This article talks about some bioinformatics challenges for genome-wide association studies.
In chapter 21, we learned that bioinformatics is a field of study that uses computers, mathematical tools, and statistical techniques to record, store, and analyze biological information. to study biological information. This fast advancing branch of biology is very interdisciplinary and incorporates principles from mathematics, statistics, information science, chemistry, and physics.
We need bioinformatics because it helps us analyze an enormous amount of data in a reasonable amount of time. By sequencing the human genome, we have been able to identify over one million single nucleotide polymorphisms (SNPs) that can all be used to carry out genome-wide association studies (GWASs). New biostatistical methods have been needed for quality control, imputation, and analysis issues with multiple testing; this is because of the large amounts of GWAS data that has accumulated.
The work has had success and allowed for the discovery of new associations that have been copied in many studies. Most of the SNPs discovered through GWAS have little effects on disease susceptibility and are therefore deemed unsuitable for improving health care through genetic testing. An explanation for the mixed results of GWAS is that the biostatistical analysis example is by design agnostic or unbiased because it does not take into account the previous information on disease pathobiology. The linear modeling framework that is employed in GWAS usually only takes one SNP into account at any given time. This ignores the genomic and environmental context.
Now there is a shift away from the biostatistical approach and more towards a holistic approach. This recognizes the complication of the genotype-phenotype relationship that is set apart by significant heterogeneity along with gene-gene and gene-environment interaction.
The article goes on to assert that bioinformatics plays an important role in addressing the complexity of the
fundamental genetic basis of common human disease. The article identifies and goes into detail about the GWAS challenges that will necessitate computational methods.
In chapter 21, we learned that bioinformatics is a field of study that uses computers, mathematical tools, and statistical techniques to record, store, and analyze biological information. to study biological information. This fast advancing branch of biology is very interdisciplinary and incorporates principles from mathematics, statistics, information science, chemistry, and physics.
We need bioinformatics because it helps us analyze an enormous amount of data in a reasonable amount of time. By sequencing the human genome, we have been able to identify over one million single nucleotide polymorphisms (SNPs) that can all be used to carry out genome-wide association studies (GWASs). New biostatistical methods have been needed for quality control, imputation, and analysis issues with multiple testing; this is because of the large amounts of GWAS data that has accumulated.
The work has had success and allowed for the discovery of new associations that have been copied in many studies. Most of the SNPs discovered through GWAS have little effects on disease susceptibility and are therefore deemed unsuitable for improving health care through genetic testing. An explanation for the mixed results of GWAS is that the biostatistical analysis example is by design agnostic or unbiased because it does not take into account the previous information on disease pathobiology. The linear modeling framework that is employed in GWAS usually only takes one SNP into account at any given time. This ignores the genomic and environmental context.
Now there is a shift away from the biostatistical approach and more towards a holistic approach. This recognizes the complication of the genotype-phenotype relationship that is set apart by significant heterogeneity along with gene-gene and gene-environment interaction.
The article goes on to assert that bioinformatics plays an important role in addressing the complexity of the
fundamental genetic basis of common human disease. The article identifies and goes into detail about the GWAS challenges that will necessitate computational methods.
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| Flowchart for bioinformatics analyses of GWAS data. The use of filter and wrapper algorithms along with computational modeling approaches is recommended in addition to parametric statistical methods. Biological knowledge in public databases has a very important role to play at all levels of the analysis and interpretation. |
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