Lection 2: Genetic variation in

transcription networks

Kristy Harmon, Lauren McIntyre, Marta Wayne, UF Gainsville, UF Gainsville

Matt Hahn, Linda Bisson, Kristy HarmonIU Bloomington

Evolution in Levels

Molecular Evolution

Quantitative

Genetics

Evolution of Phenotype

• Intraspecific Transcriptome Variation

• Components of Variation

• Dimensionality of Variation

• Phenotypic Effects?

Affymetrix microarrays: 13,966 genes

Heritability of expression: 663 genes:

0.470.39 0.60

cis + trans: 8.7%

trans: 6.8%

P=0.0397

Intraspecific Variation

Male-Female Differences in Splicing

v1. Genetics v2. ?

v3. Genome Biology

Male-Female Differences in Splicing

Total Above Line & sex Line SexProbe type genes control significance signif. signif. Alter. Trans. 2768 2479 1471 250 1336 Gene family 177 162 118 31 103Singleton 12912 8265 4602 296 4387 Total 15894 10933 6202 349 5832% of “above control” n/a 57% 3% 53%

Probe type Above Line, sex & probe Line&probe Sex&probe control significance significance significance

Altern. Trans. 828 182 26 177

Gene family 91 23 4 22

Total 919 205 30 199

% of “above control” 23% 3% 21%

Intraspecific Transcriptome Variation:Components of Variation.

1 2 3 4 5 6 7 8 9

1 1,2 1,3 1,4 1,5 1,6 1,7 1,8 1,9

2 2,1 2,3 2,4 2,5 2,6 2,7 2,8 2,9

3 3,1 3,2 3,4 3,5 3,6 3,7 3,8 3,9

4 4,1 4,2 4,3 4,5 4,6 4,7 4,8 4,9

5 5,1 5,2 5,3 5,4 5,6 5,7 5,8 5,9

6 6,1 6,2 6,3 6,4 6,5 6,7 6,8 6,9

7 7,1 7,2 7,3 7,4 7,5 7,6 7,8 7,9

8 8,1 8,2 8,3 8,4 8,5 8,6 8,7 8,9

9 9,1 9,2 9,3 9,4 9,5 9,6 9,7 9,8

Sires

Dam

s

Diallel data summary• Agilent 60b oligos• 9,863 genes were included in analysis

(requirements: no multiple splice products, and probes higher than negative controls)– 1,609 X linked genes– 8,213 “autosomal” genes (2, 3 only)– 35 4th chromosome– 6 Y chromosome

• Models run for sexes separately; evaluated significance at FDR = 0.20

P = 2

GCA + SCA + 2

RGCA + RSCA +

Conclusions: more genes genetically vary for transcript level in females

Male Female

X Autosome X Autosome

Genetic 192 1337 604 2720

GCA 185 1336 88 630

SCA 1 2 361 1284

rGCA 44 23 1 1

rSCA 2 1 341 1380

GCA, SCA

1 2 3 4 5 6 7 8 9

1 1,2 1,3 1,4 1,5 1,6 1,7 1,8 1,9

2 2,1 2,3 2,4 2,5 2,6 2,7 2,8 2,9

3 3,1 3,2 3,4 3,5 3,6 3,7 3,8 3,9

4 4,1 4,2 4,3 4,5 4,6 4,7 4,8 4,9

5 5,1 5,2 5,3 5,4 5,6 5,7 5,8 5,9

6 6,1 6,2 6,3 6,4 6,5 6,7 6,8 6,9

7 7,1 7,2 7,3 7,4 7,5 7,6 7,8 7,9

8 8,1 8,2 8,3 8,4 8,5 8,6 8,7 8,9

9 9,1 9,2 9,3 9,4 9,5 9,6 9,7 9,8

Sires

Dam

s

Conclusions: but more genes have heritable variation in transcript level in males

