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SUMMARY: A website for performing power calculations for the design of linkage and association genetic mapping studies of complex traits. AVAILABILITY: The package is made available athttp://statgen.iop.kcl.ac.uk/gpc/.
Great advances in the field of genetics have been made in the last few years. However, resolving the complexity that underlies the susceptibility to many polygenic human diseases remains a major challenge to researchers. The fast increase in availability of genetic data and the better understanding of the clinical and pathological heterogeneity of many autoimmune diseases such as multiple sclerosis, but also Parkinson's disease, Alzheimer's disease, and many more, have changed our views on their pathogenesis and diagnosis, and begins to influence clinical management. At the same time, more powerful methods that allow the analysis of large numbers of genes and proteins simultaneously open opportunities to examine their complex interactions. Using multiple sclerosis as a prototype, we review here how new methodologies such as gene expression profiling can be exploited to gain insight into complex trait diseases.
A two-locus segregation and linkage-analysis approach was used to characterize the genetic control of a complex trait (Q1) and to localize the genes that have detectable effects. The results suggested that a two-locus Mendelian model fit the data significantly better than a one-locus model. The linkage results based on the most parsimonious two-locus model revealed linkage of Q1 to two areas (MG2 and MG3), while there was less evidence for linkage using one-locus models. Results also suggested that the subphenotypes (Q2 and Q3) provided useful information for further analysis of Q1 using two-locus models.
Aluminum (Al) toxicity, which is caused by the solubilization of Al3+ in acid soils resulting in inhibition of root growth and nutrient/water acquisition, is a serious limitation to crop production, because up to one-half of the world's potentially arable land is acidic. To date, however, no Al tolerance genes have yet been cloned. The physiological mechanisms of tolerance are somewhat better understood; the major documented mechanism involves the Al-activated release of Al-binding organic acids from the root tip, preventing uptake into the primary site of toxicity. In this study, a quantitative trait loci analysis of Al tolerance in Arabidopsis was conducted, which also correlated Al tolerance quantitative trait locus (QTL) with physiological mechanisms of tolerance. The analysis identified two major loci, which explain approximately 40% of the variance in Al tolerance observed among recombinant inbred lines derived from Landsberg erecta (sensitive) and Columbia (tolerant). We characterized the mechanism by which tolerance is achieved, and we found that the two QTL cosegregate with an Al-activated release of malate from Arabidopsis roots. Although only two of the QTL have been identified, malate release explains nearly all (95%) of the variation in Al tolerance in this population. Al tolerance in Landsberg erecta x Columbia is more complex genetically than physiologically, in that a number of genes underlie a single physiological mechanism involving root malate release. These findings have set the stage for the subsequent cloning of the genes responsible for the Al tolerance QTL, and a genomics-based cloning strategy and initial progress on this are also discussed.
Hundreds of thousands of SNP markers are being generated with the purpose of carrying out case-control association studies for complex traits, which are thought to be due to multiple underlying susceptibility genes. The number of markers is typically much larger than the number of observations so that joint analysis of marker genotypes and their interactions is not feasible. We discuss a two-stage approach to first select a small subset of markers and then model the effects of the selected markers on disease. Examples of two procedures for marker selection are given with subsequent modeling of main and interaction effects. The approaches are applied to a data set with 89 SNPs in lieu of a genome screen with many more markers.
Although many biotechnological advancements have been made in the past decade, there has been very limited success in unraveling the genetic component of complex traits. Heavily invested research has been initiated based on etiological models of unrealistic simplicity and conducted under poor experimental designs, on data sets of insufficient size, leading to an overestimation of the effect sizes of genetic variants and the quantity and quality of linkage disequilibrium (LD). Arguments about whether families or unrelated individuals provide more power for gene mapping have been erroneously debated as issues of whether linkage or LD are more detectable sorts of correlation. Although the latter issue may be subject to debate, there is no doubt that family-based analysis is more powerful for detecting linkage and/or LD. If the recent advances in biotechnology are to be exploited effectively, vastly improved study designs will be imperative, as the reasons for the lack of success to date have much more to do with biology than technology, an issue that has become increasingly clear with the findings of the past years.
Combining congenic mapping with microarray expression profiling offers an opportunity to establish functional links between genotype and phenotype for complex traits such as type 1 diabetes (T1D). We used high-density oligonucleotide arrays to measure the relative expression levels of >39,000 genes and ESTs in the NOD mouse (a murine model of T1D and other autoimmune conditions), four NOD-derived diabetes-resistant congenic strains, and two nondiabetic control strains. We developed a simple, yet general, method for measuring differential expression that provides an objective assessment of significance and used it to identify >400 gene expression differences and eight new candidates for the Idd9.1 locus. We also discovered a potential early biomarker for autoimmune hemolytic anemia that is based on different levels of erythrocyte-specific transcripts in the spleen. Overall, however, our results suggest that the dramatic disease protection conferred by six Idd loci (Idd3, Idd5.1, Idd5.2, Idd9.1, Idd9.2, and Idd9.3) cannot be rationalized in terms of global effects on the noninduced immune system. They also illustrate the degree to which regulatory systems appear to be robust to genetic variation. These observations have important implications for the design of future microarray-based studies in T1D and, more generally, for studies that aim to combine genome-wide expression profiling and congenic mapping.
