Genomic data in lmt - Revision history

127.0.0.1: /* Genetic Groups and Meta-founders */

2022-05-12T00:57:12Z

Genetic Groups and Meta-founders

← Older revision		Revision as of 00:57, 12 May 2022
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	==Genetic Groups and Meta-founders==		==Genetic Groups and Meta-founders==

	If the pedigree constituting $$A$$ contains phantom parents, genetic groups are automatically fitted for all different Single-Step co-variance structures. Alternatively, genetic groups can be fitted as an extra factor.		If the pedigree constituting $$A$$ contains phantom parents<ref name="Westell1988"/>, genetic groups are automatically fitted for all different Single-Step co-variance structures. Alternatively, genetic groups can be fitted as an extra factor.

	Meta-founders are fitted automatically if a meta-founder co-variance matrix has been supplied.		Meta-founders<ref name="Garcia2017"/> are fitted automatically if a meta-founder co-variance matrix has been supplied.

	==Single-Step SNP-BLUP==		==Single-Step SNP-BLUP==

127.0.0.1: /* Single-Step SNP-BLUP */

2022-05-12T00:56:11Z

Single-Step SNP-BLUP

← Older revision		Revision as of 00:56, 12 May 2022
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	==Single-Step SNP-BLUP==		==Single-Step SNP-BLUP==

	lmt's Single-Step SNP-BLUP model uses the approach described in Liu and Goddard 2014<ref name="Liu2014">, where the adverse of the Singe-Step SNP-BLUP variance structure can be written as		lmt's Single-Step SNP-BLUP model uses the approach described in Liu and Goddard 2014<ref name="Liu2014"/>, where the adverse of the Singe-Step SNP-BLUP variance structure can be written as

	$$		$$

127.0.0.1 at 00:55, 12 May 2022

2022-05-12T00:55:12Z

← Older revision		Revision as of 00:55, 12 May 2022
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	lmt can account for genomic data via genomic relationship matrices $$G$$ and single step relationship matrices $$H$$ where ssGBLUP<ref name="Christensen2010/>, ssGTBLUP<ref name="Mäntysaari2017"/>, and ssSNPBLUP<ref name="Liu2014"/> are supported. lmt accepts plain marker data and will calculate all necessary derivatives required by the model. Genotypes can be scaled using average or marker specific allele content variance (2pq), and allele frequencies are either calculated from the data, or read from user specified input files. Futher, if requested $$G$$ can be adjusted to fit $$A$$<ref name="Christensen2012"/>, even for models where $$G$$ is not build(e.g. ssSNP-BLUP).		lmt can account for genomic data via genomic relationship matrices $$G$$ and single step relationship matrices $$H$$ where ssGBLUP<ref name="Christensen2010/>, ssGTBLUP<ref name="Mäntysaari2017"/>, and ssSNPBLUP<ref name="Liu2014"/> are supported. lmt accepts plain marker data and will calculate all necessary derivatives required by the model. Genotypes can be scaled using average or marker specific allele content variance (2pq), and allele frequencies are either calculated from the data, or read from user specified input files. Futher, if requested $$G$$ can be adjusted to fit $$A$$<ref name="Christensen2012"/>, even for models where $$G$$ is not build(e.g. ssSNP-BLUP).

	==Genetic Groups==		==Genetic Groups and Meta-founders==

	If the pedigree constituting $$A$$ contains phantom parents, genetic groups are automatically fitted for all different Single-Step co-variance structures. Alternatively, genetic groups can be fitted as an extra factor.		If the pedigree constituting $$A$$ contains phantom parents, genetic groups are automatically fitted for all different Single-Step co-variance structures. Alternatively, genetic groups can be fitted as an extra factor.

			Meta-founders are fitted automatically if a meta-founder co-variance matrix has been supplied.

