1. Analysis of whole genome association studies in pedigreed populations
Goutam Sahana
Genetics and Biotechnology
Faculty of Agricultural Sciences
Aarhus University, 8830 Tjele, Denmark

2. Concept of mapping
Identification of genetic variant underlying disease susceptibility or a trait value
Evidence for the
location of the gene
= Causal variant

3. Approaches to Mapping Candidate gene studies Genome-wide studies
Association
Resequencing approaches
Genome-wide studies
Linkage analysis
Genome-wide association studies (Linkage disequilibrium, LD mapping)

4. Linkage mapping
Look for marker alleles that are correlated with the phenotype within a pedigree
Different alleles can be connected with the trait in the different pedigrees

5. Association mapping
Marker 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.

6. Linkage vs. association
Unlikely to exist
Linkage analysis
Effect
Association
study
Very difficult
Freq. of causal variant
Modified from D. Altschuler

7. Linkage vs. association
Potential Advantage
Linkage
Association
No prior information regarding gene function required +
Localization to small genomic region -
Not susceptible to effects of stratification
-/+
Sufficient power to detect common alleles of modest effect (MAFs>5%)
Ability to detect rare allele (MAFs<1%)
Tools for analysis available
+/-
Hirschhorn & Daly, Nature Rev. Genet. 2005

8. Allelic Association Direct Association Indirect Association
Allele of interest is itself involved in phenotype
Indirect Association
Allele itself is not involved, but due to LD with the functional variant
Spurious association
Confounding factors (e.g., population stratification)

9. Linkage disequilibrium
Non 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

10. Linkage disequilibrium
Locus A: Alleles A & a; freq. PA & Pa
Locus B: Alleles B & b; freq. PB & Pb A B A b a B a b
Possible haplotyoes
Expected frequencies: pApB pApb papB papb
Observed frequencies: pAB pAb paB pab
D = pAB - pApB ≠ 0

11. LD variation across genome
The extent of LD is highly variable across the genome
The determinants of LD are not fully understood.
Factors that are believed to influence LD
Genetic drift
Population growth
Admixture or migration
Selection
Variable recombination rates

12. Haplotype Genotypes Locus1 2 4 Locus2 1 3 Locus3 3 2 Locus4 4 1
Haplotypes
Identification
of phase 4 1 3 2 2 3 4 1
PHASE
BEAGLE

13. Haplotype-based analysis
Increased 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

14. Monogenic Complex traits
verses
Complex traits

15. Monogenic trait
Mutation 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

16. Complex trait
Multiple 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

17. Some examples Mendelian/ Complex Disease No. of genes Incidence
Cystic fibrosis M 1 40
Huntington disease
5-10
Diabetes, type 2 C ?
10,000 – 20,000
Alzheimer
20,000
Schizophrenia
1000

18. Quantitative Trait
A 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

19. Assessing genetic contributions to complex traits
Continuous characters (wt, blood pressure)
Heritability: Proportion of observed variance in phenotype explained by genetic factors
Discrete characters (disease)
Relative risk ratio: λ= risk to relative of an affected individual/risk in general population
λ encompasses all genetic and environmental effects, not just those due to any single locus

20. Factors that influence identification of allelic association
Effect size
Linkage disequilibrium
Disease and marker allele frequencies
Sample Size
Reviewed by Zondervar & Cardon, Nature Rev. Genet. 2004

21. Odds ratio

22. Sample size
Disease allele freq.
Marker allele freq.
Odd ratio
3.0
2.0
1.3
0.2
150
360
2900
0.5
430
1250
11,000
0.05
1170
4150
40,000
4200
15000
160,000
No. of cases= no. of controls; D’=0.7; power 80%;  =0.001
Zondervar & Cardon (Nature Rev. Genet. 2004)

23. Population stratification
Consider two case/control samples, genotyped at a marker with alleles M and m
Sample A
Sample B M m
Freq.
Affected 50
0.10
Unaffec.
450
0.90
0.50 M m
Freq.
Affected 1 9
0.01
Unaffec. 99
891
0.99
0.10
0.90
2 NS
2 NS

24. Population stratification
Sample A Sample B M m
Freq.
Affected 50
0.10
Unaffec.
450
0.90
0.50 M m
Freq.
Affected 1 9
0.01
Unaffec. 99
891
0.99
0.10
0.90 M m
Freq.
Affected 51 59
0.055
Unaffec.
549
1341
0.945
0.30
0.70
2 =14.8
P<0.001

25. Dealing with population structure
Genomic control (Devlin and Roeder, 1999)
Inflate the distribution of the test statistic by λ.
λ estimated from data
Unlinked ‘null’ markers
Test locus 2
No stratification
E(2) 2
E(2)
Stratification
Adjust test statistics

