Multi-Trait Exome-Wide Association Study of Back Pain-Related Phenotypes

Back pain (BP) is a major contributor to disability worldwide, with heritability estimated at 40-60%. However, less than half of the heritability is explained by common genetic variants identified by genome-wide association studies. More powerful methods and rare and ultra-rare variant analysis may offer additional insight. This study utilized exome sequencing data from the UK Biobank to perform a multi-trait gene-based association analysis of three BP-related phenotypes: chronic back pain, dorsalgia, and intervertebral disc disorder. We identified the SLC13A1 gene as a contributor to chronic back pain via loss-of-function (LoF) and missense variants. This gene has been previously detected in two studies. A multi-trait approach uncovered the novel FSCN3 gene and its impact on back pain through LoF variants. This gene deserves attention because it is only the second gene shown to have an effect on back pain due to LoF variants and represents a promising drug target for back pain therapy.

Noncoding rare variants in PANX3 are associated with chronic back pain

ABSTRACT

Back pain is the leading cause of years lived with disability worldwide, yet surprisingly, little is known regarding the biology underlying this condition. The impact of genetics is known for chronic back pain: its heritability is estimated to be at least 40%. Large genome-wide association studies have shown that common variation may account for up to 35% of chronic back pain heritability; rare variants may explain a portion of the heritability not explained by common variants. In this study, we performed the first gene-based association analysis of chronic back pain using UK Biobank imputed data including rare variants with moderate imputation quality. We discovered 2 genes, SOX5 and PANX3 , influencing chronic back pain. The SOX5 gene is a well-known back pain gene. The PANX3 gene has not previously been described as having a role in chronic back pain. We showed that the association of PANX3 with chronic back pain is driven by rare noncoding intronic polymorphisms. This result was replicated in an independent sample from UK Biobank and validated using a similar phenotype, dorsalgia, from FinnGen Biobank. We also found that the PANX3 gene is associated with intervertebral disk disorders. We can speculate that a possible mechanism of action of PANX3 on back pain is due to its effect on the intervertebral disks.

Loci Associated with Negative Heterosis for Viability and Meat Productivity in Interspecific Sheep Hybrids

Negative heterosis can occur on different economically important traits, but the exact biological mechanisms of this phenomenon are still unknown. The present study focuses on determining the genetic factors associated with negative heterosis in interspecific hybrids between domestic sheep (Ovis aries) and argali (Ovis ammon). One locus (rs417431015) associated with viability and two loci (rs413302370, rs402808951) associated with meat productivity were identified. One gene (ARAP2) was prioritized for viability and three for meat productivity (PDE2A, ARAP1, and PCDH15). The loci associated with meat productivity were demonstrated to fit the overdominant inheritance model and could potentially be involved int negative heterosis mechanisms.

A Novel Framework for Analysis of the Shared Genetic Background of Correlated Traits

We propose a novel effective framework for the analysis of the shared genetic background for a set of genetically correlated traits using SNP-level GWAS summary statistics. This framework called SHAHER is based on the construction of a linear combination of traits by maximizing the proportion of its genetic variance explained by the shared genetic factors. SHAHER requires only full GWAS summary statistics and matrices of genetic and phenotypic correlations between traits as inputs. Our framework allows both shared and unshared genetic factors to be effectively analyzed. We tested our framework using simulation studies, compared it with previous developments, and assessed its performance using three real datasets: anthropometric traits, psychiatric conditions and lipid concentrations. SHAHER is versatile and applicable to summary statistics from GWASs with arbitrary sample sizes and sample overlaps, allows for the incorporation of different GWAS models (Cox, linear and logistic), and is computationally fast.

The limits of normal approximation for adult height

Adult height inspired the first biometrical and quantitative genetic studies and is a test-case trait for understanding heritability. The studies of height led to formulation of the classical polygenic model, that has a profound influence on the way we view and analyse complex traits. An essential part of the classical model is an assumption of additivity of effects and normality of the distribution of the residuals. However, it may be expected that the normal approximation will become insufficient in bigger studies. Here, we demonstrate that when the height of hundreds of thousands of individuals is analysed, the model complexity needs to be increased to include non-additive interactions between sex, environment and genes. Alternatively, the use of log-normal approximation allowed us to still use the additive effects model. These findings are important for future genetic and methodologic studies that make use of adult height as an exemplar trait.

Gene-based association analysis identifies 190 genes affecting neuroticism

Neuroticism is a personality trait, which is an important risk factor for psychiatric disorders. Recent genome-wide studies reported about 600 genes potentially influencing neuroticism. Little is known about the mechanisms of their action. Here, we aimed to conduct a more detailed analysis of genes that can regulate the level of neuroticism. Using UK Biobank-based GWAS summary statistics, we performed a gene-based association analysis using four sets of within-gene variants, each set possessing specific protein-coding properties. To guard against the influence of strong GWAS signals outside the gene, we used a specially designed procedure called “polygene pruning”. As a result, we identified 190 genes associated with neuroticism due to the effect of within-gene variants rather than strong GWAS signals outside the gene. Thirty eight of these genes are new. Within all genes identified, we distinguished two slightly overlapping groups obtained from using protein-coding and non-coding variants. Many genes in the former group included potentially pathogenic variants. For some genes in the latter group, we found evidence of pleiotropy with gene expression. Using a bioinformatics analysis, we prioritized the neuroticism genes and showed that the genes that contribute to neuroticism through their within-gene variants are the most appropriate candidate genes.

Gene-based association tests using GWAS summary statistics

MOTIVATION

A huge number of genome-wide association studies (GWAS) summary statistics freely available in databases provide a new material for gene-based association analysis aimed at identifying rare genetic variants. Only a few of the many popular gene-based methods developed for individual genotype and phenotype data are adapted for the practical use of the GWAS summary statistics as input.

RESULTS

We analytically prove and numerically illustrate that all popular powerful methods developed for gene-based association analysis of individual phenotype and genotype data can be modified to utilize GWAS summary statistics. We have modified and implemented all of the popular methods, including burden and kernel machine-based tests, multiple and functional linear regression, principal components analysis and others, in the R package sumFREGAT. Using real summary statistics for coronary artery disease, we show that the new package is able to detect genes not found by the existing packages.

