scholarly journals MiDAS - Meaningful Immunogenetic Data at Scale

2021 ◽  
Author(s):  
Maciej Migdal ◽  
Dan Fu Ruan ◽  
William F. Forrest ◽  
Amir Horowitz ◽  
Christian Hammer

Human immunogenetic variation in the form of HLA and KIR types has been shown to be strongly associated with a multitude of immune-related phenotypes. We present MiDAS, an R package enabling statistical association analysis and using immunogenetic data transformation functions for HLA amino acid fine mapping, analysis of HLA evolutionary divergence as well as HLA-KIR interactions. MiDAS closes the gap between inference of immunogenetic variation and its efficient utilization to make meaningful discoveries.

2021 ◽  
Vol 17 (7) ◽  
pp. e1009131
Author(s):  
Maciej Migdal ◽  
Dan Fu Ruan ◽  
William F. Forrest ◽  
Amir Horowitz ◽  
Christian Hammer

Human immunogenetic variation in the form of HLA and KIR types has been shown to be strongly associated with a multitude of immune-related phenotypes. However, association studies involving immunogenetic loci most commonly involve simple analyses of classical HLA allelic diversity, resulting in limitations regarding the interpretability and reproducibility of results. We here present MiDAS, a comprehensive R package for immunogenetic data transformation and statistical analysis. MiDAS recodes input data in the form of HLA alleles and KIR types into biologically meaningful variables, allowing HLA amino acid fine mapping, analyses of HLA evolutionary divergence as well as experimentally validated HLA-KIR interactions. Further, MiDAS enables comprehensive statistical association analysis workflows with phenotypes of diverse measurement scales. MiDAS thus closes the gap between the inference of immunogenetic variation and its efficient utilization to make relevant discoveries related to immune and disease biology. It is freely available under a MIT license.


Author(s):  
Wanson Choi ◽  
Yang Luo ◽  
Soumya Raychaudhuri ◽  
Buhm Han

Abstract Summary Fine-mapping human leukocyte antigen (HLA) genes involved in disease susceptibility to individual alleles or amino acid residues has been challenging. Using information regarding HLA alleles obtained from HLA typing, HLA imputation or HLA inference, our software expands the alleles to amino acid sequences using the most recent IMGT/HLA database and prepares a dataset suitable for fine-mapping analysis. Our software also provides useful functionalities, such as various association tests, visualization tools and nomenclature conversion. Availability and implementation https://github.com/WansonChoi/HATK.


1997 ◽  
Vol 61 (1) ◽  
pp. 90-104
Author(s):  
P P Dennis ◽  
L C Shimmin

Halophilic (literally salt-loving) archaea are a highly evolved group of organisms that are uniquely able to survive in and exploit hypersaline environments. In this review, we examine the potential interplay between fluctuations in environmental salinity and the primary sequence and tertiary structure of halophilic proteins. The proteins of halophilic archaea are highly adapted and magnificently engineered to function in an intracellular milieu that is in ionic balance with an external environment containing between 2 and 5 M inorganic salt. To understand the nature of halophilic adaptation and to visualize this interplay, the sequences of genes encoding the L11, L1, L10, and L12 proteins of the large ribosome subunit and Mn/Fe superoxide dismutase proteins from three genera of halophilic archaea have been aligned and analyzed for the presence of synonymous and nonsynonymous nucleotide substitutions. Compared to homologous eubacterial genes, these halophilic genes exhibit an inordinately high proportion of nonsynonymous nucleotide substitutions that result in amino acid replacement in the encoded proteins. More than one-third of the replacements involve acidic amino acid residues. We suggest that fluctuations in environmental salinity provide the driving force for fixation of the excessive number of nonsynonymous substitutions. Tinkering with the number, location, and arrangement of acidic and other amino acid residues influences the fitness (i.e., hydrophobicity, surface hydration, and structural stability) of the halophilic protein. Tinkering is also evident at halophilic protein positions monomorphic or polymorphic for serine; more than one-third of these positions use both the TCN and the AGY serine codons, indicating that there have been multiple nonsynonymous substitutions at these positions. Our model suggests that fluctuating environmental salinity prevents optimization of fitness for many halophilic proteins and helps to explain the unusual evolutionary divergence of their encoding genes.


