REVIEWS

Genetics

of

global

gene

expression

Matthew

V.

Rockman

and

Leonid

Kruglyak

Abstract

A

new

field

of

genetic

analysis

of

global

gene

expression

has

emerged

in

recent

years

driven

by

the

realization

that

traditional

techniques

of

linkage

and

association

analysis

can

be

applied

to

thousands

of

transcript

levels

measured

by

microarrays

Genetic

dissection

of

transcript

abundance

has

shed

light

on

the

architecture

of

quantitative

traits

provided

a

new

approach

for

connecting

DNA

sequence

variation

with

phenotypic

variation

and

improved

our

understanding

of

transcriptional

regulation

and

regulatory

variation

Complex

and

quantitative

traits

Phenotypes

that

are

shaped

by

multiple

and

possibly

interacting

genetic

and

environmental

factors

Quantitative

traits

-LRB-

as

distinguished

from

discrete

traits

-RRB-

are

measured

on

continuous

scales

Effect

size

The

magnitude

of

contribution

of

a

locus

to

variation

in

a

phenotype

Lewis-Sigler

Institute

for

Integrative

Genomics

and

Department

of

Ecology

and

Evolutionary

Biology

Princeton

University

Princeton

New

Jersey

USA

Correspondence

to

L.K.

e-mail

leonid@genomics

princeton.edu

doi

More

than

a

century

after

the

rediscovery

of

Mendel

the

genetic

basis

of

complex

and

quantitative

traits

resists

generalization

Basic

questions

remain

unanswered

including

the

number

of

loci

that

underlie

variation

in

heritable

phenotypes

the

distribution

of

their

effect

sizes

their

molecular

natures

and

mechanisms

of

action

and

interaction

and

their

dependence

on

environmental

variables

These

questions

are

at

the

centre

of

pressing

issues

in

medical

and

agricultural

genetics

as

well

as

in

basic

evolutionary

biology

in

which

the

outstanding

unresolved

question

concerns

the

forces

that

create

maintain

and

sort

heritable

phenotypic

variation

Now

an

emerging

approach

genetic

mapping

of

genome-wide

gene

expression

-LRB-

BOX

-RRB-

is

beginning

to

provide

the

requisite

empirical

data

to

address

these

questions

Since

the

first

empirical

linkage

study

of

global

transcript

levels

was

published

in

-LRB-

REF

-RRB-

many

general

principles

have

been

established

and

represent

solid

ground

on

which

further

work

can

build

Although

small-scale

studies

of

the

genetics

of

gene

expression

have

a

long

and

rich

history

-LRB-

BOX

-RRB-

modern

large-scale

studies

owe

their

existence

to

the

development

of

microarray

technology

in

the

Microarrays

were

first

applied

to

the

study

of

genetic

variation

in

They

revealed

that

gene

expression

differs

between

strains

in

both

yeast

and

and

that

such

differences

segregate

in

Subsequent

studies

documented

abundant

heritable

variation

in

gene

expression

in

Drosophila

melanogaster

and

By

the

time

Jansen

and

proposed

genetic

mapping

of

genome-wide

gene

expression

such

work

was

well

underway

in

several

research

groups

and

the

first

empirical

study

mapping

global

gene

expression

in

a

yeast

cross

appeared

early

the

following

Since

then

further

studies

have

documented

heritable

variation

in

genome-wide

gene

expression

in

more

than

a

dozen

species

and

have

mapped

the

loci

for

many

expression

traits

in

yeast

mice

maize

humans

rats

Eucalyptus

and

Arabidopsis

This

diversity

of

model

systems

promises

to

reveal

important

connections

between

genome-wide

gene

expression

and

features

of

population

biology

population

sizes

breeding

systems

demographic

histories

and

patterns

of

natural

selection

Differences

can

already

be

seen

among

species

most

species

show

ubiquitous

heritable

variation

in

expression

whereas

the

malarial

parasite

Plasmodium

falciparum

shows

remarkably

The

abundance

of

a

transcript

is

a

quantitative

trait

and

like

all

such

traits

its

inheritance

can

be

described

using

the

classical

methods

of

biometrical

genetics

and

its

genetic

basis

can

be

discovered

using

linkage

and

association

mapping

However

transcript

abundance

is

in

many

ways

an

extraordinary

phenotype

with

special

attributes

that

confer

particular

importance

on

an

understanding

of

its

genetics

The

primary

transformative

potential

of

genome-wide

gene

expression

genetics

is

the

sheer

number

of

traits

thousands

that

can

be

assayed

simultaneously

Whereas

studies

of

one

or

a

few

traits

offer

only

anecdotal

examples

of

the

underlying

genetic

architectures

studying

thousands

of

traits

allows

a

detailed

description

of

the

distribution

over

the

landscape

of

all

possible

architectures

Individual

traits

are

typically

preselected

on

the

basis

of

their

phenotypic

divergence

or

biological

interest

whereas

genome-wide

expression

studies

provide

data

on

a

large

and

unbiased

set

of

traits

The

radical

increase

in

the

number

of

traits

accessible

to

study

has

raised

new

challenges

to

analysis

and

interpretation

and

genome-wide

genetic

mapping

of

gene

expression

has

consequently

become

a

central

proving

ground

for

new

statistical

genetics

Another

special

feature

of

transcript

abundance

as

a

phenotype

is

that

it

represents

the

phenotype

most

immediately

connected

to

DNA

sequence

variation

NOVEMBER

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Publishing

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Box

Genetics

of

global

gene

expression

a

primer

A

study

of

the

genetics

of

global

gene

expression

begins

with

a

mapping

population

Several

choices

are

available

and

have

been

used

progeny

from

a

cross

between

two

parent

strains

recombinant

inbred

lines

collections

of

pedigrees

and

samples

of

unrelated

individuals

The

study

population

is

then

genotyped

for

a

set

of

polymorphic

markers

that

cover

the

genome

RNA

is

extracted

from

each

individual

or

strain

and

the

abundance

of

each

transcript

is

measured

typically

by

hybridization

to

microarrays

-LRB-

panel

a

illustrates

the

experimental

design

for

a

cross

between

two

yeast

-RRB-

High

levels

of

measurement

replication

are

built

into

the

design

because

each

allele

at

each

QTL

will

be

present

in

a

large

number

of

samples

the

effect

of

the

QTL

on

gene

expression

will

therefore

be

measured

many

times

The

resulting

data

set

is

then

analysed

to

find

genetic

loci

that

affect

transcript

abundance

The

simple

approach

is

to

treat

the

abundance

of

each

transcript

as

a

separate

quantitative

trait

and

to

carry

out

conventional

linkage

or

association

analysis

with

all

the

markers

tested

either

individually

or

using

multipoint

methods

Panel

b

shows

how

at

a

given

genomic

location

the

samples

are

separated

according

to

the

inherited

marker

alleles

and

linkage

-LRB-

or

association

-RRB-

is

declared

if

the

groups

differ

significantly

in

expression

level

-LRB-

in

this

case

of

the

gene

at

the

centre

of

the

small

array

section

-RRB-

An

example

of

an

actual

linkage

from

a

yeast

cross

is

shown

in

panel

c.

