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Published by soedito, 2017-09-05 00:29:35

300_ASAS_MICROARRAY_30 (1)

300_ASAS_MICROARRAY_30 (1)

Uses of micr
related meth

animal b

Bruce Walsh, jbwa

University

(Depts. of Ecology & Evolutionary Bi
Plant Sciences, Animal Sciences, a

roarrays and
hodologies in
breeding

[email protected]

of Arizona

iology, Molecular & Cellular Biology,
and Epidemology & Biostatistics)

The basic idea
expressio

• With a complete (or p
sequence in hand, one
from genes of interes
slide, or a membrane

• mRNA is extracted fr
and hybridized to the

• Genes showing differe
can be detected

a behind gene
on arrays

partial) genome
can array sequences

st on small chip, glass

rom cells of interest
array

ent levels of mRNA

Types of m

• Synthetic oligonucleo

– Chemically synthesize ol
directly on slide/chip/me
photolithography)

– Affymetrix, Agilent

• Spotted cDNA array

– PCR products from clone
are spotted on a glass sli

– Extracted cellular mRNA
transcribed into cDNAs

microarrays

otide arrays

igonucleotide sequences
embrane (e.g., using

ys

es of genes of interest
ide using a robot
A is reverse-
for hybridization

Cell type 1

Extract mR

Label mRNA with red
fluorescent dye (Cy5)

Cell type 2

RNA

Label mRNA with Green
fluorescent dye (Cy3)

Cell
Type 1

Hybridize mRNA to array

Each spo
correspo
gene

Cell type 2

ot (or feature)
onds to a different

The color of the spot
corresponds mtoRNtAhemeqauianllymix
relative concferonmtrcCeaelltlTiToyypnpeess
of mRNAs fo12rantdh2at gene
in the two cell types



gimneigmGtAmRnnceyNRebReenpcnNuNslAeeelenslsAsmAtsldysmtsaofopynorofffepecrrrrreeoe2otaommh1biuaneugbttsnbhuhhedloneyeatdsshneaeetqncutealll

Analysis of mic

• Image processing and
• Detecting significant
• Clustering and classif

– Clustering: detecting
genes

– Classification: finding
changes in mRNA expr
phenotype

croarray data

d normalization
t changes in expression
fication

g groups of co-expressed

g those genes at which
ression level predicts

Significance t

Yklijk = u + Ak +Rkl + T

ArrRaeyplkic
ktr-ethatsmpIgeoenntitnntteieniargrioarnaco
k

testing-- GLM

Ti + Gj + TGij +elkijk

caTterelaGtemneenjt i
rnacoantfryideogkpntelrnibeceeaatttjwmeueenlendontefrjarray

Problem of very m
vs. few actual

• Expectation: A large
interactions will be si

– Controlling experiment
overly conservative (fu
strongly correlated)

• Generating a reduced
future consideration (

– FDR (false discovery r
– PFP (proportion of fals
– Empirical Bayes approa

many tests (genes)
l data vectors

number of the GxT
ignificant

t-wide p value is very
urther, tests may be

d set of genes for
(data mining)

rate)
se positives)
aches

Which loci contro
changes in mRN

• Cis-acting factors

– Control regions immed
gene

• Trans-acting factors

– Diffusable factors un
to the gene of interes

• Global (Master) regul

– Trans-acting factors
number of genes

ol array-detected
NA expression?

diately adjacent to the

s

nlinked (or loosely linked)
st

lators

that influence a large

David Treadgill
exper

• Recombinant Inbre
cross of DBA/2J a

• The level of mRNA
(measured by array
treated as a quanti
QTL analysis perfo
gene in the array

