Computation of ancestry scores with mixed families and unrelated individuals

Variants of IL6, IL10, FCN2, RNASE3, IL12B and IL17B loci are associated with Schistosoma mansoni worm burden in the Albert Nile region of Uganda

BACKGROUND

Individuals genetically susceptible to high schistosomiasis worm burden may contribute disproportionately to transmission and could be prioritized for control. Identifying genes involved may guide development of therapy.

METHODOLOGY/PRINCIPAL FINDINGS

A cohort of 606 children aged 10-15 years were recruited in the Albert Nile region of Uganda and assessed for Schistosoma mansoni worm burden using the Up-Converting Particle Lateral Flow (UCP-LF) test detecting circulating anodic antigen (CAA), point-of-care Circulating Cathodic Antigen (POC-CCA) and Kato-Katz tests. Whole genome genotyping was conducted on 326 children comprising the top and bottom 25% of worm burden. Linear models were fitted to identify variants associated with worm burden in preselected candidate genes. Expression quantitative trait locus (eQTL) analysis was conducted for candidate genes with UCP-LF worm burden included as a covariate. Single Nucleotide Polymorphism loci associated with UCP-LF CAA included IL6 rs2066992 (OR = 0.43, p = 0.0006) and rs7793163 (OR = 2.0, p = 0.0007); IL21 SNP kgp513476 (OR 1.79, p = 0.0025) and IL17B SNP kgp708159 (OR = 0.35, p = 0.0028). A haplotype in the IL10 locus was associated with lower worm burden (OR = 0.53, p = 0.015) and overlapped SNPs rs1800896, rs1800871 and rs1800872. Significant haplotypes (p<0.05, overlapping significant SNP) associated with worm burden were observed in IL6 and the Th17 pathway IL12B and IL17B genes. There were significant eQTL in the IL6, IL5, IL21, IL25 and IFNG regions.

CONCLUSIONS

Variants associated with S. mansoni worm burden were in IL6, FCN2, RNASE3, IL10, IL12B and IL17B gene loci. However only eQTL associations remained significant after Bonferroni correction. In summary, immune balance, pathogen recognition and Th17 pathways may play a role in modulating Schistosoma worm burden. Individuals carrying risk variants may be targeted first in allocation of control efforts to reduce the burden of schistosomiasis in the community.

Identifying suicidal subtypes and dynamic indicators of increasing and decreasing suicide risk in active duty military personnel: Study protocol

Objectives

Several recent studies have demonstrated that posttraumatic stress disorder (PTSD) and insomnia treatments are associated with significant reductions in suicidal ideation (SI) among service members. However, few investigations have evaluated the manner in which suicide risk changes over time among military personnel receiving PTSD or insomnia treatments. This paper describes the study protocol for a project with these aims: (1) explore potential genetic, clinical, and demographic subtypes of suicide risk in a large cohort of deployed service members; (2) explore subtype change in SI as a result of evidence-based psychotherapies for PTSD and insomnia; (3) evaluate the speed of change in suicide risk; and (4) identify predictors of higher- and lower-risk for suicide.

Methods

Active duty military personnel were recruited for four clinical trials (three for PTSD treatment and one for insomnia treatment) and a large prospective epidemiological study of deployed service members, all conducted through the South Texas Research Organizational Network Guiding Studies on Trauma and Resilience (STRONG STAR Consortium). Participants completed similar measures of demographic and clinical characteristics and subsets provided blood samples for genetic testing. The primary measures that we will analyze are the Beck Scale for Suicide Ideation, Beck Depression Inventory, and the PTSD Checklist for DSM-IV.

Discussion

Results from this study will offer new insights into the presence of discrete subtypes of suicide risk among active duty personnel, changes in risk over time among those subtypes, and predictors of subtypes. Findings will inform treatment development for military service members at risk for suicide.

A Review and Tutorial of Machine Learning Methods for Microbiome Host Trait Prediction

With the growing importance of microbiome research, there is increasing evidence that host variation in microbial communities is associated with overall host health. Advancement in genetic sequencing methods for microbiomes has coincided with improvements in machine learning, with important implications for disease risk prediction in humans. One aspect specific to microbiome prediction is the use of taxonomy-informed feature selection. In this review for non-experts, we explore the most commonly used machine learning methods, and evaluate their prediction accuracy as applied to microbiome host trait prediction. Methods are described at an introductory level, and R/Python code for the analyses is provided.

A survey of high dimension low sample size asymptotics

Peter Hall's work illuminated many aspects of statistical thought, some of which are very well known including the bootstrap and smoothing. However, he also explored many other lesser known aspects of mathematical statistics. This is a survey of one of those areas, initiated by a seminal paper in 2005, on high dimension low sample size asymptotics. An interesting characteristic of that first paper, and of many of the following papers, is that they contain deep and insightful concepts which are frequently surprising and counter-intuitive, yet have mathematical underpinnings which tend to be direct and not difficult to prove.

Eigenvalue significance testing for genetic association

Genotype eigenvectors are widely used as covariates for control of spurious stratification in genetic association. Significance testing for the accompanying eigenvalues has typically been based on a standard Tracy-Widom limiting distribution for the largest eigenvalue, derived under white-noise assumptions. It is known that even modest local correlation among markers inflates the largest eigenvalues, even in the absence of true stratification. In addition, a few sample eigenvalues may be extreme, creating further complications in accurate testing. We explore several methods to identify appropriate null eigenvalue thresholds, while remaining sensitive to eigenvalues corresponding to population stratification. We introduce a novel block permutation approach, designed to produce an appropriate null eigenvalue distribution by eliminating long-range genomic correlation while preserving local correlation. We also propose a fast approach based on eigenvalue distribution modeling, using a simple fit criterion and the general Marčenko-Pastur equation under a simple discrete eigenvalue model. Block permutation and the model-based approach work well for pure simulations and for data resampled from the 1000 Genomes project. In contrast, we find that the standard approach of computing an "effective" number of markers does not perform well. The performance of the methods is also demonstrated for a motivating example from the International Cystic Fibrosis Consortium.