GENLIB: new function to simulate haplotype transmission in large complex genealogies.

SUMMARY: Founder populations with deep genealogical data are well suited for investigating genetic variants contributing to diseases. Here, we present a major update of the genealogical analysis R package GENLIB, centered around a new function which can simulate the transmission of haplotypes from founders to probands along very large and complex user-specified genealogies. AVAILABILITY AND IMPLEMENTATION: The latest update of the GENLIB package (v1.1.9) contains the new gen.simuHaplo() function and is available on the CRAN repository and from https://github.com/R-GENLIB/GENLIB. Examples can be accessed at https://github.com/R-GENLIB/simuhaplo_functions.

A single historical substitution drives an increase in acetylcholine receptor complexity.

Human adult muscle-type acetylcholine receptors are heteropentameric ion channels formed from four different, but evolutionarily related, subunits. These subunits assemble with a precise stoichiometry and arrangement such that two chemically distinct agonist-binding sites are formed between specific subunit pairs. How this subunit complexity evolved and became entrenched is unclear. Here we show that a single historical amino acid substitution is able to constrain the subunit stoichiometry of functional acetylcholine receptors. Using a combination of ancestral sequence reconstruction, single-channel electrophysiology, and concatenated subunits, we reveal that an ancestral ß-subunit can not only replace the extant ß-subunit but can also supplant the neighboring δ-subunit. By forward evolving the ancestral ß-subunit with a single amino acid substitution, we restore the requirement for a δ-subunit for functional channels. These findings reveal that a single historical substitution necessitates an increase in acetylcholine receptor complexity and, more generally, that simple stepwise mutations can drive subunit entrenchment in this model heteromeric protein.

Comparison of mixed model based approaches for correcting for population substructure with application to extreme phenotype sampling.

BACKGROUND: Mixed models are used to correct for confounding due to population stratification and hidden relatedness in genome-wide association studies. This class of models includes linear mixed models and generalized linear mixed models. Existing mixed model approaches to correct for population substructure have been previously investigated with both continuous and case-control response variables. However, they have not been investigated in the context of extreme phenotype sampling (EPS), where genetic covariates are only collected on samples having extreme response variable values. In this work, we compare the performance of existing binary trait mixed model approaches (GMMAT, LEAP and CARAT) on EPS data. Since linear mixed models are commonly used even with binary traits, we also evaluate the performance of a popular linear mixed model implementation (GEMMA). RESULTS: We used simulation studies to estimate the type I error rate and power of all approaches assuming a population with substructure. Our simulation results show that for a common candidate variant, both LEAP and GMMAT control the type I error rate while CARAT's rate remains inflated. We applied all methods to a real dataset from a Québec, Canada, case-control study that is known to have population substructure. We observe similar type I error control with the analysis on the Québec dataset. For rare variants, the false positive rate remains inflated even after correction with mixed model approaches. For methods that control the type I error rate, the estimated power is comparable. CONCLUSIONS: The methods compared in this study differ in their type I error control. Therefore, when data are from an EPS study, care should be taken to ensure that the models underlying the methodology are suitable to the sampling strategy and to the minor allele frequency of the candidate SNPs.

Modeling gene-environment interactions in longitudinal family studies: a comparison of methods and their application to the association between the IGF pathway and childhood obesity.

BACKGROUND: The interactive effect of the IGF pathway genes with the environment may contribute to childhood obesity. Such gene-environment interactions can take on complex forms. Detecting those relationships using longitudinal family studies requires simultaneously accounting for correlations within individuals and families. METHODS: We studied three methods for detecting interaction effects in longitudinal family studies. The twin model and the nonparametric partition-based score test utilized individual outcome averages, whereas the linear mixed model used all available longitudinal data points. Simulation experiments were performed to evaluate the methods' power to detect different gene-environment interaction relationships. These methods were applied to the Quebec Newborn Twin Study data to test for interaction effects between the IGF pathway genes (IGF-1, IGFALS) and environmental factors (physical activity, daycare attendance and sleep duration) on body mass index outcomes. RESULTS: For the simulated data, the twin model with the mean time summary statistic yielded good performance overall. Modelling an interaction as linear when the true model had a different relationship influenced power; for certain non-linear interactions, none of the three methods were effective. Our analysis of the IGF pathway genes showed suggestive association for the joint effect of IGF-1 variant at position 102,791,894 of chromosome 12 and physical activity. However, this association was not statistically significant after multiple testing correction. CONCLUSIONS: The analytical approaches considered in this study were not robust to different gene-environment interactions. Methodological innovations are needed to improve the current methods' performances for detecting non-linear interactions. More studies are needed in order to better understand the IGF pathway's role in childhood obesity development.

