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Supplementary Table 1 from Enrichment of Low Penetrance Susceptibility Loci in a Dutch Familial Colorectal Cancer Cohort

Recent genome-wide association studies have identified several loci that confer an increased risk of colorectal cancer (CRC). We studied the role of the 8q24.21 (rs6983267), 18q21.1 (rs12953717), 15q13.3 (rs4779584), 11q23.1 (rs3802842), 8q23.3 (rs16892766), and 10p14 (rs10795668) risk variants in a series of 995 Dutch CRC cases and 1340 controls. The CRC cases were selected on basis of having a family history of CRC and/or early-onset disease. The detailed clinical and molecular data available on the cases allowed us to examine the relationship between risk variants and clinicopathologic characteristics. We replicated the association with an increased risk of CRC cancer for all loci, except 10p14. The association with the variant on chromosome 15q13.3 was confirmed for the first time. The risks associated with variants in our series were higher (not significant) than those previously reported, consistent with our series reflecting genetic enrichment. Moreover, we show that familial CRC cases possess an increased number of risk alleles compared with solitary CRC cases (early-onset; mean age at diagnosis of 48.5 years). We also identified a significant increase in the number of risk alleles in families with early-onset disease (≤50 years) compared with late-onset families (>50 years). In solitary CRC patients, enrichment for risk alleles was not observed, suggesting that other causes of increased CRC risk play a role in these cases. Overall, our results suggest that clustering of low-risk variants may explain part of the excess risk in CRC families. (Cancer Epidemiol Biomarkers Prev 2009;18(11):3062–7)

Supplementary Figure 1 from High-Throughput Characterization of 10 New Minor Histocompatibility Antigens by Whole Genome Association Scanning

Supplementary Tables 1-3 from High-Throughput Characterization of 10 New Minor Histocompatibility Antigens by Whole Genome Association Scanning

Patients with malignant diseases can be effectively treated with allogeneic hematopoietic stem cell transplantation (allo-SCT). Polymorphic peptides presented in HLA molecules, the so-called minor histocompatibility antigens (MiHA), play a crucial role in antitumor immunity as targets for alloreactive donor T cells. Identification of multiple MiHAs is essential to understand and manipulate the development of clinical responses after allo-SCT. In this study, CD8+ T-cell clones were isolated from leukemia patients who entered complete remission after allo-SCT, and MiHA-specific T-cell clones were efficiently selected for analysis of recognition of a panel of EBV-transformed B cells positive for the HLA restriction elements of the selected T-cell clones. One million single nucleotide polymorphisms (SNP) were determined in the panel cell lines and investigated for matching with the T-cell recognition data by whole genome association scanning (WGAs). Significant association with 12 genomic regions was found, and detailed analysis of genes located within these genomic regions revealed SNP disparities encoding polymorphic peptides in 10 cases. Differential recognition of patient-type, but not donor-type, peptides validated the identification of these MiHAs. Using tetramers, distinct populations of MiHA-specific CD8+ T cells were detected, demonstrating that our WGAs strategy allows high-throughput discovery of relevant targets in antitumor immunity after allo-SCT. Cancer Res; 70(22); 9073–83. ©2010 AACR.

Cytochrome P450cam (CYP101A1) catalyzes the region- and stereo-specific 5-exo-hydroxylation of camphor via a multistep catalytic cycle that involves two-electron transfer steps, with an absolute requirement that the second electron be donated by the ferrodoxin, putidaredoxin (Pdx). Whether P450cam, once camphor has bound to the active site and the substrate entry channel has closed, opens up upon Pdx binding, during the second electron transfer step, or it remains closed is still a matter of debate. A potential allosteric site for camphor binding has been identified and postulated to play a role in the binding of Pdx. Here, we have revisited paramagnetic NMR spectroscopy data and determined a heterogeneous ensemble of structures that explains the data, provides a complete representation of the P450cam/Pdx complex in solution, and reconciles alternative hypotheses. The allosteric camphor binding site is always present, and the conformational changes induced by camphor binding to this site facilitates Pdx binding. We also determined that the state to which Pdx binds comprises an ensemble of structures that have features of both the open and closed state. These results demonstrate that there is a finely balanced interaction between allosteric camphor binding and the binding of Pdx at high camphor concentrations.

