Information, Coding, and Biological Function: The Dynamics of Life

In the mid-20th century, two new scientific disciplines emerged forcefully: molecular biology and information-communication theory. At the beginning, cross-fertilization was so deep that the term genetic code was universally accepted for describing the meaning of triplets of mRNA (codons) as amino acids. However, today, such synergy has not taken advantage of the vertiginous advances in the two disciplines and presents more challenges than answers. These challenges not only are of great theoretical relevance but also represent unavoidable milestones for next-generation biology: from personalized genetic therapy and diagnosis to Artificial Life to the production of biologically active proteins. Moreover, the matter is intimately connected to a paradigm shift needed in theoretical biology, pioneered a long time ago, that requires combined contributions from disciplines well beyond the biological realm. The use of information as a conceptual metaphor needs to be turned into quantitative and predictive models that can be tested empirically and integrated in a unified view. Successfully achieving these tasks requires a wide multidisciplinary approach, including Artificial Life researchers, to address such an endeavour.

Statistical integration of multi-omics and drug screening data from cell lines

Data integration methods are used to obtain a unified summary of multiple datasets. For multi-modal data, we propose a computational workflow to jointly analyze datasets from cell lines. The workflow comprises a novel probabilistic data integration method, named POPLS-DA, for multi-omics data. The workflow is motivated by a study on synucleinopathies where transcriptomics, proteomics, and drug screening data are measured in affected LUHMES cell lines and controls. The aim is to highlight potentially druggable pathways and genes involved in synucleinopathies. First, POPLS-DA is used to prioritize genes and proteins that best distinguish cases and controls. For these genes, an integrated interaction network is constructed where the drug screen data is incorporated to highlight druggable genes and pathways in the network. Finally, functional enrichment analyses are performed to identify clusters of synaptic and lysosome-related genes and proteins targeted by the protective drugs. POPLS-DA is compared to other single- and multi-omics approaches. We found that HSPA5, a member of the heat shock protein 70 family, was one of the most targeted genes by the validated drugs, in particular by AT1-blockers. HSPA5 and AT1-blockers have been previously linked to α-synuclein pathology and Parkinson's disease, showing the relevance of our findings. Our computational workflow identified new directions for therapeutic targets for synucleinopathies. POPLS-DA provided a larger interpretable gene set than other single- and multi-omic approaches. An implementation based on R and markdown is freely available online.

High-Resolution Hydrogen-Deuterium Protection Factors from Sparse Mass Spectrometry Data Validated by Nuclear Magnetic Resonance Measurements

Experimental measurement of time-dependent spontaneous exchange of amide protons with deuterium of the solvent provides information on the structure and dynamical structural variation in proteins. Two experimental techniques are used to probe the exchange: NMR, which relies on different magnetic properties of hydrogen and deuterium, and MS, which exploits the change in mass due to deuteration. NMR provides residue-specific information, that is, the rate of exchange or, analogously, the protection factor (i.e., the unitless ratio between the rate of exchange for a completely unstructured state and the observed rate). MS provides information that is specific to peptides obtained by proteolytic digestion. The spatial resolution of HDX-MS measurements depends on the proteolytic pattern of the protein, the fragmentation method used, and the overlap between peptides. Different computational approaches have been proposed to extract residue-specific information from peptide-level HDX-MS measurements. Here, we demonstrate the advantages of a method recently proposed that exploits self-consistency and classifies the possible sets of protection factors into a finite number of alternative solutions compatible with experimental data. The degeneracy of the solutions can be reduced (or completely removed) by exploiting the additional information encoded in the shape of the isotopic envelopes. We show how sparse and noisy MS data can provide high-resolution protection factors that correlate with NMR measurements probing the same protein under the same conditions.

Statistical integration of two omics datasets using GO2PLS

BACKGROUND

Nowadays, multiple omics data are measured on the same samples in the belief that these different omics datasets represent various aspects of the underlying biological systems. Integrating these omics datasets will facilitate the understanding of the systems. For this purpose, various methods have been proposed, such as Partial Least Squares (PLS), decomposing two datasets into joint and residual subspaces. Since omics data are heterogeneous, the joint components in PLS will contain variation specific to each dataset. To account for this, Two-way Orthogonal Partial Least Squares (O2PLS) captures the heterogeneity by introducing orthogonal subspaces and better estimates the joint subspaces. However, the latent components spanning the joint subspaces in O2PLS are linear combinations of all variables, while it might be of interest to identify a small subset relevant to the research question. To obtain sparsity, we extend O2PLS to Group Sparse O2PLS (GO2PLS) that utilizes biological information on group structures among variables and performs group selection in the joint subspace.