AVAILABILITY AND IMPLEMENTATION

The R package sumFREGAT is freely and publicly available at: https://CRAN.R-project.org/package=sumFREGAT.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A combined linkage, microarray and exome analysis suggests MAP3K11 as a candidate gene for left ventricular hypertrophy

BACKGROUND

Electrocardiographic measures of left ventricular hypertrophy (LVH) are used as predictors of cardiovascular risk. We combined linkage and association analyses to discover novel rare genetic variants involved in three such measures and two principal components derived from them.

METHODS

The study was conducted among participants from the Erasmus Rucphen Family Study (ERF), a Dutch family-based sample from the southwestern Netherlands. Variance components linkage analyses were performed using Merlin. Regions of interest (LOD > 1.9) were fine-mapped using microarray and exome sequence data.

RESULTS

We observed one significant LOD score for the second principal component on chromosome 15 (LOD score = 3.01) and 12 suggestive LOD scores. Several loci contained variants identified in GWAS for these traits; however, these did not explain the linkage peaks, nor did other common variants. Exome sequence data identified two associated variants after multiple testing corrections were applied.

CONCLUSIONS

We did not find common SNPs explaining these linkage signals. Exome sequencing uncovered a relatively rare variant in MAPK3K11 on chromosome 11 (MAF = 0.01) that helped account for the suggestive linkage peak observed for the first principal component. Conditional analysis revealed a drop in LOD from 2.01 to 0.88 for MAP3K11, suggesting that this variant may partially explain the linkage signal at this chromosomal location. MAP3K11 is related to the JNK pathway and is a pro-apoptotic kinase that plays an important role in the induction of cardiomyocyte apoptosis in various pathologies, including LVH.

Weighted functional linear regression models for gene-based association analysis

Functional linear regression models are effectively used in gene-based association analysis of complex traits. These models combine information about individual genetic variants, taking into account their positions and reducing the influence of noise and/or observation errors. To increase the power of methods, where several differently informative components are combined, weights are introduced to give the advantage to more informative components. Allele-specific weights have been introduced to collapsing and kernel-based approaches to gene-based association analysis. Here we have for the first time introduced weights to functional linear regression models adapted for both independent and family samples. Using data simulated on the basis of GAW17 genotypes and weights defined by allele frequencies via the beta distribution, we demonstrated that type I errors correspond to declared values and that increasing the weights of causal variants allows the power of functional linear models to be increased. We applied the new method to real data on blood pressure from the ORCADES sample. Five of the six known genes with P < 0.1 in at least one analysis had lower P values with weighted models. Moreover, we found an association between diastolic blood pressure and the VMP1 gene (P = 8.18×10-6), when we used a weighted functional model. For this gene, the unweighted functional and weighted kernel-based models had P = 0.004 and 0.006, respectively. The new method has been implemented in the program package FREGAT, which is freely available at https://cran.r-project.org/web/packages/FREGAT/index.html.

Nonsynonymous Variation in NKPD1 Increases Depressive Symptoms in European Populations

BACKGROUND

Despite high heritability, little success was achieved in mapping genetic determinants of depression-related traits by means of genome-wide association studies.

METHODS

To identify genes associated with depressive symptomology, we performed a gene-based association analysis of nonsynonymous variation captured using exome-sequencing and exome-chip genotyping in a genetically isolated population from the Netherlands (n = 1999). Finally, we reproduced our significant findings in an independent population-based cohort (n = 1604).

RESULTS

We detected significant association of depressive symptoms with a gene NKPD1 (p = 3.7 × 10^-08). Nonsynonymous variants in the gene explained 0.9% of sex- and age-adjusted variance of depressive symptoms in the discovery study, which is translated into 3.8% of the total estimated heritability (h² = 0.24). Significant association of depressive symptoms with NKPD1 was also observed (n = 1604; p = 1.5 × 10^-03) in the independent replication sample despite little overlap with the discovery cohort in the set of nonsynonymous genetic variants observed in the NKPD1 gene. Meta-analysis of the discovery and replication studies improved the association signal (p = 1.0 × 10^-09).

CONCLUSIONS

Our study suggests that nonsynonymous variation in the gene NKPD1 affects depressive symptoms in the general population. NKPD1 is predicted to be involved in the de novo synthesis of sphingolipids, which have been implicated in the pathogenesis of depression.

A Combined Linkage and Exome Sequencing Analysis for Electrocardiogram Parameters in the Erasmus Rucphen Family Study

Electrocardiogram (ECG) measurements play a key role in the diagnosis and prediction of cardiac arrhythmias and sudden cardiac death. ECG parameters, such as the PR, QRS, and QT intervals, are known to be heritable and genome-wide association studies of these phenotypes have been successful in identifying common variants; however, a large proportion of the genetic variability of these traits remains to be elucidated. The aim of this study was to discover loci potentially harboring rare variants utilizing variance component linkage analysis in 1547 individuals from a large family-based study, the Erasmus Rucphen Family Study (ERF). Linked regions were further explored using exome sequencing. Five suggestive linkage peaks were identified: two for QT interval (1q24, LOD = 2.63; 2q34, LOD = 2.05), one for QRS interval (1p35, LOD = 2.52) and two for PR interval (9p22, LOD = 2.20; 14q11, LOD = 2.29). Fine-mapping using exome sequence data identified a C > G missense variant (c.713C > G, p.Ser238Cys) in the FCRL2 gene associated with QT (rs74608430; P = 2.8 × 10^-4, minor allele frequency = 0.019). Heritability analysis demonstrated that the SNP explained 2.42% of the trait’s genetic variability in ERF (P = 0.02). Pathway analysis suggested that the gene is involved in cytosolic Ca²⁺ levels (P = 3.3 × 10^-3) and AMPK stimulated fatty acid oxidation in muscle (P = 4.1 × 10^-3). Look-ups in bioinformatics resources showed that expression of FCRL2 is associated with ARHGAP24 and SETBP1 expression. This finding was not replicated in the Rotterdam study. Combining the bioinformatics information with the association and linkage analyses, FCRL2 emerges as a strong candidate gene for QT interval.