Synucleinopathies, such as Parkinson’s disease and multiple system atrophy, are characterized byα-synuclein aggregation in the brain. There is no disease-modifying therapy against synucleinopathies yet. To understand the neurobiological events underlyingα-synuclein aggregation, LUHMES cell lines have been utilized to measure transcriptomics and proteomics data and screen 1600 FDA-approved drugs for their toxicity-reducing ability. However, rather than an integrative approach, the statistical analyses were performed per dataset.We propose a novel statistical data integration method, POPLS-DA, to jointly model the transcriptomics and proteomics data and discriminate disease cells from controls. We also performed a dose-response test to validate screened FDA-approved drugs. We integrated the validated drugs with the selected genes/proteins using integrative bioinformatic analyses.We identified clusters of synaptic and lysosome-related genes targeted by the validated drugs. Furthermore,HSPA5, a member of the heat shock protein 70 family, was one of the most targeted genes. HSPA5 has been previously linked toα-synuclein pathology, showing the relevance of our findings.Our integrative approach results provide new directions for therapeutic targets for synucleinopathies.

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.

Modelling the relationship between the microbiome (MB) and a continuous marker is not straightforward due to the compositional structure of the MB and the fact that the MB is measured with an error. Moreover, the MB often represents the health status of an individual; hence, it might be an endogenous variable. When longitudinal data are available, joint modelling addresses these issues and is the choice for modelling a continuous marker and its association with the MB. We will develop two joint regression models for this type of data. The approaches differ in the used submodel for the categorical counts. The first approach uses only normally distributed random effects for the correlation structure in the data. The second approach is a combined model where the overdispersion is modelled with the conjugate distribution and the correlation between the categories and the continuous outcome with a normally distributed random effect. The code to fit these models will be described and provided. The performance of the models is studied via simulations. Both joint models appear to outperform the naive model, where one of the MB categories is included as a covariate in a linear mixed model. Both joint models are fitted to data from our motivating longitudinal study on the relationship between MB and cytokine response in Indonesia’s helminth–endemic area. The two joint models yield the same conclusion regarding the significance of fixed and random effects for the cytokine response. The estimates of the parameters modelling the association between the MB and the cytokine response are small, suggesting that there is no relationship between the cytokine response and the MB. The model with only normally distributed random effects appears better to represent the observed correlation than the combined model.

Advancement of gene expression measurements in longitudinal studies enables the identification of genes associated with disease severity over time. However, problems arise when the technology used to measure gene expression differs between time points. Observed differences between the results obtained at different time points can be caused by technical differences. Modeling the two measurements jointly over time might provide insight into the causes of these different results. Our work is motivated by a study of gene expression data of blood samples from Huntington disease patients, which were obtained using two different sequencing technologies. At time point 1, DeepSAGE technology was used to measure the gene expression, with a subsample also measured using RNA‐Seq technology. At time point 2, all samples were measured using RNA‐Seq technology. Significant associations between gene expression measured by DeepSAGE and disease severity using data from the first time point could not be replicated by the RNA‐Seq data from the second time point. We modeled the relationship between the two sequencing technologies using the data from the overlapping samples. We used linear mixed models with either DeepSAGE or RNA‐Seq measurements as the dependent variable and disease severity as the independent variable. In conclusion, (1) for one out of 14 genes, the initial significant result could be replicated with both technologies using data from both time points; (2) statistical efficiency is lost due to disagreement between the two technologies, measurement error when predicting gene expressions, and the need to include additional parameters to account for possible differences.