RESULTS

The simulation study showed that introducing sparsity improved the feature selection performance. Furthermore, incorporating group structures increased robustness of the feature selection procedure. GO2PLS performed optimally in terms of accuracy of joint score estimation, joint loading estimation, and feature selection. We applied GO2PLS to datasets from two studies: TwinsUK (a population study) and CVON-DOSIS (a small case-control study). In the first, we incorporated biological information on the group structures of the methylation CpG sites when integrating the methylation dataset with the IgG glycomics data. The targeted genes of the selected methylation groups turned out to be relevant to the immune system, in which the IgG glycans play important roles. In the second, we selected regulatory regions and transcripts that explained the covariance between regulomics and transcriptomics data. The corresponding genes of the selected features appeared to be relevant to heart muscle disease.

CONCLUSIONS

GO2PLS integrates two omics datasets to help understand the underlying system that involves both omics levels. It incorporates external group information and performs group selection, resulting in a small subset of features that best explain the relationship between two omics datasets for better interpretability.

Use of shared gamma frailty model in analysis of survival data in twins

In survival analysis, the effect of a covariate on the outcome is reported in a hazard rate. However, hazards rates are hard to interpret. Here we consider differences in survival probabilities instead. Using data on twins is interesting due to the fact that many observed and unobserved factors are controlled or matched. To model the correlation between twins, some authors have proposed survival models with frailties or random effects. However, there is a potential danger of bias in the estimation if the frailty distribution is misspecified. Frailties are often assumed to follow a gamma distribution. To safeguard us from the impact of the misspecification of this distribution, we consider a flexible non-parametric baseline hazard in addition to a parametric one. We will apply this methodology to the TwinsUK cohort to predict the probability of experiencing a fracture in the next five or ten years, given their bone mineral densities (BMD) and their frailty index. The models with parametric and non-parametric baseline hazards yield very close results in estimating survival probabilities and thus a choice of parametric baseline hazard is generally preferred. We find that bone mineral density is a significant predictor in the model whereas frailty index is not. Low BMD leads to a larger probability of fracture; e.g, in 10 years, the probability of fracture is 21% for low BMD group, 16% for medium BMD group and 8% for high BMD group.

Estimation of the effect of surrogate multi-omic biomarkers

Multiple technologies which measure the same omics data set but are based on different aspects of the molecules exist. In practice, studies use different technologies and have therefore different biomarkers. An example is the glycan age index, which is constructed by three different ultra-performance liquid chromatography (UPLC) IgG glycans, and is a biomarker for biological age. A second technology is liquid chromatography- mass spectrometry (LCMS). To estimate the effect of a biomarker on an outcome variable, two issues need to be addressed. Firstly, a measurement error is needed to map one technology to the other one using a calibration study. Here, we consider two approaches, namely one based on the chemical properties of the two technologies and one based on the estimation of this relationship using O2PLS. Secondly, the use of an approximation of the biomarker in the main study needs to be taken into account by use of a regression calibration method. The performance of the two approaches is studied via simulations. The methods are used to estimate the relationship between glycan age and menopause. We have data from two cohorts, namely Korcula and Vis. In conclusion, (1) both measurement error models give similar results and suggest that there is an association between the glycan age index and the menopause status, (2) the chemical mapping approach outperforms O2PLS in the low measurement error variance, while on the larger measurement error variance, O2PLS works better, (3) statistical efficiency is lost due to increased noise level by adding irrelevant information.

Nonparametric clustering for longitudinal functional data with the application to H-NMR spectra of kidney transplant patients. Longitudinal functional data clustering

Longitudinal functional data are increasingly common in the health domain. The motivated dataset for this paper comprises H-NMR spectra of kidney transplant patients [8]. Our aim is to cluster patients into different clinical outcome subgoups to reveal the success of the transplantation. The NMR spectra of each patient at each time point are functional data and the data are longitudinally collected at up to nine different time points. Existing methods are available for functional data collected at one time point, but not for longitudinal functional data collected at a grid of time points subject to missingness. We therefore first apply a method to extract the same number of functional feactures for each subject. Next we propose a novel nonparametric clustering method for mulitivariate functional data. We applied our proposed clustering method to the kidney transplant dataset both to a subset of the raw data with only two time points and the extacted functional features. It appeared that the proposed method achieves better clustering performance on the extracted functional features than on the subset of raw data. A data simulation study was performed to further evaluate the method. The design mimiced the kidney transplant dataset but with a larger sample size. Scenarios which have different levels of noise were considered. The simulation study showed the accuarcy of our proposed method.

