1. Introduction to Gene-Finding: Linkage and Association
Danielle Dick, Sarah Medland, (Ben Neale)

2. Aim of QTL mapping…
LOCALIZE and then IDENTIFY a locus that regulates a trait (QTL)
Locus: Nucleotide or sequence of nucleotides with variation in the population, with different variants associated with different trait levels.

3. Location and Identification
Linkage
localize region of the genome where a QTL that regulates the trait is likely to be harboured
Family-specific phenomenon: Affected individuals in a family share the same ancestral predisposing DNA segment at a given QTL

4. Location and Identification
Association
identify a QTL that regulates the trait
Population-specific phenomenon: Affected individuals in a population share the same ancestral predisposing DNA segment at a given QTL

5. Linkage
Overview

6. Progress of the Human Genome Project
Human Chromosome 4

7. Genetic markers (DNA polymorphisms)
ATGCTTGCCACGCE
ATGCTTCTTGCCATGCE
Microsatellite Markers
can be di(2), tri(3), or tetra (4)
nucleotide repeats
ATGCTTGCCACGCE
ATGCTTGCCATGCE
Single Nucleotide Polymorphism

8. DNA polymorphisms Can occur in gene, but be silent
Can change gene product (protein)
Alter amino acid sequence (a lot or a little)
Can regulate gene product
Upregulate or downregulate protein production
Turn off or on gene
Can occur in noncoding region
This happens most often!

9. Mutations

10. How do we map genes?
Deviation from Mendel’s Independent Assortment Law
Aa & Bb = ¼ AB, ¼ Ab, ¼ aB, ¼ ab
We’re looking for variation from this

11. Recombination

12. Recombination Another way of introducing genetic diversity
Allows us to map genes!
Crossovers more likely to occur between genes that are further away; likelihood of a recombination event is proportional to the distance
Interference – tend not to see 2 crossovers in a small area
Alleles that are very close together are more likely to stay together, don’t assort independently

13. Linkage Mapping (is a marker “linked” to the disease gene)
Collect families with affected individuals
Genome Scan - Test markers evenly spaced across the entire genome (~every 10cM, ~400 markers)
Lod score (“log of the odds”) – what are the odds of observing the family marker data if the marker is linked to the disease (less recombination than expected) compared to if the marker is not linked to the disease

14. Thomas Hunt Morgan – discoverer of linkage

15. Linkage = Co-segregation
A1A2
A3A4
A1A3
A2A4
A2A3
Marker allele A1
cosegregates with
dominant disease
A1A2
A1A4
A3A4
A3A2

16. Lod scores >3.0 evidence for linkage <-2.0 can rule out linkage
In between – inconclusive, collect more families

17. Linkage = Co-segregation
Parametric Linkage used very successfully to map disease genes for Mendelian disorders
Problematic for complex disorders: requires disease model, penetrance, assumes gene of major effect, phenotypic precision
A1A2
A3A4
A1A3
A2A4
A2A3
A1A2
A1A4
A3A4
A3A2

18. Nonparametric Linkage
Based on allele-sharing
More appropriate for phenotypes with multiple genes of small effect, environment, no disease model assumed
Basic unit of data: affected relative (often sibling) pairs

19. x
Graphing can also be done in Microsoft's charting program. Again you will have to experiment a bit or come and talk to me. But it is an easy program to use, unlike Corel Chart.
You can also insert graph and charts from other programs via the insert function.
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20. IDENTITY BY DESCENT Sib 1 2 1 1 1 2 1 Sib 2 1 2 1 1 1 21 2 1
Sib 2 1 2 1 1 1 2
4/16 = 1/4 sibs share BOTH parental alleles IBD = 2
8/16 = 1/2 sibs share ONE parental allele IBD = 1
4/16 = 1/4 sibs share NO parental alleles IBD = 0

21. Genotypic similarity between relatives
IBS Alleles shared Identical By State “look the same”, may have the
same DNA sequence but they are not necessarily derived from a
known common ancestor - focus for association M1 M2 M3 M3
IBD Alleles shared
Identical By Descent
are a copy of the
same ancestor allele
- focus for linkage Q1 Q2 Q3 Q4 M1 M2 M3 M3 Q1 Q2 Q3 Q4
IBS
IBD M1 M3 M1 M3 2 1 Q1 Q3 Q1 Q4

