DNA methylation is an important epigenetic mechanism that has been linked to complex disease and is of great interest to researchers as a potential link between genome, environment, and disease. As the scale of DNA methylation association studies approaches that of genome-wide association studies (GWAS), issues such as population stratification will need to be addressed. It is well-documented that failure to adjust for population stratification can lead to false positives in genetic association studies, but population stratification is often unaccounted for in DNA methylation studies. Here, we propose several approaches to correct for population stratification using principal components from different subsets of genome-wide methylation data. We first illustrate the potential for confounding due to population stratification by demonstrating widespread associations between DNA methylation and race in 388 individuals (365 African American and 23 Caucasian). We subsequently evaluate the performance of our principal-components approaches and other methods in adjusting for confounding due to population stratification. Our simulations show that 1) all of the methods considered are effective at removing inflation due to population stratification, and 2) maximum power can be obtained with SNP-based principal components, followed by methylation-based principal components, which out-perform both surrogate variable analysis and genomic control. Among our different approaches to computing methylation-based principal components, we find that principal components based on CpG sites chosen for their potential to proxy nearby SNPs can provide a powerful and computationally efficient approach to adjustment for population stratification in DNA methylation studies when genome-wide SNP data are unavailable.

Increasing empirical evidence suggests that many genetic variants influence multiple distinct phenotypes. When cross-phenotype effects exist, multivariate association methods that consider pleiotropy are often more powerful than univariate methods that model each phenotype separately. Although several statistical approaches exist for testing cross-phenotype effects for common variants, there is a lack of similar tests for gene-based analysis of rare variants. In order to fill this important gap, we introduce a statistical method for cross-phenotype analysis of rare variants using a nonparametric distance-covariance approach that compares similarity in multivariate phenotypes to similarity in rare-variant genotypes across a gene. The approach can accommodate both binary and continuous phenotypes and further can adjust for covariates. Our approach yields a closed-form test whose significance can be evaluated analytically, thereby improving computational efficiency and permitting application on a genome-wide scale. We use simulated data to demonstrate that our method, which we refer to as the Gene Association with Multiple Traits (GAMuT) test, provides increased power over competing approaches. We also illustrate our approach using exome-chip data from the Genetic Epidemiology Network of Arteriopathy.

BackgroundIn 2014, over half (54%) of the world’s population lived in urban areas and this proportion will increase to 66% by 2050. This urbanizing trend has been accompanied by an increasing number of people living in urban poor communities and slums. Lower immunization coverage is found in poorer urban dwellers in many contexts. This study aims to identify factors associated with immunization coverage in poor urban areas and slums, and to identify interventions to improve coverage.MethodsWe conducted a systematic review, searching Medline, Embase, Global Health, CINAHL, Web of Science and The Cochrane Database with broad search terms for studies published between 2000 and 2016.ResultsOf 4872 unique articles, 327 abstracts were screened, leading to 63 included studies: 44 considering factors and 20 evaluating interventions (one in both categories) in 16 low or middle-income countries. A wide range of socio-economic characteristics were associated with coverage in different contexts. Recent rural-urban migration had a universally negative effect. Parents commonly reported lack of awareness of immunization importance and difficulty accessing services as reasons for under-immunization of their children. Physical distance to clinics and aspects of service quality also impacted uptake. We found evidence of effectiveness for interventions involving multiple components, especially if they have been designed with community involvement. Outreach programmes were effective where physical distance was identified as a barrier. Some evidence was found for the effective use of SMS (text) messaging services, community-based education programmes and financial incentives, which warrant further evaluation. No interventions were identified that provided services to migrants from rural areas.ConclusionDifferent factors affect immunization coverage in different urban poor and slum contexts. Immunization services should be designed in collaboration with slum-dwelling communities, considering the local context. Interventions should be designed and tested to increase immunization in migrants from rural areas.Electronic supplementary materialThe online version of this article (doi:10.1186/s12889-017-4473-7) contains supplementary material, which is available to authorized users.

Schizophrenia occurs in about one in four individuals with 22q11.2 deletion syndrome (22q11.2DS). The aim of this International Brain and Behavior 22q11.2DS Consortium (IBBC) study was to identify genetic factors that contribute to schizophrenia, in addition to the ~20-fold increased risk conveyed by the 22q11.2 deletion. Using whole-genome sequencing data from 519 unrelated individuals with 22q11.2DS, we conducted genome-wide comparisons of common and rare variants between those with schizophrenia and those with no psychotic disorder at age ≥25 years. Available microarray data enabled direct comparison of polygenic risk for schizophrenia between 22q11.2DS and independent population samples with no 22q11.2 deletion, with and without schizophrenia (total n=35,182). Polygenic risk for schizophrenia within 22q11.2DS was significantly greater for those with schizophrenia (p adj =6.73x10-6). Novel reciprocal case-control comparisons between the 22q11.2DS and population-based cohorts showed that polygenic risk score was significantly greater in individuals with psychotic illness, regardless of the presence of the 22q11.2 deletion. Within the 22q11.2DS cohort, results of gene-set analyses showed some support for rare variants affecting synaptic genes. No common or rare variants within the 22q11.2 deletion region were significantly associated with schizophrenia. These findings suggest that in addition to conferring a greatly increased risk to schizophrenia, the risk is higher when the 22q11.2 deletion and common polygenic risk factors that contribute to schizophrenia in the general population are both present.

Many case-control tests of rare variation are implemented in statistical frameworks that make correction for confounders like population stratification difficult. Simple permutation of disease status is unacceptable for resolving this issue because the replicate data sets do not have the same confounding as the original data set. These limitations make it difficult to apply rare-variant tests to samples in which confounding most likely exists, e.g., samples collected from admixed populations. To enable the use of such rare-variant methods in structured samples, as well as to facilitate permutation tests for any situation in which case-control tests require adjustment for confounding covariates, we propose to establish the significance of a rare-variant test via a modified permutation procedure. Our procedure uses Fisher's noncentral hypergeometric distribution to generate permuted data sets with the same structure present in the actual data set such that inference is valid in the presence of confounding factors. We use simulated sequence data based on coalescent models to show that our permutation strategy corrects for confounding due to population stratification that, if ignored, would otherwise inflate the size of a rare-variant test. We further illustrate the approach by using sequence data from the Dallas Heart Study of energy metabolism traits. Researchers can implement our permutation approach by using the R package BiasedUrn.