Whole-Genome

Patterns

of

Common

DNA

Variation

in

Three

Human

Populations

David

A.

Hinds

et

al.

Science

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DOI

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RESEARCH

ARTICLES

R.

Sharer

D.

Grove

Eds.

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Perspectives

on

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Press

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Grove

J.

World

Prehistory

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Cyphers

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to

Aztec

Settlement

Patterns

in

the

Ancient

Gulf

Lowlands

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Stark

P.

Arnold

III

Eds

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of

Arizona

Press

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AZ

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pp.

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Flannery

J.

Marcus

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Pottery

of

the

Valley

of

Oaxaca

Mexico

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of

the

Museum

of

Anthropology

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Univ.

of

Michigan

Ann

Arbor

MI

-RRB-

J.

Blomster

Etlatongo

Social

Complexity

Interaction

and

Village

Life

in

the

Mixteca

Alta

of

Oaxaca

Mexico

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Wadsworth

Belmont

CA

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Blomster

Ancient

Mesoamerica

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Coe

R.

Diehl

In

the

Land

of

the

Olmec

Vol

The

Archaeology

of

San

Lorenzo

Tenochtitlan

-LRB-

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of

Texas

Press

Austin

TX

-RRB-

K.

Flannery

in

Dumbarton

Oaks

Conference

on

the

Olmec

E.

Benson

Ed

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Oaks

Washington

DC

-RRB-

pp.

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Flannery

J.

Marcus

J.

Anthropol

Archaeol

-LRB-

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H.

Neff

M.

Glascock

Report

on

Instrumental

Neutron

Activation

Analysis

of

Olmec

Pottery

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Research

Reactor

Center

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of

Missouri

Columbia

MO

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Herrera

H.

Neff

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Glascock

J.

Elam

J.

Archaeol

Science

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Thieme

in

Procesos

de

Cambio

y

Conceptualizacion

del

Tiempo

Memoria

de

la

Primera

Mesa

Redonda

de

a

Monte

Alban

N.

Robles

Garci

Ed

-LRB-

Instituto

Nacional

a

de

Antropologi

e

Historia

Mexico

City

-RRB-

pp.

J.

Lorenzo

Rev.

Mex

Estud

Antropol

-LRB-

-RRB-

M.

Kirkby

The

Physical

Environment

of

the

Nochixtlan

Valley

Oaxaca

Vanderbilt

University

Publications

in

Anthropology

-LRB-

Vanderbilt

Univ.

Nashville

TN

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M.

Glascock

in

Chemical

Characterization

of

Ceramic

Pastes

in

Archaeology

H.

Neff

Ed

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Prehistory

Press

Madison

WI

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pp.

H.

Neff

in

Modern

Analytical

Methods

in

Art

and

Archaeology

E.

Ciliberto

G.

Spoto

Eds

-LRB-

Wiley

New

York

-RRB-

pp.

Materials

and

methods

are

available

as

supporting

material

on

Science

Online

R.

Bishop

H.

Neff

in

Archaeological

Chemistry

IV

R.

Allen

Ed

-LRB-

American

Chemical

Society

Washington

DC

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pp.

P.

Joralemon

A

Study

of

Olmec

Iconography

Studies

in

Pre-Columbian

Art

and

Archaeology

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Dumbarton

Oaks

Washington

DC

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Clark

M.

Pye

in

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Art

and

Archaeology

in

Mesoamerica

J.

Clark

M.

Pye

Eds

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National

Gallery

of

Art

Washington

DC

-RRB-

pp.

We

thank

the

technicians

at

MURR

and

the

archaeologists

in

Mexico

who

provided

samples

for

this

study

as

well

as

funding

agencies

that

made

their

collections

possible

The

support

and

permissions

of

a

the

Instituto

Nacional

de

Antropologi

e

Historia

aespecially

L.

Mirambell

M.

Serra

Puche

J.

Garci

Barcena

E.

Lopez

Calzada

and

N.

Robles

Garcia

have

been

invaluable

Funding

for

the

samples

in

table

came

primarily

from

the

Charles

J.

MacCurdy

Endowment

Yale

University

the

non-Etlatongo

samples

were

provided

by

M.

Winter

and

M.

Coe

The

research

at

the

MURR

Archaeometry

Lab

has

been

supported

by

NSF

-LRB-

no

-RRB-

We

are

grateful

for

the

support

of

the

Sainsbury

Research

Unit

University

of

East

Anglia

We

thank

R.

Diehl

A.

Joyce

G.

Lau

and

an

anonymous

reviewer

for

comments

Supporting

Online

Material

Materials

and

Methods

Tables

to

References

November

accepted

January

science

Whole-Genome

Patterns

of

Common

DNA

Variation

in

Three

Human

Populations

David

A.

Hinds

Laura

L.

Stuve

Geoffrey

B.

Nilsen

Eran

Halperin

Eleazar

Eskin

Dennis

G.

Ballinger

Kelly

A.

Frazer

David

R.

Individual

differences

in

DNA

sequence

are

the

genetic

basis

of

human

variability

We

have

characterized

whole-genome

patterns

of

common

human

DNA

variation

by

genotyping

single-nucleotide

polymorphisms

-LRB-

SNPs

-RRB-

in

Americans

of

European

African

and

Asian

ancestry

Our

results

indicate

that

these

SNPs

capture

most

common

genetic

variation

as

a

result

of

linkage

disequilibrium

the

correlation

among

common

SNP

alleles

We

observe

a

strong

correlation

between

extended

regions

of

linkage

disequilibrium

and

functional

genomic

elements

Our

data

provide

a

tool

for

exploring

many

questions

that

remain

regarding

the

causal

role

of

common

human

DNA

variation

in

complex

human

traits

and

for

investigating

the

nature

of

genetic

variation

within

and

between

human

populations

Single-nucleotide

polymorphisms

-LRB-

SNPs

-RRB-

are

the

most

abundant

form

of

DNA

variation

in

the

human

genome

It

has

been

estimated

that

there

are

million

common

SNPs

with

a

minor

allele

frequency

-LRB-

MAF

-RRB-

of

at

least

across

the

entire

human

population

-LRB-

-RRB-

Most

common

SNPs

are

to

be

found

in

most

major

populations

although

the

frequency

of

any

allele

may

vary

considerably

between

populations

-LRB-

-RRB-

An

additional

million

SNPs

exist

with

a

MAF

between

and

In

addition

there

are

innumerable

very

rare

Perlegen

Sciences

Inc.

