1. 中国畜牧兽医学会信息技术分会
2009年会. 哈尔滨
Haplotype inference and haplotype-based transmission disequilibrium test (Hap-TDT)
Hello,, everyone.
Nowadays, reconstruct haplotype become more and more prevail, becasue haplotypes can give more information than single locus, they are useful in QTL fine mapping, evolution and so on.
Daughter and grand-daughter designs are two very powerful designs for QTL mapping in cattle. Here, I will show you an algorithms to reconstruct haplotype in daughter and grand-daughter designs.
丁向东
中国农业大学动物科技学院

2. Outline Haplotype inference Transmission Disequilibrium Test (TDT)
Introduction of important methods
Parsimony (Clark,1990)
EM (Excoffier and Slatkin, 1995)
Bayesian (Stephens and Donnelly,2001)
Haplotype inference using family information
Transmission Disequilibrium Test (TDT)
Haplotype-based TDT (Hap-TDT)

3. Genotype and Haplotype
Haplotype: A collection of alleles from same chromosome
Allele & Genotype
Haplotype & diplotype
Loc1 2 13
Loc2 1 6 … 9 15 4 17
Loc7 2 13 6 1 9 15 17 4
Haplotype
reconstruction
Chromosome phase is unkown
Chromosome phase is kown

4. Algorithms for haplotype inference
Statistical methods
Parsimony (Clark,1990) EM
Excoffier and Slatkin (1995); Qin et al. (2002)
Bayesian
Stephens and Donnelly (2001); Niu et al. (2002)
Rule-based methods
Minimum recombination principle
Qian and Beckmann (2002); Baruch, et al. (2006)
So far, there are two categories of algorithms for haplotype inference: statistical methods and Rule-based methods. Statistical methods are usually very time consuming. Parsimony, maximum likelihood via EM algorithm and Bayesian methods are three pupolar statistic approaches in statistical methods. On the other hand, by utilizing some reasonable biological assumptions ,such as minimum recombination principle,rule-based methods give the inference of haplotypes. Generally,rule-based approaches are very fast compare with statistical methods.

5. Parsimony (Clark,1990) Start from a homozygote
A1A1
A1A2
A2A3
A3A4
A3A5
A6A7
Start from a homozygote
Determine any other ambiguous sequence using the definitive haplotype from 1
Continue this procedure until all haplotypes are resolved or until no more new haplotypes can be found

6. EM algorithm
Numerical method of finding maximum likelihood estimates for parameters given incomplete data.
Initial parameter values: haplotype frequencies
Expectation step: compute expected values of missing data based on initial data
Maximization step: compute MLE for parameters from the complete data
Repeat with updated set of parameters until changes in the parameter estimates are negligible.

7. EM algorithm efficiency
Heavy computational burden with large number of loci
Partition-ligation algorithm (Niu et al., 2002)
PL-EM (Qin et al., 2002)
Accuracy and departures from HWE
Assumption of HWE in most EM-based methods
Robust to departure from HWE (Fallin and Schork, 2000)

8. Bayesian method PHASE (Stephens and Donnelly, 2001)
Based on coalescent model
Use Gibbs sampling
So far, very accurate, but also complicated.
fastPHASE (Scheet and Stephens,2006)
Revised version of PHASE
Much faster, missing genotype

9. Comparison of Parsimony,EM and PHASE
PHASE performs better than parsimony and EM (Stephen, 2001)
PHASE and EM-based methods exhibited similar performances (Zhang et al. 2001; Xu et al. 2002)

10. Haplotype inference using family data (1)
Haplotype inference based on close relatives
Reduces haplotype ambiguity and improves the efficiency
Rohde and Fuerst (2001) – EM algorithm
Families with both parents and their children
The genotyped offspring reduce the number of potential haplotype pairs for both parents.
Ding et al. (2006) - EM algorithm
Families with only one parent available

11. Haplotype inference using family data (2)
Ding et al. – EM algorithm
Families with only sibs
genotypes of sibs
Parental information
Haplotype pairs of sibs
Mixed family data
Complete families
Incomplete families
One parent
Only sibs
the sibs are considered jointly as an independent pair, their parental information can be inferred firstly, and the haplotype pairs of sibs can be updated given the posterior parental information