Male FemaleX Autosome X

AutosomeGenetic 192 1337 604 2720GCA 185 1336 88 630SCA 1 2 361 1284rGCA 44 23 1 1rSCA 2 1 341 1380(and heritable variation is underrepresented on X)

rGCA; rSCA

1 2 3 4 5 6 7 8 9

1 1,2 1,3 1,4 1,5 1,6 1,7 1,8 1,9

2 2,1 2,3 2,4 2,5 2,6 2,7 2,8 2,9

3 3,1 3,2 3,4 3,5 3,6 3,7 3,8 3,9

4 4,1 4,2 4,3 4,5 4,6 4,7 4,8 4,9

5 5,1 5,2 5,3 5,4 5,6 5,7 5,8 5,9

6 6,1 6,2 6,3 6,4 6,5 6,7 6,8 6,9

7 7,1 7,2 7,3 7,4 7,5 7,6 7,8 7,9

8 8,1 8,2 8,3 8,4 8,5 8,6 8,7 8,9

9 9,1 9,2 9,3 9,4 9,5 9,6 9,7 9,8

Sires

Dam

s

Conclusions: males possess ~no epistatic variation in transcript levels, while

females are overwhelmed with it

Male FemaleX Autosome X

AutosomeGenetic 192 1337 604 2720GCA 185 1336 88 630SCA 1 2 361 1284rGCA 44 23 1 1rSCA 2 1 341 1380(and it is overrepresented on the X chromosome)

Overall “conclusions”

• Heritable variation in transcript level is “consistent” with mutation-selection balance;

• Dominance / epistatic variation might (?) be consistent with “antagonistic arms race”.

Benefit to males females

Harmful for females males

Dominance condition recessive dominant

• Intraspecific Transcriptome Variation

• Components of Variation

• Dimensionality of Variation

• Phenotypic Effects?

Intraspecific Transcriptome Variation: Phenotypic Effects.

9 lines eggs (5+8h) Affymetrix chips.

Binding sites of segmentation genes (from

Schroeder et al. 2004)

Model Model Fit Parameter Estimate Pr > F / Significance (P)oc = bcd gt kr kni .0152 1.44 -0.24 0.63 -0.60 .006 .185 .082 .097oc = tor gt kni .0849 -0.03 0.22 0.23 .893 .143 .446ems = kni <.0001 0.93 .0001{btd}*cnc = Tor Bcd Gt Kr Kni .0459 -0.30 0.73 -0.51 1.18 -0.34 .140 .341 .052 .026 .321 {hb}

Kr = Gt Kni Cad .0050 0.29 0.77 0.03 .093 .022 .834Gt = Bcd {Gt} Kr Kni .0110 -2.78 {} 0.85 -1.19 .051 .289 .116Kni = Tor Bcd Kr .0287 -0.19 0.06 0.64 .528 .940 .163{eve}h = Kr <.0001 1.27 .0001h = Kr Kni Cad .0054 0.90 0.47 -0.00 .099 .371 .998h = Gt Cad .0188 0.27 0.47 .094 .009run = Bcd Kr Kni .0041 -1.69 0.55 -0.58 .036 .220 .149ftz = Bcd Gt .0018 -0.67 0.48 Cad = Gt Kni {Cad} .0177 -0.57 -0.88 {}

.350 .07 6.093 .133

{spl2} {nub}pdm2 = Bcd Kr Cad .0350 0.79 0.61 -0.52

D = Bcd Gt Kr Kni Cad .0163 1.61 0.48 1.36 1.44 0.14 .647 .315 .205 .476 .315 .157 .141 .714{hb} {eve}