Stierlin

Court

Mountain

View

CA

USA

Computer

Science

Institute

Berkeley

CA

USA

of

Computer

Science

and

Engineering

University

of

California

San

Diego

La

Jolla

CA

USA

To

whom

correspondence

should

be

addressed

E-mail

single-base

variants

most

of

which

exist

in

only

a

single

individual

The

relationship

between

DNA

variation

and

human

phenotypic

differences

-LRB-

such

as

height

eye

color

and

disease

susceptibility

-RRB-

is

poorly

understood

Although

there

is

evidence

that

both

common

SNPs

and

rare

variants

contribute

to

the

observed

variation

in

complex

human

traits

-LRB-

-RRB-

the

relative

contribution

of

common

versus

rare

variants

remains

to

be

determined

The

structure

of

genetic

variation

between

populations

and

its

relationship

to

phenotypic

variation

is

unclear

Similarly

the

relative

contribution

to

complex

human

traits

of

DNA

variants

that

alter

protein

structure

by

amino

acid

replacement

versus

variants

that

alter

the

spatial

or

temporal

pattern

of

gene

expression

without

altering

protein

structure

is

unknown

In

SCIENCE

some

cases

these

issues

have

been

studied

in

limited

genomic

intervals

but

comprehensive

genomic

analyses

have

not

been

possible

Genome-wide

association

studies

to

identify

alleles

contributing

to

complex

traits

of

medical

interest

are

currently

performed

with

subsets

of

common

SNPs

and

thus

they

rely

on

the

expectation

that

a

disease

allele

is

likely

to

be

correlated

with

an

allele

of

an

assayed

SNP

Although

studies

have

shown

that

variants

in

close

physical

proximity

are

often

strongly

correlated

this

correlation

structure

or

linkage

disequilibrium

-LRB-

LD

-RRB-

is

complex

and

varies

from

one

region

of

the

genome

to

another

as

well

as

between

different

populations

-LRB-

-RRB-

Selection

of

a

maximally

informative

subset

of

common

SNPs

for

use

in

association

studies

is

necessary

to

provide

sufficient

power

to

assess

the

causal

role

of

common

DNA

variation

in

complex

human

traits

Although

a

large

fraction

of

all

common

human

SNPs

are

available

in

public

databases

lack

of

information

concerning

SNP

allele

frequencies

and

the

correlation

structure

of

SNPs

within

and

between

human

populations

has

made

it

difficult

to

select

an

optimal

subset

Here

we

examine

the

SNP

allele

frequencies

and

patterns

of

LD

between

SNPs

distributed

uniformly

across

the

human

genome

in

unrelated

individuals

of

European

African

and

Asian

ancestry

Our

primary

aim

was

to

create

a

resource

for

further

investigation

of

the

structure

of

human

genetic

variation

and

its

relationship

to

phenotypic

differences

A

dense

SNP

map

To

characterize

a

panel

of

markers

that

would

be

informative

in

whole-genome

association

studies

we

selected

a

total

of

SNPs

likely

to

be

common

in

individuals

of

diverse

ancestry

-LRB-

-RRB-

We

identified

the

majority

-LRB-

-RRB-

of

the

SNPs

by

performing

array-based

resequencing

FEBRUARY

VOL

Downloaded

from

on

May

RESEARCH

ARTICLES

of

human

DNA

samples

of

diverse

ancestry

-LRB-

-RRB-

These

SNPs

were

supplemented

with

SNPs

chosen

from

public

databases

to

obtain

a

more

uniform

physical

distribution

across

the

human

genome

Further

details

of

the

SNP

ascertainment

are

given

in

the

supporting

online

material

-LRB-

-RRB-

We

designed

high-density

oligonucleotide

arrays

for

genotyping

these

SNPs

-LRB-

-RRB-

and

roughly

long-range

polymerase

chain

reaction

-LRB-

PCR

-RRB-

primer

pairs

covering

the

selected

SNPs

with

an

average

of

eight

SNPs

per

individual

region

being

amplified

by

PCR

The

amplicons

had

an

average

length

of

kb

and

covered

of

the

available

human

genome

An

average

of

amplicons

derived

from

a

single

individual

were

pooled

and

hybridized

to

a

single

highdensity

oligonucleotide

array

producing

genotypes

for

SNPs

We

genotyped

unrelated

individuals

from

three

populations

European

Americans

African

Americans

and

Han

Chinese

from

the

Los

Angeles

area

The

individuals

genotyped

here

were

not

related

to

the

individuals

previously

used

for

SNP

discovery

DNA

samples

were

selected

from

the

Coriell

Cell

Repositories

Human

Variation

Collection

and

we

relied

on

Coriell

's

determinations

of

sample

populations

We

complied

with

all

Coriell

policies

for

research

use

DNA

of

samples

from

named

populations

Each

SNP

was

scored

with

a

combination

of

metrics

that

had

been

shown

to

correlate

with

genotype

quality

on

our

platform

and

data

for

poorly

performing

SNPs

was

rejected

These

metrics

included

the

call

rate

the

number

of

observed

genotype

clusters

the

existence

of

near-perfect

matches

for

SNP

flanking

sequences

elsewhere

in

the

genome

the

presence

of

other

known

SNPs

in

probeflanking

sequences

and

consistency

with

Hardy

Weinberg

equilibrium

Tests

for

Hardy

Weinberg

equilibrium

are

very

effective

for

identifying

some

types

of

genotyping

artifacts

-LRB-

-RRB-

however

because

we

used

these

tests

for

quality

control

our

genotype

data

are

unsuitable

for

investigating

biologically

interesting

true

deviations

from

Hardy

Weinberg

equilibrium

Further

details

of

our

genotype

quality

control

are

described

in

the

supporting

online

material

-LRB-

-RRB-

A

