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Concept of mappingIdentification of genetic variant underlying disease susceptibility or a trait value = Causal variantEvidence for the location of the gene
Approaches to MappingCandidate gene studiesAssociationResequencing approaches
Genome-wide studiesLinkage analysis Genome-wide association studies (Linkage disequilibrium, LD mapping)
Linkage mappingLook for marker alleles that are correlated with the phenotype within a pedigree
Different alleles can be connected with the trait in the different pedigrees
Association mappingMarker alleles are correlated with a trait on a population level
Can detect association by looking at unrelated individuals from a population
Does not necessarily imply that markers are linked to (are close to) genes influencing the trait.
Linkage vs. associationEffectFreq. of causal variantVery difficultLinkage analysisUnlikely to existAssociation studyModified from D. Altschuler
Linkage vs. association Hirschhorn & Daly, Nature Rev. Genet. 2005
Allelic AssociationDirect AssociationAllele of interest is itself involved in phenotype
Indirect AssociationAllele itself is not involved, but due to LD with the functional variant
Spurious associationConfounding factors (e.g., population stratification)
Linkage disequilibriumNon random association between alleles at different loci. Loci are in LD if alleles are present on haplotypes in different proportions than expected based on allele frequencies
Two alleles that are in LD are occurring together more often than would be expected by chance
Linkage disequilibriumLocus A: Alleles A & a; freq. PA & PaLocus B: Alleles B & b; freq. PB & PbPossible haplotyoesA
BA
ba
Ba
bExpected frequencies: pApB pApb papB papbObserved frequencies: pAB pAb paB pabD = pAB - pApB 0
LD variation across genomeThe extent of LD is highly variable across the genomeThe determinants of LD are not fully understood.Factors that are believed to influence LDGenetic driftPopulation growthAdmixture or migrationSelectionVariable recombination rates
HaplotypeGenotypes
Locus12 4Locus21 3Locus33 2Locus44 1Locus52 3Locus61 2Identification of phase413122232431HaplotypesPHASE
BEAGLE
Haplotype-based analysisIncreased ability to identify regions that are shared identical by descent among affected individuals
Haplotypes may the causative composite allele rather than a particular nucleotide at a particular SNP
Haplotype analysis is meaningful only if SNPS are in themselves in LD
Monogenic
verses
Complex traits
Monogenic traitMutation in single gene is both necessary and sufficient to produce the phenotype or to cause the disease
The impact of the gene on genetic risk is the same in all families
Follow clear segregation pattern in families
Typically rare in population
Complex traitMultiple genes lead to genetic predisposition to a phenotype
Pedigree reveals no Mendelian pattern
Any particular gene mutation is neither sufficient nor necessary to explain the phenotype
Environment has major contribution
We study the relative impact of individual gene on the phenotype
Some examples
Quantitative TraitA biological trait that shows continuous variation rather than falling into distinct categories
Quantitative trait locus (QTL) - Genetic locus that is associated with variation in such quantitative trait
Assessing genetic contributions to complex traitsContinuous characters (wt, blood pressure)Heritability: Proportion of observed variance in phenotype explained by genetic factors
Discrete characters (disease)Relative risk ratio: = risk to relative of an affected individual/risk in general population encompasses all genetic and environmental effects, not just those due to any single locus
Factors that influence identification of allelic associationEffect sizeLinkage disequilibriumDisease and marker allele frequenciesSample Size
Reviewed by Zondervar & Cardon, Nature Rev. Genet. 2004
Odds ratio
Sample sizeZondervar & Cardon (Nature Rev. Genet. 2004)No. of cases= no. of controls; D=0.7; power 80%; =0.001
Population stratificationConsider two case/control samples, genotyped at a marker with alleles M and mSample A2 NS2 NSSample B
Population stratificationSample ASample B2 =14.8
P
Dealing with population structureGenomic control (Devlin and Roeder, 1999)Inflate the distribution of the test statistic by . estimated from data
Dealing with population structureStructured association (Pritchard et al., 2000)Discover structure from set of unlinked markers, i.e. assign probabilities of ancestry from k populations to each individual, and then control for it.
Association analysis approachesCasecontrol studies Markers frequencies are determined in a group of affected individuals and compared with allele frequencies in a control population
Family based methods Based on unequal transmission of alleles from parents to a single affected child in each family. Associations are summed over many unrelated families
Case-Control studies: 2 testGenotypesAlleles2x3 contingency table2x2 contingency tableTest of independence:2 = (O-E)2/E with 2 or 1 df
Family based testsGenotypes from independent family trios where the child is affected
Use the non-transmitted genotypes or alleles as internal controls to the transmitted ones
Family-based association studies
1 4 transmitted
2 3 non-transmitted??1 23 41 4control
Is an allele transmitted more often than its not transmitted to affected offspring ?