Kr = Bcd Gt Kni Cad .0228 -0.34 0.24 0.71 0.07 h = Gt Cad .0663 0.16 -0.49 .814 .386 .114 .768 .674 .186Kr = Tor Bcd Gt Kni .0234 -0.06 0.05 0.27 0.71 h = Bcd Kr Kni Cad .0016 2.52 1.54 0.42 -0.43 .839 .965 .338 .120 .025 .008 .189 .054Gt = Kr Kni .0248 2.04 -1.04 h = Kr Kni Cad .0054 0.90 0.46 -0.00 .035 .273 .099 .371 .998{eve} h = Kr Kni .0008 0.91 0.47 run = Tor Bcd Kr Kni Cad .028 0.42 -0.13 0.80 -0.53 -0.58 .051 .317 .363 .910 .129 .208 .199 odd = Bcd Kr Cad <0.0001 -0.61 0.52 -0.29 run = Kr Cad .0023 0.41 -0.35 .043 .001 .002 .156 .054Gt = Kr Cad .0284 0.75 -0.36 run = Bcd Kr Kni Cad .0097 -0.78 0.67 -0.64 -0.26 .241 .331 .422 .147 .114 .248Kni = Bcd Kr {Kni} .0085 -0.16 0.70 {} odd = Gt Kr Kni Cad <0.0001 0.11 0.54 0.06 -0.35 .829 .107 .085 .010 .642 .001

Factor Analysis for Expression Data

Factor is a linear combination Measurements “load” on

of measurements factors

In every genotype, the value of the factor can be calculated

and correlated with the trait value genes with high “loads”

How many dimensions? (among 9 genotypes)

Flies Yeast

• No tissue problems;• Bunch of phenotypes ethanol;

temperature; sulfates; ….

30 genotypes (4 reps including dye swaps);Log phase;Agilent arrays.

Does variation in TF expression level account for variation in expression of targets?

ADR1 Transcription Factor ABF1 ADR1 AFT2 CHA4

CRZ1 CTH1 DAL80 DAL80 FAP1 FHL1 FKH1 FKH2 FZF1 GAT2 GAT3 GAT4 GIS1 GIS2 GLN3 GLN3 GZF3 HAC1 HMS1 HSF1 IFH1 MAL13 MAL33 MBP1 YER028C MSN2 MSN4 NDT80 PHO2 PHO2 PHO4 PLM2 PPR1 RAP1 RCS1 RCS1 RDR1 RDS1 RDS2 RDS3 SFP1 SFP1 SKN7 SKN7 SMP1 SOK2 STB5 STE12 STP1 SUT1 SWI4 TBF1 TOS4 TOS8 TYE7 TYE7 UGA3 WAR1 XBP1 YAP1 YAP1 YAP3 ZMS1

Factors: 6 (how much variance is explained)

Correlations: ADR1 X Factor 1: 0.581 (P=0.0008)

ADR1 X Factor 2: 0.656 (P<0.0001)

ADR1 X Factor ? – non significant.

Transcripts to explore or confirm.

Exploratory Factor Analysis

• ADR1 Transcription Factor

Which of the regulated genes are real?

Loading Gene: Factor 1 Factor 2

ABF1 -17 -7

AFT2 -4 54 *

CHA4 -5 1

CRZ1 -7 54*

FZF1 50 * 10

GAT2 54* 64*

GAT3 46* -1

GAT4 65* -21

Confirmatory Factor Analysis, Structural Equation

31 Exogenous variables 12 Endogenous parameters

Yeast Pathways Are “Well”-Established

V1 = 0.54V2 + E1

t = 10.18

V2 = 0.61V3 + E2

t = 9.69

V3 = 6.60V4 – 2.57V5 +E3

t = 4.19 t=1.95

model phenotype;

Direct selection on V1 indirect selection.

Yeast Confirmatory Factor Analysis

• Genetic / metabolic Small natural mutations introduce

are networks non-linear; linear deviations (natural variation is mostly additive);

• To parameterize the Nature supplies unlimited number of

model, many degrees of segregating alleles;

freedom are required;

• Pathways are never Latent variables can be used

complete, many variables instead.

can not be measured.

33 Exogenous variables 13 Endogenous parameters

Yeast Confirmatory Factor Analysis, Latent Variables

1. Intraspecific Transcriptome Variation

• 10-20% genes significantly vary in transcript level among populations, heritability is high;

• there is a similar level of variation for alternative splicing levels;

• transcriptome variation is profoundly: heritable in males, epistatic in females;

• antagonistic effects of alleles on two sexes may contribute to the overabundance of X-linked variation;

• factors explaining ITV is a promising analysis to identify genes and networks controlling QTs.