26. Dealing with population structure
Structured 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.

27. Association analysis approaches
Case–control 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

28. Case-Control studies: 2 test
Alleles
Genotypes 1 2
Total
Case n1 n2 2N
Ctrl m1 m2 2M T1 T2
2(N+M) 11 12 22
Total
Case
n11
n12
n22 N
Ctrl
m11
m12
m22 M
T11
T12
T22
N+M
2x3 contingency table 2x2 contingency table
Test of independence:
2 = (O-E)2/E with 2 or 1 df

29. Family based tests
Genotypes from independent family trios where the child is affected
Use the non-transmitted genotypes or alleles as internal controls to the transmitted ones

30. Family-based association studies? ?
1 4 transmitted
non-transmitted
1 2
3 4
control
1 4
Is an allele transmitted more often than
it’s not transmitted to affected offspring ?

31. TDT: Transmission Disequilibrium Test
Non-transmitted
G g
G/G
G/g
a b
c d G g
Transmitted
G/g
TDTG = (TG-NTG)2/(TG+NTG)
=(b-c)2/(b+c) ~ 21

32. TDT: Transmission Disequilibrium Test
Multiallelic markers
ETDT (Sham & Curtis, 1995)
Missing parent genotypes
TRANSMIT (Cayton,1999)
Haplotypes
TDTHAP (Clayton & Jones, 1999)
Sibs
TDT/STDT (Spielman & Ewens, 1998)
Pedigrees
PBAT (Martin et al, 2000)
Quantitative traits
QTDT (Abecasis et al. 2000)

33. Some limitations Subjects – random or structure family
Parents not available
Difficult when there are very many genes individually of small effect
Environmental influence may obscure genetic effects
Genetic heterogeneity underlying disease phenotype
Hidden (unaccounted) relationship

34. Rare allele A a Single family is segregating B b Offspring group I
Offspring group II

35. Complex pedigree & Quantitative traits

36. Complex pedigree Non-independence among pedigree members
Only polygenic relationship is not sufficient
Association analysis should account for the point-wise relationship among individuals
Identical-by-decent probabilities

38. Methods Combined linkage and LD Generalized linear models
Mixed-model (Yu et al. 2006)
Bayesian approach

39. Combined linkage and LD
Phenotype= Fixed factors + Polygene + Haplotype
Polygene – the whole relationship in pedigree is used
Identical-by-descend coefficients were estimated for point-wise relationship
Phase determination - GDQTL
QTL mapping - DMU

40. QTL for Clinical Mastitis in cattleLA

41. QTL for Clinical Mastitis in cattleLA LD

42. QTL for Clinical Mastitis in cattle
LD/LA LA LD

43. Simulation 100 half-sib families (Dairy cattle pedigree) 2000 progeny
5 chromosomes – 100 cM (each)
SNP – 5000
15 QTL (1QTL-10%, 4QTL-5 %, 10QTL–2%)
50% of the genetic variance
Heritability – 30%

44. Generalized linear models
Phenotype= Sire-family + genotype
Software – TASSEL

45. Generalized linear models

46. Generalized linear models

47. Generalized linear models

48. Mixed-model (Yu et al. 2006) 1 2 SAS mixed model (Gael Pressoir)
Phenotype= Fixed factors + SNP + Population +
polygene 1 2
STRUCTURE
Relationship
SAS mixed model (Gael Pressoir)

49. Mixed-model

50. Mixed-model

51. Mixed-model

52. Software – iBays (Janss LLG, 2007)
Bayesian approach
Phenotype= Fixed factors + Polygene + Allele or
Haplotype
All markers are fitted simultaneously, search for marker combination that explains the trait variation
Avoid multiple testing
Software – iBays (Janss LLG, 2007)

53. Bayesian approach

54. Bayesian approach

55. Multiple testing

56. Multiple testing
Performing one test at an alpha level of 0.05 implies 5% chance of rejecting a true null hypothesis (false positive)
Performing 100 tests at  = 0.05 when all 100 H0 are true, we expect 5 of the tests to give FP results
Pr(at least one FP)=1-Pr(no FP)= 1- (0.95)100 = 0.994
(if the tests are independent)

57. Multiple testing Bonferroni correction Permutation test
Rejection level of each test is i  /m
Permutation test
False discovery rate (FDR)
What proportion of rejections are when H0 is true?
Of all the times you reject H0 how often is H0 true?
q value (Storey et al. PNAS 2003)

58. Summary
4 methods
LD and linkage
GLM
Mixed-model
Bayesian approach

59. Project team Goutam Sahana Bernt Guldbrandtsen Luc Janss
Mogens Sandø Lund