subset

of

SNPs

was

successfully

genotyped

based

on

our

quality

criteria

with

two

alleles

each

observed

at

least

once

among

the

individuals

In

total

more

than

million

individual

genotypes

were

determined

for

these

SNPs

There

were

no

missing

genotypes

for

of

these

SNPs

and

of

these

SNPs

had

less

than

missing

data

The

overall

frequency

of

successful

genotype

calls

was

SNP

assay

details

and

individual

genotypes

have

been

deposited

in

the

National

Center

for

Biotechnology

Information

-LRB-

NCBI

-RRB-

's

SNP

database

-LRB-

dbSNP

build

accession

nos

to

-RRB-

Genotypes

for

SNPs

for

nine

of

the

EuropeanAmerican

individuals

that

were

part

of

this

project

had

been

previously

determined

by

the

International

HapMap

Project

using

a

variety

of

genotyping

platforms

-LRB-

-RRB-

Our

data

for

these

million

genotypes

is

concordant

with

the

HapMap

project

data

The

distribution

of

discordant

genotypes

is

very

nonrandom

only

of

the

SNPs

account

for

of

all

the

discrepancies

and

we

estimate

that

of

the

SNPs

in

the

complete

data

set

have

no

incorrect

genotypes

Haplotype

analyses

in

particular

will

generally

benefit

from

this

error

distribution

because

accurate

inference

of

haplotypes

requires

consistent

genotypes

across

large

groups

of

nearby

markers

The

distribution

of

the

million

highquality

genotyped

SNPs

-LRB-

table

-RRB-

is

similar

to

that

of

a

previously

reported

map

of

million

SNPs

-LRB-

-RRB-

More

than

of

the

genome

is

in

inter-SNP

intervals

of

less

than

kb

and

roughly

two-thirds

of

the

sequenced

genome

is

covered

by

inter-SNP

intervals

of

kb

or

less

-LRB-

table

-RRB-

The

average

distance

between

adjacent

SNPs

is

base

pairs

-LRB-

bp

-RRB-

Although

repetitive

elements

are

underrepresented

in

our

collection

we

genotyped

SNPs

within

repetitive

elements

where

the

SNP

flanking

sequences

could

be

uniquely

mapped

There

are

SNPs

-LRB-

-RRB-

in

genic

regions

of

the

genome

which

we

define

as

being

within

kb

of

the

transcribed

intervals

for

protein-coding

genes

in

release

of

NCBI

's

build

annotations

At

least

one

SNP

is

present

in

of

all

transcripts

When

the

region

of

DNA

upstream

and

downstream

of

each

transcript

is

included

of

all

the

protein-coding

genes

contain

at

least

one

SNP

A

total

of

SNPs

-LRB-

-RRB-

are

present

in

amino

acid

coding

sequences

and

of

these

SNPs

are

nonsynonymous

leading

to

an

amino

acid

change

-LRB-

table

-RRB-

Although

our

SNP

ascertainment

is

not

random

this

subset

of

SNPs

is

quite

uniformly

distributed

throughout

the

human

genome

with

respect

to

annotated

proteincoding

genes

as

well

as

physical

distance

Common

SNPs

in

three

populations

Table

illustrates

our

success

in

obtaining

a

set

of

common

SNPs

that

are

informative

in

human

populations

of

diverse

ancestry

Most

of

the

SNPs

with

highquality

genotypes

are

polymorphic

in

each

of

the

three

population

samples

genotyped

in

this

study

Ninety-four

percent

of

the

SNPs

-LRB-

SNPs

-RRB-

have

two

alleles

in

the

African-American

sample

-LRB-

SNPs

-RRB-

have

two

alleles

in

the

EuropeanAmerican

sample

and

-LRB-

SNPs

-RRB-

have

two

alleles

in

the

Han

Chinese

sample

In

each

population

the

majority

of

the

segregating

SNPs

have

a

MAF

greater

than

ranging

from

of

all

segregating

SNPs

in

the

African-American

sample

to

of

all

segregating

SNPs

in

the

Han

Chinese

sample

Only

of

the

SNPs

-LRB-

-RRB-

have

a

MAF

of

less

than

in

all

three

of

the

population

samples

The

distributions

of

MAFs

we

see

in

the

three

populations

is

very

similar

for

the

European-American

and

Han

Chinese

samples

with

a

higher

frequency

of

rarer

alleles

in

the

African-American

sample

-LRB-

fig

-RRB-

Consistent

with

previous

studies

-LRB-

-RRB-

we

observed

the

greatest

genetic

diversity

in

individuals

of

African

descent

Our

SNP

ascertainment

strategy

makes

it

difficult

to

make

more

definitive

statements

regarding

the

precise

distribution

of

SNP

allele

frequencies

in

different

populations

Although

the

small

sample

sizes

in

this

study

preclude

any

definite

conclusion

regarding

the

complete

absence

of

a

particular

allele

in

any

given

population

we

observed

SNPs

-LRB-

-RRB-

that

were

segregating

in

only

one

population

sample

-LRB-

private

SNPs

-RRB-

Most

of

these

private

SNPs

-LRB-

-RRB-

were

segregating

in

the

African-American

sample

although

private

SNPs

were

observed

for

each

of

the

three

population

samples

-LRB-

Table

-RRB-

Although

private

SNPs

tend

to

have

lower

MAFs

than

other

SNPs

in

our

collection

a

substantial

fraction

are

common

or

have

MAF

To

quantify

genetic

variation

within

and

between

populations

we

calculated

FST

for

each

SNP

in

each

pair

of

populations

as

well

as

combined

values

across

all

three

popula

Table

SNPs

segregating

in

the

three

genotyped

populations

Percentages

are

of

genotyped

SNPs

or

of

private

SNPs

Segregating

SNPs

All

SNPs

African-American

European-American

Han

Chinese

Private

SNPs

African-American

European-American

Han

Chinese

MAF

SNPs

MAF

SNPs

Population

SCIENCE

VOL

FEBRUARY

Downloaded

from

on

May

RESEARCH

ARTICLES

tions

-LRB-

-RRB-

FST

measures

the

genetic

variance

between

populations

as

