Signals of natural selection in the HapMap project data

The International HapMap Consortium

Gil McVean

Department of Statistics, Oxford University

The International HapMap Project

• To facilitate the design and analysis of association studies

• A genome-wide map of genetic variation across 270 individuals from four populations

– CEPH families from Utah– Yoruba from Nigeria– Han Chinese from Beijing– Japanese from the Tokyo region

• Phase I collected data on approximately 1.2 million SNPs

• Phase II increases SNP density to more than one per kb

• All data publicly available at www.hapmap.org

Looking for selection

• A genome-wide map of variation can also be used to hunt for regions of the genome where natural selection has acted

– Selective sweeps– Balancing selection– Local adaptation

• Why?– Interest– Functional polymorphism– The signal of selection we observe tells us about the genetic architecture of traits

Methods for mapping selection

• Model-based– Compare genetic variation to ‘neutral’ model

• Purely empirical– Consider the ‘most extreme’ genomic regions

• ‘Calibrated’– Compare to examples of (very few) proven selective importance

In what way are selected regions unusual?

(in the HapMap data)

HLA

17q21 inversion

Lactase

Duffy

HLA and resistance to infectious disease

The HLA region shows extremely high levels of polymorphism

HLA

17q21 inversion and reproductive success

The inversion has multiple (66) SNPs in perfect association (r2 = 1)

LCT and lactase persistence

The LCT gene shows an extended haplotype structure in European populations

The Duffy locus and resistance to Plasmodium vivax

The FY gene shows extreme population differentiation

Different selective histories leave different footprints in genetic variation

How much of the genome looks as ‘unusual’ as these selected loci?

Heterozygosity as extreme as HLA

HLA

Sets of perfect proxies as extreme as the 17q21 inversion

Inversion

EHH as extreme as LCT

Lactase

Differentiation as extreme as the Duffy locus (NB not FY*O)

Duffy

For ¾ cases, the selected locus is at the very extreme of the genome-wide distribution

What can we learn from the unusual, but less extreme cases?

Heterozygosity across the genome

Bottom 1%

Top 1%

Top 5%

Top 10%

Bottom 10%

Bottom 5%

Elevated heterozygosity on 8p

Chromosome 8

Chromosome 6

MHC

8p23 inversion

Distribution of long runs of perfect proxies

≥ 50 SNPs

20 – 50 SNPs

10-20 SNPs 17q21 Inversion

An inversion on the X chromosome?

Distribution of EHH

Top 1%

Top 10%

Top 0.1%

A selective sweep on chromosome 5?

Distribution of differentiation

Top 1%

Top 10%

Top 0.1%

SLC24A5

Lamason et al (Science 2005)

Unusual regions of the genome suggest interesting biology

BUT

The hypothesis of historical selection is fundamentally untestable

What hypothesis can we test?

Signals of selection should tend to occur near regions of known functional importance

i.e. genes

Are genes over-represented in regions of high heterozygosity?

Are genes over-represented in regions of high proxy number?

Are genes over-represented in regions of high EHH?

Are genes over-represented in regions of high differentiation?

Only differentiation shows a tendency for an increased density of ‘selection’ near genes

The wild speculation

Selection on standing variation

• Why should we see an excess of one type of signal of adaptive evolution near genes, but not another?

• Perhaps the signals are sensitive to assumptions about selection occurs?

• EHH methods will be most powerful for identifying selection on a single, novel mutation

• Differentiation will pick cases where an already polymorphic mutation, present on multiple haplotype backgrounds, becomes favoured in one geographic region

• Perhaps most selection has been on standing variation?

Acknowledgements

• The International HapMap Consortium

• Oxford Statistics– Peter Donnelly, Simon Myers, Chris Spencer, Raphaelle Chaix

• Funding agencies– NIH, TSC, The Wellcome Trust, BBSRC, the Fyssen Foundation

Distribution of Fay and Wu’s H statistic

Bottom 1%

Bottom 10%

Bottom 0.1%

Distribution of Tajima D statistic

Bottom 1%

Top 1%

Top 5%

Top 10%

Bottom 10%

Bottom 5%

Tajima D (negative) Fay and Wu H (negative)

Numbers of SNPs

Chromosome #SNPs in common files

#SNPs QC’ed, polymorphic and with ancestral inferred

Percent converted

Chromosome Length

Approx. SNP spacing

1 75850 64107 0.8451813 246043912 3.842 82565 74829 0.9063041 243407499 3.253 59417 52523 0.8839726 199282781 3.794 53219 47878 0.8996411 191710711 4.005 53324 48504 0.9096092 180825316 3.736 61829 55344 0.8951139 170902878 3.097 42588 35240 0.8274631 158542415 4.508 65506 60306 0.920618 146305419 2.439 51906 47285 0.9109737 136326725 2.88

10 46073 41185 0.8939075 135035657 3.2811 41299 36687 0.8883266 134481573 3.6712 38433 34895 0.9079437 132017602 3.7813 33757 30779 0.9117813 113025098 3.6714 27143 24487 0.9021479 105260053 4.3015 24615 22124 0.8988015 100133324 4.5316 23400 20779 0.8879915 89915381 4.3317 23235 20576 0.8855606 81724082 3.9718 35931 33137 0.9222398 76114138 2.3019 16505 14246 0.8631324 63788762 4.4820 19275 15700 0.8145266 63686957 4.0621 17933 16281 0.9078793 46956357 2.8822 17244 15196 0.8812341 49375569 3.25

X PAR 1 408 5 0.0122549 2689596 537.92X non PAR 53594 41682 0.7777363 150671647 3.61X PAR 2 45 0 0 328507 NATotals 965094 853775 0.8846548 3018551959 3.54