Multivariate functional partial least squares for classification using longitudinal data

The use of statistical methods to predict outcomes using high dimensional datasets in medicine is becoming increasingly popular for forecasting and monitoring patient health. Our work is motivated by a longitudinal dataset containing 1H NMR spectra of metabolites of 18 patients undergoing a kidney transplant alongside their graft outcomes that fall into one of three categories: acute rejection, delayed graft function and primary function. We proposed a functional partial least squares (FPLS) model that extends existing PLS methods for the analysis of longitudinally measured scalar omics datasets to the case of longitudinally measured functional datasets. We designed an iterative algorithm to link multiple time points, and then applied our proposed method to analyse the data from kidney transplant patients. Finally, we compared the AUC of our method to the AUC of the univariate methods which only use the information of one time-point information. It appeared that our method outperforms the existing methods. A simulation study was performed to mimic the kidney transplant dataset but with a larger sample size and different scenarios performed to evaluate the performance of the new method in larger datasets. We consider scenarios which vary in the difficulty to distinguish the two groups. It appeared that the three time-points model performs better than any of the individual models with average AUCs of 0.909 and 0.811 respectively.

Integrating omics datasets with the OmicsPLS package

BACKGROUND

With the exponential growth in available biomedical data, there is a need for data integration methods that can extract information about relationships between the data sets. However, these data sets might have very different characteristics. For interpretable results, data-specific variation needs to be quantified. For this task, Two-way Orthogonal Partial Least Squares (O2PLS) has been proposed. To facilitate application and development of the methodology, free and open-source software is required. However, this is not the case with O2PLS.

RESULTS

We introduce OmicsPLS, an open-source implementation of the O2PLS method in R. It can handle both low- and high-dimensional datasets efficiently. Generic methods for inspecting and visualizing results are implemented. Both a standard and faster alternative cross-validation methods are available to determine the number of components. A simulation study shows good performance of OmicsPLS compared to alternatives, in terms of accuracy and CPU runtime. We demonstrate OmicsPLS by integrating genetic and glycomic data.

CONCLUSIONS

We propose the OmicsPLS R package: a free and open-source implementation of O2PLS for statistical data integration. OmicsPLS is available at https://cran.r-project.org/package=OmicsPLS and can be installed in R via install.packages("OmicsPLS").

Genomic prediction for numerically small breeds, using models with pre-selected and differentially weighted markers

BACKGROUND

Genomic prediction (GP) accuracy in numerically small breeds is limited by the small size of the reference population. Our objective was to test a multi-breed multiple genomic relationship matrices (GRM) GP model (MBMG) that weighs pre-selected markers separately, uses the remaining markers to explain the remaining genetic variance that can be explained by markers, and weighs information of breeds in the reference population by their genetic correlation with the validation breed.

METHODS

Genotype and phenotype data were used on 595 Jersey bulls from New Zealand and 5503 Holstein bulls from the Netherlands, all with deregressed proofs for stature. Different sets of markers were used, containing either pre-selected markers from a meta-genome-wide association analysis on stature, remaining markers or both. We implemented a multi-breed bivariate GREML model in which we fitted either a single multi-breed GRM (MBSG), or two distinct multi-breed GRM (MBMG), one made with pre-selected markers and the other with remaining markers. Accuracies of predicting stature for Jersey individuals using the multi-breed models (Holstein and Jersey combined reference population) was compared to those obtained using either the Jersey (within-breed) or Holstein (across-breed) reference population. All the models were subsequently fitted in the analysis of simulated phenotypes, with a simulated genetic correlation between breeds of 1, 0.5, and 0.25.

RESULTS

The MBMG model always gave better prediction accuracies for stature compared to MBSG, within-, and across-breed GP models. For example, with MBSG, accuracies obtained by fitting 48,912 unselected markers (0.43), 357 pre-selected markers (0.38) or a combination of both (0.43), were lower than accuracies obtained by fitting pre-selected and unselected markers in separate GRM in MBMG (0.49). This improvement was further confirmed by results from a simulation study, with MBMG performing on average 23% better than MBSG with all markers fitted.

CONCLUSIONS

With the MBMG model, it is possible to use information from numerically large breeds to improve prediction accuracy of numerically small breeds. The superiority of MBMG is mainly due to its ability to use information on pre-selected markers, explain the remaining genetic variance and weigh information from a different breed by the genetic correlation between breeds.

Utility of whole-genome sequence data for across-breed genomic prediction

Background

Genomic prediction (GP) across breeds has so far resulted in low accuracies of the predicted genomic breeding values. Our objective was to evaluate whether using whole-genome sequence (WGS) instead of low-density markers can improve GP across breeds, especially when markers are pre-selected from a genome-wide association study (GWAS), and to test our hypothesis that many non-causal markers in WGS data have a diluting effect on accuracy of across-breed prediction.