22. Genotypic similarity – basic principals
Loci that are close together are more likely to be inherited together than loci that are further apart
Loci are likely to be inherited in context – ie with their surrounding loci
Because of this, knowing that a loci is transmitted from a common ancestor is more informative than simply observing that it is the same allele
Critical to have parental data when possible

23. Linkage Markers…

24. For disease traits (affected/unaffected) Affected sib pairs selected
1000
750
500
250
IBD = 2
Expected 1 2 3
127
310
IBD = 1
Markers
IBD = 0

25. For continuous measures
Unselected sib pairs
1.00
0.25
0.75
0.50
IBD = 0
IBD = 1
IBD = 2
Correlation between sibs
0.00

26. So how does all this fit into Mx?

27. IDENTITY BY DESCENT Sib 1 2 1 1 1 2 1 Sib 2 1 2 1 1 1 21 2 1
Sib 2 1 2 1 1 1 2
4/16 = 1/4 sibs share BOTH parental alleles IBD = 2
8/16 = 1/2 sibs share ONE parental allele IBD = 1
4/16 = 1/4 sibs share NO parental alleles IBD = 0

28. In biometrical modeling A is correlated at 1 for MZ twins and
In biometrical modeling A is correlated at 1 for MZ twins and .5 for DZ twins
.5 is the average genome-wide sharing of genes between full siblings (DZ twin relationship)

29. In linkage analysis we will be estimating an additional variance component Q
For each locus under analysis the coefficient of sharing for this parameter will vary for each pair of siblings
The coefficient will be the probability that the pair of siblings have both inherited the same alleles from a common ancestor

30. PTwin1 PTwin2 MZ=1.0 DZ=0.5 MZ & DZ = 1.0 Q A C E E C A Q q a c e e c

31. How do we do this? 1.Genotyping data.
Linkage
How do we do this?
1.Genotyping data.

32. Microsatellite data
Ideally positioned at equal genetic distances across chromosome
Mostly di/tri nucleotide repeats

33. Microsatellite data Raw data consists of allele lengths/calls (bp)
Different primers give different lengths
So to compare data you MUST know which primers were used

34. Binning Raw allele lengths are converted to allele numbers or lengths
Example:D1S1646 tri-nucleotide repeat size range
Logically: Work with binned lengths
Commonly: Assign allele 1 to 130 allele, 2 to 133 allele …
Commercially: Allele numbers often assigned based on reference populations CEPH. So if the first CEPH allele was 136 that would be assigned 1 and 130 & 133 would assigned the next free allele number
Conclusions: whenever possible start from the RAW allele size and work with allele length

35. Error checking After binning check for errors An iterative process
Family relationships (GRR, Rel-pair)
Mendelian Errors (Sib-pair)
Double Recombinants (MENDEL, ASPEX, ALEGRO)
An iterative process

36. ‘Clean’ data
ped file
Family, individual, father, mother, sex, dummy, genotypes

37. Estimating genotypic sharing…
The ped file is used with ‘map’ files to obtain estimates of genotypic sharing between relatives at each of the locations under analysis

38. Estimating genotypic sharing…
Merlin will give you probabilities of sharing 0, 1, 2 alleles for every pair of individuals.

39. Estimating genotypic sharing…
Output

40. Estimating genotypic sharing…
Output
Why isn’t P0, P1, P2 exact
for everyone?

41. Estimating genotypic sharing…
Output
Why isn’t P0, P1, P2 exact
for everyone?
-missing parental genotypes
-low informativeness at marker
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42. PTwin1 PTwin2 MZ=1.0 DZ=0.5 MZ & DZ = 1.0 Q A C E E C A Q q a c e e c

43. Genotypic similarity between relatives
IBD Alleles shared Identical By Descent are a copy of the same ancestor allele
Pairs of siblings may share 0, 1 or 2 alleles IBD
The probability of a pair of relatives
being IBD is known as pi-hat M1 M2 M3 M3 Q1 Q2 Q3 Q4 M1 M2 M3 M3 Q1 Q2 Q3 Q4
IBS
IBD M1 M3 M1 M3 2 1 Q1 Q3 Q1 Q4

44. Estimating genotypic sharing…
Output

45. Distribution of pi-hat
Adult Dutch DZ pairs: distribution of pi-hat at 65 cM on chromosome 19
< 0.25: IBD=0 group
> 0.75: IBD=2 group
others: IBD=1 group
pi65cat= (0,1,2)

