Lecture 15 - QTL Mapping - Part 2

• Doerge (2001) Nature Genetics Reviews3:43-52

• Neale, chapter 18• Liu, chapters 13-14

Edwards et al. 1987, Genetics, 113-125

Single factor QTL mapping

Advantages of Single FactorQTL Mapping

• No map needed• Standard stat packages, SAS

Disdvantages of Single FactorQTL Mapping

• Map position not precisely determined• Biased estimates of a and d• Phenotypic effect overestimated• Multiple testing

Interval Mapping Fig 21.1 from Falconer and Mackay. Pg 364Recombination frequencies between two marker loci, M and N, and a QTL, A

M1 N1A1

M2 N2A2

c2c1

c

Table 21. 3. Falconer and Mackay

Advantages of Interval QTLMapping

• More precise location of QTL• Better estimates of %PVE

Disdvantages of Interval QTLMapping

• Computationally demanding• Custom software

ewsglwsgvol%emfalmfaecw

clcw

c

DETEC

TION

VERIFI

CATIO

NREL

ATED

UNRELATED

Aco_10.0

PtIFG_3012_4312.715.0

PtIFG_2150_A19.619.9 PtIFG_2885_B20.1

estPtIFG_8569_a29.5PtIFG_2538_B30.2

PtIFG_2564_A40.3PtIFG_1A7_A42.6estPtIFG_9022_a43.1PtIFG_2536_146.5PtIFG_1A7_D46.8

estPtIFG_48_a58.3estPaINRA_PAXY13_a59.5estPtIFG_464_a62.2

PtIFG_1633_a66.0

PtIFG_48_178.4estPtIFG_8939_aPtIFG_3006_183.4

PtIFG_1918_h83.886.186.3

PtIFG_1623_A90.9

estPtIFG_66_a92.894.6

PtIFG_1626_a95.4

PtIFG_2986_A102.7PtIFG_1D11_A104.0

PtIFG_1165_a121.1

6Pgd_11140.7

estPpaINRA_AS01C10-1_a154.6

LG 2

PtIFG_2006_C0.0estPtIFG_1934_a0.3PtIFG_2145_13.4

PtIFG_2068_A7.8PtIFG_2897_d10.4PtIFG_975_312.2

estPtIFG_8500_a18.8

PtIFG_138_B24.1

estPtNCS_22C5_a30.1PtIFG_2588_132.5estPtNCS_C612F_a33.8

PtIFG_2718_344.8

PtIFG_2745_154.2

PtIFG_1918_357.459.5

estPtIFG_8612_a64.2PtIFG_2090_2

67.669.4

PtIFG_1636_370.1

78.2

PtIFG_2988_2183.6

PtIFG_2718_186.8

estPtIFG_2889_a95.7

PtIFG_2889_2198.9

estPtIFG_8781_a104.1

PtIFG_2145_76107.4PtIFG_2145_5109.0

113.4 PtIFG_1D9_2113.6116.2

LG 3

C4H-1

Pta14A9

SAMS-1

DETEC

TION

VERIFI

CATIO

NREL

ATED

UNRELATED

DETEC

TION

VERIFI

CATIO

NREL

ATED

UNRELATED

PtIFG_2819_12PtIFG_653_dPtIFG_2086_13PtIFG_1626_c

PtIFG_2697_A

PtIFG_2006_A

estPtINCS_20G2_aestPtIFG_9053_aestPtIFG_8843_aPtUME_Ps3_A

estPtIFG_8537_a

estPtIFG_2253_aestPpINR_AS01G01_aestPtIFG_1576_aPtIFG_2253_A

PtIFG_2782_31

PtIFG_1457_b

estPtIFG_9198_aestPtIFG_8496_a

PtIFG_2146_31

PtIFG_2441_1estPtIFG_107_aPtIFG_2931_bestPtNCS_6N3E_aPtIFG_2393_1PtIFG_2931_A

PtIFG_851_1

LG 1

LAC

GlyHMT

PtNCS_CAD-08_b

SCALE

0 cM

10 cM

Brown et al. 2003 Genetics164:1537-46

Quantitative TraitLoci Controlling

Light and HormoneResponse in Two

Accessions ofArabidopsis thaliana

Justin O. Borevitz1,a,b, Julin N. Maloof1,a,Jason Lutesa,c, Tsegaye Dabia, Joanna L.

Redferna,Gabriel T. Trainera,c, Jonathan D. Wernera,b,

Tadao Asamid, Charles C. Berrye, DetlefWeigela,f, and Joanne Chorya,c

What can be learned from a QTLmapping experiment

• Estimate of number of genes controllingcomplex trait

• Location of genes in the genome• Estimates of a and d• Estimate of %PVE

Genome

Transcriptome

Proteome

Metabolome

Phenome

Methods for Measuring mRNAAbundance

• Northern Blot• RNase protection• SAGE, serial analysis of gene expression• Microarrys• RT-PCR

Quantitative Variation in GeneExpression

• Cell type• Tissue type• Developmental stage• Inductive condition• GENOTYPE!!!

Factors Effecting GeneExpression

• cis regulatory - promoters• trans regulatory - transcription factors

0

500

1000

1500

2000

2500

A

B

trans

crip

tsnu

mbe

r of g

enes

genetic location of eQTLs (cM)

I II III IV V

Figure 1: Genomic architecture of eQTLs across five Arabidopsis chromosomes. Vertical dotted lines separate thefive Arabidopsis thaliana linkage groups (I-V).A. Heat map of Likelihood Ratio (LR) Test Statistic scores obtained by composite interval mapping (CIM) eQTL analysisin 211 RILs (Bay-0 x Shahdara) for 22,595 nuclear-encoded transcripts (y-axis) plotted against 464 genetic markerintervals (x-axis) across five chromosomes. Colors indicate chromosomal regions where LR scores were significantlygreater than the global LR permutation threshold (GPT>12) at P < 0.05. Red indicates a positive effect of the presenceof the Sha allele, and green indicates a positive effect of the Bay-0 allele.B. Numbers of genes for which eQTLs are detected. Number of genes is indicated on the y-axis, plotted against thegenetic location of the eQTLs in cM on the x-axis.

marker intervals

Considerations when choosing QTLmapping software

• Single-factor versus interval• Maximum likelihood versus regression• Flanking marker versus all marker• Composite interval mapping