a

fraction

of

the

total

genetic

variance

Because

African

Americans

are

a

relatively

admixed

population

with

substantial

but

heterogeneous

European

genetic

contributions

-LRB-

-RRB-

the

FST

estimates

for

comparisons

with

this

group

will

be

more

variable

but

should

generally

underestimate

the

results

that

would

be

obtained

with

a

native

African

sample

The

distribution

of

pairwise

FST

is

very

similar

for

the

African-American

versus

European-American

and

EuropeanAmerican

versus

Han

Chinese

samples

with

more

large

FST

values

between

the

AfricanAmerican

and

Han

Chinese

samples

-LRB-

fig

-RRB-

These

findings

are

consistent

with

prior

studies

-LRB-

-RRB-

showing

that

most

common

DNA

variation

is

shared

across

human

populations

with

differences

in

allele

frequencies

between

populations

Markers

with

large

between-population

variance

will

be

useful

for

admixture

mapping

studies

to

identify

genetic

variants

causing

phenotypic

differences

-LRB-

-RRB-

Admixture

mapping

exploits

relatively

long-range

allelic

correlations

in

a

recently

admixed

population

to

identify

functional

variants

that

have

different

prevalences

in

the

ancestral

populations

whether

because

of

genetic

drift

or

local

natural

selection

The

technique

requires

selection

and

genotyping

of

limited

numbers

of

ancestryinformative

markers

Our

identification

of

large

numbers

of

such

markers

removes

one

of

the

major

barriers

to

practical

use

of

this

promising

but

largely

untested

technique

Evidence

for

natural

selection

between

populations

It

has

been

suggested

that

natural

selection

distorts

the

observed

distribution

of

FST

across

the

human

genome

and

that

large

FST

values

can

be

used

to

identify

candidate

loci

likely

to

have

undergone

local

selection

-LRB-

-RRB-

If

this

is

true

then

larger

FST

values

should

be

found

near

functional

genetic

elements

We

looked

at

the

distribution

of

FST

for

SNPs

that

were

genic

or

nongenic

coding

or

noncoding

and

synonymous

or

nonsynonymous

We

performed

the

analysis

within

subsets

of

SNPs

grouped

by

MAF

so

that

effectively

we

looked

at

the

fraction

of

between-population

variance

for

SNPs

with

the

same

total

genetic

variance

-LRB-

fig

-RRB-

Common

SNPs

in

genic

regions

do

have

slightly

but

significantly

higher

FST

values

than

nongenic

SNPs

with

the

same

MAF

-LSB-

analysis

of

variance

-LRB-

ANOVA

-RRB-

P

-RSB-

and

common

coding

SNPs

have

slightly

higher

FST

values

than

noncoding

SNPs

in

genic

regions

-LRB-

ANOVA

P

-RRB-

We

did

not

see

a

significant

difference

in

FST

between

synonymous

and

nonsynonymous

coding

SNPs

but

our

sensitivity

is

limited

by

the

small

sample

sizes

and

expected

correlations

among

SNPs

within

the

same

transcript

These

results

are

consistent

with

local

selection

changing

the

distribution

of

FST

near

functional

sequences

However

because

the

distributions

of

FST

among

genic

and

nongenic

SNPs

are

very

similar

large

FST

values

by

themselves

appear

to

be

very

weak

evidence

of

selection

We

performed

a

similar

analysis

to

see

if

there

is

also

an

association

between

private

SNPs

and

functional

genetic

elements

When

conditioned

on

MAF

we

saw

no

difference

in

frequency

of

private

SNPs

among

genic

and

nongenic

SNPs

or

among

coding

and

noncoding

SNPs

-LRB-

fig

-RRB-

This

indicates

that

the

SNPs

responsible

for

evidence

of

local

selection

in

the

FST

analysis

tend

not

to

be

private

and

instead

are

segregating

in

multiple

populations

Although

there

are

known

examples

linking

population-specific

SNP

alleles

to

phenotypic

differences

-LRB-

-RRB-

our

results

are

more

consistent

with

the

conclusion

that

most

functional

human

genetic

variation

is

not

population-specific

Correlation

structure

of

common

SNPs

DNA

variants

in

physical

proximity

along

a

chromosome

tend

to

be

correlated

and

these

correlations

are

known

as

linkage

disequilibrium

LD

results

from

a

combination

of

processes

including

mutation

natural

selection

and

genetic

drift

It

can

initially

extend

over

very

long

genomic

distances

but

is

steadily

broken

down

over

time

by

recombination

The

observed

structure

of

LD

in

any

particular

genomic

interval

thus

depends

on

a

complex

interplay

of

demographic

history

stochastic

events

and

functional

constraints

Several

metrics

exist

for

measuring

LD

between

pairs

of

SNPs

we

used

r

the

squared

correlation

coefficient

for

a

by

table

of

haplotype

frequencies

-LRB-

-RRB-

We

have

used

a

modification

of

a

previously

described

algorithm

to

identify

bins

of

common

SNPs

that

are

in

very

strong

LD

where

each

bin

has

at

least

one

tag

SNP

with

an

r

of

at

least

with

every

other

SNP

in

the

bin

-LRB-

-RRB-

This

greedy

algorithm

works

by

iteratively

identifying

the

largest

possible

subset

with

these

properties

from

a

list

of

available

SNPs

then

removing

those

SNPs

from

the

list

used

in

the

next

Fraction

of

SNPs

G

G

G

G

Table

LD

bin

statistics

in

three

populations

Bins

were

classified

by

the

number

of

SNPs

they

contained

Size

to

to

Total

to

to

Total

to

to

Total

Bins

Bins

kby

SNPs

SNPs

G

European

Chinese

African

G

G

G

G

G

G

G

50 100 150

Minimum

bin

size

kb

Fig.