46. Linkage Analyses Advantage Disadvantages
Systematically scan the genome
Disadvantages
Not very powerful
Need hundreds – thousands of family member
Broad peaks

47. Lod scores
1cM = 1MB
1MB=1000kb
1kb=1000bp
1cM = 1,000,000 bp

48. Strategy 1. Ascertain families with multiple affecteds
2. Linkage analyses to identify chromosomal regions
 allele-sharing among affecteds
within a family
3. Association analyses to identify specific genes
Gene A
Gene B
Gene C

49. BREAK

50. Linkage vs. Association
Linkage analyses look for relationship between a marker and disease within a family (could be different marker in each family)
Association analyses look for relationship between a marker and disease between families (must be same marker in all families)

51. Extension of linkage to the population
Allelic Association:
Extension of linkage to the population
3/5
2/6
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3/2
5/2
Both families are ‘linked’ with the marker, but a different allele is involved

52. Extension of linkage to the population
Allelic Association
Extension of linkage to the population
3/5
2/6
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4/6
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3/2
6/2
6/6
6/6
All families are ‘linked’ with the marker
Allele 6 is ‘associated’ with disease

53. Localization
Linkage analysis yields broad chromosome regions harbouring many genes
Resolution comes from recombination events (meioses) in families assessed
‘Good’ in terms of needing few markers, ‘poor’ in terms of finding specific variants involved
Association analysis yields fine-scale resolution of genetic variants
Resolution comes from ancestral recombination events
‘Good’ in terms of finding specific variants, ‘poor’ in terms of needing many markers

54. Allelic Association Three Common Forms Direct Association
Mutant or ‘susceptible’ polymorphism
Allele of interest is itself involved in phenotype
Indirect Association
Allele itself is not involved, but a nearby correlated
marker changes phenotype
Spurious association
Apparent association not related to genetic aetiology
(most common outcome…)

55. Indirect and Direct Allelic Association
Direct Association M1 M2 Mn
Assess trait effects on D via correlated markers (Mi) rather than susceptibility/etiologic variants. D
Indirect Association & LD D *
Measure disease relevance (*) directly, ignoring correlated markers nearby
Semantic distinction between
Linkage Disequilibrium: correlation between (any) markers in population
Allelic Association: correlation between marker allele and trait

56. Decay of Linkage Disequilibrium
Reich et al., Nature 2001

57. Average Levels of LD along chromosomes
CEPH
W.Eur
Estonian
So why did we use human chromosome 22? Since this was the first human chromosome to be fully sequenced we have now accrued a wealth of information about the genomic landscape – which we can correlate with LD measures. Many of you will have heard Ian Dunham’s talk on Wednesday evening when he showed the current status of the 582 genes that have been annotated. In addition, there are an abundance of SNPs and other variants between which to measure LD. Chromosome 22 has been the focus of 2 chromosome-specific SNP finding projects – the Sanger pilot study for the SNP consortium, which found 2, 730 SNPs. And a project in which we found just over 12,000, variants in the sequence of the overlaps between clones used for the whole chromosome sequence. In addition, the genome wide efforts of the SNP consortium found another 8,300 SNPs. With the efforts since these publications and other snps in the public domain, we estimate that there are SNPs and other small sequence variants on chromomse 22, giving an average density of 1 SNP every 1.3 kb. With such a high density, along the length of the chromosome, we are able to effectivley study long-range haplotypes.
Chr22
Dawson et al
Nature 2002

58. Characterizing Patterns of Linkage Disequilibrium
Average LD decay vs physical distance
Mean trends along chromosomes
Haplotype Blocks

59. Linkage Disequilibrium Maps & Allelic Association1 2 3 D n
Marker LD
Primary Aim of LD maps: Use relationships amongst background markers (M1, M2, M3, …Mn) to learn something about D for association studies
Something = * Efficient association study design by reduced genotyping
* Predict approx location (fine-map) disease loci
* Assess complexity of local regions
* Attempt to quantify/predict underlying (unobserved)
patterns
···

60. Deliverables: Sets of haplotype tagging SNPs

61. Building Haplotype Maps for Gene-finding
1. Human Genome Project
 Good for consensus,
not good for individual
differences
Sept 01
Feb 02
April 04
Oct 04
2. Identify genetic variants
 Anonymous with respect to
traits.
April 1999 – Dec 01
3. Assay genetic variants
 Verify polymorphisms,
catalogue correlations
amongst sites
 Anonymous with respect to
traits
Oct present