Because

millions

of

statistical

tests

are

carried

out

-LRB-

thousands

of

transcripts

are

each

tested

against

hundreds

or

thousands

of

markers

in

linkage

studies

and

up

to

a

million

markers

in

association

studies

-RRB-

careful

control

of

false

positives

through

multiple

testing

correction

is

essential

Empirical

significance

levels

obtained

through

permutation

should

be

used

whenever

possible

to

account

for

the

complex

correlations

in

the

data

Approaches

that

are

based

on

the

falsediscovery

rate

-LRB-

FDR

-RRB-

are

very

Methods

are

available

to

search

for

multiple

loci

that

affect

a

given

trait

either

additively

or

through

interactions

but

even

greater

statistical

care

is

Methods

that

use

dimensional

reduction

-LRB-

for

example

clustering

or

principal

component

analysis

-RRB-

can

lower

the

number

of

tests

and

can

potentially

improve

mapping

power

by

combining

multiple

transcripts

that

behave

similarly

into

single

Depending

on

the

study

design

and

sample

size

loci

are

identified

for

anywhere

from

a

few

transcripts

to

thousands

of

transcripts

with

up

to

half

of

all

transcripts

showing

linkage

in

some

Following

locus

identification

the

studies

take

several

paths

The

genetic

architectures

of

the

traits

can

be

assessed

by

examining

heritabilities

detection

rates

numbers

of

loci

identified

and

their

effect

Loci

can

be

classified

according

to

whether

they

affect

transcripts

encoded

at

the

same

genomic

location

or

elsewhere

in

the

genome

and

whether

they

affect

many

transcripts

or

Bioinformatic

approaches

can

be

used

to

identify

functional

relationships

among

the

transcripts

affected

by

common

loci

and

to

investigate

the

structure

of

the

underlying

regulatory

Finally

the

causative

polymorphisms

responsible

for

variation

in

transcript

abundance

can

be

pursued

using

molecular

genetics

tools

-LRB-

panel

d

-RRB-

-LRB-

B@marker

R@marker

the

allele

carried

by

the

segregant

at

the

marker

is

the

BY

allele

-LRB-

B

-RRB-

or

the

RM

allele

-LRB-

R

-RRB-

respectively

chr

chromosome

seg

segregants

-RRB-

Panel

b

and

c

reproduced

with

permission

from

REF

-LRB-

-RRB-

American

Association

for

the

Advancement

of

Sciences

a

Laboratory

-LRB-

BY

-RRB-

Wine

-LRB-

RM

-RRB-

Determine

genome

segregation

Profile

gene

expression

Correlate

genotype

with

transcript

abundance

b

Seg

Seg

Seg

Seg

Seg

Linkage

Seg

Seg

Seg

Seg

Seg

No

linkage

Inherit

BY

Inherit

RM

c

Fold

change

-LRB-

-RRB-

All

seg

BY

parent

RM

parent

Seg

inherit

B@marker

Seg

inherit

R@marker

d

Chr

locus

inherit

RM

Chr

locus

inherit

BY

-LRB-

p

-RRB-

linkage

Daughter-cell

specificity

RM

BY

-LRB-

II

-RRB-

Genome

location

Saccharomyces

paradoxus

Saccharomyces

mikatae

Saccharomyces

bayanus

Saccharomyces

servazzii

Zygosaccharomyces

rouxii

Kluyveromyces

lactis

Pichia

angusta

DNFLLRLSQS

IPNLKHLDLR

ACDNVSDSGV

VCIALNCPKL

KTFNIGRHRR

DNFLLRLSQS

IPNLKHLVLR

ACDNVSDSGV

VCIALNCPKL

KTFNIGRHRR

D

D

D

D

D

D

D

NATURE

REVIEWS

GENETICS

VOLUME

NOVEMBER

Nature

Publishing

Group

REVIEWS

Box

Foundational

work

in

the

genetics

of

natural

variation

in

gene

expression

Dramatic

recent

progress

in

understanding

genetic

variation

in

gene

expression

builds

on

a

rich

history

dating

at

least

to

Haldane

's

prescient

treatment

of

variation

in

the

timing

of

gene

Decades

before

the

discovery

of

the

molecular

gene

Haldane

recognized

that

genetic

variation

in

a

gene

's

activity

could

be

due

to

variation

in

the

gene

itself

or

to

variation

at

an

unlinked

locus

Thinking

in

terms

of

physiological

genetics

he

pointed

to

membrane-spanning

ion

channels

as

candidate

sites

for

mutations

that

could

act

on

other

genes

in

trans

by

altering

cellular

pH.

The

earliest

genetic

mapping

of

natural

variation

in

gene

expression

appeared

immediately

after

Jacob

and

introduced

a

mechanism

for

gene

regulation

showed

that

variation

in

the

time

of

activity

of

an

esterase

gene

in

maize

depends

on

variation

that

is

tightly

linked

to

the

structural

locus

Linkage

disequilibrium

between

the

regulatory

locus

and

electrophoretically

distinguishable

alleles

of

the

enzyme

showed

that

the

regulatory

variation

acted

in

cis

Jacob

and

Monod

's

model

inspired

much

speculation

about

variation

in

gene

regulation

and

its

importance

to

but

empirical

research

really

took

off

only

after

King

and

Wilson

's

analysis

refocused

attention

on

the

Using

amylase

genes

as

models

drosophilists

documented

distant

linking

trans-acting

regulatory

variation

affecting

spatial

heritable

variation

in

enzyme

and

fitness

differences

attributable

to

regulatory

Parallel

results

for

other

enzymes

and

taxa

rapidly

Genetic

analysis

of

gene

expression

variation

entered

a

new

era

with

the

precocious

introduction

of

genomic

approaches

by

Damerval

et

al.

