Multivariate QTL linkage analysis suggests a QTL for platelet count on chromosome 19q

It's in the Bloody Genes!

Blood cell concentrations for most cell types are highly heritable. Data from Nick Martin's twin registry provided much of the data for the early heritability and linkage studies of blood cell related traits and have contributed significantly to more recent genomewide association studies that have successfully identified individual genetic loci.

Genetics of platelet traits in ischaemic stroke: focus on mean platelet volume and platelet count

Purpose/Aim of the study: The aim of this review is to summarize the role of genetic variants affecting mean platelet volume (MPV) and platelet count (PLT) leading to higher platelet reactivity and in turn to thrombotic events like stroke and cardiovascular diseases.

MATERIALS AND METHODS

A search was conducted in PUBMED, MEDLINE, EMBASE, PROQUEST, Science Direct, Cochrane Library, and Google Scholar related to the studies focussing on genome-wide association studies (GWAS), whole exome sequencing (WES), whole genome sequencing (WGS), phenome-wide association studies (PheWAS) and multi-omic analysis that have been employed to identify the genetic variants influencing MPV and PLT.

RESULTS

Antiplatelet agents underscore the crucial role of platelets in the pathogenesis of stroke. Higher platelet reactivity in terms of mean platelet volume (MPV) and platelet count (PLT) contributes significantly to the interindividual variation in platelet reaction at the site of vessel wall injury. Some individuals encounter thrombotic events as platelets get occluded at the site of vessel wall injury whereas others heal the injury without occluding the circulation. Evidence suggests that MPV and PLT have a strong genetic component. High throughput techniques including genome-wide association studies (GWAS), whole exome sequencing (WES), whole genome sequencing (WGS), phenome-wide association studies (PheWAS) and multi-omic analysis have identified different genetic variants influencing MPV and PLT.

CONCLUSIONS

Identification of complex genetic cross talks affecting PLT and MPV might help to develop novel treatment strategies in treating neurovascular diseases like stroke.

The genetics of platelet count and volume in humans

The last decade has witnessed an explosion in the depth, variety, and amount of human genetic data that can be generated. This revolution in technical and analytical capacities has enabled the genetic investigation of human traits and disease in thousands to now millions of participants. Investigators have taken advantage of these advancements to gain insight into platelet biology and the platelet's role in human disease. To do so, large human genetics studies have examined the association of genetic variation with two quantitative traits measured in many population and patient based cohorts: platelet count (PLT) and mean platelet volume (MPV). This article will review the many human genetic strategies-ranging from genome-wide association study (GWAS), Exomechip, whole exome sequencing (WES), to whole genome sequencing (WGS)-employed to identify genes and variants that contribute to platelet traits. Additionally, we will discuss how these investigations have examined and interpreted the functional implications of these newly identified genetic factors and whether they also impart risk to human disease. The depth and size of genetic, phenotypic, and other -omic data are primed to continue their growth in the coming years and provide unprecedented opportunities to gain critical insights into platelet biology and how platelets contribute to disease.

The genetics of common variation affecting platelet development, function and pharmaceutical targeting

Common variant effects on human platelet function and response to anti-platelet treatment have traditionally been studied using candidate gene approaches involving a limited number of variants and genes. These studies have often been undertaken in clinically defined cohorts. More recently, studies have applied genome-wide scans in larger population samples than prior candidate studies, in some cases scanning relatively healthy individuals. These studies demonstrate synergy with some prior candidate gene findings (e.g., GP6, ADRA2A) but also uncover novel loci involved in platelet function. Here, I summarise findings on common genetic variation influencing platelet development, function and therapeutics. Taken together, candidate gene and genome-wide studies begin to account for common variation in platelet function and provide information that may ultimately be useful in pharmacogenetic applications in the clinic. More than 50 loci have been identified with consistent associations with platelet phenotypes in ≥ 2 populations. Several variants are under further study in clinical trials relating to anti-platelet therapies. In order to have useful clinical applications, variants must have large effects on a modifiable outcome. Regardless of clinical applications, studies of common genetic influences, even of small effect, offer additional insights into platelet biology including the importance of intracellular signalling and novel receptors. Understanding of common platelet-related genetics remains behind parallel fields (e.g., lipids, blood pressure) due to challenges in phenotype ascertainment. Further work is necessary to discover and characterise loci for platelet function, and to assess whether these loci contribute to disease aetiologies or response to therapeutics.