Sampletrees and Rsampletrees: sampling gene genealogies conditional on SNP genotype data.

UNLABELLED: : The program sampletrees is a Markov chain Monte Carlo sampler of gene genealogies conditional on either phased or unphased SNP genotype data. The companion program Rsampletrees is for pre- and post-processing of sampletrees files, including setting up the files for sampletrees and storing and plotting the output of a sampletrees run. AVAILABILITY AND IMPLEMENTATION: sampletrees is implemented in C ++. The source code, documentation and test files are available at http://stat.sfu.ca/statgen/research/sampletrees.html The R package Rsampletrees is available on CRAN http://cran.r-project.org/web/packages/Rsampletrees/index.html CONTACT: : kburkett@uottawa.ca SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Using gene genealogies to detect rare variants associated with complex traits.

BACKGROUND AND OBJECTIVE: Standard population genetic theory says that deleterious genetic variants are likely rare and fairly recently introduced. However, can this expectation lead to more powerful tests of association between diseases and rare genetic variation? The gene genealogy describes the relationships between haplotypes sampled from the general population. Although ancestral tree-based methods, inspired by the gene genealogy concept, have been developed for finding associations with common genetic variants, here we ask whether gene genealogies can help in identifying genomic regions containing multiple rare causal variants. METHODS: With data simulated under several demographic models and using known gene genealogies, we developed and compared several tree-based statistics to determine which, if any, could detect the type of clustering expected with rare causal variants and whether the genealogic tree provides additional information about disease associations. RESULTS AND CONCLUSIONS: We found that a novel statistic based on the scaled distance between the tips of a tree performed better than other tree-based statistics. When data were simulated with mild population growth, this statistic outperformed two standard non-tree-based methods, showing that an ancestral tree-based approach has potential for rare variant discovery.

Markov chain Monte Carlo sampling of gene genealogies conditional on unphased SNP genotype data.

The gene genealogy is a tree describing the ancestral relationships among genes sampled from unrelated individuals. Knowledge of the tree is useful for inference of population-genetic parameters and has potential application in gene-mapping. Markov chain Monte Carlo approaches that sample genealogies conditional on observed genetic data typically assume that haplotype data are observed even though commonly-used genotyping technologies provide only unphased genotype data. We have extended our haplotype-based genealogy sampler, sampletrees, to handle unphased genotype data. We use the sampled haplotype configurations as a diagnostic for adequate sampling of the tree space based on the reasoning that if haplotype sampling is restricted, sampling from the tree space will also be restricted. We compare the distributions of sampled haplotypes across multiple runs of sampletrees, and to those estimated by the phase inference program, PHASE. Performance was excellent for the majority of individuals as shown by the consistency of results across multiple runs. However, for some individuals in some datasets, sampletrees had problems sampling haplotype configurations; longer run lengths would be required for these datasets. For many datasets though, we expect that sampletrees will be useful for sampling from the posterior distribution of gene genealogies given unphased genotype data.

Correspondence Between Genomic- and Genealogical/Coalescent-Based Inference of Homozygosity by Descent in Large French-Canadian Genealogies.

