scholarly journals CCR1 regulatory variants linked to pulmonary macrophage recruitment in severe COVID-19

2021 ◽  
Author(s):  
Bernard Stikker ◽  
Grégoire Stik ◽  
Rudi Hendriks ◽  
Ralph Stadhouders

AbstractGenome-wide association studies have identified 3p21.31 as the main risk locus for severe symptoms and hospitalization in COVID-19 patients. To elucidate the mechanistic basis of this genetic association, we performed a comprehensive epigenomic dissection of the 3p21.31 locus. Our analyses pinpoint activating variants in regulatory regions of the chemokine receptor-encoding CCR1 gene as potentially pathogenic by enhancing infiltration of monocytes and macrophages into the lungs of patients with severe COVID-19.

Brain ◽  
2019 ◽  
Vol 142 (12) ◽  
pp. 3694-3712 ◽  
Author(s):  
Regina H Reynolds ◽  
John Hardy ◽  
Mina Ryten ◽  
Sarah A Gagliano Taliun

How can we best translate the success of genome-wide association studies for neurological and neuropsychiatric diseases into therapeutic targets? Reynolds et al. critically assess existing brain-relevant functional genomic annotations and the tools available for integrating such annotations with summary-level genetic association data.


2017 ◽  
Vol 28 (7) ◽  
pp. 1927-1941
Author(s):  
Jiyuan Hu ◽  
Wei Zhang ◽  
Xinmin Li ◽  
Dongdong Pan ◽  
Qizhai Li

In the past decade, genome-wide association studies have identified thousands of susceptible variants associated with complex human diseases and traits. Conducting follow-up genetic association studies has become a standard approach to validate the findings of genome-wide association studies. One problem of high interest in genetic association studies is to accurately estimate the strength of the association, which is often quantified by odds ratios in case-control studies. However, estimating the association directly by follow-up studies is inefficient since this approach ignores information from the genome-wide association studies. In this article, an estimator called GFcom, which integrates information from genome-wide association studies and follow-up studies, is proposed. The estimator includes both the point estimate and corresponding confidence interval. GFcom is more efficient than competing estimators regarding MSE and the length of confidence intervals. The superiority of GFcom is particularly evident when the genome-wide association study suffers from severe selection bias. Comprehensive simulation studies and applications to three real follow-up studies demonstrate the performance of the proposed estimator. An R package, “GFcom”, implementing our method is publicly available at https://github.com/JiyuanHu/GFcom .


2016 ◽  
Vol 48 (11) ◽  
pp. 1418-1424 ◽  
Author(s):  
Ying Jin ◽  
Genevieve Andersen ◽  
Daniel Yorgov ◽  
Tracey M Ferrara ◽  
Songtao Ben ◽  
...  

PLoS ONE ◽  
2012 ◽  
Vol 7 (2) ◽  
pp. e29613 ◽  
Author(s):  
Mara M. Abad-Grau ◽  
Nuria Medina-Medina ◽  
Rosana Montes-Soldado ◽  
Fuencisla Matesanz ◽  
Vineet Bafna

2016 ◽  
Vol 6 (12) ◽  
pp. 3995-4007 ◽  
Author(s):  
Ferdouse Begum ◽  
Reshmi Chowdhury ◽  
Vivian G Cheung ◽  
Stephanie L Sherman ◽  
Eleanor Feingold

Abstract Meiotic recombination is an essential step in gametogenesis, and is one that also generates genetic diversity. Genome-wide association studies (GWAS) and molecular studies have identified genes that influence of human meiotic recombination. RNF212 is associated with total or average number of recombination events, and PRDM9 is associated with the locations of hotspots, or sequences where crossing over appears to cluster. In addition, a common inversion on chromosome 17 is strongly associated with recombination. Other genes have been identified by GWAS, but those results have not been replicated. In this study, using new datasets, we characterized additional recombination phenotypes to uncover novel candidates and further dissect the role of already known loci. We used three datasets totaling 1562 two-generation families, including 3108 parents with 4304 children. We estimated five different recombination phenotypes including two novel phenotypes (average recombination counts within recombination hotspots and outside of hotspots) using dense SNP array genotype data. We then performed gender-specific and combined-sex genome-wide association studies (GWAS) meta-analyses. We replicated associations for several previously reported recombination genes, including RNF212 and PRDM9. By looking specifically at recombination events outside of hotspots, we showed for the first time that PRDM9 has different effects in males and females. We identified several new candidate loci, particularly for recombination events outside of hotspots. These include regions near the genes SPINK6, EVC2, ARHGAP25, and DLGAP2. This study expands our understanding of human meiotic recombination by characterizing additional features that vary across individuals, and identifying regulatory variants influencing the numbers and locations of recombination events.


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