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PubMed2004

Genetic dissection of complex traits with chromosome substitution strains of mice.

Chromosome substitution strains (CSSs) have been proposed as a simple and powerful way to identify quantitative trait loci (QTLs) affecting developmental, physiological, and behavioral processes. Here, we report the construction of a complete CSS panel for a vertebrate species. The CSS panel consists of 22 mouse strains, each of which carries a single chromosome substituted from a donor strain (A/J) onto a common host background (C57BL/6J). A survey of 53 traits revealed evidence for 150 QTLs affecting serum levels of sterols and amino acids, diet-induced obesity, and anxiety. These results demonstrate that CSSs greatly facilitate the detection and identification of genes that control the wide diversity of naturally occurring phenotypic variation in the A/J and C57BL/6J inbred strains.

Amino Acids↗
PubMed2005

Identifying genetic variation affecting a complex trait in simulated data: a comparison of meta-analysis with pooled data analysis.

We explored the power and consistency to detect linkage and association with meta-analysis and pooled data analysis using Genetic Analysis Workshop 14 simulated data. The first 10 replicates from Aipotu population were used. Significant linkage and association was found at all 4 regions containing the major loci for Kofendrerd Personality Disorder (KPD) using both combined analyses although no significant linkage and association was found at all these regions in a single replicate. The linkage results from both analyses are consistent in terms of the significance level of linkage test and the estimate of locus location. After correction for multiple-testing, significant associations were detected for the same 8 single-nucleotide polymorphisms (SNP) in both analyses. There were another 2 SNPs for which significant associations with KPD were found only by pooled data analysis. Our study showed that, under homogeneous condition, the results from meta-analysis and pooled data analysis are similar in both linkage and association studies and the loss of power is limited using meta-analysis. Thus, meta-analysis can provide an overall evaluation of linkage and association when the original raw data is not available for combining.

Computer Simulation↗
PubMed2006

Chromosome substitution strains: a new way to study genetically complex traits.

Many biological traits and heritable diseases are multifactorial, involving combinations of genetic variants and environmental factors. To dissect the genetic basis for these traits and to characterize their functional consequences, mouse models are widely used, not only because of their genetic and physiological similarity to humans, but also because an extraordinary variety of genetic resources enable rigorous functional studies. Chromosome substitution strains (CSSs) are a powerful complement to existing resources for studying multigenic traits. By partitioning the genome into a panel of new inbred strains with single chromosome substitutions, one strain for each of the autosomes, the X and Y chromosome, and the mitochondria, unique experimental designs and considerable statistical power are possible. Multigenic trait genes (or quantitative trait loci [QTLs]) with weak effects are easily detected, linkage and congenic crosses can be quickly made, gene interactions are readily characterized, and discovery of QTLs is greatly accelerated. Several published studies demonstrate the considerable utility of these strains and new applications for CSSs continue to be discovered.

Animals↗
PubMed2001

Shifting paradigms in gene-mapping methodology for complex traits.

The analysis of genetic linkage has been highly successful in the mapping of the genes responsible for Mendelian diseases. In the past decade, attempts have been made to extend this approach to multifactorial disorders and other health-related traits. It has proved difficult, however, to obtain strong and replicable linkage findings for the common forms of heritable diseases. This, together with the rapid pace of development of molecular technology and expansion of genome sequence information, has resulted in significant shifts in research paradigm. There is an increasing recognition of the need to understand the population genetics and biometrical properties of clinically relevant traits so that phenotypes can be defined in such a way that maximises the chances of successful gene mapping. There is a trend towards systematic association analysis with increasing sophistication in the analysis of pooled DNA samples and multi-locus haplotypes, and in the use of unlinked background markers to protect against spurious associations. We can expect increasing integration between genetics, epidemiology and clinical trials leading to genetically informative designs that will not only identify susceptibility genes but also clarify how the environment influences their effects and how they may modify the response to therapeutic interventions.

Animals↗
PubMed2006

Association mapping of complex trait loci with context-dependent effects and unknown context variable.

A novel method for Bayesian analysis of genetic heterogeneity and multilocus association in random population samples is presented. The method is valid for quantitative and binary traits as well as for multiallelic markers. In the method, individuals are stochastically assigned into two etiological groups that can have both their own, and possibly different, subsets of trait-associated (disease-predisposing) loci or alleles. The method is favorable especially in situations when etiological models are stratified by the factors that are unknown or went unmeasured, that is, if genetic heterogeneity is due to, for example, unknown genes x environment or genes x gene interactions. Additionally, a heterogeneity structure for the phenotype does not need to follow the structure of the general population; it can have a distinct selection history. The performance of the method is illustrated with simulated example of genes x environment interaction (quantitative trait with loosely linked markers) and compared to the results of single-group analysis in the presence of missing data. Additionally, example analyses with previously analyzed cystic fibrosis and type 2 diabetes data sets (binary traits with closely linked markers) are presented. The implementation (written in WinBUGS) is freely available for research purposes from http://www.rni.helsinki.fi/ approximately mjs/.

Alleles↗
PubMed1999

An efficient, robust and unified method for mapping complex traits (III): combined linkage/linkage-disequilibrium analysis.