Methods

Estimated breeding values for stature and bovine high-density (HD) genotypes were available for 595 Jersey bulls from New Zealand, 957 Holstein bulls from New Zealand and 5553 Holstein bulls from the Netherlands. BovineHD genotypes for all bulls were imputed to WGS using Beagle4 and Minimac2. Genomic prediction across the three populations was performed with ASReml4, with each population used as single reference and as single validation sets. In addition to the 50k, HD and WGS, markers that were significantly associated with stature in a large meta-GWAS analysis were selected and used for prediction, resulting in 10 prediction scenarios. Furthermore, we estimated the proportion of genetic variance captured by markers in each scenario.

Results

Across breeds, 50k, HD and WGS markers resulted in very low accuracies of prediction ranging from − 0.04 to 0.13. Accuracies were higher in scenarios with pre-selected markers from a meta-GWAS. For example, using only the 133 most significant markers in 133 QTL regions from the meta-GWAS yielded accuracies ranging from 0.08 to 0.23, while 23,125 markers with a − log10(p) higher than 7 resulted in accuracies of up 0.35. Using WGS data did not significantly improve the proportion of genetic variance captured across breeds compared to scenarios with few but pre-selected markers.

Conclusions

Our results demonstrated that the accuracy of across-breed GP can be improved by using markers that are pre-selected from WGS based on their potential causal effect. We also showed that simply increasing the number of markers up to the WGS level does not increase the accuracy of across-breed prediction, even when markers that are expected to have a causal effect are included.

Improving stability of prediction models based on correlated omics data by using network approaches

Building prediction models based on complex omics datasets such as transcriptomics, proteomics, metabolomics remains a challenge in bioinformatics and biostatistics. Regularized regression techniques are typically used to deal with the high dimensionality of these datasets. However, due to the presence of correlation in the datasets, it is difficult to select the best model and application of these methods yields unstable results. We propose a novel strategy for model selection where the obtained models also perform well in terms of overall predictability. Several three step approaches are considered, where the steps are 1) network construction, 2) clustering to empirically derive modules or pathways, and 3) building a prediction model incorporating the information on the modules. For the first step, we use weighted correlation networks and Gaussian graphical modelling. Identification of groups of features is performed by hierarchical clustering. The grouping information is included in the prediction model by using group-based variable selection or group-specific penalization. We compare the performance of our new approaches with standard regularized regression via simulations. Based on these results we provide recommendations for selecting a strategy for building a prediction model given the specific goal of the analysis and the sizes of the datasets. Finally we illustrate the advantages of our approach by application of the methodology to two problems, namely prediction of body mass index in the DIetary, Lifestyle, and Genetic determinants of Obesity and Metabolic syndrome study (DILGOM) and prediction of response of each breast cancer cell line to treatment with specific drugs using a breast cancer cell lines pharmacogenomics dataset.

Familial Clustering of Factor VIII and von Willebrand Factor Levels

Recently, we found that high levels of clotting factor VIII (>150 IU/dl) are common and make an important contribution to thrombotic risk. The determinants of high factor VIII:C are unclear and might be partly genetic. Therefore, we tested the influence of age, blood group and von Willebrand factor (VWF) levels on factor VIII:C levels, and investigated whether factor VIII:C levels are genetically determined. We performed an analysis of 564 female relatives of hemophilia A patients, who had visited our center for genetic counseling. In univariate analysis, AB0 blood group, age and VWF antigen (VWF:Ag) levels all influenced factor VIII:C levels. After adjustment for the effect of VWF:Ag levels, both blood group and age still had an effect on factor VIII:C levels. In sister pairs, the Pearson correlation coefficient between factor VIII:C levels was 0.17 (p = 0.024) and this correlation remained positive (0.15, p = 0.046) after correction for the influence of VWF:Ag. In mother-daughter pairs, no correlation of factor VIII:C levels was found. The correlation of VWF:Ag levels in sisterpairs was 0.41 (p <0.001) and in mother-daughter pairs 0.44 (p <0.001), in line with the assumption that VWF:Ag levels are under control of autosomal genes. Familial influence on plasma factor VIII:C and VWF:Ag levels was investigated with a recently developed familial aggregation test. This test verifies whether familial aggregation of a particular parameter exists in a set of pedigrees. In 435 women from 168 families, factor VIII:C as well as VWF:Ag levels correlated significantly within families, which suggests a familial influence. The familial aggregation was more prominent for VWF:Ag levels than for factor VIII:C levels, possibly because the genetic effect on VWF:Ag levels is larger than on factor VIII:C levels. Our results support the presence of a familial influence on factor VIII:C as well as on VWF:Ag levels.

