Simulation of African and non-African low and high coverage whole genome sequence data to assess variant calling approaches.

Current variant calling (VC) approaches have been designed to leverage populations of long-range haplotypes and were benchmarked using populations of European descent, whereas most genetic diversity is found in non-European such as Africa populations. Working with these genetically diverse populations, VC tools may produce false positive and false negative results, which may produce misleading conclusions in prioritization of mutations, clinical relevancy and actionability of genes. The most prominent question is which tool or pipeline has a high rate of sensitivity and precision when analysing African data with either low or high sequence coverage, given the high genetic diversity and heterogeneity of this data. Here, a total of 100 synthetic Whole Genome Sequencing (WGS) samples, mimicking the genetics profile of African and European subjects for different specific coverage levels (high/low), have been generated to assess the performance of nine different VC tools on these contrasting datasets. The performances of these tools were assessed in false positive and false negative call rates by comparing the simulated golden variants to the variants identified by each VC tool. Combining our results on sensitivity and positive predictive value (PPV), VarDict [PPV = 0.999 and Matthews correlation coefficient (MCC) = 0.832] and BCFtools (PPV = 0.999 and MCC = 0.813) perform best when using African population data on high and low coverage data. Overall, current VC tools produce high false positive and false negative rates when analysing African compared with European data. This highlights the need for development of VC approaches with high sensitivity and precision tailored for populations characterized by high genetic variations and low linkage disequilibrium.

Multivariate approach for longitudinal analysis of brain metabolite levels from ages 5-11 years in children with perinatal HIV infection.

Treatment guidelines recommend that children with perinatal HIV infection (PHIV) initiate antiretroviral therapy (ART) early in life and remain on it lifelong. As part of a longitudinal study examining the long-term consequences of PHIV and early ART on the developing brain, 89 PHIV children and a control group of 85 HIV uninfected children (HIV-) received neuroimaging at ages 5, 7, 9 and 11 years, including single voxel magnetic resonance spectroscopy (MRS) in three brain regions, namely the basal ganglia (BG), midfrontal gray matter (MFGM) and peritrigonal white matter (PWM). We analysed age-related changes in absolute metabolite concentrations using a multivariate approach traditionally applied to ecological data, the Correlated Response Model (CRM) and compared these to results obtained from a multilevel mixed effect modelling (MMEM) approach. Both approaches produce similar outcomes in relation to HIV status and age effects on longitudinal trajectories. Both methods found similar age-related increases in both PHIV and HIV- children in almost all metabolites across regions. We found significantly elevated GPC+PCh across regions (95% CI=[0.033; 0.105] in BG; 95% CI=[0.021; 0.099] in PWM; 95% CI=[0.059; 0.137] in MFGM) and elevated mI in MFGM (95% CI=[0.131; 0.407]) among children living with PHIV compared to HIV- children; additionally the CRM model also indicated elevated mI in BG (95% CI=[0.008; 0.248]). These findings suggest persistent inflammation across the brain in young children living with HIV despite early ART initiation.

Latent Classes of Anthropometric Growth in Early Childhood Using Uni- and Multivariate approaches in a South African Birth Cohort.

Background: Conventional methods for modelling longitudinal growth data focus on the analysis of mean longitudinal trends or the identification of abnormal growth based on cross-sectional standardized z-scores. Latent Class Mixed Modelling (LCMM) considers the underlying heterogeneity in growth profiles and allows for the identification of groups of subjects that follow similar longitudinal trends. Methods: LCMM was used to identify underlying latent profiles of growth for univariate responses of standardized height, standardized weight, standardized body mass index and standardized weight-for-length/height measurements and multivariate response of joint standardized height and standardized weight measurements from birth to five years for a sample of 1143 children from a South African birth cohort, the Drakenstein Child Health Study (DCHS). Allocations across latent growth classes were compared to better understand the differences and similarities across the classes identified given different composite measures of height and weight as input. Results: Four classes of growth within standardized height (n1=516, n2=112, n3=187, n4=321) and standardized weight (n1=263, n2=150, n3=584, n4=142), three latent growth classes within Body Mass Index (BMI) (n1=481, n2=485, n3=149) and Weight for length/height (WFH) (n1=321, n2=710, n3=84) and five latent growth classes within the multivariate response of standardized height and standardized weight (n1=318, n2=205, n3=75, n4=296, n5=242) were identified, each with distinct trajectories over childhood. A strong association was found between various growth classes and abnormal growth features such as rapid weight gain, stunting, underweight and overweight. Conclusions: With the identification of these classes, a better understanding of distinct childhood growth trajectories and their predictors may be gained, informing interventions to promote optimal childhood growth.

A broad survey of DNA sequence data simulation tools.

In silico DNA sequence generation is a powerful technology to evaluate and validate bioinformatics tools, and accordingly more than 35 DNA sequence simulation tools have been developed. With such a diverse array of tools to choose from, an important question is: Which tool should be used for a desired outcome? This question is largely unanswered as documentation for many of these DNA simulation tools is sparse. To address this, we performed a review of DNA sequence simulation tools developed to date and evaluated 20 state-of-art DNA sequence simulation tools on their ability to produce accurate reads based on their implemented sequence error model. We provide a succinct description of each tool and suggest which tool is most appropriate for the given different scenarios. Given the multitude of similar yet non-identical tools, researchers can use this review as a guide to inform their choice of DNA sequence simulation tool. This paves the way towards assessing existing tools in a unified framework, as well as enabling different simulation scenario analysis within the same framework.