Scoring

spot

intensities

on

two-dimensional

gels

as

measures

of

abundance

of

proteins

in

the

progeny

of

a

maize

line

cross

Damerval

et

al.

mapped

QTLs

that

underlie

the

observed

variation

including

epistatic

interactions

among

the

loci

Because

the

two-dimensional

gels

allow

allele-specific

protein

quantification

the

study

could

distinguish

between

cis-acting

and

trans-acting

variation

In

the

past

several

years

genome-wide

genetic

analyses

of

gene

expression

have

far

surpassed

Damerval

et

al.

in

the

number

of

traits

examined

and

in

the

number

and

precision

of

the

QTLs

discovered

but

much

of

the

conceptual

model

for

these

studies

is

present

in

their

classic

paper

Recombinant

inbred

lines

Panels

of

genetically

mosaic

but

homozygous

strains

generated

by

crossing

parental

strains

and

inbreeding

the

progeny

False-discovery

rate

The

fraction

of

results

declared

significant

at

a

given

threshold

that

are

expected

to

be

false

positives

Dimensional

reduction

A

class

of

mathematical

techniques

for

summarizing

the

main

characteristics

of

multivariate

data

with

fewer

variables

QTL

Quantitative

trait

locus

a

region

of

the

genome

that

contributes

to

variation

in

a

quantitative

trait

Beavis

effect

A

statistical

artefact

that

is

due

to

the

deviation

of

estimates

from

true

values

by

random

error

In

a

mapping

experiment

the

loci

that

are

deemed

significant

are

enriched

for

those

in

which

the

estimated

effects

benefit

from

random

error

that

happens

to

fall

in

the

right

direction

Therefore

significant

QTLs

are

disproportionately

those

in

which

the

effect

sizes

are

inflated

by

chance

the

road

from

genotype

to

phenotype

runs

through

gene

expression

Regulatory

sequence

variation

including

both

variation

in

a

regulatory

region

of

a

gene

that

affects

its

own

expression

and

variation

in

the

coding

region

of

a

gene

that

affects

expression

of

other

genes

is

probably

the

main

mediator

of

phenotypic

divergence

in

Moreover

the

intermediate

position

of

gene

expression

between

genotype

and

organismal

phenotype

makes

it

ideally

suited

to

serve

as

a

bridge

between

the

two

in

mapping

studies

Genetic

correlations

between

expression

phenotypes

and

organismal

phenotypes

point

to

the

molecular

pathways

that

underlie

the

organismal

phenotypes

whereas

colocalization

of

QTLs

for

expression

and

organismal

phenotypes

speeds

up

the

identification

of

causal

mutations

Gene

expression

also

provides

a

universal

subphenotype

for

complex

and

heterogeneous

organismal

In

this

review

we

first

discuss

what

the

genetics

of

global

gene

expression

has

taught

us

about

the

genetic

architecture

of

quantitative

traits

We

then

describe

the

features

of

the

two

types

of

regulatory

sequence

variation

that

underlie

differences

in

gene

expression

local

variation

which

maps

close

to

the

physical

location

of

the

affected

gene

and

distant

variation

which

maps

elsewhere

in

the

genome

We

quantify

the

prevalence

of

each

type

and

delineate

the

difference

between

the

local

and

distant

distinction

which

is

based

on

location

and

the

mechanistic

distinctions

-LRB-

such

as

cis-acting

and

transacting

or

cis-regulatory

and

protein-coding

-RRB-

which

are

based

on

the

function

altered

by

the

variants

Having

discussed

the

insights

provided

by

studies

of

global

gene

expression

we

look

at

the

future

of

the

genetics

of

global

molecular

phenotypes

loci

and

with

environmental

variables

A

key

finding

from

multiple

studies

of

the

genetics

of

gene

expression

in

different

species

is

that

complex

inheritance

is

also

consistently

observed

for

the

thousands

of

transcript-level

traits

Despite

their

close

connection

to

DNA

sequence

transcript

abundances

exhibit

substantial

genetic

complexity

QTL

number

and

effect

size

Two

important

and

seemingly

simple

questions

are

how

many

QTLs

underlie

a

quantitative

trait

and

how

much

of

the

heritable

variation

in

the

trait

does

each

QTL

explain

These

questions

turn

out

to

be

surprisingly

difficult

to

answer

because

of

the

following

methodological

problem

Unless

a

study

samples

a

very

large

number

of

individuals

which

has

not

been

practical

when

the

phenotype

is

genome-wide

gene

expression

only

QTLs

with

the

largest

effect

on

the

trait

can

be

detected

Therefore

the

observed

number

of

loci

is

usually

an

extreme

underestimate

of

the

actual

number

and

the

observed

effect

sizes

represent

the

high

end

of

the

overall

distribution

of

effect

sizes

Moreover

a

common

statistical

artefact

known

as

the

Beavis

effect

causes

the

overestimation

of

effect

sizes

of

the

detected

Nevertheless

useful

estimates

can

be

made

The

genetic

architecture

of

most

expression

traits

involves

multiple

QTLs

and

most

of

these

QTLs

explain

a

minority

of

trait

variation

Because

all

mapping

studies

until

now

have

detected

only

a

single

locus

for

most

traits

this

conclusion

is

based

primarily

on

what

has

not

been

detected

The

argument

runs

as

follows

For

a

trait

with

measurable

heritability

it

is

straightforward

to

calculate

the

probability

that

a

QTL

that

explains

a

certain

fraction

of

trait

variation

will

be

detected

in

a

study

with

a

given

sample

size

For

a

single

trait

a

QTL

is

either

detected

or

not

but

when

many

traits

are

examined

simultaneously

as

in

the

case

of

genome-wide

expression

we

can

ask

whether

the

fraction

with

detected

QTLs

is

as

expected

from

the

detection

probability

If

the

detected

fraction

is

Genetic

complexity

of

transcript

levels

Historically

most

quantitative

phenotypes

have

proved

to

be

genetically

complex

explicable

only

by

multiple

underlying

loci

and

possibly

interactions

among

the

NOVEMBER

VOLUME

Nature

Publishing

Group

REVIEWS

Fraction

of

loci

Fraction

of

genetic

variance

Low

-LRB-

-RRB-

Seg

BY

RM

Seg

BY

Seg

RM

Average

-LRB-

-RRB-

Seg

BY

RM

Seg

BY

Seg

RM

High

-LRB-

-RRB-

Seg

BY

RM

Seg

BY

Seg

RM

Figure

Most

gene

expression

traits

are

affected

by

multiple

loci

Each

bar

represents

the

fraction

of

QTLs

that

explain

a

percentage

of

genetic

variance

in

the

range

on

the

x

axis

For

each

trait

with

significant

linkage

-LRB-

s

-RRB-

only

the

single

most

significant

QTL

is

included

Data

are

derived

from

the

first

table

in

REF

The

panels

below

the

plot

show

examples

of

QTLs

that

explain

from

left

to

right

low

average

and

high

percentages