Our results support the presence of a familial influence on factor VIII:C as well as on VWF:Ag levels.

Monoamine Oxidase A (MAOA) Gene and Personality Traits from Late Adolescence through Early Adulthood: A Latent Variable Investigation

Very few molecular genetic studies of personality traits have used longitudinal phenotypic data, therefore molecular basis for developmental change and stability of personality remains to be explored. We examined the role of the monoamine oxidase A gene (MAOA) on extraversion and neuroticism from adolescence to adulthood, using modern latent variable methods. A sample of 1,160 male and 1,180 female participants with complete genotyping data was drawn from a British national birth cohort, the MRC National Survey of Health and Development (NSHD). The predictor variable was based on a latent variable representing genetic variations of the MAOA gene measured by three SNPs (rs3788862, rs5906957, and rs979606). Latent phenotype variables were constructed using psychometric methods to represent cross-sectional and longitudinal phenotypes of extraversion and neuroticism measured at ages 16 and 26. In males, the MAOA genetic latent variable (AAG) was associated with lower extraversion score at age 16 (β = −0.167; CI: −0.289, −0.045; p = 0.007, FDRp = 0.042), as well as greater increase in extraversion score from 16 to 26 years (β = 0.197; CI: 0.067, 0.328; p = 0.003, FDRp = 0.036). No genetic association was found for neuroticism after adjustment for multiple testing. Although, we did not find statistically significant associations after multiple testing correction in females, this result needs to be interpreted with caution due to issues related to x-inactivation in females. The latent variable method is an effective way of modeling phenotype- and genetic-based variances and may therefore improve the methodology of molecular genetic studies of complex psychological traits.

Gene coexpression network analysis for family studies based on a meta-analytic approach

For a better understanding of the biological mechanisms involved in complex traits or diseases, networks are often useful tools in genetic studies: coexpression networks based on pairwise correlations between genes are commonly used. In case of a family-based design, it can be problematic when there is a large between-family variation in expression levels. We propose here a gene coexpression network analysis for family studies. We build a coexpression network for each family and then combine the results. We applied our approach to data provided for analysis in the Genetic Analysis Workshop 19 and compared it to 2 naïve approaches—ignoring correlations among the expressions and decorrelating the gene expression by using the residuals of a mixed model—and a single-probe analysis. Our approach seemed to better deal with heterogeneity with regard to the naïve approaches. The naïve approaches did not provide any significant results, while our approach detected genes via indirect effects. It also detected more genes than the single-probe analysis.

Making sense of big data in health research: Towards an EU action plan

Medicine and healthcare are undergoing profound changes. Whole-genome sequencing and high-resolution imaging technologies are key drivers of this rapid and crucial transformation. Technological innovation combined with automation and miniaturization has triggered an explosion in data production that will soon reach exabyte proportions. How are we going to deal with this exponential increase in data production? The potential of "big data" for improving health is enormous but, at the same time, we face a wide range of challenges to overcome urgently. Europe is very proud of its cultural diversity; however, exploitation of the data made available through advances in genomic medicine, imaging, and a wide range of mobile health applications or connected devices is hampered by numerous historical, technical, legal, and political barriers. European health systems and databases are diverse and fragmented. There is a lack of harmonization of data formats, processing, analysis, and data transfer, which leads to incompatibilities and lost opportunities. Legal frameworks for data sharing are evolving. Clinicians, researchers, and citizens need improved methods, tools, and training to generate, analyze, and query data effectively. Addressing these barriers will contribute to creating the European Single Market for health, which will improve health and healthcare for all Europeans.

Evaluation of O2PLS in Omics data integration

Background

Rapid computational and technological developments made large amounts of omics data available in different biological levels. It is becoming clear that simultaneous data analysis methods are needed for better interpretation and understanding of the underlying systems biology. Different methods have been proposed for this task, among them Partial Least Squares (PLS) related methods. To also deal with orthogonal variation, systematic variation in the data unrelated to one another, we consider the Two-way Orthogonal PLS (O2PLS): an integrative data analysis method which is capable of modeling systematic variation, while providing more parsimonious models aiding interpretation.

