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
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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