基于數(shù)據(jù)降維技術(shù)的全基因組區(qū)域化關(guān)聯(lián)分析統(tǒng)計(jì)推斷方法研究.pdf

1、Many common human diseases, such as cancer, schizophrenia, essential hypertension, type 2 diabetes, and cardiovascular disease, are known to be complex diseases. Complex diseases, also known as multifactorial diseases, a

2、re controlled by multiple genetic and environmental factors. Although they often show a tendency for family aggregation, complex diseases do not have a clear-cut pattem ofinheritance. This
　　 makes it difficult to de

3、termine one's risk ofinheriting or passing on these disorders.
　　 Recently with rapid improvements in high-throughout genotyping techniques and the growing number of available markers, genome-wide association studies

4、 (GWAS),
　　 which genotype hundreds ofthousands of single nucleotide polymorphisms (SNPs) on
　　 thousands ofparticipants, are emerging as promising approaches for the identification of SNPs that are marginally as

5、sociated with complex diseases. On the other hand, researches on gene-gene interactions (epistasis) in GWAS have shed light on some disease-associated pathways and networks to some extent, and improved our understanding

6、of the genetic basis of complex diseases despite the computational challenge. However, there are still many analytic and interpretation challenges in
　　 GWAS. It is customary to run SNP-based association or interacti

7、on tests in the whole
　　 genome to identify causal or associated SNPs with strong marginal or jointly epistasis
　　 effects on disease or traits. In other words, the unit of association is the SNP. However,
　　

8、 such a SNP-based analysis usuallyleads to computational burden and the well-known
　　 multiplicity problem, with a highly inflated risk of type I error and decreased ability to detect modest effects. In the present s