Results

A simulation study to assess the performance of O2PLS showed positive results in both low and higher dimensions. More noise (50 % of the data) only affected the systematic part estimates. A data analysis was conducted using data on metabolomics and transcriptomics from a large Finnish cohort (DILGOM). A previous sequential study, using the same data, showed significant correlations between the Lipo-Leukocyte (LL) module and lipoprotein metabolites. The O2PLS results were in agreement with these findings, identifying almost the same set of co-varying variables. Moreover, our integrative approach identified other associative genes and metabolites, while taking into account systematic variation in the data. Including orthogonal components enhanced overall fit, but the orthogonal variation was difficult to interpret.

Conclusions

Simulations showed that the O2PLS estimates were close to the true parameters in both low and higher dimensions. In the presence of more noise (50 %), the orthogonal part estimates could not distinguish well between joint and unique variation. The joint estimates were not systematically affected. Simultaneous analysis with O2PLS on metabolome and transcriptome data showed that the LL module, together with VLDL and HDL metabolites, were important for the metabolomic and transcriptomic relation. This is in agreement with an earlier study. In addition more gene expression and metabolites are identified being important for the joint covariation.

The transcriptional landscape of age in human peripheral blood

Disease incidences increase with age, but the molecular characteristics of ageing that lead to increased disease susceptibility remain inadequately understood. Here we perform a whole-blood gene expression meta-analysis in 14,983 individuals of European ancestry (including replication) and identify 1,497 genes that are differentially expressed with chronological age. The age-associated genes do not harbor more age-associated CpG-methylation sites than other genes, but are instead enriched for the presence of potentially functional CpG-methylation sites in enhancer and insulator regions that associate with both chronological age and gene expression levels. We further used the gene expression profiles to calculate the ‘transcriptomic age' of an individual, and show that differences between transcriptomic age and chronological age are associated with biological features linked to ageing, such as blood pressure, cholesterol levels, fasting glucose, and body mass index. The transcriptomic prediction model adds biological relevance and complements existing epigenetic prediction models, and can be used by others to calculate transcriptomic age in external cohorts.

Ageing increases the risk of many diseases. Here the authors compare blood cell transcriptomes of over 14,000 individuals and identify a set of about 1,500 genes that are differently expressed with age, shedding light on transcriptional programs linked to the ageing process and age-associated diseases.

Marginal genetic effects estimation in family and twin studies using random-effects models

Random-effects models are often used in family-based genetic association studies to properly capture the within families relationships. In such models, the regression parameters have a conditional on the random effects interpretation and they measure, e.g., genetic effects for each family. Estimating parameters that can be used to make inferences at the population level is often more relevant than the family-specific effects, but not straightforward. This is mainly for two reasons: First the analysis of family data often requires high-dimensional random-effects vectors to properly model the familial relationships, for instance when members with a different degree of relationship are considered, such as trios, mix of monozygotic and dizygotic twins, etc. The second complication is the biased sampling design, such as the multiple cases families design, which is often employed to enrich the sample with genetic information. For these reasons deriving parameters with the desired marginal interpretation can be challenging. In this work we consider the marginalized mixed-effects models, we discuss challenges in applying them in ascertained family data and propose penalized maximum likelihood methodology to stabilize the parameter estimation by using external information on the disease prevalence or heritability. The performance of our methodology is evaluated via simulation and is illustrated on data from Rheumatoid Arthritis patients, where we estimate the marginal effect of HLA-DRB1*13 and shared epitope alleles across three different study designs and combine them using meta-analysis.

A retrospective likelihood approach for efficient integration of multiple omics factors in case-control association studies

Integrative omics, the joint analysis of outcome and multiple types of omics data, such as genomics, epigenomics, and transcriptomics data, constitute a promising approach for powerful and biologically relevant association studies. These studies often employ a case-control design, and often include nonomics covariates, such as age and gender, that may modify the underlying omics risk factors. An open question is how to best integrate multiple omics and nonomics information to maximize statistical power in case-control studies that ascertain individuals based on the phenotype. Recent work on integrative omics have used prospective approaches, modeling case-control status conditional on omics, and nonomics risk factors. Compared to univariate approaches, jointly analyzing multiple risk factors with a prospective approach increases power in nonascertained cohorts. However, these prospective approaches often lose power in case-control studies. In this article, we propose a novel statistical method for integrating multiple omics and nonomics factors in case-control association studies. Our method is based on a retrospective likelihood function that models the joint distribution of omics and nonomics factors conditional on case-control status. The new method provides accurate control of Type I error rate and has increased efficiency over prospective approaches in both simulated and real data.