[Functional linear models for region-based association analysis]

Regional association analysis is one of the most powerful tools for gene mapping because instead analysis of individual variants it simultaneously considers all variants in the region. Recent development of the models for regional association analysis involves functional data analysis approach. In the framework of this approach, genotypes of variants within region as well as their effects are described by continuous functions. Such approach allows us to use information about both linkage and linkage disequilibrium and reduce the influence of noise and/or observation errors. Here we define a functional linear mixed model to test association on independent and structured samples. We demonstrate how to test fixed and random effects of a set of genetic variants in the region on quantitative trait. Estimation of statistical properties of new methods shows that type I errors are in accordance with declared values and power is high especially for models with fixed effects of genotypes. We suppose that new functional regression linear models facilitate identification of rare genetic variants controlling complex human and animal traits. New methods are implemented in computer software FREGAT which is available for free download at http://mga.bionet.nsc.ru/soft/FREGAT/.

Some pitfalls in application of functional data analysis approach to association studies

One of the most effective methods for gene-based mapping employs functional data analysis, which smoothes data using standard basis functions. The full functional linear model includes a functional representation of genotypes and their effects, while the beta-smooth only model smoothes the genotype effects only. Benefits and limitations of the beta-smooth only model should be studied before using it in practice. Here we analytically compare the full and beta-smooth only models under various scenarios. We show that when the full model employs two sets of basis functions equal in type and number, genotypes smoothing is eliminated from the model and it becomes analytically equivalent to the beta-smooth only model. If the basis functions differ only in type, genotypes smoothing is also eliminated from the full model, but the type of basis functions used for smoothing genotype effects becomes redefined. This leads to misinterpretation of the results and may reduce statistical power. When basis functions differ in number, no analytical comparison of the full and beta-smooth only models is possible. However, we show that the numbers of basis functions set unequal can become equal during the analysis, and the full model becomes disadvantageous.

FREGAT: an R package for region-based association analysis

Several approaches to the region-based association analysis of quantitative traits have recently been developed and successively applied. However, no software package has been developed that implements all of these approaches for either independent or structured samples. Here we introduce FREGAT (Family REGional Association Tests), an R package that can handle family and population samples and implements a wide range of region-based association methods including burden tests, functional linear models, and kernel machine-based regression. FREGAT can be used in genome/exome-wide region-based association studies of quantitative traits and candidate gene analysis. FREGAT offers many useful options to empower its users and increase the effectiveness and applicability of region-based association analysis.

AVAILABILITY AND IMPLEMENTATION

https://cran.r-project.org/web/packages/FREGAT/index.html

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics Online.

CONTACT

belon@bionet.nsc.ru.

Region-Based Association Test for Familial Data under Functional Linear Models

Region-based association analysis is a more powerful tool for gene mapping than testing of individual genetic variants, particularly for rare genetic variants. The most powerful methods for regional mapping are based on the functional data analysis approach, which assumes that the regional genome of an individual may be considered as a continuous stochastic function that contains information about both linkage and linkage disequilibrium. Here, we extend this powerful approach, earlier applied only to independent samples, to the samples of related individuals. To this end, we additionally include a random polygene effects in functional linear model used for testing association between quantitative traits and multiple genetic variants in the region. We compare the statistical power of different methods using Genetic Analysis Workshop 17 mini-exome family data and a wide range of simulation scenarios. Our method increases the power of regional association analysis of quantitative traits compared with burden-based and kernel-based methods for the majority of the scenarios. In addition, we estimate the statistical power of our method using regions with small number of genetic variants, and show that our method retains its advantage over burden-based and kernel-based methods in this case as well. The new method is implemented as the R-function 'famFLM' using two types of basis functions: the B-spline and Fourier bases. We compare the properties of the new method using models that differ from each other in the type of their function basis. The models based on the Fourier basis functions have an advantage in terms of speed and power over the models that use the B-spline basis functions and those that combine B-spline and Fourier basis functions. The 'famFLM' function is distributed under GPLv3 license and is freely available at http://mga.bionet.nsc.ru/soft/famFLM/.

FFBSKAT: fast family-based sequence kernel association test

The kernel machine-based regression is an efficient approach to region-based association analysis aimed at identification of rare genetic variants. However, this method is computationally complex. The running time of kernel-based association analysis becomes especially long for samples with genetic (sub) structures, thus increasing the need to develop new and effective methods, algorithms, and software packages. We have developed a new R-package called fast family-based sequence kernel association test (FFBSKAT) for analysis of quantitative traits in samples of related individuals. This software implements a score-based variance component test to assess the association of a given set of single nucleotide polymorphisms with a continuous phenotype. We compared the performance of our software with that of two existing software for family-based sequence kernel association testing, namely, ASKAT and famSKAT, using the Genetic Analysis Workshop 17 family sample. Results demonstrate that FFBSKAT is several times faster than other available programs. In addition, the calculations of the three-compared software were similarly accurate. With respect to the available analysis modes, we combined the advantages of both ASKAT and famSKAT and added new options to empower FFBSKAT users. The FFBSKAT package is fast, user-friendly, and provides an easy-to-use method to perform whole-exome kernel machine-based regression association analysis of quantitative traits in samples of related individuals. The FFBSKAT package, along with its manual, is available for free download at http://mga.bionet.nsc.ru/soft/FFBSKAT/.

Development and application of genomic control methods for genome-wide association studies using non-additive models

Genome-wide association studies (GWAS) comprise a powerful tool for mapping genes of complex traits. However, an inflation of the test statistic can occur because of population substructure or cryptic relatedness, which could cause spurious associations. If information on a large number of genetic markers is available, adjusting the analysis results by using the method of genomic control (GC) is possible. GC was originally proposed to correct the Cochran-Armitage additive trend test. For non-additive models, correction has been shown to depend on allele frequencies. Therefore, usage of GC is limited to situations where allele frequencies of null markers and candidate markers are matched.

In this work, we extended the capabilities of the GC method for non-additive models, which allows us to use null markers with arbitrary allele frequencies for GC. Analytical expressions for the inflation of a test statistic describing its dependency on allele frequency and several population parameters were obtained for recessive, dominant, and over-dominant models of inheritance. We proposed a method to estimate these required population parameters. Furthermore, we suggested a GC method based on approximation of the correction coefficient by a polynomial of allele frequency and described procedures to correct the genotypic (two degrees of freedom) test for cases when the model of inheritance is unknown. Statistical properties of the described methods were investigated using simulated and real data. We demonstrated that all considered methods were effective in controlling type 1 error in the presence of genetic substructure. The proposed GC methods can be applied to statistical tests for GWAS with various models of inheritance. All methods developed and tested in this work were implemented using R language as a part of the GenABEL package.

