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Related Concept Videos

Epistasis01:39

Epistasis

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In addition to multiple alleles at the same locus influencing traits, numerous genes or alleles at different locations may interact and influence phenotypes in a phenomenon called epistasis. For example, rabbit fur can be black or brown depending on whether the animal is homozygous dominant or heterozygous at a TYRP1 locus. However, if the rabbit is also homozygous recessive at a locus on the tyrosinase gene (TYR), it will have an unshaded coat that appears white, regardless of its TYRP1...
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Epistasis Analysis01:09

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Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. The...
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Genome-wide Association Studies-GWAS01:11

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Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
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Genomics02:02

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Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
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Genomic Imprinting and Inheritance02:30

Genomic Imprinting and Inheritance

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Diploid organisms inherit genetic material through chromosomes from both parents. Copies of the same gene are known as alleles. In most cases, both alleles are simultaneously expressed and allow various cellular processes to function optimally. If one of the alleles is missing or mutated, the expression of the other allele can compensate; however, this is not true for all genes.
The expression of some genes depends on which parent passed the gene to the offspring, through a phenomenon known as...
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Genome Size and the Evolution of New Genes03:21

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While every living organism has a genome of some kind (be it RNA, or DNA), there is considerable variation in the sizes of these blueprints. One major factor that impacts genome size is whether the organism is prokaryotic or eukaryotic. In prokaryotes, the genome contains little to no non-coding sequence, such that genes are tightly clustered in groups or operons sequentially along the chromosome. Conversely, the genes in eukaryotes are punctuated by long stretches of non-coding sequence.
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SuperDCA for genome-wide epistasis analysis.

Santeri Puranen1,2, Maiju Pesonen2,1, Johan Pensar1

  • 12​Department of Mathematics and Statistics, Helsinki Institute of Information Technology (HIIT), FI-00014 University of Helsinki, Finland.

Microbial Genomics
|May 30, 2018
PubMed
Summary
This summary is machine-generated.

A new method, SuperDCA, enables scalable genome-wide epistasis modeling for millions of polymorphisms. This approach reveals novel biological insights and selection signals in bacterial populations, advancing systems biology.

Keywords:
epistasislinkage disequilibriumpopulation genomics

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Area of Science:

  • Genomics
  • Computational Biology
  • Evolutionary Biology

Background:

  • Genome-wide epistasis modeling is gaining traction due to advances in population sequencing and statistical interaction models.
  • Direct Coupling Analysis (DCA) has been successful for single protein structures and bacterial genome-wide analyses, identifying co-evolutionary interactions.
  • Previous computational DCA methods lacked scalability for large numbers of polymorphisms (10^4-10^5) common in bacterial species.

Purpose of the Study:

  • To introduce a novel, scalable inference method (SuperDCA) for genome-wide epistasis modeling.
  • To demonstrate SuperDCA's ability to identify significant biological findings in bacterial populations.
  • To show SuperDCA can detect selection signals missed by traditional genome-wide association analysis.

Main Methods:

  • Developed SuperDCA, a novel inference method incorporating a new scoring principle, efficient parallelization, optimization, and phylogenetic filtering.
  • Achieved scalability for modeling up to 10^5 polymorphisms.
  • Applied SuperDCA to two large population samples of Streptococcus pneumoniae.

Main Results:

  • SuperDCA successfully scaled to analyze large numbers of polymorphisms, enabling new biological discoveries in Streptococcus pneumoniae.
  • The method uncovered significant biological findings related to resistance, virulence, and core genome elements.
  • SuperDCA identified signals of selection undetectable by genome-wide association analysis without requiring phenotypic data.

Conclusions:

  • SuperDCA offers a scalable solution for genome-wide epistasis modeling, overcoming limitations of previous DCA methods.
  • The method enhances understanding of bacterial populations and their evolutionary dynamics.
  • SuperDCA holds significant potential for systems-level biological understanding across diverse organisms.