The lod score method for localizing mendelian disease genes in human genetics is reviewed. Current ways of applying this technique to hypothesized genes underlying complex traits are discussed. In contrast to these parametric methods, allele-sharing methods using affected sib pairs are reviewed. Based on such allele-sharing data, a particular type of analysis is outlined which can identify sets of disease loci. This method makes use of pattern-recognition techniques as implemented in artificial neural networks (ANNs).
While the methodology for the mapping of Mendelian disorders is well established, the practical and theoretical steps required for successful gene identification in a complex trait are still difficult to predict. A number of analytical models and simulations based on repetitive drawings from predefined statistical distributions are available. To supplement these analytical models, we developed an integrated simulation approach by directly simulating entire populations under a disease model based on epidemiological data. Random mating, nonoverlapping populations and the absence of differential fitness were assumed. Samples were drawn from these homogeneous and heterogeneous populations and analyzed with established analysis tools. We investigated the properties of linkage and association studies in inflammatory bowel disease - modeled as a six-locus polygenic disorder - as an example of this approach. In nonparametric linkage studies, lod scores varied widely, with the median required sample size depending on the locus-specific relative sibling risk. A fine mapping resolution <4 cM was found to require nonparametric lod scores >10. Family-based association studies (TDT test) and case-control studies showed a similar sensitivity and can identify risk loci in populations with moderate levels of linkage disequilibrium in sample sizes of 500-800 triplets. Case-control association studies were prone to false-positive results if applied in heterogeneous populations, with the false-positive rate increasing with sample size because population heterogeneity is detected with increasing power.
In this review, we present some studies on genetic analysis of proteome and transcriptome variations, which exemplify new strategies for a better understanding of the molecular and genetic bases of complex traits. A large genetic variability was revealed at the proteome expression level, which raised the possibility to predict phenotypical performance on the basis of gene product variability. This approach yielded limited results, but could be re-newed by extensive identification of proteins now allowed by mass spectrometry. The dissection of the genetic basis of the variation of individual protein amounts proves very powerful to select 'candidate' proteins, physiologically relevant for a given phenotypical trait, as shown by a study on the effect of water stress in maize. In order to investigate factors of grain quality in maize, we selected a regulatory locus known to control the expression of several storage protein genes, Opaque-2, and investigated the relationships between variability in zein amount and composition and the molecular polymorphism at this locus. Moreover, a QTL analysis revealed that the variability in Opaque-2 transcript abundance was controlled by several polymorphic trans-acting regulators unlinked to the Opaque-2 structural gene. Such genetic approaches should represent additional tools for physiological analysis of the huge amounts of data generated by transcritome and proteome projects.
Linkage disequilibrium (LD) analysis has been promoted as a method of mapping disease genes, particularly in isolated populations, but has not yet been used for genome-screening studies of complex disorders. We present results of a study to investigate the feasibility of LD methods for genome screening using a sample of individuals affected with severe bipolar mood disorder (BP-I), from an isolated population of the Costa Rican central valley. Forty-eight patients with BP-I were genotyped for markers spaced at approximately 6-cM intervals across chromosome 18. Chromosome 18 was chosen because a previous genome-screening linkage study of two Costa Rican families had suggested a BP-I locus on this chromosome. Results of the current study suggest that LD methods will be useful for mapping BP-I in a larger sample. The results also support previously reported possible localizations (obtained from a separate collection of patients) of BP-I-susceptibility genes at two distinct sites on this chromosome. Current limitations of LD screening for identifying loci for complex traits are discussed, and recommendations are made for future research with these methods.
BACKGROUND: The striatum plays a pivotal role in modulating motor activity and higher cognitive function. We analyzed variation in striatal volume and neuron number in mice and initiated a complex trait analysis to discover polymorphic genes that modulate the structure of the basal ganglia. RESULTS: Brain weight, brain and striatal volume, neuron-packing density and number were estimated bilaterally using unbiased stereological procedures in five inbred strains (A/J, C57BL/6J, DBA/2J, BALB/cJ, and BXD5) and an F2 intercross between A/J and BXD5. Striatal volume ranged from 20 to 37 mm3. Neuron-packing density ranged from approximately 50,000 to 100,000 neurons/mm3, and the striatal neuron population ranged from 1.4 to 2.5 million. Inbred animals with larger brains had larger striata but lower neuron-packing density resulting in a narrow range of average neuron populations. In contrast, there was a strong positive correlation between volume and neuron number among intercross progeny. We mapped two quantitative trait loci (QTLs) with selective effects on striatal architecture. Bsc10a maps to the central region of Chr 10 (LRS of 17.5 near D10Mit186) and has intense effects on striatal volume and moderate effects on brain volume. Stnn19a maps to distal Chr 19 (LRS of 15 at D19Mit123) and is associated with differences of up to 400,000 neurons among animals. CONCLUSION: We have discovered remarkable numerical and volumetric variation in the mouse striatum, and we have been able to map two QTLs that modulate independent anatomic parameters.