Size

distribution

of

LD

bins

We

show

for

a

given

minimum

bin

size

the

fraction

of

SNPs

in

bins

of

that

size

or

larger

The

size

distributions

for

the

European-American

and

Han

Chinese

LD

maps

are

essentially

identical

African-American

European-American

Han

Chinese

The

number

of

SNPs

per

LD

bin

Average

distance

spanned

by

the

SNPs

in

each

LD

bin

in

kb

FEBRUARY

VOL

SCIENCE

Downloaded

from

on

May

iteration

By

assaying

a

reduced

set

of

tag

SNPs

the

genotyping

burden

of

an

association

study

may

be

substantially

reduced

while

retaining

most

of

the

power

to

discover

disease

associations

of

the

entire

SNP

set

Unlike

haplotype

blocks

which

are

defined

as

contiguous

groups

of

SNPs

the

SNPs

that

make

up

a

bin

may

be

interdigitated

with

SNPs

that

are

part

of

other

bins

Table

summarizes

bin

characteristics

across

the

genome

excluding

the

Y

chromosome

for

each

of

the

three

population

samples

We

focused

on

common

SNPs

with

MAF

in

this

analysis

because

estimates

of

LD

for

variants

with

lower

MAF

are

unreliable

unless

large

numbers

of

individuals

are

genotyped

-LRB-

-RRB-

Although

most

LD

bins

contained

just

one

SNP

these

isolated

SNPs

were

a

small

proportion

of

all

SNPs

and

most

SNPs

were

tightly

correlated

with

multiple

other

SNPs

In

the

European-American

data

of

bins

contained

one

SNP

however

these

constituted

only

of

the

common

SNPs

A

substantial

portion

of

all

SNPs

qualified

as

tag

SNPs

by

having

a

high

r

value

with

every

other

bin

member

indicating

that

the

bins

are

generally

quite

densely

connected

For

the

African-American

sample

there

were

substantially

fewer

bins

made

up

of

large

numbers

of

SNPs

extending

over

large

distances

-LRB-

Fig.

-RRB-

It

should

be

kept

in

mind

that

the

LD

structure

we

observed

is

based

on

an

analysis

of

only

of

all

common

SNPs

in

the

genome

Although

the

sizes

of

longer

intervals

of

LD

should

be

relatively

robust

to

our

incomplete

ascertainment

the

proportion

of

all

common

SNPs

in

high

LD

with

other

SNPs

may

be

substantially

underestimated

from

our

data

LD

and

functional

elements

We

observed

a

strong

relationship

between

extended

intervals

of

LD

and

functional

genomic

fea

RESEARCH

ARTICLES

tures

-LRB-

Table

-RRB-

Large

bins

were

significantly

overpopulated

with

genic

versus

nongenic

SNPs

-LRB-

trend

test

P

-RRB-

and

in

genic

regions

coding

SNPs

were

significantly

enriched

over

noncoding

SNPs

-LRB-

trend

test

P

-RRB-

Large

bins

were

also

overrepresented

for

nonsynonymous

versus

synonymous

SNPs

-LRB-

trend

test

P

-RRB-

This

result

is

consistent

with

the

hypothesis

of

an

association

between

selection

and

some

regions

of

extended

LD

-LRB-

-RRB-

and

suggests

that

some

genomic

regions

of

extended

LD

may

play

a

particularly

important

role

in

determining

the

genetic

basis

of

human

phenotypic

differences

We

identified

five

bins

of

more

than

SNPs

each

and

genomic

intervals

containing

bins

that

span

more

than

kb

in

one

or

more

populations

-LRB-

tables

and

-RRB-

Several

of

these

large

bins

spanned

similarly

large

genes

The

bin

with

the

most

SNPs

was

on

chromosome

in

the

European-American

map

and

had

an

unusual

pattern

of

variation

with

two

previously

reported

haplotypes

extending

across

SNPs

and

spanning

a

distance

of

kb

-LRB-

-RRB-

The

rarer

haplotype

had

a

frequency

of

in

the

European-American

sample

and

a

frequency

in

the

AfricanAmerican

sample

and

was

absent

in

the

Han

Chinese

sample

This

bin

includes

the

gene

for

microtubule-associated

protein

tau

mutations

of

which

are

associated

with

a

variety

of

neurodegenerative

disorders

a

gene

coding

for

a

protease

similar

to

presenilins

mutations

of

which

result

in

Alzheimer

's

disease

and

the

gene

for

corticotropin-releasing

hormone

receptor

which

mediates

immune

endocrine

autonomic

and

behavioral

responses

to

stress

-LRB-

-RRB-

Large-scale

patterns

of

LD

The

distribution

of

SNPs

and

LD

across

the

entire

human

genome

is

shown

in

Fig.