Research on the genetics of complex traits overwhelmingly focuses on the additive effects of genes. Yet, animal studies have shown that non-additive effects, in particular homozygosity effects, can shape complex traits. Recent investigations in human studies found some significant homozygosity effects. However, most human populations display restricted ranges of homozygosity by descent (HBD), making the identification of homozygosity effects challenging. Founder populations give rise to higher HBD levels. When deep genealogical data are available in a founder population, it is possible to gain information on the time to the most recent common ancestor (MRCA) from whom a chromosomal segment has been transmitted to both parents of an individual and in turn to that individual. This information on the time to MRCA can be combined with the time to MRCA inferred from coalescent models of gene genealogies. HBD can also be estimated from genomic data. The extent to which the genomic HBD measures correspond to the genealogical/coalescent measures has not been documented in founder populations with extensive genealogical data. In this study, we used simulations to relate genomic and genealogical/coalescent HBD measures. We based our simulations on genealogical data from two ongoing studies from the French-Canadian founder population displaying different levels of inbreeding. We simulated single-nucleotide polymorphisms (SNPs) in a 1-Mb genomic segment from a coalescent model in conjunction with the observed genealogical data. We compared genealogical/coalescent HBD to two genomic methods of HBD estimation based on hidden Markov models (HMMs). We found that genomic estimates of HBD correlated well with genealogical/coalescent HBD measures in both study genealogies. We described generation time to coalescence in terms of genomic HBD estimates and found a large variability in generation time captured by genomic HBD when considering each SNP. However, SNPs in longer segments were more likely to capture recent time to coalescence, as expected. Our study suggests that estimating the coalescent gene genealogy from the genomic data to use in conjunction with observed genealogical data could provide valuable information on HBD.

Contingency between Historical Substitutions in the Acetylcholine Receptor Pore.

Human adult muscle-type acetylcholine receptors incorporating a reconstructed ancestral ß-subunit exhibit reduced single-channel conductance when compared to wild-type. The ancestral and wild-type ß-subunits differ by 132 amino acids, including substitution of residues that line the lumen of the channel pore, near its narrowest constriction. Here we show that a single historical substitution in this region of the ancestral ß-subunit accounts for the difference in conductance. Furthermore, the contribution of the substituted residue to conductance is dependent upon its ancestral or wild-type background, and it can be modulated by a neighboring residue that has also evolved throughout the ß-subunit history. Using an expanded molecular phylogeny, we track the order in which these two mutations occurred and then show that the order in which they are installed upon the ancestral, but not the human, background determines their individual contribution to conductance. Our results show how the contribution of amino acids to acetylcholine receptor conductance is contingent upon their evolutionary history and that the order in which substitutions occurred was important for shaping conductance in the modern-day receptor.

A Cautionary Note on the Effects of Population Stratification Under an Extreme Phenotype Sampling Design.

Extreme phenotype sampling (EPS) is a popular study design used to reduce genotyping or sequencing costs. Assuming continuous phenotype data are available on a large cohort, EPS involves genotyping or sequencing only those individuals with extreme phenotypic values. Although this design has been shown to have high power to detect genetic effects even at smaller sample sizes, little attention has been paid to the effects of confounding variables, and in particular population stratification. Using extensive simulations, we demonstrate that the false positive rate under the EPS design is greatly inflated relative to a random sample of equal size or a "case-control"-like design where the cases are from one phenotypic extreme and the controls randomly sampled. The inflated false positive rate is observed even with allele frequency and phenotype mean differences taken from European population data. We show that the effects of confounding are not reduced by increasing the sample size. We also show that including the top principal components in a logistic regression model is sufficient for controlling the type 1 error rate using data simulated with a population genetics model and using 1,000 Genomes genotype data. Our results suggest that when an EPS study is conducted, it is crucial to adjust for all confounding variables. For genetic association studies this requires genotyping a sufficient number of markers to allow for ancestry estimation. Unfortunately, this could increase the costs of a study if sequencing or genotyping was only planned for candidate genes or pathways; the available genetic data would not be suitable for ancestry correction as many of the variants could have a true association with the trait.

Contribution of alpha- and beta-defensins to lung function decline and infection in smokers: an association study.

