Skip to main content

Table 11 Average r2 and predication accuracy of rules of different length on three populations.

From: FastTagger: an efficient algorithm for genome-wide tag SNP selection using multi-marker linkage disequilibrium

  

#rules

average r2

average accuracy

len

model

HCB

JPT

CEU

HCB

JPT

CEU

HCB

JPT

CEU

1

pairwise

85961

84123

69083

0.978

0.942

0.865

0.995

0.989

0.966

2

co-occurrence

1563176

1472654

1014934

0.965

0.878

0.745

0.993

0.977

0.938

2

one-vs-the-rest

1560181

1469765

1012699

0.965

0.881

0.753

0.993

0.977

0.940

3

co-occurrence

26182522

24495802

16064120

0.952

0.790

0.665

0.990

0.960

0.913

3

one-vs-the-rest

7074493

6269985

3955224

0.970

0.791

0.659

0.994

0.970

0.919

  1. The rules are generated from Han Chinese population with min_r 2 = 0.9. Some rules may become invalid in the other two populations because the MAF of some SNPs in the other two populations may be smaller than 5%. When only pairwise LD is used, all algorithms generate the same set of rules. When multi-markers are considered, FastTagger-COOC and MMTagger generate the same set of rules using the co-occurrence model; FastTagger-avsR and MultiTag generate the same set of rules using the one-vs-the-rest model.
Back to article page

BMC Bioinformatics

ISSN: 1471-2105

Contact us