A framework for structural equation models in general pedigrees

BACKGROUND/AIMS

Structural Equation Modeling (SEM) is an analysis approach that accounts for both the causal relationships between variables and the errors associated with the measurement of these variables. In this paper, a framework for implementing structural equation models (SEMs) in family data is proposed.

METHODS

This framework includes both a latent measurement model and a structural model with covariates. It allows for a wide variety of models, including latent growth curve models. Environmental, polygenic and other genetic variance components can be included in the SEM. Kronecker notation makes it easy to separate the SEM process from a familial correlation model. A limited information method of model fitting is discussed. We show how missing data and ascertainment may be handled. We give several examples of how the framework may be used.

RESULTS

A simulation study shows that our method is computationally feasible, and has good statistical properties.

CONCLUSION

Our framework may be used to build and compare causal models using family data without any genetic marker data. It also allows for a nearly endless array of genetic association and/or linkage tests. A preliminary Matlab program is available, and we are currently implementing a more complete and user-friendly R package.

Genetic regulation of platelet receptor expression and function: application in clinical practice and drug development

Understanding genetic contributions to platelet function could have profound clinical ramifications for personalizing platelet-directed pharmacotherapy, by providing insight into the risks and possible benefits associated with specific genotypes. This article represents an integrated summary of presentations related to genetic regulation of platelet receptor expression and function given at the Fifth Annual Platelet Colloquium in January 2010. It is supplemented with additional highlights from the literature covering (1) approaches to determining and evidence for the associations of genetic variants with platelet hypo- and hyperresponsive phenotypes, (2) the ramifications of these polymorphisms with regard to clinical responses to antiplatelet therapies, and (3) the role of platelet function/genetic testing in guiding antiplatelet therapy.

Reconsidering the asymptotic null distribution of likelihood ratio tests for genetic linkage in multivariate variance components models under complete pleiotropy

Accurate knowledge of the null distribution of hypothesis tests is important for valid application of the tests. In previous papers and software, the asymptotic null distribution of likelihood ratio tests for detecting genetic linkage in multivariate variance components models has been stated to be a mixture of chi-square distributions with binomial mixing probabilities. For variance components models under the complete pleiotropy assumption, we show by simulation and by theoretical arguments based on the geometry of the parameter space that all aspects of the previously stated asymptotic null distribution are incorrect-both the binomial mixing probabilities and the chi-square components. Correcting the null distribution gives more conservative critical values than previously stated, yielding P values that can easily be 10 times larger. The true mixing probabilities give the highest probability to the case where all variance parameters are estimated positive, and the mixing components show severe departures from chi-square distributions. Thus, the asymptotic null distribution has complex features that raise challenges for the assessment of significance of multivariate linkage findings. We propose a method to generate an asymptotic null distribution that is much faster than other empirical methods such as permutation, enabling us to obtain P values with higher precision more efficiently.

Calculating asymptotic significance levels of the constrained likelihood ratio test with application to multivariate genetic linkage analysis

The asymptotic distribution of the multivariate variance component linkage analysis likelihood ratio test has provoked some contradictory accounts in the literature. In this paper we confirm that some previous results are not correct by deriving the asymptotic distribution in one special case. It is shown that this special case is a good approximation to the distribution in many situations. We also introduce a new approach to simulating from the asymptotic distribution of the likelihood ratio test statistic in constrained testing problems. It is shown that this method is very efficient for small p-values, and is applicable even when the constraints are not convex. The method is related to a multivariate integration problem. We illustrate how the approach can be applied to multivariate linkage analysis in a simulation study. Some more philosophical issues relating to one-sided tests in variance components linkage analysis are discussed.

A genome-wide scan for Eysenckian personality dimensions in adolescent twin sibships: psychoticism, extraversion, neuroticism, and lie

We report the first genome-wide scan of adolescent personality. We conducted a genome-wide scan to detect linkage for measures of adolescent Psychoticism, Extraversion, Neuroticism, and Lie from the Junior Eysenck Personality Questionnaire. Data are based on 1,280 genotyped Australian adolescent twins and their siblings. The highest linkage peaks were found on chromosomes 16 and 19 for Neuroticism, on chromosomes 1, 7, 10, 13 m, and 18 for Psychoticism, and on chromosomes 2 and 3 for Extraversion.