Background Social anxiety disorder (SAD) is a disabling psychiatric condition with a genetic background. Brain alterations in gray matter (GM) related to SAD have been previously reported, but it remains to be elucidated whether GM measures are candidate endophenotypes of SAD. Endophenotypes are measurable characteristics on the causal pathway from genotype to phenotype, providing insight in genetically-based disease mechanisms. Based on a review of existing evidence, we examined whether GM characteristics meet two endophenotype criteria, using data from a unique sample of SAD-patients and their family-members of two generations. First, we investigated whether GM characteristics co-segregate with social anxiety within families genetically enriched for SAD. Secondly, heritability of the GM characteristics was estimated. Methods Families with a genetic predisposition for SAD participated in the Leiden Family Lab study on SAD; T1-weighted MRI brain scans were acquired (n = 110, 8 families). Subcortical volumes, cortical thickness and cortical surface area were determined for a-priori determined regions of interest (ROIs). Next, associations with social anxiety and heritabilities were estimated. Findings Several subcortical and cortical GM characteristics, derived from frontal, parietal and temporal ROIs, co-segregated with social anxiety within families (uncorrected p-level) and showed moderate to high heritability. Interpretation These findings provide preliminary evidence that GM characteristics of multiple ROIs, which are distributed over the brain, are candidate endophenotypes of SAD. Thereby, they shed light on the genetic vulnerability for SAD. Future research is needed to confirm these results and to link them to functional brain alterations and to genetic variations underlying these GM changes. Fund Leiden University Research Profile ‘Health, Prevention and the Human Life Cycle’., Highlights • Gray matter (GM) characteristics were examined exploring their potential as endophenotypes of social anxiety disorder (SAD). • Several GM characteristics co-segregated with social anxiety within families genetically enriched for SAD and were heritable. • These findings suggest that GM characteristics are candidate endophenotypes of SAD.

Purpose: Biallelic pathogenic variants in the mismatch repair (MMR) genes cause a recessive childhood cancer predisposition syndrome known as constitutional mismatch repair deficiency (CMMRD). Family members with a heterozygous MMR variant have Lynch syndrome. We aimed at estimating cancer risk in these heterozygous carriers as a novel approach to avoid complicated statistical methods to correct for ascertainment bias.Methods: Cumulative colorectal cancer incidence was estimated in a cohort of PMS2- and MSH6-associated families, ascertained by the CMMRD phenotype of the index, by using mutation probabilities based on kinship coefficients as analytical weights in a proportional hazard regression on the cause-specific hazards. Confidence intervals (CIs) were obtained by bootstrapping at the family level.Results: The estimated cumulative colorectal cancer risk at age 70 years for heterozygous PMS2 variant carriers was 8.7% (95% CI 4.3-12.7%) for both sexes combined, and 9.9% (95% CI 4.9-15.3%) for men and 5.9% (95% CI 1.6-11.1%) for women separately. For heterozygous MSH6 variant carriers these estimates are 11.8% (95% CI 4.5-22.7%) for both sexes combined, 10.0% (95% CI 1.83-24.5%) for men and 11.7% (95% CI 2.10-26.5%) for women.Conclusion: Our findings are consistent with previous reports that used more complex statistical methods to correct for ascertainment bias. These results underline the need for MMR gene-specific surveillance protocols for Lynch syndrome.

Emerging evidence suggests that the basis for variation in late-life mobility is attributable, in part, to genetic factors, which may become increasingly important with age. Our objective was to systematically assess the contribution of genetic variation to gait speed in older individuals. We conducted a meta-analysis of gait speed GWASs in 31,478 older adults from 17 cohorts of the CHARGE consortium, and validated our results in 2,588 older adults from 4 independent studies. We followed our initial discoveries with network and eQTL analysis of candidate signals in tissues. The meta-analysis resulted in a list of 536 suggestive genome wide significant SNPs in or near 69 genes. Further interrogation with Pathway Analysis placed gait speed as a polygenic complex trait in five major networks. Subsequent eQTL analysis revealed several SNPs significantly associated with the expression of PRSS16, WDSUB1 and PTPRT, which in addition to the meta-analysis and pathway suggested that genetic effects on gait speed may occur through synaptic function and neuronal development pathways. No genome-wide significant signals for gait speed were identified from this moderately large sample of older adults, suggesting that more refined physical function phenotypes will be needed to identify the genetic basis of gait speed in aging.