of

genetic

variance

In

each

panel

the

left-most

column

shows

the

relative

expression

of

the

corresponding

gene

in

all

segregants

-LRB-

seg

-RRB-

the

next

two

columns

show

the

expression

in

replicates

of

the

two

parent

strains

and

the

last

two

columns

show

the

expression

in

the

segregants

that

inherit

the

QTL

allele

from

the

first

and

second

parent

strains

Heritability

The

fraction

of

total

phenotypic

variance

that

is

attributable

to

additive

genetic

effects

Estimators

with

different

technical

definitions

and

biological

meanings

abound

This

is

not

an

inherent

property

of

a

trait

heritability

depends

on

the

nature

of

the

genetic

sample

-LRB-

for

example

intercross

inbred

lines

twins

and

random

populations

-RRB-

and

the

space

of

environments

surveyed

Transgressive

segregation

A

distribution

of

trait

values

for

a

segregating

population

that

extends

significantly

beyond

the

range

defined

by

the

progenitor

strains

lower

typical

QTLs

must

be

weaker

than

assumed

in

the

calculation

and

because

no

individual

QTL

can

explain

most

of

the

genetic

variation

there

must

be

multiple

QTLs

Such

arguments

have

been

used

to

estimate

that

in

a

yeast

cross

only

of

expression

traits

are

consistent

with

single-locus

inheritance

that

most

traits

require

more

than

two

additive

QTLs

and

that

segregation

of

many

traits

can

only

be

explained

by

very

complex

In

an

cross

of

mice

QTLs

were

detected

for

only

of

genes

with

significant

genetic

differences

in

expression

implying

considerable

genetic

complexity

given

the

study

's

high

power

to

detect

QTLs

for

traits

with

relatively

simple

A

combination

of

genetic

complexity

and

low

statistical

power

probably

explains

the

low

detection

rates

in

the

human

studies

carried

out

so

Direct

evidence

of

genetic

complexity

comes

from

detecting

multiple

QTLs

for

at

least

some

expression

traits

Moreover

even

the

detected

QTLs

typically

explain

only

a

minority

of

trait

variation

In

yeast

the

median

phenotypic

effect

of

a

detected

QTL

was

of

genetic

-LRB-

heritable

-RRB-

variance

explained

and

only

of

traits

had

a

QTL

that

explained

of

genetic

-LRB-

FIG.

-RRB-

Similarly

in

mapped

QTLs

explained

on

average

of

the

variance

in

expression

of

the

corresponding

genes

In

humans

effect-size

estimates

averaging

have

been

reported

for

loci

near

the

affected

Therefore

even

the

strongest

QTLs

that

underlie

variation

in

gene

expression

typically

explain

a

quarter

or

less

of

the

variation

Nevertheless

routine

observation

of

QTLs

that

explain

a

substantial

fraction

of

phenotypic

variance

contradicts

the

infinitesimal

theory

of

in

which

quantitative

traits

are

determined

by

a

very

large

number

of

loci

with

very

small

effects

QTLs

with

appreciable

effects

are

also

reported

in

studies

of

non-expression

and

can

not

all

be

explained

away

by

the

Beavis

effect

The

empirical

results

are

better

described

by

the

exponential

model

developed

by

in

which

large-effect

mutations

represent

the

expected

initial

steps

in

adaptation

as

discussed

by

and

by

Barton

and

Keightley

one

implication

is

that

strong

QTLs

might

be

observed

because

of

natural

selection

and

not

despite

it

The

many

forms

of

genetic

complexity

The

correspondence

between

genes

alleles

and

environments

on

the

one

hand

and

phenotypes

on

the

other

can

be

complex

in

many

ways

with

a

parallel

proliferation

in

terminology

Studies

of

the

genetics

of

global

gene

expression

have

illuminated

the

prevalence

of

each

type

of

complexity

-LRB-

FIG.

-RRB-

In

yeast

most

heritable

transcripts

show

transgressive

which

is

consistent

with

the

accumulation

NATURE

REVIEWS

GENETICS

VOLUME

NOVEMBER

Nature

Publishing

Group

REVIEWS

a

Directional

genetics

b

Transgressive

segregation

c

Interacting

loci

-LRB-

sample/reference

-RRB-

All

seg

BY

parent

RM

parent

All

seg

BY

parent

RM

parent

All

seg

BY

parent

RM

parent

Most

segregants

fall

between

parent

means

Most

segregants

fall

outside

parent

means

Segregant

mean

differs

from

mid-parent

mean

Figure

Types

of

complex

inheritance

of

transcript

levels

a

Directional

genetics

with

most

segregants

-LRB-

seg

-RRB-

showing

expression

between

the

two

parent

values

b

Transgressive

segregation

with

most

segregants

showing

expression

outside

the

two

parent

values

c

Genetic

interaction

with

the

segregant

average

expression

differing

from

the

mid-parent

mean

expression

In

each

panel

the

first

column

shows

the

relative

expression

of

the

corresponding

gene

in

all

segregants

and

the

next

two

columns

show

the

expression

in

replicates

of

the

two

parent

strains

Modified

with

permission

from

REF

-LRB-

-RRB-

National

Academy

of

Sciences

USA

Directional

genetics

A

distribution

of

trait

values

for

a

segregating

population

that

is

significantly

concentrated

within

the

range

defined

by

the

progenitor

strains

Non-additivity

A

property

of

alleles

at

a

locus

such

that

the

trait

value

of

heterozygous

individuals

is

not

the

average

of

the

trait

values

of

homozygotes

for

each

allele

Genetic

interaction

A

property

of

alleles

at

different

loci

such

that

their

combined

effect

on

a

phenotype

deviates

from

the

sum

of

their

individual

effects

-LRB-

this

is

often

called

epistasis

-RRB-

Allelic

heterogeneity

The

phenomenon

in

which

a

genetically

diverse

population

harbours

many

different

alleles

at

a

QTL

Gene-by-environment

interaction

The

effect

of

a

locus

on

a

trait

depends

on

the

environment

and

the

effect

of

the

environment

on

the

trait

depends

on

the

locus

Pleiotropy

The

capacity

of

a

single

mutation

to

affect

multiple

traits

in

the

parental

strains

of

a

large

number

of

alleles

of

modest

effect

A

smaller

fraction

of

traits

show

directional

which

is

suggestive

of

directional

selection

that

drives

the

phenotypic

divergence

of

the

parental

strains

Several

have

investigated

the

prevalence

of

non-additivity

where

gene

expression

in

heterozygotes

differs

from

the

mid-value

of

the

homozygous

parental

strains

Questions

of

additivity

depend

on

the

measurement

scale

values

can

be

additive

on

some

scales

but

non-additive

on

others

Moreover

microarrays

yield

linear

measures

of

gene

expression

only

within

certain

ranges

Despite

these

caveats

experimental

follow-up

has

validated

the

finding

from

microarrays

that

nonadditivity

is

common

in

D.

melanogaster

A.