The Genome of the Netherlands: design, and project goals

Within the Netherlands a national network of biobanks has been established (Biobanking and Biomolecular Research Infrastructure-Netherlands (BBMRI-NL)) as a national node of the European BBMRI. One of the aims of BBMRI-NL is to enrich biobanks with different types of molecular and phenotype data. Here, we describe the Genome of the Netherlands (GoNL), one of the projects within BBMRI-NL. GoNL is a whole-genome-sequencing project in a representative sample consisting of 250 trio-families from all provinces in the Netherlands, which aims to characterize DNA sequence variation in the Dutch population. The parent-offspring trios include adult individuals ranging in age from 19 to 87 years (mean=53 years; SD=16 years) from birth cohorts 1910-1994. Sequencing was done on blood-derived DNA from uncultured cells and accomplished coverage was 14-15x. The family-based design represents a unique resource to assess the frequency of regional variants, accurately reconstruct haplotypes by family-based phasing, characterize short indels and complex structural variants, and establish the rate of de novo mutational events. GoNL will also serve as a reference panel for imputation in the available genome-wide association studies in Dutch and other cohorts to refine association signals and uncover population-specific variants. GoNL will create a catalog of human genetic variation in this sample that is uniquely characterized with respect to micro-geographic location and a wide range of phenotypes. The resource will be made available to the research and medical community to guide the interpretation of sequencing projects. The present paper summarizes the global characteristics of the project.

Estimation of genetic effects in multiple cases family studies using penalized maximum likelihood methodology

Family studies are often used in genetic research to explore associations between genetic markers and various phenotypes. A commonly used design oversamples families enriched with the disease under study for efficient data collection and estimation. For instance, in a multiple cases family study, families are selected based on the number of affected relatives. In such cases, valid inference for the model parameters relies on the proper modeling of both the within family correlations and the outcome-dependent sampling, also known as ascertainment. A flexible modeling approach is the ascertainment-corrected mixed-effects model, but it is known to only be asymptotically identifiable, because in small samples the available data do not provide sufficient information to estimate both the intercept and the genetic variance. To deal with this issue, we propose a penalized maximum likelihood estimation procedure which reliably estimates the model parameters in small family studies by using external population-based information.

Identifying rare variants from exome scans: the GAW17 experience

Genetic Analysis Workshop 17 (GAW17) provided a platform for evaluating existing statistical genetic methods and for developing novel methods to analyze rare variants that modulate complex traits. In this article, we present an overview of the 1000 Genomes Project exome data and simulated phenotype data that were distributed to GAW17 participants for analyses, the different issues addressed by the participants, and the process of preparation of manuscripts resulting from the discussions during the workshop.

Plasma protein N-glycan profiles are associated with calendar age, familial longevity and health

The development of medical interventions for the preservation of disease-free longevity would be facilitated by markers that predict healthy aging. Altered protein N-glycosylation patterns have been found with increasing age and several disease states. Here we investigate whether glycans derived from the total glycoprotein pool in plasma mark familial longevity and distinguish healthy from unhealthy aging. Total plasma N-glycan profiles of 2396 middle aged participants in the Leiden Longevity Study (LLS) were obtained by glycan release, labeling, and subsequent HPLC analysis with fluorescence detection. After normalization and batch correction, several regression strategies were applied to evaluate associations between glycan patterns, familial longevity, and healthy aging. Two N-glycan features (LC-7 and LC-8) were identified to be more abundant in plasma of the offspring of long-lived individuals as compared to controls. These results were not confounded by the altered lipid status or glucose homeostasis of the offspring. Furthermore, a decrease in levels of LC-8 was associated with the occurrence of myocardial infarction (p = 0.049, coefficient = -0.065), indicating that plasma glycosylation patterns do not only mark familial longevity but may also reflect healthy aging. In conclusion, we describe two glycan features, of which increased levels mark familial longevity and decreased levels of one of these features mark the presence of cardiovascular disease.

Replication of the tumor necrosis factor receptor-associated factor 1/complement component 5 region as a susceptibility locus for rheumatoid arthritis in a European family-based study

OBJECTIVE

We recently showed, using a candidate gene approach in a case-control association study, that a 65-kb block encompassing tumor necrosis factor receptor-associated factor 1 (TRAF1) and C5 is strongly associated with rheumatoid arthritis (RA). Compared with case-control association studies, family-based studies have the added advantage of controlling potential differences in population structure and are not likely to be hampered by variation in population allele frequencies, as is seen for many genetic polymorphisms, including the TRAF1/C5 locus. The aim of this study was to confirm this association in populations of European origin by using a family-based approach.

METHODS

A total of 1,356 western European white individuals from 452 "trio" families were genotyped for the rs10818488 polymorphism, using the TaqMan allelic discrimination assay.