TDT: Transmission Disequilibrium TestG/GG/gG/gNon-transmitted G gTransmittedG
ga b
c dTDTG = (TG-NTG)2/(TG+NTG)
=(b-c)2/(b+c) ~ 21
TDT: Transmission Disequilibrium TestMultiallelic markersETDT (Sham & Curtis, 1995)Missing parent genotypesTRANSMIT (Cayton,1999)HaplotypesTDTHAP (Clayton & Jones, 1999)SibsTDT/STDT (Spielman & Ewens, 1998)PedigreesPBAT (Martin et al, 2000)Quantitative traitsQTDT (Abecasis et al. 2000)
Some limitationsSubjects random or structure familyParents not availableDifficult when there are very many genes individually of small effectEnvironmental influence may obscure genetic effectsGenetic heterogeneity underlying disease phenotypeHidden (unaccounted) relationship
Rare alleleSingle family is segregating
AaOffspring group IOffspring group IIBb
Complex pedigree & Quantitative traits
Complex pedigreeNon-independence among pedigree membersOnly polygenic relationship is not sufficientAssociation analysis should account for the point-wise relationship among individualsIdentical-by-decent probabilities
Methods Combined linkage and LD Generalized linear modelsMixed-model (Yu et al. 2006)Bayesian approach
Combined linkage and LDPolygene the whole relationship in pedigree is used Identical-by-descend coefficients were estimated for point-wise relationship
Phenotype= Fixed factors + Polygene + HaplotypePhase determination - GDQTLQTL mapping - DMU
QTL for Clinical Mastitis in cattleLA
Chart1
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2.544403
2.844403
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3.044403
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2.444403
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10.644403
8.944403
5.844403
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6.844403
6.844403
5.544403
LA
Morgan
LRT
profile_M1_90h10
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profile_M1_90h10
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LDLA
LA
LD
QTL Profile-Mast-Red-
QTL for Clinical Mastitis in cattleLALD
Chart1
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LA
LD
Morgan
LRT
profile_M1_90h10
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0.6821009.12.18E-020.375630.619449.80E-059.24E-035.92E-031013.3444034.2444030.7971002.710.6444030.10
0.690751010.51.55E-020.357370.643626.36E-058.55E-035.55E-031013.3444032.8444030.810251004.48.944403-0.00
0.693751013.21.86E-030.403110.625331.37E-059.52E-036.08E-031013.3444030.1444030.8271007.55.844403-0.00
0.694251013.21.57E-030.405040.624181.19E-059.56E-036.11E-031013.3444030.1444030.84851008.25.144403-0.00
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0.7171007.21.25E-020.349220.649953.65E-058.18E-035.36E-031013.3444036.1444030.909751006.56.8444030.48
0.7221010.68.27E-030.385280.629342.82E-059.15E-035.89E-031013.3444032.7444030.9511007.85.544403-0.00
0.7311004.73.08E-020.30030.663771.23E-047.57E-034.96E-031013.3444038.6444031.70
0.73925998.444.35E-020.396760.593962.70E-049.40E-036.00E-031013.34440314.9044038.94
0.740751000.72.89E-020.359220.626561.16E-048.45E-035.51E-031013.34440312.6444033.41
0.74351004.52.52E-020.345970.641839.93E-058.11E-035.33E-031013.3444038.8444031.29
0.77151002.37.32E-020.263810.6236.50E-041.20E-026.15E-031013.34440311.044403-0.00
0.798251005.43.07E-020.339660.627761.77E-049.73E-035.92E-031013.3444037.944403-0.00
0.810251004.46.21E-020.270220.634835.22E-041.11E-025.89E-031013.3444038.944403-0.00
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0.84851007.45.72E-020.273680.640435.88E-041.13E-025.78E-031013.3444035.9444030.20
0.86551008.83.05E-020.351950.620062.51E-041.05E-026.18E-031013.3444034.544403-0.00
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0.894251004.83.67E-020.355180.605542.35E-041.06E-026.35E-031013.3444038.5444031.77