Stroke (brain attack) is currently the third leading cause of death in Western societies. Recent advances in molecular genetics have finally demonstrated what has long been suggested by the clinical observation, that is, stroke is not only the complication of major pathologic conditions such as atherosclerosis, hypertension, or cardiac diseases, but rather it represents a complex trait itself. Thus, the pathogenesis of stroke is often the result of the combined effects of genes exerting a direct contributory role and of their interactions with several environmental determinants. A genetic dissection of stroke has been attempted in suitable animal models and in humans. With this approach, the genetic defects underlying monogenic disorders associated with stroke were identified. Moreover, important findings have recently highlighted the contribution of genes encoding cardiovascular hormones, such as the atrial natriuretic peptide, for the pathogenesis of multifactorial, polygenic forms of stroke. A more thorough understanding of the fine mechanisms, dependent from mutations within stroke susceptibility genes and underlying the disease pathogenesis, may help to introduce new specific tools to achieve better prevention and treatment of stroke.
Detailed genetic maps, based upon molecular markers (in particular, on restriction fragment length polymorphisms-RFLPs) have now been constructed for a number of crop plant species, and permit a range of genetic analyses hitherto considered impossible. The availability of such maps has made it possible to approach the dissection and manipulation of both simply-inherited and complex characteristics. Even those characters that show apparently 'quantitative' inheritance (displaying essentially continuous variation within a segregating family) can frequently be resolved into a handful of major gene effects. Once tagged with molecular markers, the genes can be assembled in any desired combination, permitting the testing of hypotheses on gene action/interaction, or the construction of varieties of plant species with improved agronomic performance. The next technological challenge is to 'walk' from RFLP markers to isolate the actual genes responsible for the complex trait, by a combination of genetical and physical mapping techniques. Such analyses will begin to clarify our picture of the relationship between genetic and physical maps, of recombination, and of the arrangement of diverse families of DNA sequences in plant genomes.
Many of the likelihoods arising in the analysis of complex genetic traits, particularly in linkage analysis, are computationally infeasible. Where exact likelihoods cannot be computed, Monte Carlo estimates of likelihoods may provide a satisfactory alternative. Although simulation on pedigrees is straightforward, simulation conditional upon observed phenotypic data is not. However, recent advances in Markov chain Monte Carlo methods have provided a method well suited to this problem. From realizations of underlying genes, simulated under a genetic model, conditional upon observed data, a Monte Carlo estimate of this likelihood surface can be formed. Various sampler and model modifications are needed to enhance the statistical efficiency of the Monte Carlo estimator; as these methods become increasingly developed, this approach becomes a useful tool in resolving the genes contributing to the phenotypes associated with genetically complex diseases.
There is accumulating evidence of host genetic control in malaria infection and, in humans, some genes have been associated with severe malaria. Nevertheless, other important genes controlling blood infection levels, malarial disease and immune responses are likely to be identified. In this paper, we focus on segregation and linkage analyses of blood infection levels in an urban population living in Burkina Faso. We found evidence of a complex genetic control and a linkage to chromosome 5q31-q33. The identification of genes controlling complex traits related to malaria infection should be helpful in understanding protective mechanisms and the relationship between infection, malaria attacks and severe malaria.
Using a three-locus model wherein two loci regulate a third, candidate locus, I examine physiological epistasis from the 'gene's eye view' of the regulated locus. I show that, depending upon genetic background at the regulatory loci, an allele at the candidate locus can be dominant, additive, recessive, neutral, over-dominant, or under-dominant in its effects on fitness. This kind of variation in allelic effect caused by variation in genetic background from population to population, from time to time in the same population, or sample to sample makes finding and mapping the genes underlying a complex phenotype difficult. The rate of evolution of such genes can also be slowed, especially in genetically subdivided metapopulations with migration. Nevertheless, understanding how variation in genetic background causes variation in allelic effects permits the genetic architecture of such complex traits to be dissected into the interacting component genes. While some backgrounds diminish allelic effects and make finding and mapping genes difficult, other backgrounds enhance allelic effects and facilitate gene mapping.
For one- and two-trait-locus models, we show that the lod score based on affected relative pairs or trios is a monotonically increasing function of the genome similarity measured by the proportion of alleles shared identical by descent (IBD) conditional on observed marker data. These results can be generalized to multitrait-locus models. Thus, we can use conditional probability of genes shared IBD as a tool to reveal chromosomal segments that are likely to harbor the genes underlying the complex traits.