Linkage of chromosome 1 markers to alcoholism-related phenotypes by sib pair linkage analysis of principal components

Genome-wide association analyses for boar taint components and testicular traits revealed regions having pleiotropic effects

Background

The aim of this study was to perform a genome-wide association analyses (GWAS) for androstenone, skatole and indole in different Pietrain sire lines and compare the results with previous findings in purebred populations. Furthermore, the genetic relationship of androstenone and skatole were investigated with respect to pleiotropy. In order to characterize the performance of intact boars, crossbred progenies of 136 Pietrain boars mated to crossbred sows from three different breeding companies were tested on four test stations. A total of 598 boars were performance tested according to the rules of stationary performance testing in Germany. Beside common fattening and carcass composition traits, the concentrations of the boar taint components and testicular size parameters were recorded. All boars were genotyped with the PorcineSNP60 Illumina BeadChip. The GWAS were performed using the whole data set as well as in sub groups according to the line of origin. Besides an univariate GWAS approach, principal component (PC) techniques were applied to identify common expression pattern affecting the biosynthesis and the metabolism of androstenone.

Results

In total, 33 SNPs were significantly associated with at least one of the boar taint components. Only one SNP was identified being significant in both subgroups. The analyses of the testes size parameters revealed 31 significant associations. The numbers of significant SNPs within the genetic groups evidenced the strong population specific effects. A multivariate approach using PC revealed 33 significant associations for five different PC.

Conclusions

Based on Pietrain sired cross bred boars, the mayor objective of our study was to identify QTL for boar taint components and to detect pleiotropy among boar taint and testes traits. The high number of identified QTL revealed that boar taint traits are influenced by a large number of loci. Analyzing pleiotropy allowed identifying a QTL affecting androstenone and the gonasomatic index. In this region, QTL for ovulation rate and age at puberty of sows have been described in literature. This supports the physiological findings that the androstenone level of boars and reproduction performance of sows might be linked by an antagonistic relationship.

Electronic supplementary material

The online version of this article (doi:10.1186/s12863-015-0194-z) contains supplementary material, which is available to authorized users.

Genetic linkage analysis of longitudinal hypertension phenotypes using three summary measures

BACKGROUND

Longitudinal data often have multiple (repeated) measures recorded along a time trajectory. For example, the two cohorts from the Framingham Heart Study (GAW13 Problem 1) contain 21 and 5 repeated measures for hypertension phenotypes as well as epidemiological risk factors, respectively. Direct modelling of a large number of serially and biologically correlated traits in the context of linkage analysis can be prohibitively complex. Alternatively, we may consider using univariate transformation for linkage analysis of longitudinal repeated measures.

RESULTS

We evaluated the utility of three conventional summary measures (mean, slope, and principal components) for genetic linkage analysis of longitudinal phenotypes by analyzing the chromosome 10 data of the Framingham Heart Study. Except for the temporal slope, all of the summary methods and the multivariate analysis identified the previously reported region, marker GATA64A09, for systolic blood pressure or high blood pressure. Further analysis revealed that this region may harbor gene(s) affecting human blood pressure at multiple stages of life.

CONCLUSION

We conclude that mean and principal components are feasible alternatives for genetic linkage analysis of longitudinal phenotypes, but the slope might have a separate genetic basis from that of the original longitudinal phenotypes.

Comparison of three methods for obtaining principal components from family data in genetic analysis of complex disease

Three multivariate techniques used to derive principal components (PCs) from family data were compared for their ability to model family data and power to detect linkage. Using the simulated data from Genetic Analysis Workshop 12, the five quantitative traits were first adjusted for age, sex, and environmental factors 1 and 2. Then, standard PCs, PCs obtained from between-family covariance, and PCs obtained from within-family genetic covariance were derived and subjected to multivariate sib pair linkage analysis. The standard PCs obtained from the overall correlation matrix allowed identification of key features of the true genetic model more readily than did the other methods. For detection of linkage, standard PCs and PCs obtained from the between-family genetic covariance performed similarly in terms of both power and type 1 error, and both methods performed better than the PCs obtained from within-family genetic covariance.

Linkage analysis of human systemic lupus erythematosus-related traits: a principal component approach

OBJECTIVE

To identify chromosomal regions containing genes involved in the susceptibility to human systemic lupus erythematosus (SLE)-related traits.

METHODS

In the context of a genome scan, we analyzed 101 SLE-affected sibpairs with respect to dermatologic, renal, immunologic, hematologic, neurologic, cardiopulmonary, and arthritic characteristics. Phenotypes were redefined in terms of principal components, which are synthetic variables composed of linear combinations of the original traits. Using 9 principal components obtained from these 7 traits plus age at SLE onset and race, we analyzed genome scan data with the multivariate version of the new Haseman-Elston regression model.

RESULTS

The largest linkage for an individual trait was on chromosome 2 at 228 cM (immunologic; P = 0.00048). The most significant linkage to an individual principal component was on chromosome 4 at 208 cM (P = 0.00007). The largest multivariate linkage was on chromosome 7 at 69 cM (P = 0.0001). Of the individual organ systems, dermatologic involvement had the largest effect (P = 0.0083) at this peak at 7p13 on chromosome 7. Further analyses revealed that malar rash, a subtype of dermatologic involvement, was linked significantly (P = 0.00458) to this location.

CONCLUSION

These results provide evidence of the presence and locations of genes that are involved in the genetic susceptibility to SLE-related traits in humans.

