Influence of control selection in genome-wide association studies: the example of diabetes in the Framingham Heart Study

Epidemiologic study designs represent a major challenge for genome-wide association studies. Most such studies to date have selected controls from the pool of participants without the disease of interest at the end of the study time. These choices can lead to biased estimates of exposure effects. Using data from the Framingham Heart Study (Genetic Analysis Workshop 16 Problem 2), we evaluate the impact on genetic association estimates for designs with control selection based on status at the end of a study (case exclusion (CE) sampling) to control selection based on incidence density (ID) sampling, when controls are selected from the pool of participants who are disease-free at the time a case is diagnosed. Cases are defined as those diagnosed with type 2 diabetes (T2D). We estimated odds ratios for 18 previously confirmed T2D variants using 189 cases selected by ID sampling and using 231 cases selected by CE sampling. We found none of these single-nucleotide polymorphisms to be significantly associated with T2D using either design. Because these empirical analyses were based on a small number of cases and on single-nucleotide polymorphisms with likely small effect sizes, we supplemented this work with simulated data sets of 500 cases from each strategies across a variety of allele frequencies and effect sizes. In our simulated datasets, we show ID sampling to be less biased than CE, although CE shows apparent increased power due to the upward bias of point estimates. We conclude that ID sampling is an appropriate option for genome-wide association studies

“Gap hunting” to characterize clustered probe signals in Illumina methylation array data

Additional file 6: Figures S26–S31. All remaining SBE site scenarios. Each additional scenario of a SBE site-mapping SNP delimited in Fig. 4 not including the scenario shown in Fig. 5. Each of these figures contains 4 plots, showing every combination of CpG site interrogations on the forward and reverse strand as well as which nucleotide is the reference nucleotide

Comparison of artificial neural network analysis with other multimarker methods for detecting genetic association

<p>Abstract</p> <p>Background</p> <p>Debate remains as to the optimal method for utilising genotype data obtained from multiple markers in case-control association studies. I and colleagues have previously described a method of association analysis using artificial neural networks (ANNs), whose performance compared favourably to single-marker methods. Here, the perfomance of ANN analysis is compared with other multi-marker methods, comprising different haplotype-based analyses and locus-based analyses.</p> <p>Results</p> <p>Of several methods studied and applied to simulated SNP datasets, heterogeneity testing of estimated haplotype frequencies using asymptotic <it>p </it>values rather than permutation testing had the lowest power of the methods studied and ANN analysis had the highest power. The difference in power to detect association between these two methods was statistically significant (<it>p </it>= 0.001) but other comparisons between methods were not significant. The raw <it>t </it>statistic obtained from ANN analysis correlated highly with the empirical statistical significance obtained from permutation testing of the ANN results and with the <it>p </it>value obtained from the heterogeneity test.</p> <p>Conclusion</p> <p>Although ANN analysis was more powerful than the standard haplotype-based test it is unlikely to be taken up widely. The permutation testing necessary to obtain a valid <it>p </it>value makes it slow to perform and it is not underpinned by a theoretical model relating marker genotypes to disease phenotype. Nevertheless, the superior performance of this method does imply that the widely-used haplotype-based methods for detecting association with multiple markers are not optimal and efforts could be made to improve upon them. The fact that the <it>t </it>statistic obtained from ANN analysis is highly correlated with the statistical significance does suggest a possibility to use ANN analysis in situations where large numbers of markers have been genotyped, since the <it>t</it> value could be used as a proxy for the <it>p </it>value in preliminary analyses.</p

Measurement of the 1S0^{1}S_{0} neutron-neutron effective range in neutron-deuteron breakup

We report the most precise determination of the $^{1}S_{0}$ neutron-neutron effective range parameter $(r_{nn})$ from neutron-neutron quasifree scattering in neutron-deuteron breakup. The experiment setup utilized a collimated beam of 15.5 MeV neutrons and an array of eight neutron detectors positioned at angles sensitive to several quasifree scattering kinematic configurations. The two neutrons emitted from the breakup reaction were detected in coincidence and time-of-flight techniques were used to determine their energies. The beam-target luminosity was measured in-situ with the yields from neutron-deuteron elastic scattering. Rigorous Faddeev-type calculations using the CD Bonn nucleon-nucleon potential were fit to our cross-section data to determine the value of $r_{nn}$. The analysis was repeated using a semilocal momentum-space regularized $N^{4}LO^{+}$ chiral interaction potential. We obtained values of $r_{nn}=2.86 \pm 0.01 (stat) \pm 0.10 (sys)$ fm and $r_{nn}=2.87 \pm 0.01 (stat) \pm 0.10 (sys)$ fm using the CD Bonn and $N^{4}LO^{+}$ potentials, respectively. Our results are consistent with charge symmetry and previously reported values of $r_{nn}$