and

can

be

examined

in

more

detail

online

The

top

track

illustrates

the

relative

uniformity

of

coverage

of

the

analyzed

SNPs

apart

from

intervals

of

centromeric

and

telomeric

heterochromatin

The

middle

track

shows

the

fraction

of

common

SNPs

that

are

in

high

LD

with

at

least

one

other

SNP

In

most

regions

we

observed

a

high

level

of

redundancy

for

the

EuropeanAmerican

and

Han

Chinese

samples

and

somewhat

less

redundancy

in

the

African-American

sample

The

bottom

track

shows

the

fraction

of

common

SNPs

observed

to

be

in

relatively

large

LD

bins

in

each

population

This

track

shows

substantial

structure

on

a

scale

of

megabases

Although

there

is

generally

good

agreement

between

populations

there

are

also

intervals

where

there

is

substantial

divergence

Our

whole-genome

analysis

reveals

that

the

large-scale

structure

of

LD

across

the

genome

is

correlated

with

large-scale

differences

in

recombination

rates

consistent

with

previous

findings

for

a

single

chromosome

-LRB-

-RRB-

In

particular

regions

of

very

strong

LD

are

mostly

located

in

regions

of

low

recombination

-LRB-

fig

-RRB-

This

correlation

of

large-scale

LD

structure

with

recombination

rate

and

the

finding

that

regions

of

extended

LD

show

evidence

of

selection

provide

strong

support

for

the

hypothesis

that

the

LD

structure

of

the

human

genome

has

functional

significance

and

is

not

simply

a

byproduct

of

random

genetic

drift

and

population

demographics

SNP

subsets

capture

most

common

variation

As

only

a

fraction

of

all

common

SNPs

in

human

populations

have

been

characterized

to

date

association

studies

based

on

available

subsets

of

SNPs

rely

on

the

expectation

that

an

undiscovered

disease-associated

variant

is

likely

to

be

correlated

with

an

allele

of

an

assayed

SNP

The

statistical

power

to

detect

an

unassayed

disease-associated

allele

indirectly

with

a

correlated

allele

of

an

assayed

SNP

is

related

to

r

Specifically

the

power

to

detect

an

association

indirectly

in

N

individuals

is

equivalent

to

the

power

to

detect

it

directly

in

Nr

individuals

-LRB-

-RRB-

The

actual

power

to

detect

a

particular

causal

variant

depends

on

that

variant

's

mode

of

action

and

penetrance

as

well

as

details

of

the

study

design

Thus

r

can

only

be

used

to

answer

the

narrower

question

of

what

is

the

sample

size

penalty

in

an

otherwise

appropriately

designed

study

for

not

directly

assaying

a

causal

variant

To

determine

our

ability

to

detect

unassayed

disease-associated

variants

with

this

SNP

collection

we

took

advantage

of

the

fact

that

the

European-American

and

AfricanAmerican

individuals

genotyped

in

this

study

were

also

sequenced

across

selected

genes

by

the

SeattleSNPs

Program

for

Genomic

Applications

-LRB-

PGA

-RRB-

-LRB-

-RRB-

For

these

individuals

this

data

provides

an

essentially

complete

assessment

of

genetic

variation

in

the

sequenced

regions

allowing

us

to

estimate

the

fraction

of

all

variation

contained

in

our

SNP

set

In

addition

the

data

allows

us

to

determine

the

coverage

of

our

genotyped

SNPs

for

the

sites

we

did

not

directly

assay

We

evaluated

data

for

sequence

variants

identified

in

genes

of

which

were

part

of

our

SNP

set

The

concordance

between

our

genotype

data

and

the

PGA

data

for

these

SNPs

was

Our

SNP

set

contained

of

all

SNPs

with

a

MAF

Q

for

these

genes

in

the

AfricanAmerican

and

European-American

samples

SNPs

with

low

MAF

are

underrepresented

in

our

data

compared

to

the

PGA

data

because

our

SNPs

were

typically

discovered

with

sequence

data

from

fewer

distinct

chromosomes

These

rarer

variants

account

for

relatively

small

fractions

of

the

total

nucleotide

diversity

In

the

PGA

data

for

the

European

Americans

of

SNPs

have

MAF

G

but

account

for

only

of

nucleotide

diversity

for

the

African

Americans

of

SNPs

have

MAF

G

and

account

for

of

nucleotide

diversity

Table

shows

the

average

r

and

the

fraction

of

r

values

exceeding

thresholds

for

Fig.