Region-based association analysis of human quantitative traits in related individuals

Regional-based association analysis instead of individual testing of each SNP was introduced in genome-wide association studies to increase the power of gene mapping, especially for rare genetic variants. For regional association tests, the kernel machine-based regression approach was recently proposed as a more powerful alternative to collapsing-based methods. However, the vast majority of existing algorithms and software for the kernel machine-based regression are applicable only to unrelated samples. In this paper, we present a new method for the kernel machine-based regression association analysis of quantitative traits in samples of related individuals. The method is based on the GRAMMAR+ transformation of phenotypes of related individuals, followed by use of existing kernel machine-based regression software for unrelated samples. We compared the performance of kernel-based association analysis on the material of the Genetic Analysis Workshop 17 family sample and real human data by using our transformation, the original untransformed trait, and environmental residuals. We demonstrated that only the GRAMMAR+ transformation produced type I errors close to the nominal value and that this method had the highest empirical power. The new method can be applied to analysis of related samples by using existing software for kernel-based association analysis developed for unrelated samples.

Refining genome-wide linkage intervals using a meta-analysis of genome-wide association studies identifies loci influencing personality dimensions

Personality traits are complex phenotypes related to psychosomatic health. Individually, various gene finding methods have not achieved much success in finding genetic variants associated with personality traits. We performed a meta-analysis of four genome-wide linkage scans (N=6149 subjects) of five basic personality traits assessed with the NEO Five-Factor Inventory. We compared the significant regions from the meta-analysis of linkage scans with the results of a meta-analysis of genome-wide association studies (GWAS) (N∼17 000). We found significant evidence of linkage of neuroticism to chromosome 3p14 (rs1490265, LOD=4.67) and to chromosome 19q13 (rs628604, LOD=3.55); of extraversion to 14q32 (ATGG002, LOD=3.3); and of agreeableness to 3p25 (rs709160, LOD=3.67) and to two adjacent regions on chromosome 15, including 15q13 (rs970408, LOD=4.07) and 15q14 (rs1055356, LOD=3.52) in the individual scans. In the meta-analysis, we found strong evidence of linkage of extraversion to 4q34, 9q34, 10q24 and 11q22, openness to 2p25, 3q26, 9p21, 11q24, 15q26 and 19q13 and agreeableness to 4q34 and 19p13. Significant evidence of association in the GWAS was detected between openness and rs677035 at 11q24 (P-value=2.6 × 10(-06), KCNJ1). The findings of our linkage meta-analysis and those of the GWAS suggest that 11q24 is a susceptible locus for openness, with KCNJ1 as the possible candidate gene.

A genome-wide linkage study of individuals with high scores on NEO personality traits

The NEO-Five-Factor Inventory divides human personality traits into five dimensions: neuroticism, extraversion, openness, conscientiousness and agreeableness. In this study, we sought to identify regions harboring genes with large effects on the five NEO personality traits by performing genome-wide linkage analysis of individuals scoring in the extremes of these traits (>90th percentile). Affected-only linkage analysis was performed using an Illumina 6K linkage array in a family-based study, the Erasmus Rucphen Family study. We subsequently determined whether distinct, segregating haplotypes found with linkage analysis were associated with the trait of interest in the population. Finally, a dense single-nucleotide polymorphism genotyping array (Illumina 318K) was used to search for copy number variations (CNVs) in the associated regions. In the families with extreme phenotype scores, we found significant evidence of linkage for conscientiousness to 20p13 (rs1434789, log of odds (LOD)=5.86) and suggestive evidence of linkage (LOD >2.8) for neuroticism to 19q, 21q and 22q, extraversion to 1p, 1q, 9p and12q, openness to 12q and 19q, and agreeableness to 2p, 6q, 17q and 21q. Further analysis determined haplotypes in 21q22 for neuroticism (P-values = 0.009, 0.007), in 17q24 for agreeableness (marginal P-value = 0.018) and in 20p13 for conscientiousness (marginal P-values = 0.058, 0.038) segregating in families with large contributions to the LOD scores. No evidence for CNVs in any of the associated regions was found. Our findings imply that there may be genes with relatively large effects involved in personality traits, which may be identified with next-generation sequencing techniques.

Rapid variance components-based method for whole-genome association analysis

The variance component tests used in genome-wide association studies (GWAS) including large sample sizes become computationally exhaustive when the number of genetic markers is over a few hundred thousand. We present an extremely fast variance components-based two-step method, GRAMMAR-Gamma, developed as an analytical approximation within a framework of the score test approach. Using simulated and real human GWAS data sets, we show that this method provides unbiased estimates of the SNP effect and has a power close to that of the likelihood ratio test-based method. The computational complexity of our method is close to its theoretical minimum, that is, to the complexity of the analysis that ignores genetic structure. The running time of our method linearly depends on sample size, whereas this dependency is quadratic for other existing methods. Simulations suggest that GRAMMAR-Gamma may be used for association testing in whole-genome resequencing studies of large human cohorts.

Linkage analysis for plasma amyloid beta levels in persons with hypertension implicates Aβ-40 levels to presenilin 2

Plasma concentrations of Aβ40 and Aβ42 rise with age and are increased in people with mutations that cause early-onset Alzheimer's disease (AD). Amyloid beta (Aβ) plasma levels were successfully used as an (endo)phenotype for gene discovery using a linkage approach in families with dominant forms of disease. Here, we searched for loci involved in Aβ plasma levels in a series of non-demented patients with hypertension in the Erasmus Rucphen Family study. Aβ40 and Aβ42 levels were determined in 125 subjects with severe hypertension. All patients were genotyped with a 6,000 single nucleotide polymorphisms (SNPs) illumina array designed for linkage analysis. We conducted linkage analysis of plasma Aβ levels. None of the linkage analyses yielded genome-wide significant logarithm of odds (LOD) score over 3.3, but there was suggestive evidence for linkage (LOD > 1.9) for two regions: 1q41 (LOD = 2.07) and 11q14.3 (LOD = 2.97), both for Aβ40. These regions were followed up with association analysis in the study subjects and in 320 subjects from a population-based cohort. For the Aβ40 region on chromosome 1, association of several SNPs was observed at the presenilin 2 gene (PSEN2) (p = 2.58 × 10(-4) for rs6703170). On chromosome 11q14-21, we found some association (p = 3.1 × 10(-3) for rs2514299). This linkage study of plasma concentrations of Aβ40 and Aβ42 yielded two suggestive regions, of which one points toward a known locus for familial AD.