BACKGROUND: Alpha-defensins, which are major constituents of neutrophil azurophilic granules, and beta-defensins, which are expressed in airway epithelial cells, could contribute to the pathogenesis of chronic obstructive pulmonary disease by amplifying cigarette smoke-induced and infection-induced inflammatory reactions leading to lung injury. In Japanese and Chinese populations, two different beta-defensin-1 polymorphisms have been associated with chronic obstructive pulmonary disease phenotypes. We conducted population-based association studies to test whether alpha-defensin and beta-defensin polymorphisms influenced smokers' susceptibility to lung function decline and susceptibility to lower respiratory infection in two groups of white participants in the Lung Health Study (275 = fast decline in lung function and 304 = no decline in lung function). METHODS: Subjects were genotyped for the alpha-defensin-1/alpha-defensin-3 copy number polymorphism and four beta-defensin-1 polymorphisms (G-20A, C-44G, G-52A and Val38Ile). RESULTS: There were no associations between individual polymorphisms or imputed haplotypes and rate of decline in lung function or susceptibility to infection. CONCLUSION: These findings suggest that, in a white population, the defensin polymorphisms tested may not be of importance in determining who develops abnormally rapid lung function decline or is susceptible to developing lower respiratory infections.

A comparison of five methods for selecting tagging single-nucleotide polymorphisms.

Our goal was to compare methods for tagging single-nucleotide polymorphisms (tagSNPs) with respect to the power to detect disease association under differing haplotype-disease association models. We were also interested in the effect that SNP selection samples, consisting of either cases, controls, or a mixture, would have on power. We investigated five previously described algorithms for choosing tagSNPS: two that picked SNPs based on haplotype structure (Chapman-haplotypic and Stram), two that picked SNPs based on pair-wise allelic association (Chapman-allelic and Cousin), and one control method that chose equally spaced SNPs (Zhai). In two disease-associated regions from the Genetic Analysis Workshop 14 simulated data, we tested the association between tagSNP genotype and disease over the tagSNP sets chosen by each method for each sampling scheme. This was repeated for 100 replicates to estimate power. The two allelic methods chose essentially all SNPs in the region and had nearly optimal power. The two haplotypic methods chose about half as many SNPs. The haplotypic methods had poor performance compared to the allelic methods in both regions. We expected an improvement in power when the selection sample contained cases; however, there was only moderate variation in power between the sampling approaches for each method. Finally, when compared to the haplotypic methods, the reference method performed as well or worse in the region with ancestral disease haplotype structure.

A Comparison of Statistical Methods for the Discovery of Genetic Risk Factors Using Longitudinal Family Study Designs.

The etiology of immune-related diseases or traits is often complex, involving many genetic and environmental factors and their interactions. While methodological approaches focusing on an outcome measured at one time point have succeeded in identifying genetic factors involved in immune-related traits, they fail to capture complex disease mechanisms that fluctuate over time. It is increasingly recognized that longitudinal studies, where an outcome is measured at multiple time points, have great potential to shed light on complex disease mechanisms involving genetic factors. However, longitudinal data require specialized statistical methods, especially in family studies where multiple sources of correlation in the data must be modeled. Using simulated data with known genetic effects, we examined the performance of different analytical methods for investigating associations between genetic factors and longitudinal phenotypes in twin data. The simulations were modeled on data from the Québec Newborn Twin Study, an ongoing population-based longitudinal study of twin births with multiple phenotypes, such as cortisol levels and body mass index, collected multiple times in infancy and early childhood and with sequencing data on immune-related genes and pathways. We compared approaches that we classify as (1) family-based methods applied to summaries of the observations over time, (2) longitudinal-based methods with simplifications of the familial correlation, and (3) Bayesian family-based method with simplifications of the temporal correlation. We found that for estimation of the genetic main and interaction effects, all methods gave estimates close to the true values and had similar power. If heritability estimation is desired, approaches of type (1) also provide heritability estimates close to the true value. Our work shows that the simpler approaches are likely adequate to detect genetic effects; however, interpretation of these effects is more challenging.

Microarray genotyping resource to determine population stratification in genetic association studies of complex disease.

