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Genetic approaches to stature, pubertal timing, and other complex traits.

The factors that regulate the timing of puberty remain largely elusive, as do the factors that modulate childhood growth and adult height. However, it is clear that these developmental processes are highly heritable--much of the natural variation in growth and timing of puberty is due to genetic variation within the population. In this review, we discuss how recent genetic and genomic advances can be exploited to help understand the genetic regulation of these processes. In particular, we describe how genome-wide linkage scans and association studies, in conjunction with haplotype-based approaches, are potentially useful tools to increase our understanding of these two complex traits. Discovery of the genetic variants that regulate these two traits would expand our understanding of human neuroendocrinology, postnatal development, and the general architecture of complex genetic traits.

Adult↗

Variance component methods for detecting complex trait loci.

Variance component-based linkage analysis has become a major statistical tool for the localization and evaluation of quantitative trait loci influencing complex phenotypes. The variance component approach has many benefits--it can, for example, be used to analyze large pedigrees, and it is able to accommodate multiple loci simultaneously in a true oligogenic model. Important biological phenomena such as genotype-environment interaction and epistasis are also examined easily in a variance component framework. In this chapter, we review the basic statistical features of variance component linkage analysis, with an emphasis on its power and robustness to distributional violations.

Analysis of Variance↗

Admixture in the Hispanics of the San Luis Valley, Colorado, and its implications for complex trait gene mapping.

Hispanic populations are a valuable resource that can and should facilitate the identification of complex trait genes by means of admixture mapping (AM). In this paper we focus on a particular Hispanic population living in the San Luis Valley (SLV) in Southern Colorado. We used a set of 22 Ancestry Informative Markers (AIMs) to describe the admixture process and dynamics in this population. AIMs are defined as genetic markers that exhibit allele frequency differences between parental populations >or=30%, and are more informative for studying admixed populations than random markers. The ancestral proportions of the SLV Hispanic population are estimated as 62.7 +/- 2.1% European, 34.1 +/- 1.9% Native American and 3.2 +/- 1.5% West African. We also estimated the ancestral proportions of individuals using these AIMs. Population structure was demonstrated by the excess association of unlinked markers, the correlation between estimates of admixture based on unlinked marker sets, and by a highly significant correlation between individual Native American ancestry and skin pigmentation (R2= 0.082, p < 0.001). We discuss the implications of these findings in disease gene mapping efforts.

Adult↗

Linkage mapping of a complex trait in the New York population of the GAW14 simulated dataset: a multivariate phenotype approach.

Multivariate phenotypes underlie complex traits. Thus, instead of using the end-point trait, it may be statistically more powerful to use a multivariate phenotype correlated to the end-point trait for detecting linkage. In this study, we develop a reverse regression method to analyze linkage of Kofendrerd Personality Disorder affection status in the New York population of the Genetic Analysis Workshop 14 (GAW14) simulated dataset. When we used the multivariate phenotype, we obtained significant evidence of linkage near four of the six putative loci in at least 25% of the replicates. On the other hand, the linkage analysis based on Kofendrerd Personality Disorder status as a phenotype produced significant findings only near two of the loci and in a smaller proportion of replicates.

Chromosome Mapping↗

The genetic structure of recombinant inbred mice: high-resolution consensus maps for complex trait analysis.

BACKGROUND: Recombinant inbred (RI) strains of mice are an important resource used to map and analyze complex traits. They have proved particularly effective in multidisciplinary genetic studies. Widespread use of RI strains has been hampered by their modest numbers and by the difficulty of combining results derived from different RI sets. RESULTS: We have increased the density of typed microsatellite markers two- to five-fold in each of several major RI sets that share C57BL/6 as a parental strain (AXB, BXA, BXD, BXH and CXB). A common set of 490 markers was genotyped in just over 100 RI strains. Genotypes of around 1,100 additional microsatellites in one or more RI sets were generated, collected and checked for errors. Consensus RI maps that integrate genotypes of approximately 1,600 microsatellite loci were assembled. The genomes of individual strains typically incorporate 45-55 recombination breakpoints. The collected RI set - termed the BXN set - contains approximately 5,000 breakpoints. The distribution of recombinations approximates a Poisson distribution and distances between breakpoints average about 0.5 centimorgans (cM). Locations of most breakpoints have been defined with a precision of < 2 cM. Genotypes deviate from Hardy-Weinberg equilibrium in only a small number of intervals. CONCLUSIONS: Consensus maps derived from RI strains conform almost exactly to theoretical expectation and are close to the length predicted by the Haldane-Waddington equation (x3.6 for a 2-3 cM interval between markers). Non-syntenic associations between different chromosomes introduce predictable distortions in quantitative trait locus (QTL) datasets that can be partly corrected using two-locus correlation matrices.

