Genomic prediction with the additive-dominant model by dimensionality reduction methods

Costa, Jaquicele Aparecida da; Azevedo, Camila Ferreira; Nascimento, Moysés; Silva, Fabyano Fonseca e; Resende, Marcos Deon Vilela de; Nascimento, Ana Carolina Campana

doi:10.1590/S1678-3921.pab2020.v55.01713

Acessibilidade / Reportar erro

Brasil

Pesquisa Agropecuária Brasileira

Español English

Brasil

Español English

sumário « anterior atual seguinte »

Sumário

Genetics • Pesq. agropec. bras. 55 • 2020 • https://doi.org/10.1590/S1678-3921.pab2020.v55.01713 copy

Genomic prediction with the additive-dominant model by dimensionality reduction methods

Predição genômica com o modelo aditivo-dominante por métodos de redução de dimensionalidade

Authorship SCIMAGO INSTITUTIONS RANKINGS

Abstract:

The objective of this work was to evaluate the application of different dimensionality reduction methods in the additive-dominant model and to compare them with the genomic best linear unbiased prediction (G-BLUP) method. The dimensionality reduction methods evaluated were: principal components regression (PCR), partial least squares (PLS), and independent components regression (ICR). A simulated data set composed of 1,000 individuals and 2,000 single-nucleotide polymorphisms was used, being analyzed in four scenarios: two heritability levels × two genetic architectures. To help choose the number of components, the results were evaluated as to additive, dominant, and total genomic information. In general, PCR showed higher accuracy values than the other methods. However, none of the methodologies are able to recover true genomic heritabilities and all of them present biased estimates, under- or overestimating the genomic genetic values. For the simultaneous estimation of the additive and dominance marker effects, the best alternative is to choose the number of components that leads the dominance genomic value to a higher accuracy.

Index terms:
dominance effect; G-BLUP; independent components regression; partial least squares; principal components regression

Embrapa Secretaria de Pesquisa e Desenvolvimento; Pesquisa Agropecuária Brasileira Caixa Postal 040315, 70770-901 Brasília DF Brazil, Tel. +55 61 3448-1813, Fax +55 61 3340-5483 - Brasília - DF - Brazil
E-mail: pab@embrapa.br

Acompanhe os números deste periódico no seu leitor de RSS

[1] *Corresponding author: camila.azevedo@ufv.br

Scenario	Genetic architecture⁽²⁾	σ ²_a	h ²_a	σ ²_d	h ²_d	σ ²_e
Scenario 1	Polygenic inheritance	35	0.20	18	0.10	124
Scenario 2	Polygenic inheritance	35	0.30	23	0.20	59
Scenario 3	Mixed inheritance	49	0.20	25	0.10	171
Scenario 4	Mixed inheritance	49	0.30	33	0.20	81

Efficiency measure	Genetic information	Expression⁽¹⁾
Accuracy	Additive	$r_{\overset{⌢}{a} a} = Cor (a, \overset{⌢}{a})$
Accuracy	Dominance	$r_{\overset{⌢}{d} d} = Cor (d, \overset{⌢}{d})$
Regression coefficients	Additive	$b_{y \overset{⌢}{a}} = \frac{Cov (y, \overset{⌢}{a})}{Var (\overset{⌢}{a})}$
Regression coefficients	Dominance	$b_{y \overset{⌢}{d}} = \frac{Cov (y, \overset{⌢}{d})}{Var (\overset{⌢}{d})}$
Heritability	Additive	$h_{aM}^{2} = \frac{σ_{a_{M}}^{2}}{σ_{a_{M}}^{2} + σ_{d_{M}}^{2} + σ_{e}^{2}}$
	Dominance	$h_{d M}^{2} = \frac{σ_{d_{M}}^{2}}{σ_{a_{M}}^{2} + σ_{d_{M}}^{2} + σ_{e}^{2}}$
	Total	$h_{g M}^{2} = h_{a m}^{2} + h_{d M}^{2}$
Relative efficiency	Additive	$E R_{a} = \frac{r_{\overset{⌢}{a} a R M}}{r_{\overset{⌢}{a} a G-BLUP}}$
Relative efficiency	Dominance	$E R_{d} = \frac{r_{\overset{⌢}{d} d R M}}{r_{\overset{⌢}{d} d G-BLUP}}$

Scenario⁽¹⁾	Method⁽²⁾	Criterion⁽³⁾	GV	Ratio
Scenario⁽¹⁾	Method⁽²⁾	Criterion⁽³⁾	GV	Additive	Dominance
Scenario 1	ICR	Exhaustive	Additive	1.00	0.92
			Dominance	0.91	1.00
			Total	0.97	0.79
	ICR	Optimized	Additive	0.98	0.79
			Dominance	0.93	0.96
			Total	0.93	0.96
	PCR	Exhaustive	Additive	1.00	0.86
			Dominance	0.97	1.00
			Total	1.00	1.00
	PLS	Exhaustive	Additive	1.00	0.94
			Dominance	0.98	1.00
			Total	1.00	0.79
Scenario 2	ICR	Exhaustive	Additive	1.00	0.81
			Dominance	0.95	1.00
			Total	0.98	0.94
	ICR	Optimized	Additive	1.00	0.87
			Dominance	0.97	0.84
			Total	0.98	0.84
	PCR	Exhaustive	Additive	1.00	0.95
			Dominance	0.97	1.00
			Total	1.00	0.98
	PLS	Exhaustive	Additive	1.00	0.70
			Dominance	0.91	1.00
			Total	0.98	1.00
Scenario 3	ICR	Exhaustive	Additive	1.00	0.45
			Dominance	0.73	1.00
			Total	0.93	0.45
	ICR	Optimized	Additive	0.96	0.73
			Dominance	0.87	0.95
			Total	0.87	0.95
	PCR	Exhaustive	Additive	1.00	0.78
			Dominance	0.95	1.00
			Total	0.98	0.90
	PLS	Exhaustive	Additive	1.00	1.00
			Dominance	1.00	1.00
			Total	0.98	0.81
Scenario 4	ICR	Exhaustive	Additive	1.00	0.89
			Dominance	0.96	1.00
			Total	0.98	0.93
	ICR	Optimized	Additive	0.98	0.89
			Dominance	0.96	0.96
			Total	0.96	0.96
	PCR	Exhaustive	Additive	1.00	0.93
			Dominance	0.95	1.00
			Total	0.98	0.98
	PLS	Exhaustive	Additive	1.00	0.93
			Dominance	0.92	1.00
			Total	0.97	0.95