The Wellcome Trust Centre
for Human Genetics
The Wellcome Trust Centre
for Human GeneticsThe technique has recently been used to map five QTL for emotionality in an outbred mouse population. The method is implemented in a C-program called HAPPY, which is available for download.
These difficulties have led to the study of animal models of human traits. Studies using experimental crosses between inbred animal strains have been successful in mapping QTLs with effects on a number of different phenotypes, including behaviour, but attempts to fine-map QTLs in animals have often foundered on the discovery that a single QTL of large effect was in fact due to multiple loci of small effect positioned within the same chromosomal region. A further potential difficulty with detecting QTLs between inbred crosses is the significant reduction in genetic heterogeneity compared to the total genetic variation present in animal populations: a QTL segregating in the wild need not be present in the experimental cross.
In an attempt to circumvent the difficulties encountered with inbred crosses, we have been using a genetically heterogeneous stock (HS) of mice for which the ancestry is known. The heterogeneous stock was established from an 8 way cross of C57BL, BALB/c, RIII, AKR, DBA/2, I, A and C3H/2 inbred strains. Since its foundation 30 years ago, the stock has been maintained by breeding from 40 pairs and, at the time of this experiment, was in its 60th generation. Thus each chromosome from an HS animal is a fine-grained genetic mosaic of the founder strains, with an average distance between recombinants of 1/60 or 1.7 cM.
Theoretically, the HS offers at least a 30 fold increase in resolution for QTL mapping compared to an F2 intercross. The high level of recombination means that fine-mapping is possible using a relatively small number of animals; for QTLs of small to moderate effect, mapping to under 0.5 cM is possible with fewer than 2,000 animals. The large number of founders increases the genetic heterogeneity, and in theory one can map all QTLs that account for progenitor strain genetic differences. Potentially, the use of the HS offers a substantial improvement over current methods for QTL mapping.
HAPPY was written to find QTLs in HS animals. It uses a multipoint analysis which offers significant improvements in statistical power to detect QTLs over that achieved by single-marker association. Further details can be found in Proc. Natl. Acad. Sci. USA, 10.1073/pnas.230304397.
HAPPY's analyis is essentially two stage; ancestral haplotype reconstruction using dynamic programming, followed by QTL testing by linear regression:
To install HAPPY, download the compressed tar file HAPPY.tar.Z, decompress it and untar it. You will find the following directory structure:
To compile:
NB: Happy works on marker interval, which are always referred to by the name of the marker at the left-end of the interval.
| argument | type | default value | function |
| -alleles | Readable File | [ ] | Name of alleles input file (see above) |
| -data | Readable File | [ ] | Name of data input file(see above) |
| -extremes | float | [ 0 ] | Only use +- extreme% phenotypes (default uses all data) |
| -seed | integer | [ 0 ] | Random number seed (defaults to system time |
| -normalize | switch | [ false ] | Transform phenotypes to be normally distributed before analysis |
| -pointwise | switch | [ false ] | Perfrom pointwise QTL tests rather than interval-wide (much slower) |
| -generations | integer | [ 60 ] | The number of generations since the HS was founded. We recommend setting this to a high value such as 500 or 1000 for maximum sensitivity, as this copes better with errors due to incorrect genotypes and wrong marker distances. |
| -partial | text | [ ] | Remove effect of QTL at interval with corresponding left-end marker before testing for qtls in other intervals. Used for examining multiple QTLs |
| -scramble | switch | [ false ] | Shuffle the phenotypes before doing any analysis |
| -permutations | integer | [ 0 ] | Do a permutation test at each marker location by shuffling the phenotypes this number of times and repeating the analysis of variance. Useful for checking that significant results are not artefacts caused by non-normality of the phenotypes. Warning: Slow. |
| -bootstrap | integer | [ 0 ] | Perform this number of bootstraps, resampling the data with replacement and repeating the analysis. Used to get confidence intervals for QTL locations. You can restrict the marker range using -bootstart, -bootstop. Warning: Very Slow. |
| -bootstart | text | [ ] | Specify marker at left end of first interval to be bootstrapped |
| -bootstop | text | [ ] | Specify marker at left end of last interval to be bootstrapped |
| -verbose | integer | [ 1 ] | Control level of output |
| -help | switch | [ ] | This help |
Testing marker interval 11,12 D1MIT264 D1MIT194
strain densities:
A/J AKR BALB C3H C57 DBA I RIII
0.1891 0.5380 0.2572 0.1896 0.2575 0.0778 0.1691 0.3217
ANOVA F 5.963372e+00 pval 9.087284e-07
tss 4.520509e+02 fss 2.404637e+01 rss 4.280046e+02 R^2 5.319394e-02
trait estimates, mean= 1.469828e-01:
1 A/J effect -8.958640e+00 se 4.572822e+00 T -1.959105e+00
2 AKR effect -2.302646e-02 se 8.002267e-02 T -2.877492e-01
3 BALB effect -7.967934e+00 se 1.104530e+01 T -7.213870e-01
4 C3H effect 9.463201e+00 se 4.479047e+00 T 2.112771e+00
5 C57 effect 7.735892e+00 se 1.102880e+01 T 7.014264e-01
6 DBA effect 9.941300e-01 se 3.721886e-01 T 2.671038e+00
7 I effect -3.325929e-01 se 5.296803e-01 T -6.279125e-01
8 RIII effect -6.170642e-01 se 1.919972e-01 T -3.213924e+00
1 A/J effect -8.958640e+00 se 4.572822e+00 T -1.959105e+00means strain A/J has effect size -8.95, standard error 4.57, T-values -1.95. Not that because sometimes strain effects are highly correlated (in regions where two strains cannot be distinguished), it is not advisable to interpret the significance of individual strain effects using a T-test; it is safer to look at the F-statistic.
The software package HAPPY is distributed in the hope that it will be useful, but in order that the University as a charitable foundation protects its assets for the benefit of its educational and research purposes, the University makes clear that no condition is made or to be implied, nor is any warranty given or to be implied, as to the accuracy of HAPPY, or that it will be suitable for any particular purpose or for use under any specific conditions, or that the content or use of HAPPY will not constitute or result in infringement of third-party rights. Furthermore, the University disclaims all responsibility for the use which is made of HAPPY.