	==Single-Step SNP-BLUP==		==Single-Step SNP-BLUP==
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	<ref name="Westell1988">Westell et al.; Genetic Groups in an Animal Model; Journal of Dairy Science; 1988</ref>		<ref name="Westell1988">Westell et al.; Genetic Groups in an Animal Model; Journal of Dairy Science; 1988</ref>
	<ref name="Christensen2010">Christensen et al.; Genomic prediction when some animals are not genotyped; Genetics Selection Evolution; 2010</ref>		<ref name="Christensen2010">Christensen et al.; Genomic prediction when some animals are not genotyped; Genetics Selection Evolution; 2010</ref>
			<ref name="Christensen2012">O.F. Christensen; Compatibility of pedigree-based and marker-based relationship matrices for single-step genetic evaluation; Genetics Selection Evolution; 2012</ref>
	<ref name="Liu2014">Liu et al.;A single-step genomic model with direct estimation of marker effects; Journal of Dairy Science;2014</ref>		<ref name="Liu2014">Liu et al.;A single-step genomic model with direct estimation of marker effects; Journal of Dairy Science;2014</ref>
	<ref name="Mäntysaari2017">Mäntysaari et al.;Efficient single-step genomic evaluation for a multibreed beef cattle population having many genotyped animals; Journal of Animal Science;2017</ref>		<ref name="Mäntysaari2017">Mäntysaari et al.;Efficient single-step genomic evaluation for a multibreed beef cattle population having many genotyped animals; Journal of Animal Science;2017</ref>
	</references>		</references>

127.0.0.1 at 00:52, 12 May 2022

2022-05-12T00:52:20Z

← Older revision		Revision as of 00:52, 12 May 2022
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	lmt can account for genomic data via genomic relationship matrices $$G$$ and single step relationship matrices $$H$$ where ~~single step G-BLUP~~, ~~single step GT-BLUP~~, and ~~single step SNP-BLUP~~ are supported. lmt accepts plain marker data and will calculate all necessary derivatives required by the model. Genotypes can be scaled using average or marker specific allele content variance (2pq), and allele frequencies are either calculated from the data, or read from user specified input files. Futher, if requested $$G$$ can be adjusted to fit $$A$$, even for models where $$G$$ is not build(e.g. ~~single step SNP~~-BLUP).		lmt can account for genomic data via genomic relationship matrices $$G$$ and single step relationship matrices $$H$$ where ssGBLUP<ref name="Christensen2010/>, ssGTBLUP<ref name="Mäntysaari2017"/>, and ssSNPBLUP<ref name="Liu2014"/> are supported. lmt accepts plain marker data and will calculate all necessary derivatives required by the model. Genotypes can be scaled using average or marker specific allele content variance (2pq), and allele frequencies are either calculated from the data, or read from user specified input files. Futher, if requested $$G$$ can be adjusted to fit $$A$$<ref name="Christensen2012"/>, even for models where $$G$$ is not build(e.g. ssSNP-BLUP).

	==~~Single step SNP-BLUP~~==		==Genetic Groups==

	lmt's Single ~~step~~ SNP-BLUP model uses the approach described in Liu and Goddard 2014, where the adverse of the ~~singe step~~ SNP-BLUP variance structure can be written as		If the pedigree constituting $$A$$ contains phantom parents, genetic groups are automatically fitted for all different Single-Step co-variance structures. Alternatively, genetic groups can be fitted as an extra factor.

			==Single-Step SNP-BLUP==

			lmt's Single-Step SNP-BLUP model uses the approach described in Liu and Goddard 2014<ref name="Liu2014">, where the adverse of the Singe-Step SNP-BLUP variance structure can be written as