Background: Soil-transmitted helminths have been shown to have the immune regulatory capacity, which they use to enhance their long term survival within their host. As these parasites reside in the gastrointestinal tract, they might modulate the immune system through altering the gut bacterial composition. Although the relationships between helminth infections or the microbiome with the immune system have been studied separately, their combined interactions are largely unknown. In this study we aim to analyze the relationship between bacterial communities with cytokine response in the presence or absence of helminth infections. Results: For 66 subjects from a randomized placebo-controlled trial, stool and blood samples were available at both baseline and 21 months after starting three-monthly albendazole treatment. The stool samples were used to identify the helminth infection status and fecal microbiota composition, while whole blood samples were cultured to obtain cytokine responses to innate and adaptive stimuli. When subjects were free of helminth infection (helminth-negative), increasing proportions of Bacteroidetes was associated with lower levels of IL-10 response to LPS {estimate [95% confidence interval (CI)] −1.96 (−3.05, −0.87)}. This association was significantly diminished when subjects were helminth-infected (helminth positive) (p-value for the difference between helminth-negative versus helminth-positive was 0.002). Higher diversity was associated with greater IFN-γ responses to PHA in helminth-negative (0.95 (0.15, 1.75); versus helminth-positive [−0.07 (−0.88, 0.73), p-value = 0.056] subjects. Albendazole treatment showed no direct effect in the association between bacterial proportion and cytokine responses, although the Bacteroidetes’ effect on IL-10 responses to LPS tended downward in the albendazole-treated group [−1.74 (−4.08, 0.59)] versus placebo [−0.11 (−0.84, 0.62); p-value = 0.193]. Conclusion: We observed differences in the relationship between gut microbiome composition and immune responses, when comparing individuals infected or uninfected with geohelminths. Although these findings are part of a preliminary exploration, the data support the hypothesis that intestinal helminths may modulate immune responses, in unison with the gut microbiota. Trial Registration: ISRCTN, ISRCTN83830814. Registered 27 February 2008 — Retrospectively registered, http://www.isrctn.com/ISRCTN83830814.

Marginal tests based on individual SNPs are routinely used in genetic association studies. Studies have shown that haplotype‐based methods may provide more power in disease mapping than methods based on single markers when, for example, multiple disease‐susceptibility variants occur within the same gene. A limitation of haplotype‐based methods is that the number of parameters increases exponentially with the number of SNPs, inducing a commensurate increase in the degrees of freedom and weakening the power to detect associations. To address this limitation, we introduce a hierarchical linkage disequilibrium model for disease mapping, based on a reparametrization of the multinomial haplotype distribution, where every parameter corresponds to the cumulant of each possible subset of a set of loci. This hierarchy present in the parameters enables us to employ flexible testing strategies over a range of parameter sets: from standard single SNP analyses through the full haplotype distribution tests, reducing degrees of freedom and increasing the power to detect associations. We show via extensive simulations that our approach maintains the type I error at nominal level and has increased power under many realistic scenarios, as compared to single SNP and standard haplotype‐based studies. To evaluate the performance of our proposed methodology in real data, we analyze genome‐wide data from the Wellcome Trust Case‐Control Consortium.

Clustered overdispersed multivariate count data are challenging to model due to the presence of correlation within and between samples. Typically, the first source of correlation needs to be addressed but its quantification is of less interest. Here, we focus on the correlation between time points. In addition, the effects of covariates on the multivariate counts distribution need to be assessed. To fulfill these requirements, a regression model based on the Dirichlet-multinomial distribution for association between covariates and the categorical counts is extended by using random effects to deal with the additional clustering. This model is the Dirichlet-multinomial mixed regression model. Alternatively, a negative binomial regression mixed model can be deployed where the corresponding likelihood is conditioned on the total count. It appears that these two approaches are equivalent when the total count is fixed and independent of the random effects. We consider both subject-specific and categorical-specific random effects. However, the latter has a larger computational burden when the number of categories increases. Our work is motivated by microbiome data sets obtained by sequencing of the amplicon of the bacterial 16S rRNA gene. These data have a compositional structure and are typically overdispersed. The microbiome data set is from an epidemiological study carried out in a helminth-endemic area in Indonesia. The conclusions are as follows: time has no statistically significant effect on microbiome composition, the correlation between subjects is statistically significant, and treatment has a significant effect on the microbiome composition only in infected subjects who remained infected.

The development of immune responses is influenced by the interaction between environmental and genetic factors. Our previous study showed a close association between maternal and young infant's cytokine responses. The question is how this association evolves over time and the contribution of genetic polymorphisms to this association. Five cytokines in mitogen-stimulated whole blood culture were measured from pregnant mothers and their children aged 2, 5, 12, 24 and 48 months. Cytokine gene polymorphisms were determined in both mothers and children. High production of maternal interleukin (IL)-10, tumour necrosis factor-alpha (TNF-alpha) and interferon-gamma (IFN-gamma) was significantly associated with higher levels of the corresponding cytokines in their children at 2 months (T2), but the association decreased over time. Maternal single-nucleotide polymorphism (SNP) in IFN-gamma gene, rs3181032, was found to be associated with child's IFN-gamma levels at T2 only, whereas maternal IL-10 rs4579758 and child's TNF-alpha rs13215091 were associated with child's corresponding cytokines at later ages but not at T2. In the final models including the gene polymorphisms, maternal cytokines were still the strongest determinant of child cytokines. Maternal cytokine during pregnancy, which could be a proxy for child's environmental factors, showed its highest impact at early age, with no or little influence from genetic factors.