Distribution

of

SNP

positions

and

LD

structure

across

the

genome

For

each

chromosome

the

top

track

shows

SNP

density

per

kb

with

a

window

size

of

kb

The

middle

track

shows

for

each

population

the

fraction

of

common

SNPs

with

MAF

that

are

in

high

LD

-LRB-

r

-RRB-

with

at

least

one

other

common

SNP

with

a

window

size

of

kb

The

bottom

track

shows

for

each

population

the

fraction

of

common

SNPs

that

are

in

an

LD

bin

extending

over

at

least

kb

with

a

window

size

of

kb

Table

Distribution

of

genic

synonymous

and

nonsynonymous

coding

SNPs

spanned

by

bins

of

extended

LD

in

any

of

the

three

population

samples

Genic

SNPs

are

defined

as

within

kb

of

a

proteincoding

gene

annotation

Longest

spanning

LD

bin

-LRB-

kb

-RRB-

to

Genic

SNPs

SNPs

Synonymous

SNPs

Nonsynonymous

SNPs

Table

LD

statistics

for

common

SNPs

genotyped

in

this

study

with

common

variants

identified

by

complete

resequencing

in

genes

Subset

Yield

-LRB-

-RRB-

y

r

r

-LRB-

-RRB-

r

-LRB-

-RRB-

r

-LRB-

-RRB-

African-American

All

Tag

European-American

All

Tag

SNPs

from

the

current

study

either

all

common

SNPs

or

a

minimal

tagging

subset

Percentage

of

all

SeattleSNPs

PGA

variants

that

were

in

the

selected

set

Across

all

PGA

variants

the

mean

maximum

r

with

a

selected

SNP

in

greater

than

the

specified

threshold

with

any

selected

the

same

locus

Percentages

of

PGA

variants

having

an

r

SNP

in

the

same

locus

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100 110 120 130

100 110 120 130

100 110 120 130

140 150 160 170

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LEGEND

Top

track

genotyped

SNPs

per

kb

Middle

track

fraction

of

common

SNPs

in

high

LD

with

another

SNP

Bottom

track

fraction

of

interval

covered

by

LD

bins

kb

Red

European

American

Green

African

American

Blue

Han

Chinese

RESEARCH

ARTICLES

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from

on

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RESEARCH

ARTICLES

any

PGA

SNP

with

the

most-correlated

SNP

in

the

same

region

that

was

included

in

our

SNP

set

These

results

indicate

that

with

the

stringent

threshold

of

r

our

SNP

set

ascertains

of

common

variation

in

the

European-American

sample

and

of

common

variation

in

the

African-American

sample

These

values

are

similar

to

those

previously

predicted

if

million

SNPs

from

public

databases

were

developed

into

genotyping

assays

-LRB-

-RRB-

This

analysis

sets

a

very

conservative

lower

bound

on

coverage

because

it

treats

SNPs

below

the

threshold

of

as

completely

uncovered

and

does

not

reward

coverage

that

exceeds

the

threshold

Using

a

less

stringent

threshold

of

coverage

would

improve

to

in

the

European-American

sample

and

in

the

African-American

sample

The

skewed

distribution

of

r

toward

high

values

is

apparent

in

the

mean

values

of

for

the

European-American

sample

and

for

the

African-American

sample

These

numbers

are

especially

impressive

considering

that

we

did

not

genotype

of

all

the

common

SNPs

in

these

intervals

Selection

of

one

tag

SNP

from

each

LD

bin

for

the

three

population

samples

yielded

of

the

SNPs

segregating

in

the

European-American

sample

-LRB-

-RRB-

of

the

SNPs

segregating

in

the

Han

Chinese

sample

-LRB-

-RRB-

and

of

the

SNPs

segregating

in

the

African-American

sample

-LRB-

-RRB-

When

tag

SNPs

from

European

Americans

and

African

Americans

were

used

to

assess

common

variation

in

the

PGA

data

for

MAF

Q

the

amount

of

all

common

variation

ascertained

was

reduced

very

little

compared

to

that

ascertained

with

the

complete

sets

of

common

SNPs

-LRB-

Table

-RRB-

These

tag

SNP

numbers

are

smaller

than

have

previously

been

predicted

with

a

similar

selection

strategy

-LRB-

-RRB-

however

we

did

not

attempt

to

achieve

coverage

as

in

that

work

Although

choosing

subsets

of

SNPs

based

on

bin

relationships

reduces

the

genotyping

burden

for

a

comprehensive

whole-genome

scan

to

some

degree

in

all

populations

these

data

indicate

that

even

taking

advantage

of

such

tag

SNP

selection

a

comprehensive

whole-genome

association

study

requires

genotyping

each

individual

for

at

least

several

hundreds

of

thousands

of

SNPs

Haplotype

block

structure

LD

maps

and

haplotype

maps

represent

somewhat

different

aspects

of

the

local

structure

of

genetic

variation

The

genetic

architecture

of

a

particular

phenotype

will

determine

which

representation

is

most

powerful

for

the

identification

of

functional

variants

-LRB-

-RRB-

In

parallel

with

our

LD

analysis

we

used

the

HAP

program

-LRB-

-RRB-

to

infer

haplotypes

from

our

diploid

genotype

data

We

partitioned

these

reconstructed

haplotypes

into

blocks

with

limited

diversity

separately

for

each

of

the

three

population

samples

These

blocks

were

defined

as

sets

of

SNPs

for

which

at

least

of

the

inferred

haplotypes

could

be

grouped

into

common

patterns

with

population

frequencies

of

at

least

Table

summarizes

the

structure

of

the

three

resulting

haplotype

maps

for

the

whole

genome

excluding

the

Y

chromosome

The

haplotype

map

statistics

across

the

three

populations

appear

qualitatively

similar

to

the

LD

maps

with

substantially

more

blocks

in

the

map

derived

from

the

African-American

sample

than

in

the

maps

from

the

EuropeanAmerican

and

Han

Chinese

samples

The

numbers

of

SNPs

required

to

represent

frequencies

of

common

haplotype

patterns

were

similar

to

the

numbers

of

tag

SNPs

identified

in

the

LD

maps

Substantial

fractions

of

LD

bins

of

two

or

more

SNPs

crossed

haplotype

block

boundaries

ranging

from

in

the

Han

Chinese

map

to

in

the

AfricanAmerican

map

The

bin

structure

for

SNPs

in

the

region

of

the

CFTR

gene

on

chromosome

-LRB-

Fig.

-RRB-

demonstrates

some

of

the

differences

between

the

LD

bin

and

haplotype

block

maps

and

further

illustrates

that

there

can

be

substantial

population

differences

in

local

map

structure

In

this

interval

the

EuropeanAmerican

and

African-American

LD

maps

have

similar

complexity

with

multiple

overlapping

bins

but

the

Han

Chinese

map

is

dominated

by

two

disjoint

bins

of

highly

correlated

SNPs

Conversely

a

break

point

near

the

position

is

shared

in

the

African-American

and

Han

Chinese

LD

maps

but

is

bridged

by

multiple

LD

groupings

in

the

European-American

map

All

three

haplotype

maps

share

this

break

point

However

the

African-American

map

contains

many

more

distinct

haplotype

blocks

than

the

maps

for

the

other

two

population

samples

Common

genetic

variation

and

human

health

Our

focus

on

common

genetic

variation

has

several

motivations

Common

variants

account

for

a

larger

share

of

human

nucleotide

diversity

than

rare

variants

and

are

more

experimentally

tractable

For

the

same

allelic

effect

a

common

variant

represents

a

larger

fraction

of

phenotypic

variance

and

population

attributable

risk

than

a

rare

one

so

common

variants

are

more

valuable

from

the

perspective

of

diagnostics

and

intervention

Finally

detecting

and

characterizing

effects

of

rare

variants

requires

very

large

sample

sizes

to

obtain

statistically

meaningful

numbers

of

individuals

carrying

Fig.

