European Human Genetics Conference 2007 June 16 – 19, 2007 ...

Concurrent Sessions
power of this method is much higher compared to TDT-based methods.
When pedigree structure is at least partly known, utilisation of this
information allows even more powerful analysis. The baseline method
is extended to include covariates, interactions and more complex genetic
effects, such as parent-of-origin and epistasis. We demonstrate
the utility of the newly developed methods using real data from a young
genetically isolated population.
C44. Optimizing information for linkage genomescreen in a large
and inbred pedigree with a high density SNP map
C. Bellenguez 1,2 , C. Ober 3 , C. Bourgain 2,1 ;
1 Univ Paris-Sud, UMR-S535, Villejuif F-94817, France, 2 INSERM, Villejuif F-
94817, France, 3 Department of Human Genetics, The University of Chicago,
Chicago, IL, United States.
Large genealogies available in isolated populations are potentially
very informative for linkage analyses, in particular when considering
high density SNP maps.
Here, we investigate different methodological aspects of linkage analysis
in a large and inbred 1840-member Hutterite pedigree, phenotyped
for asthma and asthma-related traits. Starting with a 5cM microsatellite
map, we first identify genome-wide significant regions (1p13, 1p31,
5q33, 6q21-23, 12q14, 13q13 and 14p11-q11), after a carefully optimized
breaking of the pedigree. Our approach is interesting since
these regions had not been significantly detected in previous analyses
of the same dataset but have been described by others.
We further analyse these linked regions using a 500K SNP map. Because
SNPs are much closer than microsatellite markers, they present
important linkage disequilibrium (LD), which bias classical nonparametric
multipoint analyses. This problem is even stronger in population
isolates where LD extends over larger regions with a more stochastic
pattern. The only method that models LD in the NPL analysis is limited
in both the pedigree size and the number of markers (Abecasis and
Wigginton, 2005) and therefore could not be used. Instead, we propose
methods that identify sets of SNPs with maximum linkage information
content in our pedigree and no LD-driven bias. Both algorithms
that directly remove pairs of SNPs in high LD and clustering methods
are evaluated. Preliminary results suggest that in such population
isolates, a careful marker selection is necessary to obtain information
content for linkage higher with SNPs than that already available with
microsatellites.
C45. Epistasis for physiological variables in admixed
populations
M. Fenger 1 , A. Linneberg 2 , O. Pedersen 3 , T. Hansen 4 , T. Jøregensen 2 ;
1 University of Copenhagen, Hvidovre, Denmark, 2 Research Centre for Prevention
and Health, Glostrup, Denmark, 3 Steno Diabetes Center, Gentofte, Denmark,
4 Steno Diabetes Centre, Gentofte, Denmark.
The metabolic syndrome and diabetes mellitus are highly influenced
by genetic factors. These conditions are heterogenous and polygenic
in nature for the vast majority of cases, each gene supposed to have
only a minor impact of the phenotypic variance in the general population.
Therefor large populations are required to identify and elucidate the
genetic structure of these conditions. A major obstacle is the heterogeneity
of most study populations, i.e. the populations consist of
a mixture of yet unidentified physiological and genetic homogenous
subpopulations. To define these subpopulations a latent class analysis
is performed. This was done in a structural equation modelling framework.
Basic physiological variables previously shown to be related to
the metabolic syndrome were included in analysis as indicators and
covariates. No genetic model is assumed. This model defined 19 subpopulations
with an entropy measure of approximately 0.9. Less than
0.1%. of all possible single-gene heritabilities were present. In contrast,
including two-gene interactions revealed that 28 of the 30 genetic
markers are involved in one and usually several epistatic interactions.
On average approximately 1/3 of all possible two-gene interactions
were significant for all the traits included in the model. Most notably,
stratifying the basic study population revealed approximately 30%
more interactions masked in the physiological mixed study population.
These results supports the notion that genome wide single-gene association
studies generally will be futile. For a genome wide screening to
be successful the populations should be physiologically stratified and
at least two-gene interactions should be included.
C46. A powerful approach to detect parent-of-origin effects in
whole-genome association scans of quantitative traits
N. M. Belonogova 1,2 , Y. S. Aulchenko 1,3 ;
1 Institute of Cytology & Genetics SD RAS, Novosibirsk, Russian Federation,
2 Department of Cytology & Genetics, Novosibirsk State University, Novosibirsk,
Russian Federation, 3 Department of Epidemiology & Biostatistics, Erasmus
MC, 3000 CA Rotterdam, The Netherlands.
Parent-of-origin effect (POE) is a widespread genetic phenomenon extensively
studied in model animals. For the genes exhibiting POE, the
trait value in a heterozygous offspring depends on the parental origin
of the alleles. There exist evidences that POE is important for such human
traits as weight, type 2 diabetes, and others. For these traits the
power of genome-wide association (GWA) analysis may be increased
by incorporating POE into the analysis model.
To study POE in GWA scans of human quantitative traits, different variants
of TDT-based methods may be applied. These, however, exploit
only within-family variation in order to avoid population stratification
bias. In homogeneous populations methods utilizing both within- and
between-family variation, such as the measured genotype (MG) approach,
are shown to have greater power. In MG analysis, genetic
polymorphism is included as a fixed effect or covariate in a mixed
linear model along with a polygenic component. To utilize POE information,
we suggest using probabilities indicating parental origin as a
covariate in the MG analysis. We suggest a fast approximation to the
full MG analysis, which is suitable for the analysis of GWA scans and
a method to estimate probabilities of parental origin in arbitrary complex
pedigrees. Using simulated data, we compare our approach to the
TDT-based methods. We show that our approach is more powerful.
For many scenarios the sample size can be reduced two times when
using our approach and the same significance level as by using the
TDT-based methods is still achieved.
C47. Quantifying the increase in human individual genome-wide
heterozygosity through isolate break-up and admixture
I. Rudan 1 , Z. Biloglav 2 , A. Vorko-Jovic 2 , I. Kolcic 2 , O. Polasek 2 , L. Zgaga 2 , T.
Zemunik 3 , R. Mulic 3 , D. Ropac 3 , D. Stojanovic 4 , D. Puntaric 5 , R. Stevanovic 6 , H.
Campbell 1 ;
1 Faculty of Medicine, Edinburgh, United Kingdom, 2 Faculty of Medicine, Zagreb,
Croatia, 3 Faculty of Medicine, Split, Croatia, 4 Faculty of Medicine, Rijeka, Croatia,
5 Faculty of Medicine, Osijek, Croatia, 6 Institute for Public Health, Zagreb,
Croatia.
Aim. The human population is undergoing a major transition from a
metapopulation structure (subdivision in many relatively isolated communities)
to a more admixed structure such as that which occurs in
large cities. We attempted to quantify the magnitude of increase in
average individual genome-wide heterozygosity (IGWH) that has occurred
through isolate break-up and admixture.
Materials and methods. We sampled 100 examinees from 9 isolated
villages on 5 Croatian islands, and an additional 101 immigrants
into those villages. Out of the larger sample of 1001 examinees, we
carefully selected 6 samples each of 23 individuals with a predicted
increasing level of genome-wide heterozygosity, which was then measured
studied using 1,200 STR markers. The first sample was from
the most isolated island, the second from less isolated island, the third
from the least isolated island, the fourth included individuals admixed
between villages, the fifth immigrants from mainland cities and the
sixth extremely outbred individuals.
Results: Relative to the mean IGWH estimate in the first sample, the
increase in the percentage of the genome that is heterozygous in the
remaining 5 samples was found to be: 2.3%, 3.8%, 5.8%, 6.7% and
7.9%. These differences are statistically highly significant (p