thaliana

and

maize

and

that

its

extreme

forms

overdominance

and

underdominance

are

not

Genetic

interactions

have

been

observed

in

several

studies

and

a

systematic

scan

for

interacting

QTLs

found

non-additive

interactions

among

loci

for

roughly

half

of

all

The

detection

of

interacting

QTLs

will

be

aided

by

methodological

advances

as

well

as

by

future

studies

with

much

larger

sample

The

detection

of

population

association

at

only

a

minority

of

loci

previously

identified

by

linkage

in

indicates

that

many

individual

QTLs

which

underlie

expression

traits

display

considerable

allelic

heterogeneity

The

effects

of

genetic

variation

on

gene

expression

are

condition-dependent

and

such

geneby-environment

interactions

have

been

documented

in

comparisons

of

inbred

strains

across

In

multicellular

organisms

the

local

conditions

differ

in

each

tissue

and

genetic

variation

with

a

cell-typedependent

influence

on

gene

expression

represents

a

special

case

of

gene-by-environment

interaction

Studies

of

gene

expression

in

mouse

haematopoietic

stem

and

and

in

rat

kidney

and

have

found

that

the

genetic

basis

of

variation

in

a

gene

's

expression

is

sometimes

shared

between

different

tissues

but

is

often

unique

to

each

Studies

in

flies

and

mice

have

also

shown

extensive

sex

dependence

of

gene

Finally

the

existence

of

loci

that

affect

the

expression

of

many

genes

-LRB-

see

below

-RRB-

or

individual

genes

across

many

conditions

establishes

pleiotropy

as

a

common

feature

of

the

genetics

of

gene

expression

Local

versus

distant

QTLs

Transcript

levels

differ

from

other

phenotypes

in

that

each

transcript

has

a

corresponding

encoding

gene

with

a

known

position

in

the

genome

Therefore

because

mapping

studies

reveal

the

locations

of

QTLs

an

expression

QTL

can

be

immediately

classified

as

local

-LRB-

near

the

genomic

location

of

the

gene

encoding

the

transcript

-RRB-

or

distant

-LRB-

elsewhere

in

the

genome

-RRB-

Because

mapping

studies

do

not

reveal

the

underlying

molecular

nature

of

QTLs

we

prefer

the

strictly

positional

terms

local

and

distant

to

the

commonly

used

terms

cis

and

trans-linking

which

have

implicit

mechanistic

connotations

In

fact

both

local

and

distant

QTLs

can

include

polymorphisms

in

cis-acting

and

trans-acting

factors

under

the

classic

definition

of

the

The

casual

conflation

of

different

usages

of

cis

and

trans

has

resulted

in

a

significant

amount

of

confusion

Some

uses

describe

the

pattern

of

co-inheritance

of

trait

and

locus

-LRB-

linking

-RRB-

whereas

others

describe

the

mechanism

of

action

of

a

locus

with

respect

to

a

trait

-LRB-

acting

-RRB-

Neither

distinction

captures

any

information

about

the

molecular

nature

of

the

loci

that

is

whether

the

mutation

alters

a

protein

or

a

functional

RNA

or

acts

at

the

level

of

DNA

in

cis-regulation

NOVEMBER

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Publishing

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a

Local

regulatory

variation

Neighbouring

gene

Cis-regulatory

-LRB-

allele-specific

-RRB-

Autoregulatory

Feedback

b

Distant

regulatory

variation

Figure

Local

and

distant

regulatory

variation

a

Local

regulatory

variation

From

left

to

right

regulation

by

a

neighbouring

gene

cis-regulatory

variation

autoregulatory

variation

and

feedback

variation

The

red

star

denotes

the

regulatory

variant

the

bar

with

arrow

denotes

the

coding

region

of

the

gene

-LRB-

dark

green

-RRB-

the

expression

of

which

is

affected

by

the

variant

and

the

circle

denotes

the

protein

product

of

the

gene

The

blue

rectangle

to

the

left

of

the

coding

region

represents

an

upstream

regulatory

element

b

Distant

regulatory

variation

Arrows

are

drawn

from

a

gene

with

regulatory

variation

to

the

gene

or

genes

it

regulates

On

the

left

each

regulatory

variant

affects

expression

of

a

different

gene

On

the

right

a

regulatory

variant

affects

expression

of

many

genes

Such

a

variant

would

show

up

as

a

linkage

hot

spot

-LRB-

FIG.

-RRB-

Some

arbitrariness

is

introduced

by

the

definition

of

a

marker

being

local

or

distant

Local

is

most

commonly

defined

as

being

within

some

physical

distance

of

a

chosen

point

in

the

gene

Statistical

arguments

can

be

used

to

choose

this

distance

so

that

the

probability

that

a

linked

marker

will

fall

that

close

to

the

gene

by

chance

is

More

sophisticated

definitions

can

include

considerations

of

whether

the

gene

is

close

to

the

linkage

peak

for

example

does

it

fall

within

the

confidence

region

for

the

linkage

Local

linkage

can

arise

as

a

result

of

several

scenarios

-LRB-

FIG.