Quantitative trait loci for porcine white blood cells and platelet-related traits in a White Duroc x Erhualian F resource population

White blood cell count and platelets are implicated as risk factors for common complex diseases. Genetic factors substantially affect these traits in humans and mice. However, little is known about the genetic architecture of these traits in pigs. To identify quantitative trait loci (QTL) for leucocyte- and platelet-related traits in pigs, the total leucocyte number and differential leucocyte counts including the fraction of basophils, eosinophils, lymphocytes, monocytes, neutrophils, and a series of platelet parameters including platelet count, mean platelet volume, platelet distribution width and plateletcrit were measured in 1033 F(2) animals on 240 days from a White Duroc x Erhualian intercross resource population. A total of 183 informative microsatellites distributed across 19 pig chromosomes (SSC) were genotyped across the entire resource population. Thirty-three QTL were identified for the examined traits, including eight genome-wide significant QTL for white blood cells and differential leucocyte counts on SSC2, 7, 8, 12 and 15 and six significant QTL for platelet-related traits on SSC2, 8, 13 and X. Erhualian or White Duroc alleles were not systematically associated with increased phenotypic values. These results not only confirmed many QTL identified previously in the mouse and swine, but also revealed a number of novel QTL for the traits recorded. Moreover, it is the first time that QTL for platelet-related traits in pigs have been reported.

QTLs identified for P3 amplitude in a non-clinical sample: importance of neurodevelopmental and neurotransmitter genes

BACKGROUND

The P3(00) event-related potential is an index of processing capacity (P3 amplitude) and stimulus evaluation (P3 latency) as well as a phenotypic marker of various forms of psychopathology where P3 abnormalities have been reported.

METHODS

A genome-wide linkage scan of 400-761 autosomal markers, at an average spacing of 5-10 centimorgans (cM), was completed in 647 twins/siblings (306 families mostly comprising dizygotic twins), mean age 16.3, range 15.4-20.1 years, for whom P3 amplitude and latency data were available.

RESULTS

Significant linkage for P3 amplitude was observed on chromosome 7q for the central recording site (logarithm-of-odds [LOD] = 3.88, p = .00002) and in the same region for both frontal (LOD = 2.19, p = .0015) and parietal (LOD = 1.67, p = .0053) sites, with multivariate analysis also identifying linkage in this region (LOD = 2.14, p = .0017). Suggestive linkage was also identified on 6p (LOD(max) = 2.49) and 12q (LOD(max) = 2.24), with other promising regions identified on 9q (LOD(max) = 2.14) and 10p (LOD(max) = 2.18). Less striking were the results for P3 latency; LOD > 1.5 were found on chromosomes 1q, 9q, 10q, 12q, and 19p.

CONCLUSIONS

This is a first step in the identification of genes for normal variation in the P3. Loci identified here for P3 amplitude suggest the possible importance of neurodevelopmental genes in addition to those influencing neurotransmitters, fitting with the evidence that P3 amplitude is sensitive to diverse types of brain abnormalities.

The HBS1L-MYB intergenic region on chromosome 6q23.3 influences erythrocyte, platelet, and monocyte counts in humans

Common sequence variants situated between the HBS1L and MYB genes on chromosome 6q23.3 (HMIP) influence the proportion of F cells (erythrocytes that carry measurable amounts of fetal hemoglobin). Since the physiological processes underlying the F-cell variability are thought to be linked to kinetics of erythrocyte maturation and differentiation, we have investigated the influence of the HMIP locus on other hematologic parameters. Here we show a significant impact of HMIP variability on several types of peripheral blood cells: erythrocyte, platelet, and monocyte counts as well as erythrocyte volume and hemoglobin content in healthy individuals of European ancestry. These results support the notion that changes of F-cell abundance can be an indicator of more general shifts in hematopoietic patterns in humans.