We have developed a robust microarray genotyping chip that will help advance studies in genetic epidemiology. In population-based genetic association studies of complex disease, there could be hidden genetic substructure in the study populations, resulting in false-positive associations. Such population stratification may confound efforts to identify true associations between genotype/haplotype and phenotype. Methods relying on genotyping additional null single nucleotide polymorphism (SNP) markers have been proposed, such as genomic control (GC) and structured association (SA), to correct association tests for population stratification. If there is an association of a disease with null SNPs, this suggests that there is a population subset with different genetic background plus different disease susceptibility. Genotyping over 100 null SNPs in the large numbers of patient and control DNA samples that are required in genetic association studies can be prohibitively expensive. We have therefore developed and tested a resequencing chip based on arrayed primer extension (APEX) from over 2000 DNA probe features that facilitate multiple interrogations of each SNP, providing a powerful, accurate, and economical means to simultaneously determine the genotypes at 110 null SNP loci in any individual. Based on 1141 known genotypes from other research groups, our GC SNP chip has an accuracy of 98.5%, including non-calls.

Gene genealogies for genetic association mapping, with application to Crohn's disease.

A gene genealogy describes relationships among haplotypes sampled from a population. Knowledge of the gene genealogy for a set of haplotypes is useful for estimation of population genetic parameters and it also has potential application in finding disease-predisposing genetic variants. As the true gene genealogy is unknown, Markov chain Monte Carlo (MCMC) approaches have been used to sample genealogies conditional on data at multiple genetic markers. We previously implemented an MCMC algorithm to sample from an approximation to the distribution of the gene genealogy conditional on haplotype data. Our approach samples ancestral trees, recombination and mutation rates at a genomic focal point. In this work, we describe how our sampler can be used to find disease-predisposing genetic variants in samples of cases and controls. We use a tree-based association statistic that quantifies the degree to which case haplotypes are more closely related to each other around the focal point than control haplotypes, without relying on a disease model. As the ancestral tree is a latent variable, so is the tree-based association statistic. We show how the sampler can be used to estimate the posterior distribution of the latent test statistic and corresponding latent p-values, which together comprise a fuzzy p-value. We illustrate the approach on a publicly-available dataset from a study of Crohn's disease that consists of genotypes at multiple SNP markers in a small genomic region. We estimate the posterior distribution of the tree-based association statistic and the recombination rate at multiple focal points in the region. Reassuringly, the posterior mean recombination rates estimated at the different focal points are consistent with previously published estimates. The tree-based association approach finds multiple sub-regions where the case haplotypes are more genetically related than the control haplotypes, and that there may be one or multiple disease-predisposing loci.

Matrix metalloproteinase expression by human alveolar macrophages in relation to emphysema.

An abnormal increase in proteolytic enzymes is thought to play a key role in pulmonary emphysema. Alveolar macrophage proteolytic enzymes include cathepsin L, cathepsin S, matrix metalloproteinase 1, 9, and 12, and a number of studies have implicated these proteinases in the alveolar destruction that characterizes emphysema. The aim of this study was to investigate cathepsin L, cathepsin S, matrix metalloproteinase 1, 9, and 12 mRNA expression in alveolar macrophages isolated from patients with varying degrees of emphysema and to correlate their level of expression with measures of emphysema. Alveolar macrophages were isolated from fifty-four patients who underwent surgical resection for lung carcinoma. The level of mRNA expression was determined using real-time PCR. Emphysema was quantified using high-resolution CT scans. Alveolar macrophages were also cultured for 24 h and 48 h; the effect of proinflammatory mediators and promoter polymorphisms on expression was analyzed. There was a significant correlation between matrix metalloproteinase 1 mRNA expression and emphysema. A higher level of matrix metalloproteinase 1 mRNA was associated with more severe emphysema. Matrix metalloproteinase 12 mRNA expression was increased in current smokers as compared with former smokers. Furthermore, there was a negative correlation between matrix metalloproteinase 12 gene expression and carbon monoxide diffusing capacity. The matrix metalloproteinase 9 C-1562T polymorphism significantly influenced matrix metalloproteinase 9 mRNA expression in alveolar macrophages. These results suggest that alveolar macrophage matrix metalloproteinase 1 and 12 may have a role in the lung structural changes leading to the development of emphysema. Furthermore, these data provide evidence to support the concept that multiple proteinases, causing both elastin and collagen degradation, are important in the pathogenesis of pulmonary emphysema.