62. Haplotype Tagging for Efficient Genotyping
Cardon & Abecasis, TIG 2003
Some genetic variants within haplotype blocks give redundant information
A subset of variants, ‘htSNPs’, can be used to ‘tag’ the conserved haplotypes with little loss of information (Johnson et al., Nat Genet, 2001)
… Initial detection of htSNPs should facilitate future genetic association studies

63. HapMap Strategy Samples Genotyping Four populations, small samples
5 kb initial density across genome (600K markers)
Subsequent focus on low LD regions
Recent NIH RFA for deeper coverage

64. Hapmap validating millions of SNPs.
Are they the right SNPs?
Distribution of allele frequencies in public markers is biased toward common alleles
Expected frequency in population
Frequency of public markers
Updated with phase 2—more similar to expectation
Phillips et al. Nat Genet 2003

65. Summary of Role of Linkage Disequilibrium on Association Studies
Marker characterization is becoming extensive and genotyping throughput is high
Tagging studies will yield panels for immediate use
Need to be clear about assumptions/aims of each panel
Density of eventual Hapmap probably cover much of genome in high LD, but not all
Challenges
Just having more markers doesn’t mean that success rate will improve
Expectations of association success via LD are too high.

66. Two types of association studies
Case-control
Family-based

67. Allelic Association Controls Cases
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Allele 6 is ‘associated’ with disease

68. Primary Concern with Case-Control Analyses
Main Blame
Primary Concern with Case-Control Analyses
Population stratification
Analysis of mixed samples having different allele frequencies is a primary concern in human genetics, as it leads to false evidence for allelic association.

69. Population Stratification
Leads to spurious association
Requirements:
Group differences in allele frequencies AND
Group differences in outcome
In epidemiology, this is a classic matching problem, with genetics as a confounding variable

70. + Population Stratification Spurious Association
c21 = 14.84, p < 0.001
Spurious Association

71. Family-based association methods
TDT – Transmission Disequilibrium Test
1/2
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2/3
50/50 chance the 2 is transmitted
Looking for overtransmission of a particular allele
across affected individuals (undertransmission to unaffecteds)

72. TDT Advantages/Disadvantages
Robust to stratification
Genotyping error detectable via Mendelian inconsistencies
Estimates of haplotypes possible
Disadvantages
Detection/elimination of genotyping errors causes bias (Gordon et al., 2001)
Uses only heterozygous parents
Inefficient for genotyping
3 individuals yield 2 founders: 1/3 information not used
Can be difficult/impossible to collect
Late-onset disorders, psychiatric conditions, pharmacogenetic applications

73. Association Studies 2000+ :
Association studies < 2000: TDT
TDT virtually ubiquitous over past decade
Grant, manuscript referees & editors mandated design
View of case/control association studies greatly
diminished due to perceived role of stratification
Association Studies :
Return to population
Case/controls, using extra genotyping
+families, when available

74. Detecting and Controlling for
Population Stratification with Genetic Markers
Idea
Take advantage of availability of large N genetic markers
Use case/control design
Genotype genetic markers across genome
(Number depends on different factors)
Look if any evidence for background population substructure exists and account for it

75. Two types of association studies
Case-control
Adv: more powerful
Disadv: population stratification
limited by case/control definition
Family-based
Adv: population stratification not a problem
Disadv: less powerful, hard to collect parents for some phenotypes

76. Association Analyses vs Linkage
Advantage
More powerful
Disadvantage
Not systematic (in the past)
Now!
Genome wide association scans

77. Current Association Study Challenges 1) Genome-wide screen or candidate gene
Hypothesis-free
High-cost: large genotyping requirements
Multiple-testing issues
Possible many false positives, fewer misses
Candidate gene
Hypothesis-driven
Low-cost: small genotyping requirements
Multiple-testing less important
Possible many misses, fewer false positives

78. Current Association Study Challenges 2) What constitutes a replication?
GOLD Standard for association studies
Replicating association results in different laboratories is often seen as most compelling piece of evidence for ‘true’ finding
But…. in any sample, we measure
Multiple traits
Multiple genes
Multiple markers in genes
and we analyse all this using multiple statistical tests
What is a true replication?

