Biology subjects

Nye, J.

Publications and source records attributed to Nye, J..

3 recordsLinked to original sources

bioRxiv2021

Chromosome X-wide analysis of positive selection in human populations: from common and private signals to selection impact on inactivated genes and enhancers-like signatures

The ability of detecting adaptive (positive) selection in the genome has opened the possibility of understanding the genetic bases of population-specific adaptations genome-wide. Here we present the analysis of recent selective sweeps specifically in the X chromosome in different human populations from the third phase of the 1000 Genomes Project using three different haplotype-based statistics. We describe numerous instances of genes under recent positive selection that fit the regimes of hard and soft sweeps, showing a higher amount of detectable sweeps in sub-Saharan Africans than in non-Africans (Europe and East Asia). A global enrichment is seen in neural-related processes while numerous genes related to fertility appear among the top candidates, reflecting the importance of reproduction in human evolution. Commonalities with previously reported genes under positive selection are found, while particularly strong new signals are reported in specific populations or shared across different continental groups. We report an enrichment of signals in genes that escape X chromosome inactivation, which may contribute to the differentiation between sexes. We also provide evidence of a widespread presence of soft-sweep-like signatures across the chromosome and a global enrichment of highly scoring regions that overlap potential regulatory elements. Among these, enhancers-like signatures seem to present putative signals of positive selection that might be in concordance with selection in their target genes. Also, particularly strong signals appear in regulatory regions that show differential activities, which might point to population-specific regulatory adaptations.

evolutionary biology↗

bioRxiv2020

Research productivity and collaboration of the NIH-funded HIV Vaccine Trials Network: a bibliometric analysis

ObjectivesTo assess the scientific productivity and impact of the HIV Vaccine Trials Network (HVTN) over the last two decades and to examine how research collaboration has evolved over this time in the HIV vaccine field. DesignThis section does not apply since this is a bibliometric study. MethodsA systematic bibliometric analysis was conducted to identify all HIV vaccine and HVTN associated publications from 1999-2019. All publications were sourced from the NLM Pubmed database and funding information was obtained from the SPIRES and iSearch databases. Both the InCites and iCite databases were utilized for impact metrics. Finally, HVTN clinical trials were obtained from clinicaltrials.gov. Multiple field normalized citation metrics, such as the relative citation ratio (RCR) and number of publications in the top 1% and 10% within their respective field, were used to gauge scientific impact of publications. Network analyses were used to examine collaboration among the most prolific researchers in the HIV vaccine research. SettingThis section does not apply since this is a bibliometric study. ParticipantsThis section does not apply since this is a bibliometric study. InterventionThis section does not apply since this is a bibliometric study. Results79 clinical trials were funded by the HVTN from 1999 to 2019. These were carried out via a network of trial sites in 23 countries and 94 cities around the world. In total, 465 publications (89.5% original research articles, 7.3% reviews, and 3.2% other) acknowledged funding from the HVTN. Impact analyses using multiple field normalized metrics revealed that HVTN publications are highly cited with a mean RCR of 1.8. 10,481 HIV vaccine related publications were used to analyze collaboration in this field. Compared to the field as a whole, publications attributed to the HVTN had significantly more authors per publication (p-value < 0.001) and our network analysis found that HVTN-associated authors also had a higher degree (p-value < 0.01). ConclusionsBibliometric analysis of the last two decades of HIV vaccine research by the HVTN revealed that in addition to conducting a large number of clinical trials worldwide, the network produced high impact publications and was associated with increased collaboration among researchers. ARTICLE SUMMARYO_ST_ABSStrengths and limitations of this studyC_ST_ABSO_LITo the best of our knowledge, this is the only study that has provided a systematic bibliometric analysis of the HVTN since its inception. C_LIO_LIStudies like this can illustrate overall outcomes of large clinical network programs. C_LIO_LIAdvanced field normalized metrics were used to provide the most accurate measures of productivity, impact, and collaboration. C_LIO_LIIdentification of HVTN publications using funding acknowledgements can lead to an underestimate of the number research articles since not all research articles include grant funding information. C_LIO_LIVariations in a single authors name across publications can make name disambiguation difficult when performing network analyses. C_LI

microbiology↗

bioRxiv2017

The power of a multivariate approach to genome-wide association studies: an example with Drosophila melanogaster wing shape

Due to the complexity of genotype-phenotype relationships, simultaneous analyses of genomic associations with multiple traits will be more powerful and more informative than a series of univariate analyses. In most cases, however, studies of genotype-phenotype relationships have analyzed only one trait at a time, even as the rapid advances in molecular tools have expanded our view of the genotype to include whole genomes. Here, we report the results of a fully integrated multivariate genome-wide association analysis of the shape of the Drosophila melanogaster wing in the Drosophila Genetic Reference Panel. Genotypic effects on wing shape were highly correlated between two different labs. We found 2,396 significant SNPs using a 5% FDR cutoff in the multivariate analyses, but just 4 significant SNPs in univariate analyses of scores on the first 20 principal component axes. A key advantage of multivariate analysis is that the direction of the estimated phenotypic effect is much more informative than a univariate one. Exploiting this feature, we show that the directions of effects were on average replicable in an unrelated panel of inbred lines. Effects of knockdowns of genes implicated in the initial screen were on average more similar than expected under a null model. Association studies that take a phenomic approach in considering many traits simultaneously are an important complement to the power of genomics. Multivariate analyses of such data are more powerful, more informative, and allow the unbiased study of pleiotropy.

genetics↗