Ped_Outlier software for automatic identification of within-family outliers

A high-throughput resequencing technology has brought family based studies back into genetic research focus. Within-family outliers (the individuals whose phenotype is very much unlike the phenotype of relatives) may carry rare variants of large effects and thus resequencing of these provides a highly powered strategy for rare variants detection. On the other hand, such outliers may complicate search for common variants of smaller effects, because they may obscure a real linkage signal. We have developed a program Ped_Outlier allowing automatic detection of within-family outliers in a sample of pedigrees of arbitrary structure and size. We tested our program by identification of within-family outliers for adult height and intracranial volume in large pedigree. Results of linkage analysis of these traits demonstrated that identification of within-family outliers is one of the important steps of pedigree analysis. The program Ped_outlier is freely available at http://mga.bionet.nsc.ru/soft/index.html.

MQScore_SNP software for multipoint parametric linkage analysis of quantitative traits in large pedigrees

We describe software for multipoint parametric linkage analysis of quantitative traits using information about SNP genotypes. A mixed model of major gene and polygene inheritance is implemented in this software. Implementation of several algorithms to avoid computational underflow and decrease running time permits application of our software to the analysis of very large pedigrees collected in human genetically isolated populations. We tested our software by performing linkage analysis of adult height in a large pedigree from a Dutch isolated population. Three significant and four suggestive loci were identified with the help of our programs, whereas variance-component-based linkage analysis, which requires the pedigree fragmentation, demonstrated only three suggestive peaks. The software package MQScore_SNP is available at http://mga.bionet.nsc.ru/soft/index.html.

A powerful genome-wide feasible approach to detect parent-of-origin effects in studies of quantitative traits

There is currently a lot of interest in the role of genomic imprinting in mammalian development. Many human diseases, such as cancer, obesity, diabetes and behavioral traits, may be related to imprinted genes. When searching for genes related to complex disorders, the power of genome-wide association analysis can be improved by introducing parent-of-origin effects into the analyses. For quantitative traits, family-based TDT analysis has successfully implemented such an approach. Although attractive for several reasons, TDT-based tests are known to be less powerful than methods based on measured genotype approaches. In this study, we describe a fast, powerful method for detecting parent-of-origin effects in studies of quantitative traits using a measured genotype framework. First, for each locus studied, we estimate the probabilities of an allele's parental origin using multipoint haplotype reconstruction. Next, we introduce the parental origin of these alleles as a covariate in regression models during the second step of GRAMMAR, a fast approximation to the measured genotype approach. We show that, compared with a TDT-based analysis, our method has a higher power to detect a locus exhibiting a parent-of-origin effect. Moreover, our method is applicable to a wider range of data, including pedigree structures that are not very informative for TDT. The method gives no false positives in the absence of parent-of-origin effects, under both additive and dominant models. As this method is an extension of the rapid GRAMMAR analysis, it is fast enough to be suitable for genome-wide association scans.

Genetic determinants of circulating sphingolipid concentrations in European populations

Sphingolipids have essential roles as structural components of cell membranes and in cell signalling, and disruption of their metabolism causes several diseases, with diverse neurological, psychiatric, and metabolic consequences. Increasingly, variants within a few of the genes that encode enzymes involved in sphingolipid metabolism are being associated with complex disease phenotypes. Direct experimental evidence supports a role of specific sphingolipid species in several common complex chronic disease processes including atherosclerotic plaque formation, myocardial infarction (MI), cardiomyopathy, pancreatic beta-cell failure, insulin resistance, and type 2 diabetes mellitus. Therefore, sphingolipids represent novel and important intermediate phenotypes for genetic analysis, yet little is known about the major genetic variants that influence their circulating levels in the general population. We performed a genome-wide association study (GWAS) between 318,237 single-nucleotide polymorphisms (SNPs) and levels of circulating sphingomyelin (SM), dihydrosphingomyelin (Dih-SM), ceramide (Cer), and glucosylceramide (GluCer) single lipid species (33 traits); and 43 matched metabolite ratios measured in 4,400 subjects from five diverse European populations. Associated variants (32) in five genomic regions were identified with genome-wide significant corrected p-values ranging down to 9.08x10(-66). The strongest associations were observed in or near 7 genes functionally involved in ceramide biosynthesis and trafficking: SPTLC3, LASS4, SGPP1, ATP10D, and FADS1-3. Variants in 3 loci (ATP10D, FADS3, and SPTLC3) associate with MI in a series of three German MI studies. An additional 70 variants across 23 candidate genes involved in sphingolipid-metabolizing pathways also demonstrate association (p = 10(-4) or less). Circulating concentrations of several key components in sphingolipid metabolism are thus under strong genetic control, and variants in these loci can be tested for a role in the development of common cardiovascular, metabolic, neurological, and psychiatric diseases.

PedStr software for cutting large pedigrees for haplotyping, IBD computation and multipoint linkage analysis

We propose an automatic heuristic algorithm for splitting large pedigrees into fragments of no more than a user-specified bit size. The algorithm specifically aims to split large pedigrees where many close relatives are genotyped and to produce a set of sub-pedigrees for haplotype reconstruction, IBD computation or multipoint linkage analysis with the help of the Lander-Green-Kruglyak algorithm. We demonstrate that a set of overlapping pedigree fragments constructed with the help of our algorithm allows fast and effective haplotype reconstruction and detection of an allele's parental origin. Moreover, we compared pedigree fragments constructed with the help of our algorithm and existing programs PedCut and Jenti for multipoint linkage analysis. Our algorithm demonstrated significantly higher linkage power than the algorithm of Jenti and significantly shorter running time than the algorithm of PedCut. The software package PedStr implementing our algorithms is available at http://mga.bionet.nsc.ru/soft/index.html.