Power and robustness of a score test for linkage analysis of quantitative traits using identity by descent data on sib pairs

Identification of genes involved in complex traits by traditional (lod score) linkage analysis is difficult due to many complicating factors. An unfortunate drawback of non-parametric procedures in general, though, is their low power to detect genetic effects. Recently, Dudoit and Speed [2000] proposed using a (likelihood-based) score test for detecting linkage with IBD data on sib pairs. This method uses the likelihood for theta, the recombination fraction between a trait locus and a marker locus, conditional on the phenotypes of the two sibs to test the null hypothesis of no linkage (theta = (1/2)). Although a genetic model must be specified, the approach offers several advantages. This paper presents results of simulation studies characterizing the power and robustness properties of this score test for linkage, and compares the power of the test to the Haseman-Elston and modified Haseman-Elston tests. The score test is seen to have impressively high power across a broad range of true and assumed models, particularly under multiple ascertainment. Assuming an additive model with a moderate allele frequency, in the range of p = 0.2 to 0.5, along with heritability H = 0.3 and a moderate residual correlation rho = 0.2 resulted in a very good overall performance across a wide range of trait-generating models. Generally, our results indicate that this score test for linkage offers a high degree of protection against wrong assumptions due to its strong robustness when used with the recommended additive model.

Enhanced efficiency of quantitative trait loci mapping analysis based on multivariate complexes of quantitative traits

An approach to increase the efficiency of mapping quantitative trait loci (QTL) was proposed earlier by the authors on the basis of bivariate analysis of correlated traits. The power of QTL detection using the log-likelihood ratio (LOD scores) grows proportionally to the broad sense heritability. We found that this relationship holds also for correlated traits, so that an increased bivariate heritability implicates a higher LOD score, higher detection power, and better mapping resolution. However, the increased number of parameters to be estimated complicates the application of this approach when a large number of traits are considered simultaneously. Here we present a multivariate generalization of our previous two-trait QTL analysis. The proposed multivariate analogue of QTL contribution to the broad-sense heritability based on interval-specific calculation of eigenvalues and eigenvectors of the residual covariance matrix allows prediction of the expected QTL detection power and mapping resolution for any subset of the initial multivariate trait complex. Permutation technique allows chromosome-wise testing of significance for the whole trait complex and the significance of the contribution of individual traits owing to: (a) their correlation with other traits, (b) dependence on the chromosome in question, and (c) both a and b. An example of application of the proposed method on a real data set of 11 traits from an experiment performed on an F(2)/F(3) mapping population of tetraploid wheat (Triticum durum x T. dicoccoides) is provided.

Genetic dissection of complex traits: an overview

Genetic dissection of even simple Mendelian traits has been sufficiently challenging. Complex traits are proving to be much more challenging and frustrating than previously thought. The concepts, methods, and strategies discussed in this volume emphasize the critical importance of study design, appropriate methods of analysis, including relatively newer and emerging methods, and issues relating to the interpretation of results from genome scans; some thoughts on the future the new millennium holds are offered, as well. This chapter overviews the key steps involved in the study of complex traits, which are discussed in detail in subsequent chapters. It is suggested that a combination of lumping and splitting strategies is more appropriate for the analysis of complex traits, and large-scale collaborations should make this possible. For example, by pooling data and/or results from multiple studies on a given disease/trait, one may attain a sample size large enough to permit the division of the data into multiple relatively more homogeneous subgroups. The sample size of the subgroups may still be sufficiently large sample, but the genetic dissection within each subgroup should be much less daunting. The expectation is that analyses within subgroups will enhance gene finding, especially when any interacting determinants are taken into account at the time of dividing the data into subgroups. Perhaps the methods are not yet optimum, but the future holds much promise. In the meantime, the cutting-edge methods discussed in this volume by leading experts should help. There is an increasing healthy tendency for investigators to collaborate by pooling materials and results across studies, with the goal of increasing the sample size and thus the power. We believe that such efforts are essential for the genetic dissection of complex traits and should contribute to greater success, especially if there is a real commitment to meaningful collaboration. After all, for most complex traits, the question is not whether there are genes, only when and how they might be found.

Power of a score test for quantitative trait linkage analysis of relative pairs

The score test of Dudoit and Speed [(2000) Biostatistics 1:1-26] to detect linkage between a trait locus and a marker locus, using identity by descent data on sib pairs, is extended to other types of relative pairs (grandparent/grandchild, avuncular, and half-sib relationships). The test is based on the likelihood of the recombination fraction theta between trait and marker loci, conditional on phenotypes of the relatives. We present results of simulation studies characterizing power and robustness properties of this linkage score test, and compare the power of the score test to that of the classical and modified Haseman-Elston tests. The score test has considerable power, particularly under sampling schemes where selection is on double probands. Use of a generic additive model [Goldstein et al., submitted] with allele frequency p = 0.2, heritability H = 0.3, and a moderate residual correlation of rho = 0.2 resulted in a very good overall performance across a wide range of trait-generating models.

Genetics of event-related brain potentials in response to a semantic priming paradigm in families with a history of alcoholism

Event-related brain potentials (ERPs) are altered in patients with a variety of psychiatric disorders and may represent quantitative correlates of disease liability that are more amenable to genetic analysis than disease status itself. Results of a genomewide linkage screen are presented for amplitude of the N4 and P3 components of the ERP, measured at 19 scalp locations in response to a semantic priming task for 604 individuals in 100 pedigrees ascertained as part of the Collaborative Study on the Genetics of Alcoholism. N4 and P3 amplitudes in response to three stimuli (nonwords, primed words [i.e., antonyms], and unprimed words) all showed significant heritabilities, the highest being.54. Both N4 and P3 showed significant genetic correlations across stimulus type at a given lead and across leads within a stimulus, indicating shared genetic influences among the traits. There were also substantial genetic correlations between the N4 and P3 amplitudes for a given lead, even across stimulus type. N4 amplitudes showed suggestive evidence of linkage in several chromosomal regions, and P3 amplitudes showed significant evidence of linkage to chromosome 5 and suggestive evidence of linkage to chromosome 4.