Alleles↗

Comparison of model-free linkage mapping strategies for the study of a complex trait.

We compared several strategies for identifying and estimating effects from a genetic locus in the etiology of a complex trait. For our analyses we used data from simulated trait 1 and chromosome 5. Results from analysis of the first 20 replicates showed that a components of variance test provided considerably better power for identifying linkage than tests that consider pair differences. We also compared the power from constructing tests with a single marker, an approximate method using five markers jointly, or a multipoint analysis using all 25 markers on chromosome 5 jointly. Results from this analysis showed substantially better power when all markers were jointly used in the analysis. Results from considering all replicates showed that all methods of estimation provided maximal test statistics at the correct marker position, but the components of variance procedure provided more power to detect the correct position than other methods.

Analysis of Variance↗

New approaches to investigating heterogeneity in complex traits.

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.

Base Sequence↗

Two-locus approach of segregation and linkage analysis in the study of complex traits.

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.

Alleles↗

Identification and characterization of aluminum tolerance loci in Arabidopsis (Landsberg erecta x Columbia) by quantitative trait locus mapping. A physiologically simple but genetically complex trait.

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.

Aluminum↗

Score test for detecting linkage to complex traits in selected samples.

We present a unified approach to selection and linkage analysis of selected samples, for both quantitative and dichotomous complex traits. It is based on the score test for the variance attributable to the trait locus and applies to general pedigrees. The method is equivalent to regressing excess IBD sharing on a function of the traits. It is shown that when population parameters for the trait are known, such inversion does not entail any loss of information. For dichotomous traits, pairs of pedigree members of different phenotypic nature (e.g., affected sib pairs and discordant sib pairs) can easily be combined as well as populations with different trait prevalences.

Analysis of Variance↗

Statistical multilocus methods for disequilibrium analysis in complex traits.

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.

Alleles↗

A power study of bivariate LOD score analysis of a complex trait and fear/discomfort with strangers.

Complex diseases are often reported along with disease-related traits (DRT). Sometimes investigators consider both disease and DRT phenotypes separately and sometimes they consider individuals as affected if they have either the disease or the DRT, or both. We propose instead to consider the joint distribution of the disease and the DRT and do a linkage analysis assuming a pleiotropic model. We evaluated our results through analysis of the simulated datasets provided by Genetic Analysis Workshop 14. We first conducted univariate linkage analysis of the simulated disease, Kofendrerd Personality Disorder and one of its simulated associated traits, phenotype b (fear/discomfort with strangers). Subsequently, we considered the bivariate phenotype, which combined the information on Kofendrerd Personality Disorder and fear/discomfort with strangers. We developed a program to perform bivariate linkage analysis using an extension to the Elston-Stewart peeling method of likelihood calculation. Using this program we considered the microsatellites within 30 cM of the gene pleiotropic for this simulated disease and DRT. Based on 100 simulations of 300 families we observed excellent power to detect linkage within 10 cM of the disease locus using the DRT and the bivariate trait.

Databases, Genetic↗

How useful is the fine-scale mapping of complex trait linkage peaks? Evaluating the impact of additional microsatellite genotyping on the posterior probability of linkage.

The two-stage linkage mapping protocol for complex traits (a primary genome scan with low marker density followed by the high-density genotyping around linkage peaks) is a near-universal practice. The behavior (an increase or a decrease) of the peak upon such fine mapping frequently leads to inferences regarding the veracity of the primary scan finding, namely a true, or a false, positive. We examined by simulation, under the null hypothesis of no linkage and the alternative hypothesis of true linkage, the inferences that can be made regarding the posterior probability of linkage given either a peak increase, or alternatively, a peak decrease, following fine mapping. We considered different models of missing genotype data, fine-mapping LOD score thresholds, and prior probabilities of linkage. Our simulations show that evidence for linkage can increase frequently upon fine mapping under both null and alternative hypotheses, although large increases in LOD scores are more common under the alternative hypothesis. Increased LOD scores accompany an increased posterior probability of linkage, and large LOD score changes and the presence of dominance at the trait locus accentuate this effect. We demonstrate that the greatest changes in the posterior probability of linkage occur when the genotyping data are least complete (and especially when parental genotypes are missing), and the LOD score threshold for fine mapping is relaxed.

Algorithms↗

A bias-ed assessment of the use of SNPs in human complex traits.

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.

Bias↗

Combining mouse congenic strains and microarray gene expression analyses to study a complex trait: the NOD model of type 1 diabetes.

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.

Animals↗

Complex traits on the map.

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).

Alleles↗

Mapping genes for polygenic disorders: considerations for study design in the complex trait of inflammatory bowel disease.

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.

Alleles↗