l’s (UNC) mouse
riment

ed lines from a
and C57BL

A expression
y analysis) is
itative trait and
ormed for each

TRANS-modifiers C

Genomic location of genes on arrayDistribution
of >12,000

gene
interactions

Genomic lo

CIS-modifiers MASTER modifiers
ocation of mRNA level modifiers

Candidate loci :
Gene Expression

• Correlate difference
expression with trait

• Map factors underlyi
expression

– These are (very) ofte

• Difference between s
regulatory alleles

– Different structural a
on an array analysis

: Differences in
n between lines

es in levels of
t levels
ing changes in

en trans-acting factors

structural alleles and

alleles may go undetected

Expanded selecti
offered by m

• GxE

– Candidate genes may be
levels of mRNA express
major environments

– With candidates in hand
selection of genes showi
expression over critical

• Breaking (or at least re
deleterious genetic cor

– Look for variation in gen
any) trans-acting effect

ion opportunities
microarrays

e suggested by examining
sion over different

d, potential for
ing reduced variance in
environments

educing) potentially
rrelations

nes that have little (if
ts on other genes

Towards t

• Selection decisions us
gene networks / pathw

• Microarrays are one t
reconstructing gene n

• Additional tools for e
protein interactions

– Two hybrid screens
– FRET & FRAP
– 2D Protein gels

the future

sing information on
ways
tool for
networks
examining protein-

Analysis and Ex
Gene and Metab

• Graph theory
• Most estimation a

unresolved
• Major (current) a

Kascer-Burns Sen

xploitation of
bolic Networks

and statistical issues
analytic tool:
nsitivity Analysis

Gene network

ks are graphs



Kascer-Burns Sen

(aka. Metabolic C

““NAolltmhoedoerylssahroeulwdrfointga, lslotm
th(Beofxa)cts are wrong” (N.

nsitivity Analysis

Control Analysis)

tmhe mfaocdteslsbeacraeuusseesfouml”e of
Bohr)

Flu

Perhaps we increase thpea
A e1 B e2 D

tKToahsetchfeiHplsruaopxatwrnhcodwbobaneBlyetsu?rmtrontHTlsooco, oiwpinenrecfcovrfrveeeiircad

ux = production rate of a

arctoinccuelanrtraptrioodnuocft,eh1ere F
D e3 E e4 F
ercada,iseseeisetnttamth,hqaeienuytfacbrlnouoetnxdicmtuteacohtnerritdveoreuaebgtsfyhiofolintuchtioeiifsonnte4

Flux Control Coe

The control coefficient f
a pathway associated with

Cij = @F i Ej
@E j fi

Roughly speaking, the
the percentage change
percentage change in e

efficients, C j
i

for the flux at step i in
h enzyme j,

= @ln Fi
@ln E j

control coefficient is
e in flux divided by
enzyme activity

FluxWhy many mutations are r
reduction in activity (the h
results in only a very smal

Activi
WWhheenn tthhee aaccttiivviitt
CC iiss cclloossee ttoo 1zero

recessive: a 50%
heterozygote)
ll change in the flux

ity
ttyy ooff EE iiss lnaeragre,zero,
o

Kacser-Bu
summation

X
C

i

•naoi•pvnleaWfrlocgCIlo•wauhraofptiieennileeTevgasaefremrsfftorcnuoeoeyiforzslcdgytnyCuioevltimravtanrntaalheouolttvuree,lseeassrbscl-(aourou>lcfriee1etmoepC.,fnrrifetnfttaoshoiirrstcniotohp

urns Flux
n theorem:

C j = 1
i

cnitohpsigrereisodn)srttetgeiraiecvniieprssn(esnlasgoeraiarecgcsareapetpeaolis)vsrteirostltayivpyhvareeseleu,treetsi,mes
ol coefficients

“rate-limiting” st

SinmcHraelela-nsKecadecs,betyrhatenherlio-mfroeitlmdin:igntchirn

f f =f = 1 °

20
0.2 0.4
18
16
14

12

10
8
6

4

2
0
0.0

Control

teps in pathways

hrneecafrsaeecatinosreafcitnbivyfitwyihsoicfhEfilsux is

11
°CEj1
°r r CEj

4 0.6 0.8 1.0

l Coefficient C

Using estima
Coefficients as

• Loci with larger C val
faster to selection

• Such loci are obvious
of natural variation (c

• Selection with reduce

– Tallis or Kempthorne - No
index

– Select on loci with large C
for other fluxes not of co

– Positive selection on C for
reduce flux changes in oth

ated Control
s selection aids

lues should respond

s targets for screens
candidate loci)
ed correlations

ordskog restricted selection

C for flux of interest, smallest C
oncern
r flux of interest, selection to
her pathways

e6

e5
A e1 B e2 D e3

A moTrheecionritrieacltapaprporaocahchm, ih
Pickttrhyeesitheep(rse) 3thoartem4,arxaitm


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