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Table 4 Prediction bias (standard error) of SNP and haplotype models for BayesA, BayesB and BayesN analyses

From: Fixed-length haplotypes can improve genomic prediction accuracy in an admixed dairy cattle population

Traita

Breedb

BayesA

BayesB (π = 0.5)

BayesN (Π = 0.5; π = 0)

SNP

Hap125c

Hap250d

SNP

Hap125c

Hap250d

SNP

Hap125c

Hap250d

Fat

HF

0.05 (0.05)

0.07 (0.05)

0.06 (0.05)

0.06 (0.05)

0.08 (0.05)

0.07 (0.05)

0.09 (0.05)

0.06 (0.05)

0.05 (0.05)

J

−0.16 (0.04)

−0.16 (0.04)

−0.17 (0.04)

−0.15* (0.04)

−0.16 (0.04)

−0.16 (0.04)

−0.13* (0.04)

−0.18* (0.04)

−0.18 (0.04)

KX

0.00 (0.04)

0.00 (0.04)

0.03 (0.04)

0.01 (0.04)

0.01 (0.04)

0.03 (0.04)

0.03 (0.04)

−0.01 (0.04)

0.01 (0.04)

Lwt

HF

−0.04 (0.06)

−0.03 (0.06)

−0.01 (0.06)

−0.03 (0.06)

−0.03 (0.06)

−0.01 (0.06)

0.00 (0.06)

−0.05 (0.06)

−0.03 (0.06)

J

−0.21 (0.05)

−0.21 (0.04)

−0.18* (0.05)

−0.20* (0.05)

−0.20 (0.04)

−0.19 (0.05)

−0.15* (0.05)

−0.21 (0.04)

−0.21 (0.04)

KX

0.00 (0.04)

−0.01 (0.04)

0.02 (0.04)

0.01 (0.04)

0.00 (0.04)

0.03 (0.04)

0.06 (0.04)

−0.02 (0.04)

0.01 (0.04)

SCS

HF

−0.05 (0.08)

−0.04 (0.08)

−0.04 (0.08)

−0.05 (0.08)

−0.04 (0.08)

−0.05 (0.08)

−0.04 (0.08)

−0.08 (0.08)

−0.08 (0.08)

J

−0.23 (0.07)

−0.22 (0.07)

−0.18* (0.07)

−0.23 (0.07)

−0.22 (0.07)

−0.18* (0.07)

−0.22 (0.07)

−0.26* (0.07)

−0.21 (0.07)

KX

−0.18 (0.07)

−0.20 (0.07)

−0.17 (0.07)

−0.18 (0.07)

−0.20 (0.07)

−0.17 (0.07)

−0.18 (0.07)

−0.23* (0.06)

−0.20 (0.06)

  1. * Significantly different bias than the BayesA SNP model (italics) for that breed and trait (P < 0.05)
  2. aTrait: Fat = Milk fat yield; Lwt = liveweight; SCS = somatic cell score
  3. bBreed: HF = predominantly Holstein–Friesian; J = predominantly Jersey; KX = admixed KiwiCross (HF/J)
  4. cHap125 = haplotypes of length 125 kb, fitting only haplotype alleles >10% frequency in training data set
  5. dHap250 = haplotypes of length 250 kb, fitting only haplotype alleles >1% frequency in training data set