-RRB-

First

the

linkage

might

be

due

to

a

polymorphism

in

a

nearby

gene

that

regulates

the

gene

for

which

expression

is

being

measured

This

can

occur

either

by

chance

or

owing

to

a

nonrandom

close

location

in

the

genome

of

regulators

and

their

targets

Statistical

arguments

can

be

used

to

ensure

that

there

is

only

a

small

probability

of

a

chance

occurrence

Second

and

more

typically

local

linkage

will

be

due

to

a

polymorphism

-LRB-

or

polymorphisms

-RRB-

in

the

gene

itself

Such

polymorphisms

might

act

in

cis

by

altering

classic

cis-acting

regulatory

elements

and

consequently

changing

transcription

they

could

also

act

in

cis

posttranscriptionally

by

altering

message

stability

or

by

altering

sites

that

are

targets

for

messenger

RNA

processing

and

decay

Polymorphisms

in

the

gene

might

also

act

in

trans

-LRB-

that

is

affecting

the

expression

of

both

alleles

in

a

heterozygous

diploid

-RRB-

by

triggering

feedback

loops

either

directly

through

changes

in

the

coding

sequence

of

an

autoregulatory

gene

or

indirectly

by

changing

the

coding

sequence

or

message

levels

that

are

sensed

and

responded

to

by

the

cell

Classic

cis/trans

tests

of

allele-specific

expression

in

a

diploid

hybrid

can

be

used

to

distinguish

among

these

Distant

regulatory

variation

typically

acts

in

trans

through

the

downstream

effects

of

coding

or

cis-regulatory

polymorphisms

in

different

types

of

genes

with

transcription

factors

being

the

most

obvious

example

But

distant

regulation

can

occur

with

many

degrees

of

indirectness

including

non-cell-autonomous

effects

and

beyond

-LRB-

for

example

a

polymorphism

that

influences

diet

choice

would

affect

the

expression

of

genes

that

respond

to

specific

nutrients

and

exogenous

molecules

-RRB-

Moreover

the

existence

of

regulatory

elements

that

are

located

far

from

the

genes

they

regulate

means

that

distant

loci

can

act

in

cis

Even

more

complications

arise

from

DNA

elements

that

act

directly

to

regulate

distant

genes

through

physical

contact

interchromosomal

interactions

can

result

in

cis-regulatory

DNA

that

acts

on

a

different

A

genetically

diffuse

version

of

this

phenomenon

is

due

to

variation

in

the

total

genome-wide

number

of

binding

motifs

for

a

transcription

factor

the

binding

of

the

factor

to

non-functional

cis-regulatory

DNA

titrates

the

proteins

out

of

the

nucleoplasm

decreasing

their

availability

with

effects

in

NATURE

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VOLUME

NOVEMBER

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Publishing

Group

REVIEWS

Box

Units

of

measure

influence

contributions

to

variation

in

gene

expression

An

explicit

statistical

model

is

central

to

any

attempt

to

address

the

relative

contributions

of

different

kinds

of

QTLs

to

phenotypic

variation

But

apart

from

the

problem

of

what

qualifies

as

significant

is

the

problem

of

what

units

are

being

counted

We

might

want

to

count

the

number

of

gene

expression

traits

that

show

each

kind

of

genetic

basis

or

we

might

want

to

count

the

number

of

genetic

loci

that

underlie

the

inferred

linkages

a

major

source

of

confusion

is

that

both

traits

and

loci

are

often

called

genes

In

other

contexts

the

quantity

of

interest

might

be

the

number

of

linkages

in

which

case

a

single

genetic

locus

could

count

multiple

times

if

it

pleiotropically

affects

multiple

gene

expression

traits

Because

linkages

are

often

called

QTLs

counts

of

linkages

and

loci

are

readily

confused

Yet

another

unit

of

measure

is

the

fraction

of

phenotypic

variance

that

is

attributable

to

a

particular

type

of

genetic

variation

Most

studies

describe

results

for

only

some

of

these

units

hindering

comparisons

among

studies

Interpretation

also

depends

on

whether

we

are

measuring

trait-by-trait

averages

or

genome-wide

global

quantities

For

example

for

each

gene

expression

trait

most

loci

will

be

distant

but

on

a

genome-wide

scale

the

majority

of

loci

might

be

owing

to

the

pleiotropic

effects

of

distant

loci

Another

variable

among

studies

is

the

phenotypic

space

over

which

the

questions

are

asked

The

sampling

properties

of

networks

are

but

relevant

to

claims

about

the

genetic

architecture

of

gene

expression

as

counts

of

traits

loci

linkages

and

variances

have

different

ranges

and

scales

For

example

the

number

of

loci

segregating

in

a

cross

is

finite

whereas

the

number

of

possible

traits

-LRB-

and

therefore

linkages

-RRB-

is

technically

The

expression

of

each

gene

is

a

suite

of

traits

distributed

over

a

space

of

environments

continuous

in

some

dimensions

such

as

temperature

and

discontinuous

in

others

such

as

cell

type

At

present

we

know

very

little

about

how

the

counts

of

loci

-LRB-

or

linkages

or

variances

-RRB-

scale

with

the

extent

of

phenotypic

state

space

explored

However

in

the

case

of

local

versus

distant

loci

there

is

a

clear

bias

The

maximum

number

of

local

loci

discovered

is

limited

by

the

space

of

traits

examined

because

current

genome-wide

linkage

methods

are

unable

to

resolve

multiple

locally

linking

loci

for

a

single

trait

As

the

number

of

traits

examined

increases

the

estimated

genome-wide

number

of

local

loci

might

increase

linearly

whereas

the

number

of

distant

loci

will

increase

less

than

linearly

as

particular

pleiotropic

loci

are

discovered

again

and

again

One

consequence

is

that

estimated

ratios

of

local

versus

distant

loci

are

biased

by

the

number

of

sampled

traits

For

example

Morley

et

al.