0.909751006.26.11E-020.278480.6296.37E-041.18E-025.97E-031013.3444037.144403-0.00
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profile_M1_90h10
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LDLA
LA
LD
QTL Profile-Mast-Red-
QTL for Clinical Mastitis in cattleLALD/LALD
Chart1
2.3444031.744403-0.000000032
2.9444032.544403-0.000000032
2.7444032.8444031.4432015598
3.2444032.944403-0.0000000321
0.0444033.0444030.58485429
0.5444033.0444030.531389156
3.3444033.044403-0.0000000321
1.8444032.4444030.0272408303
1.6444031.9444030.2057662723
0.5444031.944403-0.0000095168
1.2444031.844403-0.0000265088
1.1444032.0444030.0162969604
0.2444032.2444030.0185906067
3.2444032.4444031.1206260126
0.9444033.5444031.0601634282
1.0444033.1444032.1779390253
0.5444032.944403-0.0001126592
0.3444032.944403-0.0000075699
0.4444033.844403-0.0000236027
0.6444037.544403-0.0000757402
0.24440310.644403-0.0000096231
2.64440310.944403-0.000000032
011.744403-0.00018067
012.344403-0.0002281395
012.944403-0.0000682414
013.534403-0.0000101858
3.04440313.144403-0.0000286556
2.74440310.144403-0.0000163377
3.84440312.144403-0.0000277397
7.94440311.944403-0.0000000319
6.44440310.9444030.5649934652
010.444403-0.0002960709
2.8444039.944403-0.0000073671
4.2444039.944403-0.000440445
5.04440310.744403-0.0000316191
4.24440310.6444030.1017906115
2.8444038.944403-0.0000471483
0.1444035.844403-0.0000482385
0.1444035.144403-0.000115464
05.444403-0.0000128659
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6.8444036.8444030.3325767712
6.1444036.8444030.4817941532
2.7444035.544403-0.0000388472
8.6444031.6982511022
14.9044038.9376605056
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11.044403-0.000000032
7.944403-0.0000278023
8.944403-0.0000572169
5.044403-0.0000372763
5.9444030.1964023707
4.544403-0.0001490375
6.744403-0.0000270704
8.5444031.772013477
7.144403-0.0000093862
5.744403-0.0000000319
LDLA
LA
LD
Morgan
LRT
profile_M1_90h10
3.70E-0210110.132350.14670.62276.43E-033.49E-026.19E-031013.3444032.3444033.70E-021011.61.744403-0.00
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0.3977510136.11E-030.405960.616545.79E-059.94E-036.28E-031013.3444030.3444030.4871010.42.944403-0.00
0.398251012.95.52E-030.405350.61775.18E-059.90E-036.26E-031013.3444030.4444030.5181009.53.844403-0.00
0.398751012.78.11E-030.402630.616196.66E-051.00E-026.29E-031013.3444030.6444030.58551005.87.544403-0.00
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0.46351010.33.84E-020.331540.623364.51E-041.12E-026.08E-031013.3444033.0444030.690751000.213.144403-0.00
0.4871010.63.48E-020.338810.623373.92E-041.09E-026.09E-031013.3444032.7444030.704251003.210.144403-0.00
0.5181009.54.54E-020.315710.625145.09E-041.13E-026.05E-031013.3444033.8444030.7171001.212.144403-0.00
0.58551005.47.20E-020.266180.623557.47E-041.24E-026.14E-031013.3444037.9444030.7221001.411.944403-0.00
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0.6631010.51.78E-020.368020.629418.12E-059.16E-035.85E-031013.3444032.8444030.74751003.49.944403-0.00
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0.6821009.12.18E-020.375630.619449.80E-059.24E-035.92E-031013.3444034.2444030.7971002.710.6444030.10
0.690751010.51.55E-020.357370.643626.36E-058.55E-035.55E-031013.3444032.8444030.810251004.48.944403-0.00
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0.73925998.444.35E-020.396760.593962.70E-049.40E-036.00E-031013.34440314.9044038.94
0.740751000.72.89E-020.359220.626561.16E-048.45E-035.51E-031013.34440312.6444033.41
0.74351004.52.52E-020.345970.641839.93E-058.11E-035.33E-031013.3444038.8444031.29
0.77151002.37.32E-020.263810.6236.50E-041.20E-026.15E-031013.34440311.044403-0.00
