Genomics and the Acute Respiratory Distress Syndrome: Current and Future Directions
Abstract
:1. Definition and Epidemiology
2. Molecular Pathophysiology
3. Genetic Association Studies
4. Causal Inferences with Mendelian Randomization
5. Transcriptomics
6. Metagenomics
7. Other Incipient Genomic Approaches
8. Future Directions
9. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Approach | Aim | Main Advantages | Main Limitations | Phenotypes Assessed |
---|---|---|---|---|
Candidate-gene association study | To identify the statistical association between genetic variants for pre-specified genes of biological interest and the trait. | Simple approximation not requiring computational skills. Hypothesizes causality of the analysed variant Reduced penalty of statistical significance. | Non-reproducibility of the findings in independent studies complicating the interpretation. | Susceptibility, outcomes |
Genome-wide association study (GWAS) | To identify the statistical association between genetic variants assessed across the genome and the trait. | Hypothesis-free approach. Allows to identify new pathogenic mechanisms, potentially leading to new therapeutic targets. Reduced proportion of false positives. | Many of the genes that are identified do not yet have a known biological implication in the trait. Large penalty of statistical significance. Large proportion of false negatives. | Susceptibility |
Whole-exome sequencing (WES) | To identify the statistical association between genetic variants assessed across exons of all genes (exome) and the trait. | Same as indicated for GWAS. Allows analysis of rare and common genetic variants. | Blind to genetic variation occurring in the regulatory regions of genes. There is no standardization of the statistical tests. Requires advanced computational skills and dedicated infrastructure. More expensive than GWAS and candidate-gene studies for a fixed sample size. | Susceptibility, outcomes |
Transcriptome-wide association study | To identify genomic loci associated with gene expression alterations related to the trait. | Same as indicated for GWAS. | Same as indicated for GWAS. | Susceptibility |
Transcriptomics | To assess alterations of the gene expression and biological pathways in disease states focusing on particular targets or using array or sequencing-based approaches. | Allows to quantify and provides precise expression levels of genes simultaneously. A variant focusing on small non-coding species is possible. If sequencing-based, it allows the distinction of isoforms and allelic expression. If sequencing-based, it allows to map transcribed regions. If sequencing-based, it allows to evaluate gene expression levels in single cells. | The RNA isolation and handling require specialized materials and skills. If sequence-based, requires abundant RNA species (e.g., rRNA) to be depleted. Effects of this on the profiles are yet unknown. If sequencing-based, requires advanced computational skills and dedicated infrastructure. If sequencing-based, there is a lack of standardization of the optimal read depth. | Susceptibility and outcomes (array-based only) |
Mendelian randomization | To assess the causality of a risk factor on a trait based on genetic predictors of the former. | Less affected by confusion or inverse causality. | Depends on many assumptions that need to be assessed for plausibility. Genetic predictors of the risk factor need to be known from previous studies. | Susceptibility |
DNA methylation | To identify methylation levels at genomic loci associated with the trait. | Allows to quantitatively evaluate environmental exposures at DNA level. Permits the evaluation of functional effects of identified elements. | There is no standardization of the statistical tests. Necessity to control for collection tissues, environmental exposures and other relevant variables that affect the results. | Susceptibility |
Metagenomics | To assess the collective microbial composition and function of environmental samples from genomic data. | Allows to characterize microbial communities (abundance, diversity and distribution) and deduce function without culturing. Allows to detect uncultivable microbes. With sufficient resolution, it allows to recover antibiotic resistance genes and virulence factors. | The same as indicated for DNA methylation. Requires advanced computational skills and dedicated infrastructure. | Susceptibility |
Whole-genome sequencing | To identify the statistical association between genetic variants assessed across the genome and the trait. | Same as indicated for WES. Allows the better analysis of structural variation and variation in non-exonic regions of the genome. | There is no standardization of the statistical tests. Requires advanced computational skills and dedicated infrastructure. More expensive than WES studies for a fixed sample size. | None |
Admixture mapping | To identify genomic regions that are associated with a trait based on ancestry markers. | Hypothesis-free approach. Reduced proportion of false positives. Reduced penalty of statistical significance. | Can only be applied in recently admixed populations and the evolutionary history must be known. Large proportion of false negatives. Identified loci at Mb resolution. There is no standardization of the statistical tests. | None |
Polygenic risks | To stratify disease risks based on the cumulative effects of genetic variants. | Allows to stratify the risk with a single score. Allows to assess the genetic overlap among traits. | Genetic risk variants need to be known from previous studies. Difficulties in the transferability among populations. | None |
Mitochondrial DNA levels | To assess its potential as a biomarker for a trait. | Simple approximation not requiring computational skills. May offer improvements for diagnostic or prognostic scores. Inexpensive approach. | Difficulties to reach optimal sensitivity and specificity. Strong dependency on sample collection and handling. | None |
Gene | Chr | Position (hg19) | rsID | Phenotype | Sample (Case/Control) | Population | Study | |
---|---|---|---|---|---|---|---|---|
Discovery | Validation | |||||||
EGLN1 | 1 | 231542656 | rs516651 | Outcome | 264 * | -- | European | Dötsch et al. [36] |
MUC5B | 11 | 1241221 | rs35705950 | Susceptibility | 234/669 | -- | Multi-ethnic | Rogers et al. [37] |
AGER | 6 | 32151693 | rs2070600 | Susceptibility | 59/405 | -- | Multi-ethnic | Jabaudon et al. [38] |
LRRC16A | 6 | 25426768 | rs9358856 | Outcome | 414 * | -- | Multi-ethnic | Wei et al. [39] |
MAP3K1 | 5 | 56177743 | rs832582 | Outcome | 306 * | 241 * | European | Morrell et al. [40] |
FLT1 | 13 | 28993669 | rs9513106 | Susceptibility | 225/899 | 661/234 | European | Hernandez-Pacheco et al. [41] |
IL17 | 6 | 52185695 | rs8193036 | Susceptibility/Outcome | 210/210 | -- | East Asian | Xie et al. [42] |
52186235 | rs2275913 | |||||||
DEFB1 | 8 | 6877901 | rs1800972 | Susceptibility | 300/240 | -- | European | Feng et al. [43] |
FER | 5 | 108402140 | rs4957796 | Outcome | 27/68 | -- | European | Hinz et al. [44] |
ANGPT2 | 8 | 6370320 | rs2442630 | Susceptibility | 178/226 | -- | European | Reilly et al. [45] |
6386620 | rs2442608 |
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Hernández-Beeftink, T.; Guillen-Guio, B.; Villar, J.; Flores, C. Genomics and the Acute Respiratory Distress Syndrome: Current and Future Directions. Int. J. Mol. Sci. 2019, 20, 4004. https://doi.org/10.3390/ijms20164004
Hernández-Beeftink T, Guillen-Guio B, Villar J, Flores C. Genomics and the Acute Respiratory Distress Syndrome: Current and Future Directions. International Journal of Molecular Sciences. 2019; 20(16):4004. https://doi.org/10.3390/ijms20164004
Chicago/Turabian StyleHernández-Beeftink, Tamara, Beatriz Guillen-Guio, Jesús Villar, and Carlos Flores. 2019. "Genomics and the Acute Respiratory Distress Syndrome: Current and Future Directions" International Journal of Molecular Sciences 20, no. 16: 4004. https://doi.org/10.3390/ijms20164004