 0 0 0 ? 0 1 0 0 0 0 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 1 0 0 1 1 1 1 0 0 0 1 1 0 1 1 0 ? 1 1 01 0 1 0 0 0 0 0 0 0 1 0 0 ? 0 1 1 0 1 1 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 1 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 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 1 1 0 1 0 0 10 0 0 1 1 1 0 0 0 0 0 0 0 1 0 0 0 1 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 1 0 0 0 1 0 0 0 0 0 1 0 1 1 0 1 1 0 1 0 0 00 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 1 1 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 0 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 1 0 1 0 0 0 0 1 10 0 0 1 1 1 1 1 1 1 0 1 1 0 0 0 1 0 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 0 0 0 1 0 0 1 1 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 01 0 1 0 1 0 0 0 0 0 0 0 0 ? 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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.

References

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