79. What is a true replication?
Replication Outcome
Explanation
Association to same trait, but different gene
Association to same trait, same gene, different SNPs (or haplotypes)
Association to same trait, same gene, same SNP – but in opposite direction (protective  disease)
Association to different, but correlated phenotype(s)
No association at all
Genetic heterogeneity
Allelic heterogeneity
Allelic heterogeneity/pop differences
Phenotypic heterogeneity
Sample size too small

80. Measuring Success by Replication
Define objective criteria for what is/is not a replication in advance
Design initial and replication study to have enough power
‘Lumper’: use most samples to obtain robust results in first place
Great initial detection, may be weak in replication
Skol et al. 2006—lumping is better for power
‘Splitter’: Take otherwise large sample, split into initial and replication groups
One good study  two bad studies.
Poor initial detection, poor replication

81. Current Association Study Challenges 3) Do we have the best set of genetic markers
There exist 6+ million putative SNPs in the public domain. Are they the right markers?
Allele frequency distribution is biased toward common alleles
Expected frequency in population
Frequency of public markers

82. Current Association Study Challenges 3) Do we have the best set of genetic markers
Tabor et al, Nat Rev Genet 2003

83. (Müller-Myhsok and Abel, 1997)
Greatest power comes from markers that match allele freq with trait loci
ls = 1.5, a = 5 x 10-8, Spielman TDT
(Müller-Myhsok and Abel, 1997)

84. Current Association Study Challenges 4) Integrating the sampling, LD and genetic effects
Questions that don’t stand alone:
How much LD is needed to detect complex disease genes?
What effect size is big enough to be detected?
How common (rare) must a disease variant(s) be to be identifiable?
What marker allele frequency threshold should be used to find complex disease genes? 1

85. Complexity of System
In any indirect association study, we measure marker alleles that are correlated with trait variants…
We do not measure the trait variants themselves
But, for study design and power, we concern ourselves with frequencies and effect sizes at the trait locus….
This can only lead to underpowered studies and inflated expectations
We should concern ourselves with the apparent effect size at the marker, which results from
1) difference in frequency of marker and trait alleles
2) LD between the marker and trait loci
3) effect size of trait allele 1

86. Practical Implications of Allele Frequencies
‘Strongest argument for using common markers is not CD-CV. It is practical:
For small effects, common markers are the only ones for which sufficient sample sizes can be collected
 There are situations where indirect association analysis will not work
Discrepant marker/disease freqs, low LD, heterogeneity, …
Linkage approach may be only genetics approach in these cases
At present, no way to know when association will/will not work
Balance with linkage

87. Current Association Study Challenges 5) How to analyse the data
Allele based test?
2 alleles  1 df
E(Y) = a + bX X = 0/1 for presence/absence
Genotype-based test?
3 genotypes  2 df
E(Y) = a + b1A+ b2D A = 0/1 additive (hom); W = 0/1 dom (het)
Haplotype-based test?
For M markers, 2M possible haplotypes  2M -1 df
E(Y) = a + bH H coded for haplotype effects
Multilocus test?
Epistasis, G x E interactions, many possibilities

88. Current Association Study Challenges 6) Multiple Testing
Candidate genes: a few tests (probably correlated)
Linkage regions: 100’s – 1000’s tests (some correlated)
Whole genome association: 100,000s – 1,000,000s tests (many correlated)
What to do?
Bonferroni (conservative)
False discovery rate?
Permutations?
….Area of active research

89. Despite challenges: upcoming association studies hold some promise
Availability of millions of genetic markers
Genotyping costs decreasing rapidly
Cost per SNP: ($0.25)  2003 ($0.10)  2004 ($0.01)
Background LD patterns being characterized
International HapMap and other projects

90. Genome Wide Association Studies (GWAS) Underway:
Genetic Analysis Information Network (GAIN)
Psoriasis, ADHD, Schizophrenia, Bipolar Disorder, Depression, Type 1 Diabetes
Welcome Trust Case Control Consortium
Bipolar Disorder, Coronary Artery Disease, Crohn’s disease, Rheumatoid Arthristis, Type 1 Diabetes, Type 2 Diabetes
Genes, Environment, & Health Initiative (Gene/Environment Association Studies: GENEVA)
Addiction, diabetes, Heart Disease, Oral Clefts, Maternal Metabolism and Birth Weight, Lung Cancer, Pre-Term Birth, Dental Carries
Genes, Environment, & Development Initiative (GEDI)