Predicting human height by Victorian and genomic methods

In the Victorian era, Sir Francis Galton showed that 'when dealing with the transmission of stature from parents to children, the average height of the two parents, ... is all we need care to know about them' (1886). One hundred and twenty-two years after Galton's work was published, 54 loci showing strong statistical evidence for association to human height were described, providing us with potential genomic means of human height prediction. In a population-based study of 5748 people, we find that a 54-loci genomic profile explained 4-6% of the sex- and age-adjusted height variance, and had limited ability to discriminate tall/short people, as characterized by the area under the receiver-operating characteristic curve (AUC). In a family-based study of 550 people, with both parents having height measurements, we find that the Galtonian mid-parental prediction method explained 40% of the sex- and age-adjusted height variance, and showed high discriminative accuracy. We have also explored how much variance a genomic profile should explain to reach certain AUC values. For highly heritable traits such as height, we conclude that in applications in which parental phenotypic information is available (eg, medicine), the Victorian Galton's method will long stay unsurpassed, in terms of both discriminative accuracy and costs. For less heritable traits, and in situations in which parental information is not available (eg, forensics), genomic methods may provide an alternative, given that the variants determining an essential proportion of the trait's variation can be identified.

An approach for cutting large and complex pedigrees for linkage analysis

Utilizing large pedigrees in linkage analysis is a computationally challenging task. The pedigree size limits applicability of the Lander-Green-Kruglyak algorithm for linkage analysis. A common solution is to split large pedigrees into smaller computable subunits. We present a pedigree-splitting method that, within a user supplied bit-size limit, identifies subpedigrees having the maximal number of subjects of interest (eg patients) who share a common ancestor. We compare our method with the maximum clique partitioning method using a large and complex human pedigree consisting of 50 patients with Alzheimer's disease ascertained from genetically isolated Dutch population. We show that under a bit-size limit our method can assign more patients to subpedigrees than the clique partitioning method, particularly when splitting deep pedigrees where the subjects of interest are scattered in recent generations and are relatively distantly related via multiple genealogic connections. Our pedigree-splitting algorithm and associated software can facilitate genome-wide linkage scans searching for rare mutations in large pedigrees coming from genetically isolated populations. The software package PedCut implementing our approach is available at http://mga.bionet.nsc.ru/soft/index.html.

Breaking loops in large complex pedigrees

For pedigrees with multiple loops, exact likelihoods could not be computed in an acceptable time frame and thus, approximate methods are used. Some of these methods are based on breaking loops and approximations of complex pedigree likelihoods using the exact likelihood of the corresponding zero-loop pedigree. Due to ignoring loops, this method results in a loss of genetic information and a decrease in the power to detect linkage. To minimize this loss, an optimal set of loop breakers has to be selected. In this paper, we present a graph theory based algorithm for automatic selection of an optimal set of loop breakers. We propose using a total relationship between measured pedigree members as a proxy to power. To minimize the loss of genetic information, we suggest selection of such breakers whose duplication in a pedigree would be accompanied by a minimal loss of total relationship between measured pedigree members. We show that our algorithm compares favorably with other existing loop-breaker selection algorithms in terms of conservation of genetic information, statistical power and CPU time of subsequent linkage analysis. We implemented our method in a software package LOOP_EDGE, which is available at http://mga.bionet.nsc.ru/nlru/.

A genomewide screen for late-onset Alzheimer disease in a genetically isolated Dutch population

Alzheimer disease (AD) is the most common cause of dementia. We conducted a genome screen of 103 patients with late-onset AD who were ascertained as part of the Genetic Research in Isolated Populations (GRIP) program that is conducted in a recently isolated population from the southwestern area of The Netherlands. All patients and their 170 closely related relatives were genotyped using 402 microsatellite markers. Extensive genealogy information was collected, which resulted in an extremely large and complex pedigree of 4,645 members. The pedigree was split into 35 subpedigrees, to reduce the computational burden of linkage analysis. Simulations aiming to evaluate the effect of pedigree splitting on false-positive probabilities showed that a LOD score of 3.64 corresponds to 5% genomewide type I error. Multipoint analysis revealed four significant and one suggestive linkage peaks. The strongest evidence of linkage was found for chromosome 1q21 (heterogeneity LOD [HLOD]=5.20 at marker D1S498). Approximately 30 cM upstream of this locus, we found another peak at 1q25 (HLOD=4.0 at marker D1S218). These two loci are in a previously established linkage region. We also confirmed the AD locus at 10q22-24 (HLOD=4.15 at marker D10S185). There was significant evidence of linkage of AD to chromosome 3q22-24 (HLOD=4.44 at marker D3S1569). For chromosome 11q24-25, there was suggestive evidence of linkage (HLOD=3.29 at marker D11S1320). We next tested for association between cognitive function and 4,173 single-nucleotide polymorphisms in the linked regions in an independent sample consisting of 197 individuals from the GRIP region. After adjusting for multiple testing, we were able to detect significant associations for cognitive function in four of five AD-linked regions, including the new region on chromosome 3q22-24 and regions 1q25, 10q22-24, and 11q25. With use of cognitive function as an endophenotype of AD, our study indicates the that the RGSL2, RALGPS2, and C1orf49 genes are the potential disease-causing genes at 1q25. Our analysis of chromosome 10q22-24 points to the HTR7, MPHOSPH1, and CYP2C cluster. This is the first genomewide screen that showed significant linkage to chromosome 3q23 markers. For this region, our analysis identified the NMNAT3 and CLSTN2 genes. Our findings confirm linkage to chromosome 11q25. We were unable to confirm SORL1; instead, our analysis points to the OPCML and HNT genes.