Quantitative trait loci for peripheral blood cell counts: a study in baboons

Increasingly, baseline peripheral blood cell counts are implicated as risk factors for common complex diseases. While genetic influences on these hematologic parameters are firmly established, the genetic architecture of the blood counts is still poorly understood. In this article we used data from 582 healthy pedigreed baboons and variance components methods to localize quantitative trait loci (QTLs) influencing complete blood count variables. Besides performing genome-wide linkage scans for each trait individually, we conducted bivariate linkage analyses for all pairwise trait combinations to also identify pleiotropic QTLs influencing several blood counts. While significant and suggestive QTLs were localized throughout the genome (LOD range: 1.5-3.5), chromosomal regions associated with the expression of various hematologic parameters stand out. In particular, our results provide significant and consistent evidence for a QTL on the orthologous human chromosome 1p that is shared by several blood counts, mainly erythrocyte parameters. In addition, multiple suggestive evidence of linkage was detected on the orthologous human chromosomes 10 (near the q-terminus) and 19 (centromeric section). Future studies should help identify the genes responsible for these QTL and elucidate their role on baseline variation in hematologic indicators of health and disease.

Regression-based multivariate linkage analysis with an application to blood pressure and body mass index

Multivariate linkage analysis has been suggested for the analysis of correlated traits, such as blood pressure (BP) and body mass index (BMI), because it may offer greater power and provide clearer results than univariate analyses. Currently, the most commonly used multivariate linkage methods are extensions of the univariate variance component model. One concern about those methods is their inherent sensitivity to the assumption of multivariate normality which cannot be easily guaranteed in practice. Another problem possibly related to all multivariate linkage analysis methods is the difficulty in interpreting nominal p-values, because the asymptotic distribution of the test statistic has not been well characterized. Here we propose a regression-based multivariate linkage method in which a robust score statistic is used to detect linkage. The p-value of the statistic is evaluated by a simple and rapid simulation procedure. Theoretically, this method can be used for any number and type of traits and for general pedigree data. We apply this approach to a genome linkage analysis of blood pressure and body mass index data from the Beaver Dam Eye Study.

Two candidate genes for low platelet count identified in an Asian Indian kindred by genome-wide linkage analysis: glycoprotein IX and thrombopoietin

A genome-wide linkage analysis of platelet count was carried out in a large Asian Indian kindred. Linkage analysis showed one marker (D3S1309) on chromosome 3q with a lod score of 3.26 and another (D3S1282) approximately 30 cM centromeric, with a lod score of 2.52. Multipoint analysis of chromosome 3q identified two peaks with maximum multipoint lod scores of 3.52 and 4.11 under markers D3S1309 and D3S1282, respectively. Two strong candidate genes for platelet variation were identified in the linked region; thrombopoietin (THPO) and glycoprotein IX (GPIX). Resequencing of four individuals revealed five single-nucleotide polymorphisms (SNPs) in THPO and one mutation in the transmembrane region of GPIX. Analysis of variance showed that the GPIX mutation and one THPO SNP accounted for 6 and 4% of the variation in platelet count, respectively. The THPO SNP lies in the 3' untranslated region of the gene and has not been previously reported. The G to A transition at nucleotide 653 resulted in an Ala 156 (GCC) to Thr (ACC) replacement in the GPIX protein. The GPIX mutation was recently identified in a Chinese patient with Bernard-Soulier syndrome (BSS), a rare recessive bleeding disorder characterized by thrombocytopenia and giant platelets. One copy of the GPIX mutation was found in 300 European individuals with platelet counts within the normal range. The results suggest that two QTLs on chromosome 3q influence platelet count variation in the Asian Indian kindred, with the GPIX transmembrane mutation and the 3' UTR SNP in THPO being strong candidates.

Fishing for Pleiotropic QTLs in a Polygenic Sea

The application of factor analysis to human genetics has the potential to discover the coordinated control of multiple traits by common environment, common polygenes, or a single major gene. Classical factor analysis explains the covariation among the components of a random vector by approximating the vector by a linear transformation of a small number of uncorrelated factors. In the current paper we show how factor analysis dovetails with the classical variance decompositions of biometrical genetics. To explore the relationships between related quantitative variables, and avoid complicated positive definiteness constraints, we employ Cholesky and factor analytic decompositions. We derive an ECM algorithm and a competing quasi-Newton algorithm for estimating parameters by maximum likelihood and propose tactics for selecting initial parameter values. We also show how parameter asymptotic standard errors under these parameterizations propagate to asymptotic standard errors of the underlying variance components. Our genetic analysis program Mendel, which now incorporates the program Fisher, has performed well on a variety of data sets. We illustrate our methods, algorithms, and models on two data sets: a bivariate quantitative genetic example using total finger ridge count data and a multivariate linkage example using insulin resistance data.