RESULTS

We observed evidence for association, demonstrating departure from Mendel's law, with an overtransmission of the rs10818488 A allele (A = 55%; P = 0.036). By taking into consideration parental phenotypes, we also observed an increased A allele frequency in affected versus unaffected parents (A = 64%; combined P = 0.015). Individuals carrying the A allele had a 1.2-fold increased risk of developing RA (allelic odds ratio 1.24, 95% confidence interval 1.04-1.50).

CONCLUSION

Using a family-based study that is robust against population stratification, we provide evidence for the association of the TRAF1/C5 rs10818488 A allele and RA in populations of European descent, further substantiating our previous findings. Future functional studies should yield insight into the biologic relevance of this locus to the pathways involved in RA.

The TRAF1-C5 region on chromosome 9q33 is associated with multiple autoimmune diseases

OBJECTIVES

The TRAF1-C5 locus has recently been identified as a genetic risk factor for rheumatoid arthritis (RA). Since genetic risk factors tend to overlap with several autoimmune diseases, a study was undertaken to investigate whether this region is associated with type 1 diabetes (TID), celiac disease (CD), systemic sclerosis (SSc) and systemic lupus erythematosus (SLE).

METHODS

The most consistently associated SNP, rs10818488, was genotyped in a total of 735 patients with T1D, 1049 with CD, 367 with SSc, 746 with SLE and 3494 ethnically- and geographically-matched healthy individuals. The replication sample set consisted of 99 patients with T1D, 272 with SLE and 482 healthy individuals from Crete.

RESULTS

A significant association was detected between the rs10818488 A allele and T1D (OR 1.14, p=0.027) and SLE (OR 1.16, p=0.016), which was replicated in 99 patients with T1D, 272 with SLE and 482 controls from Crete (OR 1.64, p=0.002; OR 1.43, p=0.002, respectively). Joint analysis of all patients with T1D (N=961) and all patients with SLE (N=1018) compared with 3976 healthy individuals yielded an allelic common OR of 1.19 (p=0.002) and 1.22 (p=2.6 x 10(-4)), respectively. However, combining our dataset with the T1D sample set from the WTCCC resulted in a non-significant association (OR 1.06, p=0.087). In contrast, previously unpublished results from the SLEGEN study showed a significant association of the same allele (OR 1.19, p=0.0038) with an overall effect of 1.22 (p=1.02 x 10(-6)) in a total of 1577 patients with SLE and 4215 healthy individuals.

CONCLUSION

A significant association was found for the TRAF1-C5 locus in SLE, implying that this region lies in a pathway relevant to multiple autoimmune diseases.

A candidate gene approach identifies the TRAF1/C5 region as a risk factor for rheumatoid arthritis

BACKGROUND

Rheumatoid arthritis (RA) is a chronic autoimmune disorder affecting approximately 1% of the population. The disease results from the interplay between an individual's genetic background and unknown environmental triggers. Although human leukocyte antigens (HLAs) account for approximately 30% of the heritable risk, the identities of non-HLA genes explaining the remainder of the genetic component are largely unknown. Based on functional data in mice, we hypothesized that the immune-related genes complement component 5 (C5) and/or TNF receptor-associated factor 1 (TRAF1), located on Chromosome 9q33-34, would represent relevant candidate genes for RA. We therefore aimed to investigate whether this locus would play a role in RA.

METHODS AND FINDINGS

We performed a multitiered case-control study using 40 single-nucleotide polymorphisms (SNPs) from the TRAF1 and C5 (TRAF1/C5) region in a set of 290 RA patients and 254 unaffected participants (controls) of Dutch origin. Stepwise replication of significant SNPs was performed in three independent sample sets from the Netherlands (ncases/controls = 454/270), Sweden (ncases/controls = 1,500/1,000) and US (ncases/controls = 475/475). We observed a significant association (p < 0.05) of SNPs located in a haplotype block that encompasses a 65 kb region including the 3' end of C5 as well as TRAF1. A sliding window analysis revealed an association peak at an intergenic region located approximately 10 kb from both C5 and TRAF1. This peak, defined by SNP14/rs10818488, was confirmed in a total of 2,719 RA patients and 1,999 controls (odds ratiocommon = 1.28, 95% confidence interval 1.17-1.39, pcombined = 1.40 x 10(-8)) with a population-attributable risk of 6.1%. The A (minor susceptibility) allele of this SNP also significantly correlates with increased disease progression as determined by radiographic damage over time in RA patients (p = 0.008).

CONCLUSIONS

Using a candidate-gene approach we have identified a novel genetic risk factor for RA. Our findings indicate that a polymorphism in the TRAF1/C5 region increases the susceptibility to and severity of RA, possibly by influencing the structure, function, and/or expression levels of TRAF1 and/or C5.