Meta Gene ◽  
2020 ◽  
Vol 23 ◽  
pp. 100631
Author(s):  
Wan Rohani Wan Taib ◽  
Nasriah Ahmad ◽  
Elinah Ali ◽  
Tengku Fatimah Murniwati Tengku Muda ◽  
Norhasiza Mat Jusoh ◽  
...  

2019 ◽  
Vol 35 (19) ◽  
pp. 3701-3708 ◽  
Author(s):  
Gulnara R Svishcheva ◽  
Nadezhda M Belonogova ◽  
Irina V Zorkoltseva ◽  
Anatoly V Kirichenko ◽  
Tatiana I Axenovich

Abstract Motivation A huge number of genome-wide association studies (GWAS) summary statistics freely available in databases provide a new material for gene-based association analysis aimed at identifying rare genetic variants. Only a few of the many popular gene-based methods developed for individual genotype and phenotype data are adapted for the practical use of the GWAS summary statistics as input. Results We analytically prove and numerically illustrate that all popular powerful methods developed for gene-based association analysis of individual phenotype and genotype data can be modified to utilize GWAS summary statistics. We have modified and implemented all of the popular methods, including burden and kernel machine-based tests, multiple and functional linear regression, principal components analysis and others, in the R package sumFREGAT. Using real summary statistics for coronary artery disease, we show that the new package is able to detect genes not found by the existing packages. Availability and implementation The R package sumFREGAT is freely and publicly available at: https://CRAN.R-project.org/package=sumFREGAT. Supplementary information Supplementary data are available at Bioinformatics online.


Author(s):  
Li Liu ◽  
Pramod Chandrashekar ◽  
Biao Zeng ◽  
Maxwell D Sanderford ◽  
Sudhir Kumar ◽  
...  

Abstract Motivation Expression quantitative trait loci (eQTL) harbor genetic variants modulating gene transcription. Fine mapping of regulatory variants at these loci is a daunting task due to the juxtaposition of causal and linked variants at a locus as well as the likelihood of interactions among multiple variants. This problem is exacerbated in genes with multiple cis-acting eQTL, where superimposed effects of adjacent loci further distort the association signals. Results We developed a novel algorithm, TreeMap, that identifies putative causal variants in cis-eQTL accounting for multisite effects and genetic linkage at a locus. Guided by the hierarchical structure of linkage disequilibrium, TreeMap performs an organized search for individual and multiple causal variants. Via extensive simulations, we show that TreeMap detects co-regulating variants more accurately than current methods. Furthermore, its high computational efficiency enables genome-wide analysis of long-range eQTL. We applied TreeMap to GTEx data of brain hippocampus samples and transverse colon samples to search for eQTL in gene bodies and in 4 Mbps gene-flanking regions, discovering numerous distal eQTL. Furthermore, we found concordant distal eQTL that were present in both brain and colon samples, implying long-range regulation of gene expression. Availability and implementation TreeMap is available as an R package enabled for parallel processing at https://github.com/liliulab/treemap. Supplementary information Supplementary data are available at Bioinformatics online.


Amino Acids ◽  
2015 ◽  
Vol 47 (12) ◽  
pp. 2623-2634 ◽  
Author(s):  
Jingjing Tao ◽  
Kunkai Su ◽  
Chengbo Yu ◽  
Xiaoli Liu ◽  
Wei Wu ◽  
...  

2015 ◽  
Vol 57 (8) ◽  
pp. 722-729 ◽  
Author(s):  
Honglang Yan ◽  
Hui Wang ◽  
Hao Cheng ◽  
Zhenbin Hu ◽  
Shanshan Chu ◽  
...  

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