Optimal peeling order for pedigrees with incomplete genotypic information

The likelihood approach is common in linkage analysis of large extended pedigrees. Various peeling procedures, based on the conditional independence of separate parts of a pedigree, are typically used for likelihood calculations. A peeling order may significantly affect the complexity of such calculations, particularly for pedigrees with loops or when many pedigrees members have unknown genotypes. Several algorithms have been proposed to address this problem for pedigrees with loops. However, the problem has not been solved for pedigrees without loops until now. In this paper, we suggest a new graph theoretic algorithm for optimal selection of peeling order in zero-loop pedigrees with incomplete genotypic information. It is especially useful when multiple likelihood calculation is needed, for example, when genetic parameters are estimated or linkage with multiple marker loci is tested. The algorithm can be easily introduced into the existing software packages for linkage analysis based on the Elston-Stewart algorithm for likelihood calculation. The algorithm was implemented in a software package PedPeel, which is freely available at http://mga.bionet.nsc.ru/nlru/.

Heritability of blood pressure traits and the genetic contribution to blood pressure variance explained by four blood-pressure-related genes

OBJECTIVE

To study the heritability of four blood pressure traits and the proportion of variance explained by four blood-pressure-related genes.

METHODS

All participants are members of an extended pedigree from a Dutch genetically isolated population. Heritability and genetic correlations of systolic blood pressure, diastolic blood pressure, mean arterial pressure and pulse pressure were assessed using a variance components approach (SOLAR). Polymorphisms of the alpha-adducin (ADD1), angiotensinogen (AGT), angiotensin II type 1 receptor (AT1R) and G protein beta3 (GNB3) genes were typed.

RESULTS

Heritability estimates were significant for all four blood pressure traits, ranging between 0.24 and 0.37. Genetic correlations between systolic blood pressure, diastolic blood pressure and mean arterial pressure were high (0.93-0.98), and those between pulse pressure and diastolic blood pressure were low (0.05). The ADD1 polymorphism explained 0.3% of the variance of pulse pressure (P = 0.07), and the polymorphism of GNB3 explained 0.4% of the variance of systolic blood pressure (P = 0.02), 0.2% of mean arterial pressure (P = 0.05) and 0.3% of pulse pressure (P = 0.06).

CONCLUSION

Genetic factors contribute to a substantial proportion of blood pressure variance. In this study, the effect of polymorphisms of ADD1, AGT, AT1R and GNB3 explained a very small proportion of the heritability of blood pressure traits. As new genes associated with blood pressure are localized in the future, their effect on blood pressure variance should be calculated.

Inheritance of litter size at birth in farmed arctic foxes (Alopex lagopus, Canidae, Carnivora)

Natural populations of the arctic fox (Alopex lagopus, Canidae, Carnivora) differ drastically in their reproductive strategy. Coastal foxes, which depend on stable food resources, produce litters of moderate size. Inland foxes feed on small rodents, whose populations are characterized by cycling fluctuation. In the years with low food supply, inland fox populations have a very low rate of reproduction. In the years with high food supply, they undergo a population explosion. To gain insight into the genetic basis of the reproductive strategy of this species, we performed complex segregation analysis of the litter size in the extended pedigree of the farmed arctic foxes involving 20,665 interrelated animals. Complex segregation analysis was performed using a mixed model assuming that the trait was under control of a major gene and a large number of additive genetic and random factors. To check the significance of any major gene effect, we used Elston-Stewart transmission probability test. Our analysis demonstrated that the inheritance of this trait can be described within the frameworks of a major gene model with recessive control of low litter size. This model was also supported by the pattern of its familial segregation and by comparison of the distributions observed in the population and that expected under our model. We suggest that a system of balanced polymorphism for litter size in the farmed population might have been established in natural populations of arctic foxes as a result of adaptation to the drastic fluctuations in prey availability.

Solution for underflow problem in linkage and segregation analysis

Finding genes for complex traits is one of the major challenges of modern human genetics. Current developments of molecular techniques facilitated use of large pedigrees and marker sets of thousands of single-nucleotide polymorphisms (SNPs). However, one of the problems occurring in statistical analysis of such large data sets is that the likelihood is very low and underflow may easily occur. In this work we describe a method permitting to avoid underflow during computation of a likelihood function, using different algorithms. Our method makes practically possible analysis of thousands of individuals and thousands of SNPs. The method is easy to implement without major change of the code of existing programs. It also helps to reduce the amount of computer memory used in analysis without noticeable alteration of the program running time. The algorithm was implemented in the software packages for segregation and linkage analysis, which are available from http://mga.bionet.nsc.ru/.

Inheritance of White Head Spotting in Natural Populations of South American Water Rat (Nectomys squamipes Rodentia: Sigmodontinae)

Specimens with white head spots are present at low frequency in the natural populations of South American water rat (Nectomys squamipes) and absent in the sibling species Nectomys rattus. We analyzed the pattern of inheritance of the phenotype using complex segregation analysis of pedigrees of a captive-bred population of N. squamipes. We found that the inheritance of the white head spot in this species can be described within the framework of the major gene recessive model with incomplete penetrance of genotypes.

miLD and booLD programs for calculation and analysis of corrected linkage disequilibrium

We describe software which calculates a set of linkage disequilibrium statistics, including multiallelic D' corrected by the bootstrap and permutation. The software also provides a tool for maximum likelihood and least squares estimation and testing of a set of hierarchical hypotheses formulated within the framework of the Malecot model of the decay of linkage disequilibrium with distance. Additionally, the bootstrap approach is used for estimation of the model parameter's confidence intervals and for hypothesis testing. The programs are available from http://www.geneticepi.com/Research/software/software.html

Inheritance of litter size at birth in the Brazilian grass mouse (Akodon cursor, Sigmodontinae, Rodentia)

By means of complex segregation analysis we studied the inheritance of litter size in two large pedigrees of captive-bred colonies of the Brazilian grass mouse Akodon cursor. Genetic analysis has revealed a highly significant influence of genetic factors on the variation of litter size (heritability, h2, was estimated as 0.44). The inheritance followed the classical polygene model: neither the major-gene model nor the polygene with unequal contribution model described the data significantly better.

Segregation analysis of Scheuermann disease in ninety families from Siberia

Scheuermann disease [OMIM number 181440] is the most common cause of structural kyphosis in adolescence. Segregation analysis using a model with gender effects was applied to 90 pedigrees from Barnaul (West Siberia, Russia) ascertained through a proband with Scheuermann disease. The transmission probability model was used to detect major gene effect. A significant contribution of a major gene to the control of the pathology was established. Inheritance of the disease can be described within the framework of a dominant major gene diallele model. According to this model, Scheuermann disease should never occur in the absence of the mutant allele. All male carriers of the mutant allele develop the disease, while only a half of female carriers manifest it. We found a high frequency of idiopathic scoliosis in the families with Scheuermann disease (0.08 vs. 0.01-0.02 in general population). We also observed a succession of idiopathic scoliosis and Scheuermann disease in consecutive generations. The familial aggregation of these two spinal pathologies in the present sample may indicate a genetic unity of Scheuermann disease and idiopathic scoliosis.