Family history of immune conditions and autism spectrum and developmental disorders: Findings from the study to explore early development

Numerous studies have reported immune system disturbances in individuals with autism and their family members; however, there is considerable variability in findings with respect to the specific immune conditions involved, their timing, and the family members affected and little understanding of variation by autism subphenotype. Using data from the Study to Explore Early Development (SEED), a multi-site case-control study of children born 2003–2006 in the United States, we examined the role of family history of autoimmune diseases, asthma, and allergies in autism spectrum disorder (ASD) as well as other developmental disorders (DD). We investigated maternal immune conditions during the pregnancy period, as well as lifetime history of these conditions in several family members (mother, father, siblings, and study child). Logistic regression analyses included 663 children with ASD, 984 children with DD, and 915 controls ascertained from the general population (POP). Maternal history of eczema/psoriasis and asthma was associated with a 20%–40% increased odds of both ASD and DD. Risk estimates varied by specific ASD subphenotypes in association with these exposures. In addition, children with ASD were more likely to have a history of psoriasis/eczema or allergies than POP controls. No association was observed for paternal history or family history of these immune conditions for either ASD or DD. These data support a link between maternal and child immune conditions and adverse neurodevelopmental outcomes, and further suggest that associations may differ by ASD phenotype of the child. Autism Research 2019, 12: 123–135. © 2018 International Society for Autism Research, Wiley Periodicals, Inc. Lay Summary: Using data from a large multi-site study in the US—the Study to Explore Early Development—we found that women with a history of eczema/psoriasis and asthma are more likely to have children with ASD or DD. In addition, children with ASD are more likely to have a history of psoriasis/eczema or allergies than typically developing children. These data support a link between maternal and child immune conditions and adverse neurodevelopmental outcomes

A Bayesian method for evaluating and discovering disease loci associations

Background: A genome-wide association study (GWAS) typically involves examining representative SNPs in individuals from some population. A GWAS data set can concern a million SNPs and may soon concern billions. Researchers investigate the association of each SNP individually with a disease, and it is becoming increasingly commonplace to also analyze multi-SNP associations. Techniques for handling so many hypotheses include the Bonferroni correction and recently developed Bayesian methods. These methods can encounter problems. Most importantly, they are not applicable to a complex multi-locus hypothesis which has several competing hypotheses rather than only a null hypothesis. A method that computes the posterior probability of complex hypotheses is a pressing need. Methodology/Findings: We introduce the Bayesian network posterior probability (BNPP) method which addresses the difficulties. The method represents the relationship between a disease and SNPs using a directed acyclic graph (DAG) model, and computes the likelihood of such models using a Bayesian network scoring criterion. The posterior probability of a hypothesis is computed based on the likelihoods of all competing hypotheses. The BNPP can not only be used to evaluate a hypothesis that has previously been discovered or suspected, but also to discover new disease loci associations. The results of experiments using simulated and real data sets are presented. Our results concerning simulated data sets indicate that the BNPP exhibits both better evaluation and discovery performance than does a p-value based method. For the real data sets, previous findings in the literature are confirmed and additional findings are found. Conclusions/Significance: We conclude that the BNPP resolves a pressing problem by providing a way to compute the posterior probability of complex multi-locus hypotheses. A researcher can use the BNPP to determine the expected utility of investigating a hypothesis further. Furthermore, we conclude that the BNPP is a promising method for discovering disease loci associations. © 2011 Jiang et al

Scaling of the surface vasculature on the human placenta

The networks of veins and arteries on the chorionic plate of the human placenta are analyzed in terms of Voronoi cells derived from these networks. Two groups of placentas from the United States are studied: a population cohort with no prescreening, and a cohort from newborns with an elevated risk of developing autistic spectrum disorder. Scaled distributions of the Voronoi cell areas in the two cohorts collapse onto a single distribution, indicating common mechanisms for the formation of the complete vasculatures, but which have different levels of activity in the two cohorts

Linkage disequilibrium in young genetically isolated Dutch population

The design and feasibility of genetic studies of complex diseases are critically dependent on the extent and distribution of linkage disequilibrium (LD) across the genome and between different populations. We have examined genomewide and region-specific LD in a young genetically isolated population identified in the Netherlands by genotyping approximately 800 Short Tandem Repeat markers distributed genomewide across 58 individuals. Several regions were an