	$$		$$
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	where $$M$$ is a centered and scaled matrix of marker counts, $$\zeta = \Omega_{m}(\Gamma_m \otimes I)\Omega_{m}^{'}$$, $$\xi = A_{n,g}A_{g,g}^{-1}$$, $$\Sigma_g$$ is the global genetic co-variance matrix, $$\lambda$$ is the polygenic weight and $$\gamma$$ is the genomic weight. In most applications $$\gamma \zeta $$ is set to $$ \gamma I\otimes \Sigma_g$$. For that case it can be shown that the above variance structure can be transferred into an ordinary single step $$H$$ matrix which uses $$G$$. Note that in the instruction file $$\zeta$$ is parametrized independently of $$\Sigma_g$$, but it is advisable to ensure that the following equality holds $$\gamma 1'\zeta 1==\gamma \Sigma_g$$.		where $$M$$ is a centered and scaled matrix of marker counts, $$\zeta = \Omega_{m}(\Gamma_m \otimes I)\Omega_{m}^{'}$$, $$\xi = A_{n,g}A_{g,g}^{-1}$$, $$\Sigma_g$$ is the global genetic co-variance matrix, $$\lambda$$ is the polygenic weight and $$\gamma$$ is the genomic weight. In most applications $$\gamma \zeta $$ is set to $$ \gamma I\otimes \Sigma_g$$. For that case it can be shown that the above variance structure can be transferred into an ordinary single step $$H$$ matrix which uses $$G$$. Note that in the instruction file $$\zeta$$ is parametrized independently of $$\Sigma_g$$, but it is advisable to ensure that the following equality holds $$\gamma 1'\zeta 1==\gamma \Sigma_g$$.

			==References==
			<references>
			<ref name="Garcia2017">Garcia et al.; Metafounders are related to F st fixation indices and reduce bias in single-step genomic evaluations; Genetics Selection Evolution; 2017</ref>
			<ref name="Westell1988">Westell et al.; Genetic Groups in an Animal Model; Journal of Dairy Science; 1988</ref>
			<ref name="Christensen2010">Christensen et al.; Genomic prediction when some animals are not genotyped; Genetics Selection Evolution; 2010</ref>
			<ref name="Liu2014">Liu et al.;A single-step genomic model with direct estimation of marker effects; Journal of Dairy Science;2014</ref>
			<ref name="Mäntysaari2017">Mäntysaari et al.;Efficient single-step genomic evaluation for a multibreed beef cattle population having many genotyped animals; Journal of Animal Science;2017</ref>
			</references>

127.0.0.1: /* Single step SNP-BLUP */

2022-03-04T03:58:33Z

Single step SNP-BLUP

← Older revision		Revision as of 03:58, 4 March 2022
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	==Single step SNP-BLUP==		==Single step SNP-BLUP==

	lmt's Single step SNP-BLUP model uses the approach described in Liu and Goddard 2014, where the adverse of the singe step SN-~~PBLUP~~ variance structure can be written as		lmt's Single step SNP-BLUP model uses the approach described in Liu and Goddard 2014, where the adverse of the singe step SNP-BLUP variance structure can be written as

	$$		$$
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	$$		$$

	where $$M$$ is a centered and scaled matrix of marker counts, $$\zeta = \Omega_{m}(\Gamma_m \otimes I)\Omega_{m}^{'}$$, $$\xi = A_{n,g}A_{g,g}^{-1}$$, $$\Sigma_g$$ is the global genetic co-variance matrix, $$\lambda$$ is the polygenic weight and $$\gamma$$ is the genomic weight. In most applications $$\gamma \zeta $$ is set to $$ \gamma I\otimes \Sigma_g$$ ~~where~~ it can be shown that the above variance structure can be transferred into an ordinary single step $$H$$ matrix which uses $$G$$. Note that in the instruction file $$\zeta$$ is parametrized independently of $$\Sigma_g$$, but it is advisable to ensure that the following equality holds $$\gamma 1'\zeta 1==\gamma \Sigma_g$$.		where $$M$$ is a centered and scaled matrix of marker counts, $$\zeta = \Omega_{m}(\Gamma_m \otimes I)\Omega_{m}^{'}$$, $$\xi = A_{n,g}A_{g,g}^{-1}$$, $$\Sigma_g$$ is the global genetic co-variance matrix, $$\lambda$$ is the polygenic weight and $$\gamma$$ is the genomic weight. In most applications $$\gamma \zeta $$ is set to $$ \gamma I\otimes \Sigma_g$$. For that case it can be shown that the above variance structure can be transferred into an ordinary single step $$H$$ matrix which uses $$G$$. Note that in the instruction file $$\zeta$$ is parametrized independently of $$\Sigma_g$$, but it is advisable to ensure that the following equality holds $$\gamma 1'\zeta 1==\gamma \Sigma_g$$.