Extending the method for linkage analysis [Zhao et al., 1998a: Am. J. Med. Genet. 77:366-383; 1998b: Am. J. Med. Genet. 79:49-61], this article describes a method for the linkage-disequilibrium analysis, and for combining linkage and linkage-disequilibrium analyses. As highly dense markers are increasingly used in genome scans, one or more markers are not only linked with the disease genes if they exist, but also likely in linkage-disequilibrium with those putative genes. Hence, linkage-disequilibrium analysis potentially offers additional information about positions of putative disease genes. Combining both linkage and linkage-disequilibrium signals, this approach is able to improve positional signals. As before, the proposed method is a model-based approach, but semiparametric via the estimating equation technique. Under the assumptions of penetrance and allele frequency, this method efficiently estimates recombination fractions for linkage analysis and odds ratios for linkage-disequilibrium analysis. As described in two previous papers, this method is relatively more robust than the lod score methods, since it requires weaker assumption than conditional independence. While the estimated recombination fractions are used for inference as part of linkage analysis, the estimated odds ratios are used for linkage-disequilibrium inference and combined linkage, and linkage-disequilibrium parameters can be used to test combined linkage/linkage-disequilibrium analysis. This approach has been implemented, named gSCAN, and its compiled version is available for trial on request via the web site (http:/lynx.fhcrc.org/qge). We applied this new approach to affected sib-pair data collected for the genome scan to localize type 1 diabetes genes. Under an assumed autosomal dominant gene model, the linkage analysis confirms an earlier suggestion of one major gene around D6S281. Interestingly, the linkage-disequilibrium analysis suggests several additional signals around D6S250, GATA30, D6S311, D6S441, D6S442, D6S415, D6S411, D6S305, and a290xh9. The linkage analysis, on the other hand, suggests a signal around D6S281, while providing supporting evidence for several other marker loci. However, the combined analysis did not provide strong support for any of the findings, implying that linkage and linkage-disequilibrium findings are not consistent.

Alleles↗
PubMed1999

The power of association studies to detect the contribution of candidate genetic loci to variation in complex traits.

The statistical power of five association study test statistics (two haplotype-based tests, two marker-based tests, and the Transmission Disequilibrium Test-Q5) to detect single nucleotide polymorphism (SNP)/phenotype associations in a linkage-disequilibrium-based candidate gene scan employing a number of SNPs is examined. Power is estimated as a function of realistic parameters expected to affect the likelihood of detecting a significant association: the number of SNPs examined, the scaled recombination size of the region examined, the proportion of variance in the trait attributable to a hidden causative polymorphism within the region, and the number of individuals or families examined. For the different combinations of parameter values, power is estimated from a large number of realizations of a simulated coalescent describing a single random mating population with mutation, random genetic drift, and recombination. This explicit population genetics model results in a distribution of DNA marker heterozygosities and linkage disequilibria that are likely to resemble those expected in actual population samples. The study concludes that (1) marker-based permutation tests are more powerful than simple haplotype-based tests, (2) there is sufficient power to detect the presence of causative polymorphisms of small effect if on the order of 500 individuals are sampled, (3) greater power is achieved by increasing the sample size than by increasing the number of polymorphisms, (4) association studies are generally more powerful than transmission disequilibrium-based tests, and (5) for the range of parameters considered association studies have a low repeatability unless sample sizes are on the order of 500 individuals. Estimates of 4Nc for a number of gene regions and human populations will be of use in determining the density of SNPs that are likely to be required for successful association studies.

Computational Biology↗
PubMed2001

Complex trait analysis of the hippocampus: mapping and biometric analysis of two novel gene loci with specific effects on hippocampal structure in mice.

Notable differences in hippocampal structure are associated with intriguing differences in development and behavioral capabilities. We explored genetic and environmental factors that modulate hippocampal size, structure, and cell number using sets of C57BL/6J (B6) and DBA/2J (D2) mice; their F1 and F2 intercrosses (n = 180); and 35 lines of BXD recombinant inbred (RI) strains. Hippocampal weights of the parental strains differ by 20%. Estimates of granule cell number also differ by approximately 20%. Hippocampal weights of RI strains range from 21 to 31 mg, and those of individual F2 mice range from 23 to 36 mg (bilateral weights). Volume and granule cell number are well correlated (r = 0.7-0.8). Significant variation is associated with differences in age and sex. The hippocampus increases in weight by 0.24 mg per month, and those of males are 0.55 mg heavier (bilateral) than those of females. Heritability of variation is approximately 50%, and half of this genetic variation is generated by two quantitative trait loci that map to chromosome 1 (Hipp1a: genome-wide p < 0.005, between 65 and 100 cM) and to chromosome 5 (Hipp5a, p < 0.05, between 15 and 40 cM). These are among the first gene loci known to produce normal variation in forebrain structure. Hipp1a and Hipp5a individually modulate hippocampal weight by 1.0-2.0 mg, an effect size greater than that generated by age or sex. The Hipp gene loci modulate neuron number in the dentate gyrus, collectively shifting the population up or down by as much as 200,000 cells. Candidate genes for the Hipp loci include Rxrg and Fgfr3.

Aging↗