Biology subjects

Meyer, D.

Publications and source records attributed to Meyer, D..

4 recordsLinked to original sources

bioRxiv2018

Interrelationships among key Reproductive Health indicators in Sub-Saharan Africa

IntroductionIndicators of reproductive health (RH) services, outputs, outcomes and impacts are expected to be related with each other and with key social determinants. As the provision of RH services is usually integrated, the effort expended to improve one component is also expected to affect the other components. There is a lack of evidence-based models demonstrating the interrelationships among these indicators and between RH indicators and social determinants.\n\nObjectiveTo examine interrelationships among key RH indicators and their relationship with key social determinants in Sub-Saharan Africa (SSA).\n\nMethodThis study used data from the most recent demographic and health survey conducted during the period from 2010 to 2016 in 391 provinces of 29 SSA countries. We focused on seven RH indicator -- antenatal care, skilled birth attendance, postnatal care, contraceptive prevalence rate (CPR), ideal number of children, birth interval and total fertility rate (TFR), along with selected socio-demographic indicators. The unit of analysis was sub-national, at provincial level. Structural equation modelling was used to examine the strength of interrelationships among the indicators based on the total standardized effect sizes. Significance tests and 95% confidence intervals for the total effects were presented using a bias-corrected bootstrap method.\n\nResultsWomens literacy rate, at the centre of the model, has direct connections with all the RH indicators included in the final model. The strongest relationship was observed between womens literacy rate and CPR with a total standardized (std.) effect size of 0.79 (95% CI: 0.74, 0.83). RH indicators are interrelated directly and/or indirectly. A strong direct effect was also observed in the relationship between CPR and birth interval ({beta}=0.63, 95%: 0.50, 0.77) and the model suggests that the reported ideal number of children is a key predictor of birth interval (Std. effect size=-0.58, 95% CI: -0.69, -0.48) and TFR (Std. effect size=0.52, 95% CI: 0.38, 0.62).\n\nConclusionRH indicators are strongly interrelated and are all associated with womens literacy. The model of interrelationships developed in this study may guide the design, implementation and evaluation of RH policies and programs.

epidemiology↗

bioRxiv2018

Expression estimation and eQTL mapping for HLA genes with a personalized pipeline

The HLA (Human Leukocyte Antigens) genes are well-documented targets of balancing selection, and variation at these loci is associated with many disease phenotypes. Variation in expression levels also influences disease susceptibility and resistance, but little information exists about the regulation and population-level patterns of expression due to the difficulty in mapping short reads to these highly polymorphic loci, and in accounting for the existence of several paralogues. We developed a computational pipeline to accurately estimate expression for HLA genes based on RNA-seq, improving both locus-level and allele-level estimates. First, reads are aligned to all known HLA sequences in order to infer HLA genotypes, then quantification of expression is carried out using a personalized index. We use simulations to show that expression estimates are not biased due to divergence from the reference genome. We applied our pipeline to GEUVADIS dataset, and compared the quantifications to those obtained with reference transcriptome, and found that a substantial portion of the variation captured by the HLA-personalized index in not captured by the standard index (23%). We describe the impact of the HLA-personalized approach on downstream analyses for seven HLA loci (HLA-A, HLA-B, HLA-C, HLA-DPB1, HLA-DQA1, HLA-DQB1, HLA-DRB1). Although the influence of the HLA-personalized approach is modest for eQTL mapping, the p-values and the causality of the eQTLs obtained are better than when the reference transcriptome is used. Finally, we integrate information on HLA-allele level expression with the eQTL findings to show that the HLA allele is an important layer of variation to understand HLA regulation.

genomics↗

bioRxiv2017

Population Differentiation at the HLA Genes

Balancing selection is defined as a class of selective regimes that maintain polymorphism above what is expected under neutrality. Theory predicts that balancing selection reduces population differentiation, as measured by FST. However, balancing selection regimes in which different sets of alleles are maintained in different populations could increase population differentiation. To tackle this issue, we investigated population differentiation at the HLA genes, which constitute the most striking example of balancing selection in humans. We found that population differentiation of single nucleotide polymorphisms (SNPs) at the HLA genes is on average lower than that of SNPs in other genomic regions. However, this result depends on accounting for the differences in allele frequency between selected and putatively neutral sites. Our finding of reduced differentiation at SNPs within HLA genes suggests a predominant role of shared selective pressures among populations at a global scale. However, in pairs of closely related populations, where genome-wide differentiation is low, differentiation at HLA is higher than in other genomic regions. This pattern was reproduced in simulations of overdominant selection. We conclude that population differentiation at the HLA genes is generally lower than genome-wide, but it may be higher for recently diverged population pairs, and that this pattern can be explained by a simple overdominance regime.

genetics↗

bioRxiv2017

Signatures of long-term balancing selection in human genomes

Balancing selection maintains advantageous diversity in populations through various mechanisms. While extensively explored from a theoretical perspective, an empirical understanding of its prevalence and targets lags behind our knowledge of positive selection. Here we describe the Non-Central Deviation (NCD), a simple yet powerful statistic to detect long-term balancing selection (LTBS) that quantifies how close frequencies are to expectations under LTBS, and provides the basis for a neutrality test. NCD can be applied to a single locus or genomic data, and can be implemented considering only polymorphisms (NCD1) or also considering fixed differences with respect to an outgroup (NCD2) species. Incorporating fixed differences improves power, and NCD2 has higher power to detect LTBS in humans under different frequencies of the balanced allele(s) than other available methods. Applied to genome-wide data from African and European human populations, in both cases using chimpanzee as an outgroup, NCD2 shows that, albeit not prevalent, LTBS affects a sizable portion of the genome: about 0.6% of analyzed genomic windows and 0.8% of analyzed positions. Significant windows (p < 0.0001) contain 1.6% of SNPs in the genome, which disproportionally fall within exons and change protein sequence, but are not enriched in putatively regulatory sites. These windows overlap about 8% of the protein-coding genes, and these have larger number of transcripts than expected by chance even after controlling for gene length. Our catalog includes known targets of LTBS but a majority of them (90%) are novel. As expected, immune-related genes are among those with the strongest signatures, although most candidates are involved in other biological functions, suggesting that LTBS potentially influences diverse human phenotypes.

genomics↗