sampled

just

human

gene

expression

traits

if

these

traits

were

downstream

of

the

major

pleiotropic

trans-acting

loci

then

most

of

the

genome

's

distant

QTLs

will

have

been

identified

whereas

at

best

less

than

of

transcripts

-LRB-

assuming

genes

-RRB-

were

examined

for

local

linkage

A

final

variable

is

the

nature

of

the

genetic

sample

that

is

the

genotypic

space

surveyed

So

far

most

studies

have

involved

simple

line

crosses

in

which

two

alleles

at

most

are

segregating

at

each

locus

Several

more

recent

studies

have

used

association

methods

in

larger

population

samples

where

more

alleles

could

be

segregating

If

the

loci

that

underlie

different

kinds

of

linkages

have

different

allele

frequency

different

kinds

of

sample

will

yield

different

answers

to

questions

about

their

prevalences

and

contributions

to

Multiple

testing

problem

The

number

of

false-positive

results

increases

when

multiple

statistical

tests

are

carried

out

requiring

more

stringent

thresholds

to

reach

the

same

level

of

significance

Linkage

disequilibrium

The

nonrandom

association

of

alleles

at

different

loci

in

a

population

QTN

Quantitative

trait

nucleotide

the

actual

sequence

polymorphism

responsible

for

variation

in

a

quantitative

trait

Empirical

results

local

regulatory

variation

As

many

as

of

all

gene

expression

traits

in

a

yeast

cross

are

affected

by

local

regulatory

Local

linkages

have

been

observed

to

account

for

anywhere

from

-LRB-

for

example

see

REF

-RRB-

to

-LRB-

for

example

see

REF

-RRB-

of

detected

loci

This

range

is

primarily

a

function

of

sample

size

as

local

linkages

on

average

explain

more

trait

variance

than

distant

linkages

and

are

therefore

more

likely

to

be

detected

in

smaller

In

addition

the

power

to

detect

local

linkages

is

higher

than

it

is

for

distant

linkages

because

the

multiple

testing

problem

is

less

severe

In

both

cases

thousands

of

transcripts

are

tested

for

linkage

but

in

the

case

of

distant

linkages

many

markers

that

cover

the

entire

genome

are

tested

for

linkage

to

each

transcript

whereas

in

the

case

of

local

linkage

only

markers

near

the

gene

encoding

the

transcript

need

to

be

tested

The

difference

is

especially

acute

for

association

studies

where

up

to

a

million

markers

are

used

to

cover

the

Several

types

of

polymorphism

in

different

locations

in

or

near

a

gene

and

acting

through

different

molecular

mechanisms

can

cause

local

variation

It

seems

that

most

but

not

all

local

regulatory

variation

acts

in

cis

with

perhaps

a

quarter

to

a

third

acting

in

Cis-acting

local

variation

can

result

from

differential

transcription

splicing

mRNA

decay

or

even

gene

copy

number

each

phenomenon

was

observed

in

a

mouse

A

variant

in

the

gene

in

Saccharomyces

cerevisiae

provides

an

example

of

a

local

but

trans-acting

effect

of

a

coding

polymorphism

on

its

corresponding

Because

transcript

levels

with

local

linkages

are

most

likely

affected

by

regulatory

variation

in

the

corresponding

gene

each

local

linkage

comes

with

an

immediate

candidate

gene

removing

the

need

for

cumbersome

positional

approaches

to

gene

identification

However

finding

functional

polymorphisms

is

still

not

straightforward

as

genes

are

likely

to

contain

multiple

polymorphisms

and

both

coding

and

regulatory

regions

need

to

be

examined

Linkage

and

linkage

disequilibrium

between

nearby

polymorphisms

further

complicate

the

identification

of

functional

polymorphisms

and

the

effect

on

expression

might

be

due

to

haplotypes

that

combine

multiple

alleles

Therefore

the

path

from

local

QTLs

to

QTNs

-LRB-

quantitative

trait

nucleotides

-RRB-

remains

tortuous

and

dependent

on

targeted

experiments

A

recent

and

unexpected

finding

from

A.

thaliana

is

the

neighbourhood

effect

whereby

a

large

number

of

physically

clustered

genes

show

linkage

to

a

local

These

hot

spots

of

local

linkage

might

be

due

to

tight

clustering

of

functionally

related

genes

or

to

coincidental

colocalization

of

a

trans-acting

variant

and

its

target

genes

but

a

plausible

alternative

is

that

the

QTLs

represent

mutations

that

alter

the

regional

structure

of

chromatin

acting

in

cis

to

influence

a

large

number

of

genes

NOVEMBER

VOLUME

Nature

Publishing

Group

REVIEWS

In

parallel

with

mapping

studies

allele-specific

measurement

techniques

which

pinpoint

genes

that

have

variable

transcription

due

to

cis-acting

allelic

variation

have

been

addressing

the

same

questions

Five

assay

techniques

allele-specific

quantitative

measurement

of

polymerase

quantitative

twodimensional

protein

allele-specific

expression

and

experimental

reporter

have

all

documented

abundant

cis-acting

genetic

variation

-LRB-

see

REF

for

a

review

of

these

techniques

-RRB-

Only

the

array-based

methods

approach

genome-wide

scope

and

the

results

are

striking

and

consistent

In

each

case

a

large

fraction

of

variable

traits

exhibit

cis-acting

variation

Pant

et

al.

by

measuring

allele-specific

expression

of

genes

in

human

white

blood

cells

found

that

more

than

half

of

the

genes

exhibited

cis-acting

heterozygosity

in

a

sample

of

individuals

and

that

on

average

individuals

exhibit

cis-acting

heterozygosity

at

of

the

genes

This

high

number

is

compatible

with

an

earlier

extrapolation

from

a

survey

of

reporter

Reporter

assays

have

allowed

the

dissection

of

cis-regulatory

haplotypes

and

have

established

that

the

single

local

QTL

that

is

found

for

a

gene

by

linkage

or

association

might

represent

haplotypes

that

differ

by

many

variants

with

individual

and

interacting

effects

on

Empirical

results

distant

regulatory

variation

In

studies

with

larger

sample

sizes

most

transcripts

link

to

loci

distant

from

the

genomic

locations

of

the

genes

that

encode

the

corresponding

transcripts

For

example

Yvert

et

al.

found

that

of

expression

traits

show

linkage

to

the

genomic

regions

at

which

they

are

transcribed

whereas

link

to

distant

loci

An

estimated

loci

accounted

for

these

distant

linkages

Because

a

particular

locus

can

influence

many

distant

genes

-LRB-

FIG.

-RRB-

the

number

of

distant

linkages

is

often

much

higher

than

the

number

of

local

linkages

even

though

as

in

this

case

the

number

of

loci

that

account

for

local

linkages

is

much

higher

than

the

number

of

distant

loci

-LRB-

BOX

-RRB-

Because

the

resolution

of

the

linkages

tends

to

be

relatively

low

it

is

difficult

to

estimate

the

number

of

loci

that

affect

gene

expression

of

distant

transcripts

in

any

given

study

as

well

as

the

number

of

transcripts

affected

by

a

typical

locus

However

one

common

feature

observed

in

multiple

studies

is

the

presence

of

hot

spots

individual

loci

that

affect

large

numbers

of

Hot

spots

are

usually

defined

as

those

loci

for

which

the

number

of

linked

or

associated

transcripts

statistically

significantly

exceeds

that

expected

if

such

transcripts

were

randomly

distributed

along

the

genetic

-LRB-

FIG.

-RRB-

Care

must

be

taken

to

correct

for

the

fact

that

many

loci

are

examined

and

for

correlation

in

expression

levels

of

different

genes

that

might

cause

apparent

hot

spots

in

the

absence

of

underlying

common

polymorphisms

in

other

words

a

false-positive

QTL

for

one

trait

will

be

a

false

positive

for

all

correlated

traits

Although

hot-spot

QTLs

are

often

called

master

regulators

the

QTLs

themselves

are

simply

mutations

that

segregate

in

populations

and

their

hot-spot

status

comes

from

the

pleiotropic

effects

of

the

mutations

rather

than

from

any

necessary

regulatory

function

of

the

genes

A

mutation

in

an

essential

structural

protein

with

no

regulatory

role

is

the

ultimate

hot

spot

a

locus

that

acts

in

trans

to

reduce

the

expression

of

every

gene

to

zero

-LRB-

by

means

of

lethality

-RRB-

Of

course

lethality

is

an

extreme

example

and

in

general

mutations

in

genes

at

the

top

of

regulatory

hierarchies

are

promising

candidates

for

hot-spot

QTLs

Finding

the

genes

that

underlie

distant

loci

is

more

challenging

than

in

the

case

of

local

loci

Several

approaches

might

prove

to

be

helpful

Some

polymorphisms

also

affect

the

expression

of

the

gene

that

contains

them

with

the

effects

on

other

genes

being

either

pleiotropic

or

a

direct

consequence

of

the

change

in

expression

Therefore

looking

for

a

local

linkage

among

the

transcripts

linking

to

a

hot

spot

might

lead

directly

to

the

causative

gene

In

mouse

for

example

a

local

QTL

influencing

expression

of

the

transcription

factor

gene

acts

as

a

distant

QTL

for

genes

known

to

be

targets

of

Combining

the

transcripts

affected

by

a

hot

spot

into

a

single

phenotype

-LRB-

for

example

by

clustering

-RRB-

can

increase

resolution

and

make

the

phenotype

simpler

-LRB-

more

monogenic

-RRB-

and

therefore

more

amenable

to

recombinational

fine

More

sophisticated

approaches

that

leverage

the

correlation

structure

among

phenotypes

for

example

by

finding

a

function

of

multiple

traits

that

maximizes

support

for

a

pleiotropic

QTL

also

hold

Because

many

regulatory

and

biochemical

networks

are

well

characterized

the

identity

and

annotation

of

the

transcripts

that

link

to

a

hot

spot

can

greatly

assist

candidate

gene

Studying

gene

expression

and

growth

rates

in

Eucalyptus

Kirst

et

al.

