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Table 3 Average imputation accuracies on the two cattle datasets

From: Fast accurate missing SNP genotype local imputation

Methods:

fPH

NN

WNN

SVM

NeuN

MC

BL

hd-0.5%

.9151

.8701

.8748

.8538

.6518

.8012

.5449

hd-1%

.9209

.8695

.8707

.8563

.6495

.7991

.5432

hd-2%

.9174

.8624

.8599

.8484

.6437

.7933

.5412

hd-5%

.9123

.8482

.8436

.8362

.6336

.7817

.5379

hd-10%

.8968

.8232

.8344

.8057

.6204

.7771

.5378

hd-20%

.8831

.7951

.8032

.7782

.6072

.7548

.5352

ld-0.5%

.9643

.8618

.8705

.8765

.6817

.7269

.6563

ld-1%

.9627

.8601

.8674

.8732

.6799

.7270

.6527

ld-2%

.9616

.8571

.8636

.8704

.6796

.7265

.6552

ld-5%

.9598

.8480

.8468

.8577

.6765

.7230

.6532

ld-10%

.9566

.8328

.8209

.8395

.6740

.7169

.6542

ld-20%

.9492

.7951

.7686

.8029

.6680

.7110

.6528

  1. Average imputation accuracies on the two cattle datasets, the high-density one is 700K and the low-density one is 60K. At each missing rate, the highest accuracy is in bold. ‘fPH, NeuN’ stand for ‘fastPHASE, NeuralNet’, respectively.
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BMC Research Notes

ISSN: 1756-0500

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