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Related Concept Videos

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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This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
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Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
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A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...
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Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
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RACE - Rapid Amplification of cDNA Ends02:35

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Rapid Amplification of cDNA Ends, or RACE, is one of the most effective methods to obtain a full-length cDNA from an mRNA sequence between a known internal region to the unknown sequence at the 5’ or 3’ end. The unknown region is cloned in the cDNA by a gene-specific primer that binds the known end, and a hybrid primer that attaches a predefined anchor sequence to the unknown end of the cDNA. The sequence in between is amplified by PCR with an anchor primer and a gene-specific...
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SparRec: An effective matrix completion framework of missing data imputation for GWAS.

Bo Jiang1, Shiqian Ma2, Jason Causey3,4

  • 1Research Center for Management Science and Data Analytics, School of Information Management and Engineering, Shanghai University of Finance and Economics, Shanghai 200433, China.

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|October 21, 2016
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Summary

SparRec offers a novel framework for imputing missing genetic data in large genome-wide association studies. Its methods achieve high accuracy and efficiency, outperforming existing statistical approaches.

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Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Genome-wide association studies (GWAS) face computational challenges with missing data imputation.
  • Advancing genotype technologies generate large datasets from multiple SNP chips.
  • Imputing data across diverse cohorts genotyped on different SNP sets is complex.

Purpose of the Study:

  • Introduce SparRec (Sparse Recovery), a new framework for missing data imputation.
  • Address computational challenges in large-scale GWAS and meta-analyses.
  • Provide a flexible imputation method applicable even without a reference panel.

Main Methods:

  • Developed optimization models based on low-rank matrix completion (LRMC) and matrix co-clustering factorization (MCCF).
  • LRMC model is similar to existing methods; MCCF model is novel.
  • Framework designed for flexibility in handling diverse, large-scale genotypic datasets.

Main Results:

  • SparRec demonstrates consistent performance and high recovery accuracy, even with up to 90% missing data.
  • The LRMC model shows comparable accuracy and efficiency to Mendel-Impute.
  • The MCCF model offers advantages in computational running time.
  • SparRec outperforms state-of-the-art methods like Beagle and fastPhase.

Conclusions:

  • SparRec provides a robust and efficient framework for missing data imputation in GWAS.
  • Its novel MCCF model offers significant advantages in speed.
  • SparRec is a competitive alternative to existing statistical imputation methods for large-scale genetic studies.