12. Comparison of four different strategies
Method name
Using family information?
Using
LD?
Handling incomplete families?
Complete-family-EM
(Rhode and Fuerst, 2001)
YES NO
Incomplete-family-EM
(Ding et al., 2006)
GENEHUNTER
(Kruglyak et al., 1996)
PHASE
(Stephens et al., 2003)
(Ding et al., 2006)

13. Result for incomplete families
Incomplete-family-EM
PHASE
GENEHUNTER
Running time of PHASE:
3.5 hs for the whole 100 datasets of 30 trios,187 SNPs (Marchini et al.,2006)
Running time will become prohibitive for large SNPs

14. Rule-based method Minimum recombination principle
Qian and Beckmann (2002); Baruch, et al. (2006)
Genetic recombination is rare
Haplotype with fewer recombinants should be preferred in a haplotype reconstruction
Rule 1
Rule 2 …
Wang et al., 2006

15. Joint EM and rule-based algorithm for (grand-) daughter design
Assumption of no recombination
EM algorithm to construct diplotype
Taking into account recombination
Minimum recombination principle
Find the diplotype of sire that minimizes the number of recombinations in the sire family

16. Example:
Possible diplotypes recom. events
A sire family with 50 offspring
phase-unknown genotype of sire:(12;12;12;12;12)

17. Results 10 sire families with 30 offspring each
HSHAP
PedPhase
SimWalk2
Error rate in sires
0.007c
0.682a
0.126b
Error rate in offspring
0.021c
0.0330b
0.0705a
Running time (m)
8.211
0.006
60.17
10 sire families with 30 offspring each
10 SNPs , 0.5 centiMorgan

18. Results - incomplete data
HSHAP
PedPhase
SimWalk2
Error rate in sires
0.0310c
0.8910a
0.0930b
Error rate in offspring
0.0159c
0.7607a
0.2164b
Running time (m)
0.01
0.002
10.65
10 sire families with 20 offspring each
5SNPs, 0.5 centiMorgan
30% individuals with one random missing locus.

19. Linkage vs Association
Family-based
Matching/ethnicity generally unimportant
Few markers for genome coverage ( STRs)
Can be weak design
Good for initial detection; poor for fine-mapping
Powerful for rare variants
Association
Families or unrelateds
Matching/ethnicity crucial
Many markers req for genome coverage (105 – 106 SNPs)
Powerful design
Poor for initial detection; good for fine-mapping
Powerful for common variants; rare variants generally impossible

20. TDT (Transmission Disequilibrium Test)
Compares the distribution of transmitted and non-transmitted alleles by parents of affected offspring (Spielman et al. 1993)
Non-transmitted allele
total
transmitted allele M1 M2 a b
a+b c d
c+d
a+c
b+d 2n
If the marker is unlinked to the causative locus then we expect b = c, else, one of the alleles will tend to be transmitted more often

21. TDT TDT (Transmission Disequilibrium Test)
Good for fine-mapping, poor for initial detection
Robust for population stratification/admixture
Widely used in genome-wide association study
Extended to all kinds of data structure
Multi allelic markers (Sham and Curtis, 1995)
Multiple siblings (Spielman,1998; Boehnke, 1998)
Missing parental data (Sun, 1999)
Extended pedigree (Martin et al., 2000)
Quantitative traits (Allison,1997; Ding et al, 2004)

22. Comparison of PDT,QTDT and PTDT
Quantitative trait
# ped
# alleles
QTL effect
Power
Type I error
PDT
QTDT
PTDT 10 2
0.1 59 18 57 4 5 1
0.3 74 61 82 7 3 8
0.5 85 81 92

23. Hap-TDT Two categories of haplotype-based TDT
Hap-TDT (Haplotype-based TDT)
Haplotype is more informative than single marker
The original TDT and most of its extensions consider one marker at a time.
Two categories of haplotype-based TDT
Haplotype reconstruction first
Sethuraman (1997); Wilson (1997); Clayton and Jones (1999); Zhao et al. (2000); Zhang et al.(2003)
Implicit haplotype reconstruction
Dudbridge (2003)

24. Hap-TDT vs TDT Zhang et al. (2003) Qualitative trait
Quantitative trait (h2 = 0.06)

25. Hap-TDT vs TDT
5SNPs, Excluding causal variant SNP

26. Hap- TDT Problem of multiple comparisons
Increase in the degree of freedom
More markers
A large number of haplotypes
A large number of degrees of freedom
Limit the power of test

27. Acknowledgement
Prof. Qin Zhang (China Agricultural University)
Prof. Henner Simianer (University of Goettingen)