Logic regression is an adaptive regression method which searches for Boolean (logic) combinations of binary variables that best explain the variability in the outcome, and thus, it reveals interaction effects which are associated with the response. In this study, we extended logic regression to longitudinal data with binary response and proposed “Transition Logic Regression Method” to find interactions related to response. In this method, interaction effects over time were found by Annealing Algorithm with AIC (Akaike Information Criterion) as the score function of the model. Also, first and second orders Markov dependence were allowed to capture the correlation among successive observations of the same individual in longitudinal binary response. Performance of the method was evaluated with simulation study in various conditions. Proposed method was used to find interactions of SNPs and other risk factors related to low HDL over time in data of 329 participants of longitudinal TLGS study.

Summary Looking young for one's age has been a desire since time immemorial. This desire is attributable to the belief that appearance reflects health and fecundity. Indeed, perceived age predicts survival [1] and associates with molecular markers of aging such as telomere length [2]. Understanding the underlying molecular biology of perceived age is vital for identifying new aging therapies among other purposes, but studies are lacking thus far. As a first attempt, we performed genome-wide association studies (GWASs) of perceived facial age and wrinkling estimated from digital facial images by analyzing over eight million SNPs in 2,693 elderly Dutch Europeans from the Rotterdam Study. The strongest genetic associations with perceived facial age were found for multiple SNPs in the MC1R gene (p −7 ). This effect was enhanced for a compound heterozygosity marker constructed from four pre-selected functional MC1R SNPs (p = 2.69 × 10 −12 ), which was replicated in 599 Dutch Europeans from the Leiden Longevity Study (p = 0.042) and in 1,173 Europeans of the TwinsUK Study (p = 3 × 10 −3 ). Individuals carrying the homozygote MC1R risk haplotype looked on average up to 2 years older than non-carriers. This association was independent of age, sex, skin color, and sun damage (wrinkling, pigmented spots) and persisted through different sun-exposure levels. Hence, a role for MC1R in youthful looks independent of its known melanin synthesis function is suggested. Our study uncovers the first genetic evidence explaining why some people look older for their age and provides new leads for further investigating the biological basis of how old or young people look.

Emerging evidence suggests that the basis for variation in late-life mobility is attributable, in part, to genetic factors, which may become increasingly important with age. Our objective was to systematically assess the contribution of genetic variation to gait speed in older individuals. We conducted a meta-analysis of gait speed GWASs in 31,478 older adults from 17 cohorts of the CHARGE consortium, and validated our results in 2,588 older adults from 4 independent studies. We followed our initial discoveries with network and eQTL analysis of candidate signals in tissues. The meta-analysis resulted in a list of 536 suggestive genome wide significant SNPs in or near 69 genes. Further interrogation with Pathway Analysis placed gait speed as a polygenic complex trait in five major networks. Subsequent eQTL analysis revealed several SNPs significantly associated with the expression of PRSS16, WDSUB1 and PTPRT, which in addition to the meta-analysis and pathway suggested that genetic effects on gait speed may occur through synaptic function and neuronal development pathways. No genome-wide significant signals for gait speed were identified from this moderately large sample of older adults, suggesting that more refined physical function phenotypes will be needed to identify the genetic basis of gait speed in aging.

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”). Electronic supplementary material The online version of this article (10.1186/s12859-018-2371-3) contains supplementary material, which is available to authorized users.