Variable DNA methylation in neonates mediates the association between prenatal smoking and birth weight

There is great interest in the role epigenetic variation induced by non-genetic exposures may play in the context of health and disease. In particular, DNA methylation has previously been shown to be highly dynamic during the earliest stages of development and is influenced by in utero exposures such as maternal smoking and medication. In this study we sought to identify the specific DNA methylation differences in blood associated with prenatal and birth factors, including birth weight, gestational age and maternal smoking. We quantified neonatal methylomic variation in 1263 infants using DNA isolated from a unique collection of archived blood spots taken shortly after birth (mean = 6.08 days; s.d. = 3.24 days). An epigenome-wide association study (EWAS) of gestational age and birth weight identified 4299 and 18 differentially methylated positions (DMPs) respectively, at an experiment-wide significance threshold of p < 1 × 10-7. Our EWAS of maternal smoking during pregnancy identified 110 DMPs in neonatal blood, replicating previously reported genomic loci, including AHRR. Finally, we tested the hypothesis that DNA methylation mediates the relationship between maternal smoking and lower birth weight, finding evidence that methylomic variation at three DMPs may link exposure to outcome. These findings complement an expanding literature on the epigenomic consequences of prenatal exposures and obstetric factors, confirming a link between the maternal environment and gene regulation in neonates. This article is part of the theme issue 'Developing differences: early-life effects and evolutionary medicine'.This article is freely available via Open Access. Click on the Publisher URL to access it via the publisher's site.This study was supported by grant no. HD073978 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institute of Environmental Health Sciences, and National Institute of Neurological Disorders and Stroke; and by the Beatrice and Samuel A. Seaver Foundation. The iPSYCH (The Lundbeck Foundation Initiative for Integrative Psychiatric Research) team acknowledges funding from The Lundbeck Foundation (grant no. R102-A9118 and R155-2014-1724), the Stanley Medical Research Institute, the European Research Council (project no: 294838), the Novo Nordisk Foundation for supporting the Danish National Biobank resource, and grants from Aarhus and Copenhagen Universities and University Hospitals, including support to the iSEQ Center, the GenomeDK HPC facility, and the CIRRAU Center. This research has been conducted using the Danish National Biobank resource, supported by the Novo Nordisk Foundation. J.M. and E.H. are supported by funding from the UK Medical Research Council (K013807).published version, accepted version, submitted versio

Decision Forest Analysis of 61 Single Nucleotide Polymorphisms in a Case-Control Study of Esophageal Cancer; a novel method

BACKGROUND: Systematic evaluation and study of single nucleotide polymorphisms (SNPs) made possible by high throughput genotyping technologies and bioinformatics promises to provide breakthroughs in the understanding of complex diseases. Understanding how the millions of SNPs in the human genome are involved in conferring susceptibility or resistance to disease, or in rendering a drug efficacious or toxic in the individual is a major goal of the relatively new fields of pharmacogenomics. Esophageal squamous cell carcinoma is a high-mortality cancer with complex etiology and progression involving both genetic and environmental factors. We examined the association between esophageal cancer risk and patterns of 61 SNPs in a case-control study for a population from Shanxi Province in North Central China that has among the highest rates of esophageal squamous cell carcinoma in the world. METHODS: High-throughput Masscode mass spectrometry genotyping was done on genomic DNA from 574 individuals (394 cases and 180 age-frequency matched controls). SNPs were chosen from among genes involving DNA repair enzymes, and Phase I and Phase II enzymes. We developed a novel adaptation of the Decision Forest pattern recognition method named Decision Forest for SNPs (DF-SNPs). The method was designated to analyze the SNP data. RESULTS: The classifier in separating the cases from the controls developed with DF-SNPs gave concordance, sensitivity and specificity, of 94.7%, 99.0% and 85.1%, respectively; suggesting its usefulness for hypothesizing what SNPs or combinations of SNPs could be involved in susceptibility to esophageal cancer. Importantly, the DF-SNPs algorithm incorporated a randomization test for assessing the relevance (or importance) of individual SNPs, SNP types (Homozygous common, heterozygous and homozygous variant) and patterns of SNP types (SNP patterns) that differentiate cases from controls. For example, we found that the different genotypes of SNP GADD45B E1122 are all associated with cancer risk. CONCLUSION: The DF-SNPs method can be used to differentiate esophageal squamous cell carcinoma cases from controls based on individual SNPs, SNP types and SNP patterns. The method could be useful to identify potential biomarkers from the SNP data and complement existing methods for genotype analyses