127.0.0.1: /* Single step SNP-BLUP */

2022-03-04T03:57:23Z

Single step SNP-BLUP

← Older revision		Revision as of 03:57, 4 March 2022
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	$$		$$

	where $$M$$ is a centered and scaled matrix of marker counts, $$\zeta = \Omega_{m}(\Gamma_m \otimes I)\Omega_{m}^{'}$$, $$\xi = A_{n,g}A_{g,g}^{-1}$$, $$\Sigma_g$$ is the global genetic co-variance matrix, $$\lambda$$ is the polygenic weight and $$\gamma$$ is the genomic weight. In most applications $$\gamma \zeta = \gamma I\otimes \Sigma_g$$. Note that in the instruction file $$\zeta$$ is parametrized independently of $$\Sigma_g$$, but it is advisable to ensure that the following equality holds $$\gamma 1'\zeta 1==\gamma \Sigma_g$$.		where $$M$$ is a centered and scaled matrix of marker counts, $$\zeta = \Omega_{m}(\Gamma_m \otimes I)\Omega_{m}^{'}$$, $$\xi = A_{n,g}A_{g,g}^{-1}$$, $$\Sigma_g$$ is the global genetic co-variance matrix, $$\lambda$$ is the polygenic weight and $$\gamma$$ is the genomic weight. In most applications $$\gamma \zeta $$ is set to $$ \gamma I\otimes \Sigma_g$$ where it can be shown that the above variance structure can be transferred into an ordinary single step $$H$$ matrix which uses $$G$$. Note that in the instruction file $$\zeta$$ is parametrized independently of $$\Sigma_g$$, but it is advisable to ensure that the following equality holds $$\gamma 1'\zeta 1==\gamma \Sigma_g$$.

Boerner: /* Single step SNP-BLUP */

2022-03-04T01:21:12Z

Single step SNP-BLUP

← Older revision		Revision as of 01:21, 4 March 2022
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	$$		$$

	where $$M$$ is a centered and scaled matrix of marker counts, $$\zeta = \Omega_{m}(\Gamma_m \otimes I)\Omega_{m}^{'}$$, $$\xi = A_{n,g}A_{g,g}^{-1}$$, $$\Sigma_g$$ is the global genetic co-variance matrix, $$\lambda$$ is the polygenic weight and $$\gamma$$ is the genomic weight. In most applications $$\gamma \zeta = \gamma I\otimes \Sigma_g$$. Note that in the instruction file $$\zeta$$ is parametrized independently of $$\Sigma_g$$, but it is advisable that the following equality holds $$\gamma 1'\zeta 1==\gamma \Sigma_g$$.		where $$M$$ is a centered and scaled matrix of marker counts, $$\zeta = \Omega_{m}(\Gamma_m \otimes I)\Omega_{m}^{'}$$, $$\xi = A_{n,g}A_{g,g}^{-1}$$, $$\Sigma_g$$ is the global genetic co-variance matrix, $$\lambda$$ is the polygenic weight and $$\gamma$$ is the genomic weight. In most applications $$\gamma \zeta = \gamma I\otimes \Sigma_g$$. Note that in the instruction file $$\zeta$$ is parametrized independently of $$\Sigma_g$$, but it is advisable to ensure that the following equality holds $$\gamma 1'\zeta 1==\gamma \Sigma_g$$.