Background: Social anxiety disorder (SAD) is the extreme fear and avoidance of one or more social situations. The goal of the current study was to investigate whether heart rate variability (HRV) during resting state and a social performance task (SPT) is a candidate endophenotype of SAD. Methods: In this two-generation family study, patients with SAD with their partner and children, and their siblings with partner and children took part in a SPT (total n = 121, 9 families, 3–30 persons per family, age range: 8–61 years, 17 patients with SAD). In this task, participants had to watch and evaluate the speech of a female peer, and had to give a similar speech. HRV was measured during two resting state phases, and during anticipation, speech and recovery phases of the SPT. We tested two criteria for endophenotypes: co-segregation with SAD within families and heritability. Results: HRV did not co-segregate with SAD within families. Root mean square of successive differences during the first resting phase and recovery, and high frequency power during all phases of the task were heritable. Limitations: It should be noted that few participants were diagnosed with SAD. Results during the speech should be interpreted with caution, because the duration was short and there was a lot of movement. Conclusions: HRV during resting state and the SPT is a possible endophenotype, but not of SAD. As other studies have shown that HRV is related to different internalizing disorders, HRV might reflect a transdiagnostic genetic vulnerability for internalizing disorders. Future research should investigate which factors influence the development of psychopathology in persons with decreased HRV.

The main goal of this paper is to estimate the effect of triglyceride levels on methylation of cytosine-phosphate-guanine (CpG) sites in multiple-case families. These families are selected because they have 2 or more cases of metabolic syndrome (primary phenotype). The methylations at the CpG sites are the secondary phenotypes. Ascertainment corrections are needed when there is an association between the primary and secondary phenotype. We will apply the newly developed secondary phenotype analysis for multiple-case family studies to identify CpG sites where methylations are influenced by triglyceride levels. Our second goal is to compare the performance of the naïve approach, which ignores the sampling of the families, SOLAR (Sequential Oligogenic Linkage Analysis Routines), which adjusts for ascertainment via probands, and the secondary phenotype approach. The analysis of possible CpG sites associated with triglyceride levels shows results consistent with the literature when using the secondary phenotype approach. Overall, the secondary phenotype approach performed well, but the comparison of the different approaches does not show significant differences between them. However, for genome-wide applications, we recommend using the secondary phenotype approach when there is an association between primary and secondary phenotypes, and to use the naïve approach otherwise.

Hydrogen/deuterium exchange monitored by mass spectrometry is a promising technique for rapidly fingerprinting structural and dynamical properties of proteins. The time-dependent change in the mass of any fragment of the polypeptide chain depends uniquely on the rate of exchange of its amide hydrogens, but determining the latter from the former is generally not possible. Here, we show that, if time-resolved measurements are available for a number of overlapping peptides that cover the whole sequence, rate constants for each amide hydrogen exchange (or equivalently, their protection factors) may be extracted and the uniqueness of the solutions obtained depending on the degree of peptide overlap. However, in most cases, the solution is not unique, and multiple alternatives must be considered. We provide a statistical method that clusters the solutions to further reduce their number. Such analysis always provides meaningful constraints on protection factors and can be used in situations in which obtaining more refined experimental data is impractical. It also provides a systematic way to improve data collection strategies to obtain unambiguous information at single-residue level (e.g., for assessing protein structure predictions at atomistic level).

Background Microbiome studies suggest the presence of an interaction between the human gut microbiome and soil-transmitted helminth. Upon deworming, a complex interaction between the anthelminthic drug, helminths and microbiome composition might occur. To dissect this, we analyse the changes that take place in the gut bacteria profiles in samples from a double blind placebo controlled trial conducted in an area endemic for soil transmitted helminths in Indonesia. Methods Either placebo or albendazole were given every three months for a period of one and a half years. Helminth infection was assessed before and at 3 months after the last treatment round. In 150 subjects, the bacteria were profiled using the 454 pyrosequencing. Statistical analysis was performed cross-sectionally at pre-treatment to assess the effect of infection, and at post-treatment to determine the effect of infection and treatment on microbiome composition using the Dirichlet-multinomial regression model. Results At a phylum level, at pre-treatment, no difference was seen in microbiome composition in terms of relative abundance between helminth-infected and uninfected subjects and at post-treatment, no differences were found in microbiome composition between albendazole and placebo group. However, in subjects who remained infected, there was a significant difference in the microbiome composition of those who had received albendazole and placebo. This difference was largely attributed to alteration of Bacteroidetes. Albendazole was more effective against Ascaris lumbricoides and hookworms but not against Trichuris trichiura, thus in those who remained infected after receiving albendazole, the helminth composition was dominated by T. trichiura. Discussion We found that overall, albendazole does not affect the microbiome composition. However, there is an interaction between treatment and helminths as in subjects who received albendazole and remained infected there was a significant alteration in Bacteroidetes. This helminth-albendazole interaction needs to be studied further to fully grasp the complexity of the effect of deworming on the microbiome. Trial registration ISRCTN Registy, ISRCTN83830814., Author summary Studying the relationship between soil-transmitted helminthiasis and gut microbiota is becoming more important as both have been implicated in modulating immune system in various inflammatory diseases. However, findings of previous studies of the effect of helminth on the microbiome are inconsistent. In this study, an optimal design, a placebo-controlled anthelminthic trial was conducted to dissect the effect of helminths and anthelminthic treatment on gut microbial profile. In addition, a novel statistical model was used to analyse the association by taking into account the correlation structures between bacterial categories by applying multivariate analysis whereby the multiple testing correction is not needed.

