Genetic Polymorphism of Matrix Metalloproteinase-9 and Susceptibility to Myocardial Infarction: A Meta-Analysis

Objective. Current findings on the association between MMP-9 rs3918242 and susceptibility to myocardial infarction (MI) are inconsistent, and their definite relationship is discussed in this meta-analysis. Methods. Eligible literatures reporting MMP-9 rs3918242 and susceptibility to MI were searched in PubMed, Cochrane Library, CNRI, and VIP using keywords such as “MMP-9”, “matrix metallopeptidase-9” and “myocardial infarction”, “acute myocardial infarction”, “AMI”, and “polymorphism”. Data from eligible literatures were extracted for calculating OR and corresponding 95% CI using RevMan 5.3 and STATA12.0. Results. Ten independent literatures reporting MMP-9 rs3918242 and susceptibility to MI were enrolled. Compared with subjects carrying CT&TT genotype of MMP-9 rs3918242, susceptibility to MI was lower in those carrying CC genotype ( OR = 1.49 , 95 % CI = 1.19 – 1.86 , P = 0.0004 ). Such a significance was observed in the overdominant ( OR = 1.27 , 95 % CI = 1.14 – 1.41 , P < 0.0001 ) and allele genetic models ( OR = 1.43 , 95 % CI = 1.17 – 1.74 , P = 0.0005 ) as well. This finding was also valid in the Asian population. Conclusions. Mutation on MMP-9 rs3918242 has a potential relevance with susceptibility to MI.

Sweeps in time: leveraging the joint distribution of branch lengths

Current methods of identifying positively selected regions in the genome are limited in two key ways: the underlying models cannot account for the timing of adaptive events and the comparison between models of selective sweeps and sequence data is generally made via simple summaries of genetic diversity. Here we develop a tractable method of describing the effect of positive selection on the genealogical histories in the surrounding genome, explicitly modeling both the timing and context of an adaptive event. In addition, our framework allows us to go beyond analyzing polymorphism data via the site frequency spectrum or summaries thereof and instead leverage information contained in patterns of linked variants. Tests on both simulations and a human data example, as well as a comparison to SweepFinder2, show that even with very small sample sizes, our analytic framework has higher power to identify old selective sweeps and to correctly infer both the time and strength of selection. Finally, we derived the marginal distribution of genealogical branch lengths at a locus affected by selection acting at a linked site. This provides a much-needed link between our analytic understanding of the effects of sweeps on sequence variation and recent advances in simulation and heuristic inference procedures that allow researchers to examine the sequence of genealogical histories along the genome.

Broad-scale variation in human genetic diversity levels is predicted by purifying selection on coding and non-coding elements

Analyses of genetic variation in many taxa have established that neutral genetic diversity is shaped by natural selection at linked sites. Whether the source of selection is primarily the fixation of strongly beneficial alleles (selective sweeps) or purifying selection on deleterious mutations (background selection) remains unknown, however. We address this question in humans by fitting a model of the joint effects of selective sweeps and background selection to autosomal polymorphism data from the 1000 Genomes Project. After controlling for variation in mutation rates along the genome, a model of background selection alone explains ~60% of the variance in diversity levels at the megabase scale. Adding the effects of selective sweeps driven by adaptive substitutions to the model does not improve the fit, and when both modes of selection are considered jointly, selective sweeps are estimated to have had little or no effect on linked neutral diversity. The regions under purifying selection are best predicted by phylogenetic conservation, with ~80% of the deleterious mutations affecting neutral diversity occurring in non-exonic regions. Thus, background selection is the dominant mode of linked selection in humans, with marked effects on diversity levels throughout autosomes.

Role of Ecologic ACE I/D Polymorphism Data Towards Prediction of COVID-19 Epidemiology

COVID-19 displays marked variability in the clinical course as well as regional epidemiology. Abnormalities in RAAS system especially stemming from genetic variability in ACE and ACE2 expression (including ACE I/D polymorphism) have been proposed to explain underlying pathogenesis and variability in SARS-CoV-2 infection. In a meta-regression data set of 30 countries, we found significant associations of ACE I/D ratio and COVID-19 prevalence, deaths and recovery rate but not when adjusted for possible confounders. This ecological study suggests potential of ACE I/D data as predictive biomarker COVID-19 risk and severity in a population specific manner, subject to validation in large genetic epidemiological and functional studies.

Sweeps in time: leveraging the joint distribution of branch lengths

Current methods of identifying positively selected regions of the genome are limited by their underlying model in two key ways: the model cannot account for the timing of the adaptive event and the analytic predictions are limited to single nucleotide polymorphisms. Here we develop a tractable method of describing the effect of positive selection on the genealogical histories in the surrounding genome, explicitly modeling both the timing and context of the adaptive event. In addition, our framework allows us to go beyond simple polymorphism data. We are able to leverage information contained in patterns of linked variants, and even with very small sample sizes, our analytic framework has high power to identify historically adaptive regions of the genome and to correctly infer both the time and strength of selection. Finally, we derived the marginal distribution of genealogical branch lengths at a locus affected by selection acting at a linked site. This provides a much-needed link between current theoretical models to recent advances in simulation procedures that have allowed researchers both to examine the evolution of genealogical histories at the level of full chromosomes and build methods that attempt to reconstruct full ancestries from genome sequence data.

Population Genomics of Filamentous Plant Pathogens—A Brief Overview of Research Questions, Approaches, and Pitfalls

With ever-decreasing sequencing costs, research on the population biology of plant pathogens is transitioning from population genetics—using dozens of genetic markers or polymorphism data of several genes—to population genomics—using several hundred to tens of thousands of markers or whole-genome sequence data. The field of population genomics is characterized by rapid theoretical and methodological advances and by numerous steps and pitfalls in its technical and analytical workflow. In this article, we aim to provide a brief overview of topics relevant to the study of population genomics of filamentous plant pathogens and direct readers to more extensive reviews for in-depth understanding. We briefly discuss different types of population genomics-inspired research questions and give insights into the sampling strategies that can be used to answer such questions. We then consider different sequencing strategies, the various options available for data processing, and some of the currently available tools for population genomic data analysis. We conclude by highlighting some of the hurdles along the population genomic workflow, providing cautionary warnings relative to assumptions and technical challenges, and presenting our own future perspectives of the field of population genomics for filamentous plant pathogens.