0.798251005.43.07E-020.339660.627761.77E-049.73E-035.92E-031013.3444037.944403-0.00
0.810251004.46.21E-020.270220.634835.22E-041.11E-025.89E-031013.3444038.944403-0.00
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0.894251004.83.67E-020.355180.605542.35E-041.06E-026.35E-031013.3444038.5444031.77
0.909751006.26.11E-020.278480.6296.37E-041.18E-025.97E-031013.3444037.144403-0.00
0.9511007.65.74E-020.291790.624376.92E-041.22E-026.07E-031013.3444035.744403-0.00
profile_M1_90h10
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LDLA
LA
LD
QTL Profile-Mast-Red-
Simulation100 half-sib families (Dairy cattle pedigree)2000 progeny5 chromosomes 100 cM (each)SNP 5000 15 QTL (1QTL-10%, 4QTL-5 %, 10QTL2%)50% of the genetic varianceHeritability 30%
Generalized linear modelsPhenotype= Sire-family + genotypeSoftware TASSELhttp://www2.maizegenetics.net/index.php?page=bioinformatics/tassel
Generalized linear models
Chart2
0.1835
0.2636
3.3594
1.3907
0.8949
0.3712
0.0882
4.0439
0.8869
0.6752
0.5823
0.8031
1.8517
0.5377
0.6115
0.22
1.6107
1.9395
0.619
0.7986
0.7547
0.4278
0.3316
0.18
0.3717
0.634
1.3364
0.9603
1.8086
0.2673
2.3059
0.2027
1.3108
4.3415
0.52
2.4894
1.3029
2.4428
0.0904
0.6761
0.1139
0.7447
0.6525
0.2554
0.2585
2.2808
0.0945
0.3285
0.4672
0.1376
1.1145
0.0274
0.6086
0.4685
0.3832
0.8612
0.5131
0.0484
0.4784
0.6376
1.5405
2.8763
0.003
4.4918
1.8993
0.9302
0.4412
0.2141
0.2422
4.9983
0.5126
1.867
1.3569
0.0364
2.9332
1.8811
0.0761
3.4407
0.539
0.965
0.7968
0.1367
5.276
0.5838
0.0889
2.9957
0.8132
1.6643
6.088
0.5927
1.0552
4.2731
4.4297
4.4438
1.4274
5.735
0.0818
0.2127
3.507
2.5715
0.2778
4.4835
0.9507
5.5326
1.2107
2.9911
2.7318
0.1502
5.039
5.5761
3.787
1.5422
6.4463
3.0693
3.635
0.501
18.2455
2.1251
4.2093
14.7655
1.824
18.6477
1.7942
14.6718
32.1298
4.5349
0.508
9.0343
32.0644
31.6787
5.1224
8.8595
0.6955
11.0301
9.9566
16.0237
5.6114
9.8432
39.6404
12.5848
18.9746
0.544
14.5334
3.0306
23.6879
12.6873
14.0837
0.1276
41.6533
29.5023
3.9711
10.8073
11.6085
19.0266
106.7306
11.8091
35.1584
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2.5129
0.0067
7.7174
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5.6775
6.1247
17.2293
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19.4616
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19.3813
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1.4008
5.5412
9.7033
17.2022
7.1866
0.5553
4.5468
6.3909
3.5692
1.2424
3.8961
3.1044
2.3213
1.057
0.895
1.098
0.2042
0.5901
1.2198
0.6945
2.0249
1.6028
1.5551
2.3086
3.0942
1.9218
0.224
0.4076
1.3798
0.4555
1.4062
2.5417
2.0746
3.2549
3.5973
1.5093
1.2301
3.2485
5.6718
1.1778
1.556
0.9168
1.8398
0.7031
0.3696
3.4078
8.1506
0.7028
0.1285
1.4838
0.2904
1.8968
1.3495
2.3725
5.1781
0.9481
1.1718
0.2679
8.1884
3.8764
1.2691
6.3645
0.1586
1.4305
1.0326
0.4524
0.2748
1.9776
5.0127
1.235
0.7182
1.653
0.1442
2.3866
3.463
3.5524
1.0191
1.9888
2.6553
0.9704
0.0646
2.5706
0.5995
0.7454
0.0458
0.0926
1.7003
0.02
0.2658
1.6579
0.5439
0.1251
1.8378
0.4512
1.7886
0.0994
2.3716
0.5947
4.4607
2.5092
1.4008
0.456
0.0824
0.8297
1.1615
5.1376
0.224
0.5261
1.3178
0.411
1.077
0.2504
2.8298
0.0942
0.7863
0.3932
1.1047
0.7532
0.44
0.1333
3.8575
0.5892
0.868
0.8949
0.6765
3.1234
2.9714
1.2476
2.6731
1.7242
0.2371
0.019
2.7175
1.0046
1.9103
0.3139
0.0789
0.2063
0.2458
1.8467
0.1016
1.1354
1.6299
0.3228
0.4942
0.582
2.47
0.8582
0.5214
3.4916
0.9695
0.5663
1.2467
1.9489
1.3815
7.0569
0.8195
1.3319
3.6651
1.2412
0.9292
3.476
4.8553
0.8918
6.7135
0.99
2.2228
0.908
4.7846
0.8252
4.9413
0.6708
2.1326
1.3551
0.6804
0.105
0.6883
2.1812
0.8459
1.9837
1.1249
4.9324
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