Social anxiety disorder (SAD) is a serious and prevalent psychiatric condition, with a heritable component. However, little is known about the characteristics that are associated with the genetic component of SAD, the so-called "endophenotypes". These endophenotypes could advance our insight in the genetic susceptibility to SAD, as they are on the pathway from genotype to phenotype. The Leiden Family Lab study on Social Anxiety Disorder (LFLSAD) is the first multiplex, multigenerational study aimed to identify neurocognitive endophenotypes of social anxiety. The LFLSAD is characterized by a multidisciplinary approach and encompasses a variety of measurements, including a clinical interview, functional and structural magnetic resonance imaging and an electroencephalography experiment. Participants are family members from 2 generations, from families genetically enriched for SAD. The sample (n = 132 participants, from 9 families) was characterized by a high prevalence of SAD, in both generations (prevalence (sub)clinical SAD: 38.3%). Furthermore, (sub)clinical SAD was positively related to self-reported social anxiety, fear of negative evaluation, trait anxiety, behavioral inhibition, negative affect, and the level of depressive symptoms. By the multidimensional character of the measurements and thorough characterization of the sample, the LFLSAD offers unique opportunities to investigate candidate neurocognitive endophenotypes of SAD. OBJECTIVES METHODS RESULTS CONCLUSIONS

In the field of aging research, family-based sampling study designs are commonly used to study the lifespans of long-lived family members. However, the specific sampling procedure should be carefully taken into account in order to avoid biases. This work is motivated by the Leiden Longevity Study, a family-based cohort of long-lived siblings. Families were invited to participate in the study if at least two siblings were ‘long-lived’, where ‘long-lived’ meant being older than 89 years for men or older than 91 years for women. As a result, more than 400 families were included in the study and followed for around 10 years. For estimation of marker-specific survival probabilities and correlations among life times of family members, delayed entry due to outcome-dependent sampling mechanisms has to be taken into account. We consider shared frailty models to model left-truncated correlated survival data. The treatment of left truncation in shared frailty models is still an open issue and the literature on this topic is scarce. We show that the current approaches provide, in general, biased estimates and we propose a new method to tackle this selection problem by applying a correction on the likelihood estimation by means of inverse probability weighting at the family level.

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.

With a rapid increase in volume and complexity of data sets, there is a need for methods that can extract useful information, for example the relationship between two data sets measured for the same persons. The Partial Least Squares (PLS) method can be used for this dimension reduction task. Within life sciences, results across studies are compared and combined. Therefore, parameters need to be identifiable, which is not the case for PLS. In addition, PLS is an algorithm, while epidemiological study designs are often outcome-dependent and methods to analyze such data require a probabilistic formulation. Moreover, a probabilistic model provides a statistical framework for inference. To address these issues, we develop Probabilistic PLS (PPLS). We derive maximum likelihood estimators that satisfy the identifiability conditions by using an EM algorithm with a constrained optimization in the M step. We show that the PPLS parameters are identifiable up to sign. A simulation study is conducted to study the performance of PPLS compared to existing methods. The PPLS estimates performed well in various scenarios, even in high dimensions. Most notably, the estimates seem to be robust against departures from normality. To illustrate our method, we applied it to IgG glycan data from two cohorts. Our PPLS model provided insight as well as interpretable results across the two cohorts., Comment: Accepted in Journal of Multivariate Analysis

