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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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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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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.
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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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The present-day mitochondrial and chloroplast genomes have retained some of the characteristics of their ancestral prokaryotes and also have acquired new attributes during their evolution within eukaryotic cells. Like prokaryotic genomes, mitochondrial and chloroplast genomes neither bind with histone-like proteins nor show complex packaging into chromosome-like structures, as observed in eukaryotes. Unlike mitotic cell divisions observed in eukaryotic cells, mitochondria and chloroplasts...
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Ultra-long Read Sequencing for Whole Genomic DNA Analysis
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A genome-wide scan statistic framework for whole-genome sequence data analysis.

Zihuai He1,2, Bin Xu3, Joseph Buxbaum4

  • 1Department of Biostatistics, Columbia University, New York, NY, 10032, USA.

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|July 11, 2019
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Summary

We developed WGScan, a new framework for analyzing whole-genome sequencing data to identify autism spectrum disorder (ASD) genetic associations. This method enhances discovery in noncoding regions and pinpoints significant genetic signals.

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Area of Science:

  • Genetics and Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Whole-genome sequencing (WGS) analysis is complex due to numerous noncoding rare variants and limited understanding of their functional impact.
  • Identifying genetic associations for complex diseases like autism spectrum disorders (ASD) from WGS data presents significant challenges.
  • Existing methods struggle with the scale and complexity of noncoding variants in genome-wide association studies.

Purpose of the Study:

  • To introduce WGScan, a novel scan statistic framework for simultaneous detection and localization of genome-wide association signals.
  • To establish a robust method for analyzing whole-genome sequencing data, particularly for identifying noncoding variant associations.
  • To derive genome-wide significance thresholds and perform enrichment analyses for autism spectrum disorders.

Main Methods:

  • Developed WGScan, a scan statistic framework for genome-wide association signal detection and localization.
  • Utilized summary statistics for meta-analysis and incorporated functional annotations to enhance noncoding region discoveries.
  • Applied WGScan to whole-genome sequencing data from 1,786 sibling pairs with autism spectrum disorders (Simons Simplex Collection).

Main Results:

  • WGScan successfully derived genome-wide significance thresholds for whole-genome sequencing studies.
  • Detected significant enrichments of association signals in promoter regions and enhancers linked to autism.
  • Identified functional categories related to autism, highlighting the utility of WGScan in pinpointing disease-associated regions.

Conclusions:

  • WGScan provides a powerful framework for analyzing whole-genome sequencing data to detect and localize genetic association signals.
  • The framework effectively enhances the discovery of noncoding variants associated with autism spectrum disorders.
  • WGScan facilitates robust genome-wide significance threshold estimation and enrichment analyses, advancing genetic studies of complex diseases.