Related Experiment Videos
Investigating single nucleotide polymorphism (SNP) density in the human genome and its implications for molecular
Zhongming Zhao1, Yun-Xin Fu, David Hewett-Emmett
1Human Genetics Center, 1200 Herman Pressler, Suite E447, University of Texas Health Science Center at Houston, Houston, TX 77030, USA.
Gene
|August 12, 2003
Summary
This study analyzed single nucleotide polymorphism (SNP) density in the human genome using two databases. Results indicate natural selection, particularly purifying selection, influences human protein coding sequences.
Area of Science:
- Human Genetics
- Population Genetics
- Genomics
Background:
- Single nucleotide polymorphisms (SNPs) are crucial for genetic variation and disease association studies.
- Understanding SNP distribution across the human genome is essential for genetic research.
Purpose of the Study:
- To investigate single nucleotide polymorphism (SNP) density across the human genome and within different genic regions.
- To compare SNP density patterns derived from two distinct SNP databases (CgsSNP and RefSNP).
- To assess the influence of natural selection on human protein coding sequences based on mutation ratios.
Main Methods:
- Utilized two SNP databases: Celera's CgsSNP (genomic sequence comparison) and Celera's RefSNP (diverse sources, disease-associated bias).
- Calculated SNP density per 10 kb in the overall genome, intergenic, and genic regions (intronic, exonic, untranslated).
- Analyzed SNP counts per chromosome, correlation with chromosome length and GC content, and mutation ratios (nonsense, missense, silent, non-synonymous, synonymous).
Main Results:
- Average SNP densities per 10 kb were 8.33 (genome), 8.44 (intergenic), and 8.09 (genic) based on CgsSNP.
- Within genic regions, SNP densities were 8.21 (intronic), 5.28 (exonic), and 7.51 (untranslated) per 10 kb.
- RefSNP showed a different density pattern than CgsSNP, highlighting its utility for genotype-phenotype studies.
- SNP density correlated with chromosome length but not significantly with GC content.
- Mutation ratios (nonsense/missense, missense/silent, non-synonymous/synonymous) were lower than predicted by neutral mutation theory, suggesting purifying selection.
Conclusions:
- SNP distribution varies across genomic and genic regions.
- The choice of SNP database impacts findings, with RefSNP suited for association studies and CgsSNP for population genetics.
- Natural selection, particularly purifying selection, plays a significant role in shaping human protein coding sequences.