Members of longevous families live longer than individuals from similar birth cohorts and delay/escape age-related diseases. Insight into this familial component of longevity can provide important knowledge about mechanisms protecting against age-related diseases. This familial component of longevity was studied in the Leiden Longevity Study which consists of 944 longevous siblings (participants), their parents (N = 842), siblings (N = 2,302), and spouses (N = 809). Family longevity scores were estimated to explore whether human longevity is transmitted preferentially through the maternal or paternal line. Standardized mortality ratios (SMRs) were estimated to investigate whether longevous siblings have a survival advantage compared with longevous singletons and we investigated whether parents of longevous siblings harbor a life-long sustained survival advantage compared with the general Dutch population by estimating lifetime SMRs (L-SMRs). We found that sibships with long-lived mothers and non-long-lived fathers had 0.41 (p = .024) less observed deaths than sibships with long-lived fathers and non-long-lived mothers and 0.48 (p = .008) less observed deaths than sibships with both parents non-long lived. Participants had 18.6 per cent less deaths compared with matched singletons and parents had a life-long sustained survival advantage (L-SMR = 0.510 and 0.688). In conclusion, genetic longevity studies may incorporate the maternal transmission pattern and genes influencing the entire life-course of individuals.

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.\ud \ud \ud \ud 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.\ud \ud \ud \ud 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.\ud \ud \ud \ud 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.

Reduced insulin/insulin-like growth factor 1 (IGF-1) signaling has been associated with longevity in various model organisms. However, the role of insulin/IGF-1 signaling in human survival remains controversial. The aim of this study was to test whether circulating IGF-1 axis parameters associate with old age survival and functional status in nonagenarians from the Leiden Longevity Study. This study examined 858 Dutch nonagenarian (males≥89 years; females≥91 years) siblings from 409 families, without selection on health or demographic characteristics. Nonagenarians were divided over sex-specific strata according to their levels of IGF-1, IGF binding protein 3 and IGF-1/IGFBP3 molar ratio. We found that lower IGF-1/IGFBP3 ratios were associated with improved survival: nonagenarians in the quartile of the lowest ratio had a lower estimated hazard ratio (95% confidence interval) of 0.73 (0.59 - 0.91) compared to the quartile with the highest ratio (ptrend=0.002). Functional status was assessed by (Instrumental) Activities of Daily Living ((I)ADL) scales. Compared to those in the quartile with the highest IGF-1/IGFBP3 ratio, nonagenarians in the lowest quartile had higher scores for ADL (ptrend=0.001) and IADL (ptrend=0.003). These findings suggest that IGF-1 axis parameters are associated with increased old age survival and better functional status in nonagenarians from the Leiden Longevity Study.

Objective To identify osteoarthritis (OA) progression–modulating pathways in articular cartilage and their respective regulatory epigenetic and genetic determinants in end-stage disease. Methods Transcriptional activity of CpG was assessed using gene expression data and DNA methylation data for preserved and lesional articular cartilage samples. Disease-responsive transcriptionally active CpG were identified by means of differential methylation between preserved and lesional cartilage. Transcriptionally relevant genetic determinants were addressed by means of single-nucleotide polymorphisms (SNPs) proximal to the OA-responsive transcriptionally active CpG. Statistical analyses were corrected for age, sex, joint, and technical covariates. A random effect was included to correct for possible correlations between paired samples. Results Of 9,838 transcribed genes in articular cartilage, 2,324 correlated with the methylation status of 3,748 transcriptionally active CpG; both negative (n = 1,741) and positive (n = 2,007) correlations were observed. Hypomethylation and hypermethylation (false discovery rate of 0.05) were observed for 62 and 25 transcriptionally active CpG, respectively, covering 70 unique genes. Enrichment for developmental and extracellular matrix maintenance pathways indicated possible reactivation of endochondral ossification. Finally, we observed 31 and 26 genes for which methylation and expression, respectively, were additionally affected by genetic variation. Conclusion We identified tissue-specific genes involved in OA disease progression, reflected by genetic and pathologic epigenetic regulation of transcription, primarily at genes involved in development. Therefore, transcriptionally active SNPs near these genes may serve as putative susceptibility alleles. Our results constitute an important step toward understanding the reported widespread epigenetic changes occurring in OA articular cartilage and toward subsequent development of treatments targeting disease-driving pathways.