Extended

LD

bin

and

haplotype

block

structure

around

the

CFTR

gene

LD

bins

where

each

bin

has

at

least

one

SNP

with

r

with

every

other

SNP

are

depicted

as

light

horizontal

bars

with

the

positions

of

constituent

SNPs

indicated

by

vertical

tick

marks

as

well

as

the

extreme

ends

of

the

bars

Isolated

SNPs

are

indicated

by

plain

tick

marks

Haplotype

blocks

within

which

at

least

of

observed

haplotypes

could

be

grouped

into

common

patterns

with

frequencies

of

at

least

are

depicted

as

dark

horizontal

bars

Unlike

haplotype

blocks

that

are

by

design

sequential

and

nonoverlapping

SNPs

in

one

LD

bin

can

be

interdigitated

with

SNPs

in

multiple

other

overlapping

bins

FEBRUARY

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RESEARCH

ARTICLES

a

rare

allele

There

is

no

doubt

that

rare

variants

play

a

role

in

the

etiology

of

common

disease

but

pursuit

of

common

variants

is

more

tractable

with

available

technologies

Common

human

diseases

such

as

cardiovascular

disease

and

psychiatric

illness

are

caused

by

the

interplay

of

multiple

genetic

and

environmental

factors

The

bounded

nature

of

the

human

genome

and

the

availability

of

the

complete

human

genome

sequence

have

resulted

in

extensive

efforts

to

define

the

genetic

basis

of

a

wide

variety

of

complex

human

traits

One

approach

for

identifying

such

genetic

risk

factors

is

the

case-control

association

study

in

which

a

group

of

individuals

with

disease

is

found

to

have

an

increased

frequency

of

a

particular

genetic

variant

compared

to

a

group

of

control

individuals

A

number

of

genetic

risk

factors

for

common

disease

have

been

identified

by

such

association

studies

-LRB-

-RRB-

These

studies

suggest

that

many

different

genes

distributed

throughout

the

human

genome

contribute

to

the

total

genetic

variability

of

a

particular

complex

trait

with

any

single

gene

accounting

for

no

more

than

a

few

percent

of

the

overall

variability

of

the

trait

-LRB-

-RRB-

Case-control

study

designs

that

include

on

the

order

of

individuals

can

provide

adequate

power

to

identify

genes

accounting

for

only

a

few

percent

of

the

overall

genetic

variability

of

a

complex

trait

even

using

the

very

stringent

significance

levels

required

when

testing

large

numbers

of

common

DNA

variants

-LRB-

-RRB-

Using

such

study

designs

in

conjunction

with

the

detailed

description

of

common

human

DNA

variation

presented

here

it

may

be

possible

to

identify

a

set

of

major

genetic

risk

factors

contributing

to

the

variability

in

a

complex

disease

and/or

treatment

response

Although

knowledge

of

a

single

genetic

risk

factor

can

seldom

be

used

to

predict

the

treatment

outcome

of

a

common

disease

knowledge

of

a

large

fraction

of

all

the

major

genetic

risk

factors

contributing

to

a

treatment

response

or

common

disease

could

have

immediate

utility

allowing

existing

treatment

options

to

be

matched

to

individual

patients

without

requiring

additional

knowledge

of

the

mechanisms

by

which

the

genetic

differences

lead

to

different

outcomes

In

our

analyses

we

selected

representations

of

the

data

including

pairwise

LD

as

well

as

a

haplotype-based

approach

that

we

felt

would

be

most

useful

for

an

initial

characterization

of

this

resource

We

focused

attention

on

pairwise

LD

analyses

because

they

provide

a

particularly

simple

framework

for

evaluating

coverage

and

information

content

of

different

SNP

collections

The

optimal

representation

of

genetic

variation

data

remains

an

area

of

active

research

Although

we

have

determined

example

haplotype

maps

of

the

human

genome

in

these

three

populations

the

most

appropriate

representation

of

the

data

depends

substantially

on

the

specific

questions

to

be

answered

There

will

be

many

maps

of

human

genetic

variation

each

tailored

for

specific

uses

Public

data

availability

We

have

implemented

an

instance

of

the

Generic

Genome

Browser

-LRB-

-RRB-

at

perlegen.com

for

viewing

the

SNP

LD

and

haplotype

data

reported

here

this

data

will

also

be

available

from

Science

upon

request

More

detailed

haplotype

analysis

results

are

available

at

wgha

and

through

dbSNP

The

data

reported

here

represent

a

massive

increase

in

the

available

number

of

SNPs

characterized

in

multiple

populations

For

comparison

although

the

public

SNP

database

dbSNP

build

contained

map

positions

for

more

than

million

human

SNPs

frequencies

were

available

for

only

of

these

SNPs

mostly

in

just

one

population

and

genotypes

were

available

for

only

SNPs

Our

data

also

complement

the

results

of

the

International

HapMap

Project

-LRB-

-RRB-

by

providing

data

for

many

more

SNPs

across

fewer

individuals

This

work

enables

detailed

analyses

of

the

structure

of

human

genetic

variation

on

a

whole-genome

scale

We

examined

genetic

variation

in

individuals

from

three

populations

with

substantially

different

histories

and

describe

general

features

of

variation

within

and

between

populations

Because

these

samples

do

not

capture

the

full

genetic

diversity

of

the

populations

from

which

they

were

selected

our

data

are

not

suitable

for

answering

many

questions

about

the

detailed

genetic

structure

of

human

populations

-LRB-

-RRB-

However

the

public

availability

of

these

data

will

enable

a

wide

variety

of

additional

analyses

to

be

carried

out

by

scientists

investigating

the

structure

of

human

genetic

variation

as

well

as

the

genetic

basis

of

human

phenotypic

differences

References

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D.

A.

Nickerson

Nature

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Romualdi

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J.

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Xiao

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J.

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et

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Nakayama

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Devlin

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Huttley

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SeattleSNPs

National

Heart

Lung

and

Blood

Institute

Program

for

Genomic

Applications

University

of

Washington

Fred

Hutchinson

Cancer

Research

Center

Seattle

WA

available

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J.

S.

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Pharmacogenomics

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We

thank

B.

Margus

and

S.

Fodor

for

many

helpful

discussions

S.

Ptak

for

comments

on

the

manuscript

and

the

following

individuals

for

expert

technical

assistance

high

throughput

genotyping

C.

Chen

P.

Chu

D.

Dalija

J.

Doshi

P.

Jain

A.

Johnson

L.

Kamigaki

J.

Karbowski

C.

Kautzer

V.

Mendoza

M.

Morenzoni

B.

Nguyen

C.

Owyang

N.

Patil

K.

Perry

R.

Patel

C.

Pethiyagoda

T.

L.

Pham

C.

Sanders

A.

Sparks

R.

Stokowski

D.

Telman

R.

Vergara

P.

Vu

and

P.-H

Wang

bioinformatics

design

W.

Barrett

H.

Huang

M.

Jen

X.

Li

B.

Mooney

and

S.

Pitts

data

analysis

A.

Berno

K.

Konvicka

A.

Ollmann

K.

Pant

and

J.

Sheehan

laboratory

information

management

R.

Gupta

E.

Jacobs

C.

Radu

and

P.

Starink

engineering

and

instrumentation

R.

Hartlage

M.

Norris

G.

Park

and

A.

Yee

computer

systems

and

operations

T.

Fleury

R.

Galvez

R.

Gordon

P.

Hickey

C.

LaPlante

J.

Nordhal

T.

Ogi

and

J.

VandenHengel

E.E.

is

supported

by

the

California

Institute

for

Telecommunications

and

Information

Technology

Supporting

Online

Material

Materials

and

Methods

Figs

to

Tables

to

References

and

Notes

September

accepted

January

science

Table

Haplotype

block

partition

results

for

the

three

populations

Population

African-American

European-American

Han

Chinese

Blocks

Average

size

kb

Required

SNPsy

Average

distance

spanned

by

segregating

sites

in

each

block

common

haplotype

patterns

with

frequencies

of

or

higher

Minimum

number

of

SNPs

required

to

distinguish

SCIENCE

VOL

FEBRUARY

Downloaded

from

on

May

O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O B O O O O O O O O B O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O B O O O O O O O O O O O O B O O O O O B O O O O O O O O O O O B O O O O O O O O O O O O B O O O O O O O O O O O O O O O O B O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O B B O O O O O O O O O O O O O O O O O O B O O O B O O O O O O O O O O O O O O O O O O O O O O O O B O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O B O O O B O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O B O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O B O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O B O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O B O O O O B O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O B O O B O O O O O O O O O O O O O B O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O B O O O O O O O O O O B O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O B O O O O O O O B O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O B O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O B O O O O B O O B O B O O O O O B O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O B O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O B O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O B O O O O B O O O O O B O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O B O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O B O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O B O O O B O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O B O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O B O O O O O B O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O B O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O B O O O B O O O O O O O O B O O O O O O O O B O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O B O O O O O 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O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O B B O O O O O O O O O B O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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