Boerner: /* Single step SNP-BLUP */

2022-03-04T01:20:20Z

Single step SNP-BLUP

← Older revision		Revision as of 01:20, 4 March 2022
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	$$		$$

	where $$\zeta = \Omega_{m}(\Gamma_m \otimes I)\Omega_{m}^{'}$$ ~~and~~ $$\xi = A_{n,g}A_{g,g}^{-1}$$, $$\Sigma_g$$ is the global genetic co-variance matrix, $$\lambda$$ is the polygenic weight and $$\gamma$$ is the genomic weight. ~~Note that when parametrizing this variance structure the following equality should hold~~ $$\gamma 1'\zeta 1==\gamma \Sigma_g$$. Note that in ~~most case~~ $$\gamma \zeta == ~~I\otimes~~ \gamma \Sigma_g$$.		where $$M$$ is a centered and scaled matrix of marker counts, $$\zeta = \Omega_{m}(\Gamma_m \otimes I)\Omega_{m}^{'}$$, $$\xi = A_{n,g}A_{g,g}^{-1}$$, $$\Sigma_g$$ is the global genetic co-variance matrix, $$\lambda$$ is the polygenic weight and $$\gamma$$ is the genomic weight. In most applications $$\gamma \zeta = \gamma I\otimes \Sigma_g$$. Note that in the instruction file $$\zeta$$ is parametrized independently of $$\Sigma_g$$, but it is advisable that the following equality holds $$\gamma 1'\zeta 1==\gamma \Sigma_g$$.

Boerner: /* Single step SNP-BLUP */

2022-03-04T01:12:49Z

Single step SNP-BLUP

← Older revision		Revision as of 01:12, 4 March 2022
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	$$		$$

	where $$\zeta = \Omega_{m}(\Gamma_m \otimes I)\Omega_{m}^{'}$$ and $$\xi = A_{n,g}A_{g,g}^{-1}$$, $$\Sigma_g$$ is the global genetic co-variance matrix, $$\lambda$$ is the polygenic weight and $$\gamma$$ is the genomic weight. Note that when parametrizing this variance structure the following equality should hold $$\gamma 1'\zeta 1==\gamma \Sigma_g$$. Note that in most case $$\zeta == I\otimes \gamma \Sigma_g$$.		where $$\zeta = \Omega_{m}(\Gamma_m \otimes I)\Omega_{m}^{'}$$ and $$\xi = A_{n,g}A_{g,g}^{-1}$$, $$\Sigma_g$$ is the global genetic co-variance matrix, $$\lambda$$ is the polygenic weight and $$\gamma$$ is the genomic weight. Note that when parametrizing this variance structure the following equality should hold $$\gamma 1'\zeta 1==\gamma \Sigma_g$$. Note that in most case $$\gamma \zeta == I\otimes \gamma \Sigma_g$$.

Boerner at 01:12, 4 March 2022

2022-03-04T01:12:12Z

← Older revision		Revision as of 01:12, 4 March 2022
Line 15:		Line 15:
	$$		$$

	where $$\zeta = \Omega_{m}(\Gamma_m \otimes I)\Omega_{m}^{'}$$ and $$\xi = A_{n,g}A_{g,g}^{-1}$$, $$\Sigma_g$$ is the global genetic co-variance matrix, $$\lambda$$ is the polygenic weight and $$\gamma$$ is the genomic weight. Note that when parametrizing this variance structure the following equality should hold $$\gamma 1'\zeta 1==\gamma \Sigma_g$$		where $$\zeta = \Omega_{m}(\Gamma_m \otimes I)\Omega_{m}^{'}$$ and $$\xi = A_{n,g}A_{g,g}^{-1}$$, $$\Sigma_g$$ is the global genetic co-variance matrix, $$\lambda$$ is the polygenic weight and $$\gamma$$ is the genomic weight. Note that when parametrizing this variance structure the following equality should hold $$\gamma 1'\zeta 1==\gamma \Sigma_g$$. Note that in most case $$\zeta == I\otimes \gamma \Sigma_g$$.