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Table 8 Results for the real data obtained with EBLASSO-NE, EBLASSO-NEG, LASSO and HyperLasso

From: Empirical Bayesian LASSO-logistic regression for multiple binary trait locus mapping

Marker/Marker pairaIDs

Position (Chr,cM)

EBLASSO-NE β ^ s β ^ b

EBLASSO-NEG β ^ s β ^ b

LASSO β ^ s β ^ b

HyperLasso β ^ s β ^ b

D1mit334d

(1,49.2)

−0.15(0.28)

−

−0.37(0.19)

−0.80(0.18)

D3mit217d

(3,43.7)

−0.20(0.30)

−0.62(0.13)

−0.42(0.20)

−

D4mit214d

(4,21.9)

−0.24(0.30)

−

−0.46(0.16)

−0.81(0.20)

D6mit261d

(6,29.5)

−0.18(0.29)

−0.42(0.12)

−0.56(0.15)

−0.78(0.18)

D9mit270d

(9,41.5)

−0.25(0.31)

−0.72(0.13)

−0.39(0.23)

−0.57(0.24)

D9mit182

(9,53.6)

−0.25(0.32)

−

−0.51(0.20)

−0.80(0.26)

D13mit228d

(13,45.9)

−0.12(0.27)c

−0.40(0.12)c

0.38(0.20)

0.91(0.19)

(D1mit19;D17mit176)

(1,37.2;17,12.0)

0.32(0.37)

0.60(0.19)

0.89(0.24)

0.92(0.30)

(D4mit31d;Dxmit208)

(4,50.3;20,18.6)

0.19(0.33)

0.72(0.18)

0.71(0.23)

0.69(0.29)

(D7mit246;D11mit242)

(7,12.0;11,31.9)

0.20(0.33)

0.48(0.16)

−

0.71(0.25)

  1. aPaired markers in parenthesis are markers involved in an epistatic effect. Only effects detected by at least three of the four algorithms are shown. All effects listed have a p-value ≤ 0.05.
  2. bParameters are λ = 0.4 for EBLASSO-NE, (a, b) = (0.01, 0.5) for EBLASSO-NEG, λ = 0.0715 for LASSO and (a,α) = (0.1, 0.01) for HyperLasso. The estimated marker effect is denoted by β ^ and the standard deviation is denoted by s β ^ .
  3. cThe estimated marker effect was obtained from a neighboring marker D13mit35 (59.0 cM).
  4. dMarkers identified previously by Masinde et al.[41].