identified

a

suite

of

genes

of

which

the

expression

explained

much

of

the

variance

in

tree

This

set

of

genes

included

nearly

the

entire

lignin

biosynthesis

network

and

their

expression

traits

share

a

set

of

common

QTLs

Although

Linkages

200 150 100

100 200 300 400

Genome

location

Figure

Hot

spots

of

distant

regulatory

variation

The

number

of

linkages

is

plotted

against

genome

location

The

yeast

genome

has

been

divided

into

bins

of

kb

shown

in

chromosomal

order

on

the

x

axis

The

number

of

distinct

transcripts

linked

to

markers

in

each

bin

is

shown

by

bar

height

Local

linkages

are

excluded

The

dashed

line

shows

the

maximum

number

of

linkages

that

would

be

expected

to

fall

into

any

one

bin

by

chance

with

a

probability

of

corrected

for

the

number

of

bins

Bins

with

bar

heights

above

this

line

represent

hot

spots

NATURE

REVIEWS

GENETICS

VOLUME

NOVEMBER

Nature

Publishing

Group

REVIEWS

a

Eucalyptus

genome

sequence

is

unavailable

Kirst

et

al.

mapped

some

of

the

lignin

biosynthesis

genes

and

found

that

one

S-adenosylmethionine

synthase

coincides

with

the

major

QTL

for

both

growth

and

the

suite

of

lignin

biosynthesis

genes

A

natural

assumption

is

that

distant

linkages

that

underlie

gene

expression

variation

are

due

to

polymorphisms

in

transcription

factors

the

archetypal

trans-acting

transcriptional

regulators

The

hypothesis

is

difficult

to

test

without

mapping

QTLs

to

the

resolution

of

single

genes

but

genome-wide

expression

mapping

provides

sufficient

data

to

allow

confident

inferences

In

a

yeast

cross

known

yeast

transcription

factors

were

not

overrepresented

near

the

markers

that

defined

QTLs

for

distant

linkages

and

an

analysis

of

Gene

Ontology

categories

indicated

that

QTLs

are

not

enriched

for

any

particular

class

of

molecular

-LRB-

these

questions

are

revisited

in

REF

-RRB-

expression

profiling

as

well

as

developing

techniques

for

rapid

and

comprehensive

mutation

and

for

exchanging

alleles

between

strains

and

directly

testing

the

effects

on

Two

other

major

directions

involve

expanding

the

genetic

samples

and

the

set

of

global

molecular

phenotypes

So

far

most

studies

have

used

linkage

analysis

in

experimental

crosses

recombinant

inbred

lines

or

reference

human

pedigrees

It

is

important

to

extend

studies

to

population

samples

in

order

to

ask

questions

about

allele

frequencies

and

selective

In

those

cases

in

which

gene

expression

variation

has

been

mapped

to

the

level

of

nucleotides

population

genetic

analysis

has

revealed

an

important

role

for

natural

selection

in

shaping

and

maintaining

Population

studies

have

begun

to

be

carried

out

but

until

now

have

been

limited

to

humans

and

have

not

yet

used

global

expression

As

global

expression

analysis

merges

with

population

genomics

we

anticipate

a

fuller

understanding

of

the

causes

of

heritable

The

expansion

of

global

molecular

phenotyping

includes

measuring

gene

expression

in

multiple

environments

developmental

stages

and

cell

and

tissue

types

Emerging

technologies

also

open

up

the

ability

to

examine

other

molecular

components

of

cells

including

and

Global

phenotyping

can

also

include

characterizing

cellular

properties

and

responses

to

large

classes

of

external

perturbations

such

as

small

Where

possible

multiple

classes

of

global

phenotypes

should

be

collected

for

the

same

study

samples

so

that

connections

might

be

drawn

between

the

multiple

levels

of

phenotypic

information

with

the

goal

of

achieving

an

understanding

of

how

changes

at

the

DNA

sequence

level

are

translated

into

changes

at

the

level

of

organismal

phenotypes

through

changes

in

the

intermediates

Clearly

exciting

times

lie

ahead

The

future

Much

has

been

learned

about

the

genetics

of

global

gene

expression

in

the

past

few

years

However

the

field

is

young

and

much

remains

to

be

discovered

So

far

most

of

the

conclusions

are

based

on

the

identification

of

genetic

loci

that

have

not

been

resolved

into

individual

genes

and

polymorphisms

that

affect

expression

Although

a

few

examples

of

detailed

molecular

characterization

of

expression

QTLs

exist

-LRB-

for

example

see

REF

-RRB-

many

more

are

needed

for

a

clear

understanding

of

the

types

of

allele

that

are

responsible

for

genetic

differences

in

gene

expression

and

of

the

location

and

nature

of

the

causative

polymorphisms

Such

high-throughput

identification

and

characterization

of

polymorphisms

that

affect

expression

remains

a

challenge

Promising

research

directions

include

greatly

increasing

sample

sizes

of

studies

by

taking

advantage

of

high-throughput

techniques

for

and

Brem

R.

B.

Yvert

G.

Clinton

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Acknowledgements

We

regret

that

space

constraints

prevented

us

from

citing

additional

work

in

the

field

We

thank

past

and

current

members

of

the

Kruglyak

laboratory

and

our

collaborators

for

discussions

Work

was

supported

in

part

by

a

grant

from

the

National

Institute

of

Mental

Health

and

a

James

S.

McDonnell

Foundation

Centennial

Fellowship

to

L.K.

M.V.R.

is

supported

by

The

Jane

Coffin

Childs

Memorial

Fund

for

Medical

Research

Competing

interests

statement

The

authors

declare

no

competing

financial

interests

DATABASES

The

following

terms

in

this

article

are

linked

online

to

Entrez

Gene

FURTHER

INFORMATION

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Ontology

Leonid

Kruglyak

's

homepage

edu/index.php

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content

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to

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