Sample size required for predefined linkage decision quality

A method for estimating the sample size required to attain a predefined linkage decision quality (type I and type II errors) is proposed using the linkage test power estimate developed by Ginsburg et al. [(1996) Genet Epidemiol 13:355-366]. The method is applicable for samples of arbitrarily structured pedigrees collected via proband. Comparison of different ascertainment schemes and pedigree structures by their consequent minimal sample size was performed. For recessive and dominant inheritance with complete penetrance, the relative ranks of the ascertainment schemes are invariant regardless of the true recombination fraction value and the trait and marker gene frequencies, which enables one to point out the better scheme. The feasibility of evaluating a sampling strategy by the cost of pedigree collection is also considered, and comparison between these two methods of sample planning is performed.

Inheritance of pendulum movements in rats

The stereotyped hyperkinesis referred to as pendulum movements (PM) may be found in up to 50% of the animals in stocks of Wistar rats. The mode of inheritance of predisposition to PM was studied by two methods: 1) a classical Mendelian analysis of hybrids of the strains PM+ and PM- bred from Wistar stock for enhancement and absence of PM, respectively, and 2) a segregation analysis of pedigree data from the archive records of breeding the cataleptic GC strain. The two methods gave the same result: the inheritance of predisposition to PM can be explained by a major gene model with an incomplete penetrance of heterozygous genotype.

Segregation analysis of idiopathic scoliosis: demonstration of a major gene effect

Segregation analysis using a model with age and gender effects was applied to 101 pedigrees ascertained through a proband with idiopathic scoliosis. The transmission probability model was used to detect major gene effect. When we analyzed the pedigrees where affected status was assigned to persons with a Cobb's angle of more than 5 degrees we did not detect a significant major gene effect. However, when the affected status was assigned to persons with pronounced forms of disease only (a curve of at least 11 degrees) a significant contribution of a major causal gene could be established and inheritance could be described according to a dominant major gene diallele model, assuming incomplete sex and age dependent penetrance of genotypes. According to this model, the pronounced forms of idiopathic scoliosis should never occur in the absence of the mutant allele. This indicates that only the carriers of the mutant allele develop pronounced forms of the disease. At the same time, only a fraction of the carriers of the mutant gene should manifest the disease (30% of males and 50% of females).

Inheritance of male hybrid sterility in the house musk shrew (Suncus murinus, Insectivora, Soricidae)

Two geographic races of the house musk shrew (Suncus murinus) were crossed and intercrossed in the laboratory. Many cases of male sterility were detected among the hybrids. Segregation analysis of the pedigree data showed that the inheritance of male sterility in interracial hybrids of S. murinus can be described within the framework of monogene polyallele model with sterility of a single allele combination. This model is similar if not identical to that proposed by Dobzhansky and Muller.

Chromosomal segregation and fertility in Robertsonian chromosomal heterozygotes of the house musk shrew (Suncus murinus, Insectivora, Soricidae)

Crucial to our understanding of chromosomal variation and evolution in mammals are detailed studies of chromosomal heterozygotes, with analyses of chromosomal segregation and chromosome-derived infertility. We studied segregation and fertility in hybrids between karyotypic races of the house musk shrew Suncus murinus. These individuals were heterozygous for up to five Robertsonian fusions (Rbs) and an insertion of heterochromatin in an autosome. All variant chromosomes showed Mendelian segregation and all Rbs segregated independently of each other in the progeny of double heterozygotes. Litter size in single and even multiple Rb heterozygotes was no smaller than that in the less fertile parental strain. The effects of genetic background were more important in determining litter size than Rb heterozygosity for the shrews that we examined.

Inheritance of litter size at birth in the house musk shrew (Suncus murinus, Insectivora, Soricidae)

In this research we estimated the contribution of a major-gene effect to the control of litter size in hybrids between two local populations of the house musk shrew (Suncus murinus). Segregation analysis was performed on the basis of a mixed polygene and major-gene model. The model presumes that two parental populations may differ from each other in gene frequencies and in the values of polygenic effects but not in the major-gene contribution of the trait. Moreover, the peculiarity of the trait--litter size--is taken into account. This trait is not an individual attribute. It characterizes the parental couple and may depend on the genotypes of both parents. Results of segregation analysis of a large hybrid pedigree of Suncus murinus indicate that the parental populations differ in the allele frequency of the major gene (one population is homozygous, while the other contains the two alleles in approximately equal proportions) and in the values of average polygenic effects. Both major-gene and polygenic components are necessary for the correct description of litter size inheritance in interracial hybrids of S murinus, inasmuch as the exclusion of either of them leads to a significant drop in likelihood. The Elston-Stewart criterion also confirms the Mendelian inheritance of the major gene.

Segregation analysis of animal pedigree data from inter-population crosses

The general method of segregation analysis of pedigree data has been developed and widely used in human genetics. We modified this method to examine pedigree data coming from inter-population crosses. These kinds of pedigrees are common in laboratory and farm animal breeding. This paper describes a rationale for the method and illustrates its application to the study of inheritance of litter size and of male sterility in hybrid stock of the house musk shrew (Suncus murinus) derived from crosses of two geographically isolated populations.

On estimation of linkage test power

Two methods of estimating linkage test power are proposed. The first method is based on the maximum likelihood (ML) estimate of the recombination fraction and is intended for use with a likelihood ratio test (LRT) in the form of a chi 2 or lod score. The power is estimated through a noncentral chi 2 distribution with a specially chosen noncentrality parameter. The second method uses the LRT constructed for a simple alternative hypothesis regarding the recombination fraction value. The approximate distribution of this test and a method of estimating its power is proposed. Using simulated pedigree data, the power estimates for these two methods were shown to be satisfactory. Comparisons among these two methods and the computer simulation approach of Boehnke [1986] are performed.