AsaMap - Revision history

Albrecht: Replaced content with "The program is available and described on github: https://github.com/e-jorsboe/asaMap"

2026-03-24T07:32:33Z

Replaced content with "The program is available and described on github: https://github.com/e-jorsboe/asaMap"

Emil: /* Example */

2019-03-23T09:08:53Z

Example

← Older revision		Revision as of 11:08, 23 March 2019
Line 17:		Line 17:
	=Example=		=Example=

	~~This an example!!~~		To be added...

	=Input Files=		=Input Files=

Emil: /* Input Files */

2019-03-23T05:40:46Z

Input Files

← Older revision		Revision as of 07:40, 23 March 2019
Line 34:		Line 34:
	</pre>		</pre>

	A covarite file can also be provided, where each column is a covariate and each row is an individual - should NOT have columns of 1s for intercept (intercept will be included automatically). This file has to have same number of rows as phenotype file and .fam file.		A covarite file can also be provided, where each column is a covariate and each row is an individual - '''should NOT have columns of 1s for intercept (intercept will be included automatically)'''. This file has to have same number of rows as phenotype file and .fam file.

	<pre>		<pre>

Emil at 19:15, 3 March 2019

2019-03-03T19:15:39Z

← Older revision		Revision as of 21:15, 3 March 2019
Line 1:		Line 1:
	This page contains information about the program asaMap, a tool for doing ancestry specific assocaition mapping for large scales genetic studies. It is based on called genotypes in the binary plink format (.bed). The program is written in C++.		This page contains information about the program '''asaMap''', a tool for doing ancestry specific assocaition mapping for large scales genetic studies. It is based on called genotypes in the binary plink format (.bed). The program is written in C++.

	=Download=		=Download=

Emil at 19:15, 3 March 2019

2019-03-03T19:15:14Z

← Older revision		Revision as of 21:15, 3 March 2019
Line 1:		Line 1:
			This page contains information about the program asaMap, a tool for doing ancestry specific assocaition mapping for large scales genetic studies. It is based on called genotypes in the binary plink format (.bed). The program is written in C++.

	=Download=		=Download=

Emil at 19:12, 3 March 2019

2019-03-03T19:12:03Z

← Older revision		Revision as of 21:12, 3 March 2019
Line 19:		Line 19:

	=Input Files=		=Input Files=
	Input files are called genotypes in the binary plink files (*.bed) format [https://www.cog-genomics.org/plink2]. And estimated admixture proportions and population specific allele frequencies. For estimating admixture proportions and population specific allele frequencies [http://software.genetics.ucla.edu/admixture/ ADMIXTURE], can be used, where .Q and .P files respectively can be given directly to asaMap.		Input files are called genotypes in the binary plink files (*.bed) format [https://www.cog-genomics.org/plink2]. And estimated admixture proportions and population specific allele frequencies. For estimating admixture proportions and population specific allele frequencies [http://software.genetics.ucla.edu/admixture/ ADMIXTURE], can be used, where '''.Q and .P files''' respectively can be given directly to asaMap.


Line 68:		Line 68:
	</pre>		</pre>

	This produces a out.log logfile and a out.res with results for each site (after filtering).		This produces a '''out.log''' logfile and a '''out.res''' with results for each site (after filtering).


Line 119:		Line 119:
	=Outputs=		=Outputs=

	A .res file with the likelihoods of each model and the estimated ~~coefficents~~ in each model is produced, here for the additive:		A '''.res''' file with the likelihoods of each model and the estimated coefficients in each model is produced, here for the additive:

	<pre>		<pre>
Line 144:		Line 144:

	P-values can be generated doing a likelihood ratio test, between the 2 desired models.		P-values can be generated doing a likelihood ratio test, between the 2 desired models.
	An Rscript "getPvalues.R" is provided that makes it easy to obtain P-values from the .res file:		An Rscript '''getPvalues.R''' is provided that makes it easy to obtain P-values from the '''.res''' file:

	<pre>		<pre>
Line 152:		Line 152:
	</pre>		</pre>

	Which produces a file with the suffix .Pvalues:		Which produces a file with the suffix '''.Pvalues''':

	<pre>		<pre>
Line 256:		Line 256:
	\|}		\|}

	beta_1 and beta_2 are the effect of the assumed effect-allele in population 1 and 2 respectively. beta_m is the recessive effect of being recessive for an allele with one copy from population 1 and one copy from population 2. delta_1 and delta_2 are the effect of the assumed non-effect-allele in population 1 and 2 respectively.		'''beta_1''' and '''beta_2''' are the effect of the assumed effect-allele in population 1 and 2 respectively. '''beta_m''' is the recessive effect of being recessive for an allele with one copy from population 1 and one copy from population 2. '''delta_1''' and '''delta_2''' are the effect of the assumed non-effect-allele in population 1 and 2 respectively.

	=Citation=		=Citation=

Emil: /* Models */

2019-03-03T19:10:11Z

Models

← Older revision		Revision as of 21:10, 3 March 2019
Line 168:		Line 168:
	For the additive model there are 6 different models:		For the additive model there are 6 different models:

	~~The output from the association analysis is a list of files called '''prefix.lrt'''. These are tab separated plain text files, with nine columns.~~
	{\| class="wikitable"		{\| class="wikitable"
	\|-		\|-
Line 209:		Line 208:
	For the recessive model there are 8 different models:		For the recessive model there are 8 different models:

	~~The output from the association analysis is a list of files called '''prefix.lrt'''. These are tab separated plain text files, with nine columns.~~
	{\| class="wikitable"		{\| class="wikitable"
	\|-		\|-

Emil: /* Models */

2019-03-03T19:09:43Z

Models

← Older revision		Revision as of 21:09, 3 March 2019
Line 177:		Line 177:
	\|-		\|-
	\| M0		\| M0
	\| (\beta_1,\beta_2,\delta_1)\in R^3		\| (beta_1, beta_2, delta_1) in R^3
	\| effect of non-assumed effect allele		\| effect of non-assumed effect allele
	\| 1		\| 1
	\|-		\|-
	\| M1		\| M1
	\| (\beta_1,\beta_2)\in R^2		\| (beta_1, beta_2) in R^2
	\| population specific effects		\| population specific effects
	\| 2		\| 2
	\|-		\|-
	\| M2		\| M2
	\| \beta_1=0,\beta_2\in R		\| beta_1=0, beta_2 in R
	\| no effect in population 1		\| no effect in population 1
	\| 1		\| 1
	\|-		\|-
	\| M3		\| M3
	\| \beta_1\in R, \beta_2=0		\| beta_1 in R, beta_2=0
	\| no effect in population 2		\| no effect in population 2
	\| 1		\| 1
	\|-		\|-
	\| M4		\| M4
	\| \beta_1=\beta_2\in R		\| beta_1=beta_2 in R
	\| same effect in both populations		\| same effect in both populations
	\| 1		\| 1
	\|-		\|-
	\| M5		\| M5
	\| \beta_1=\beta_2=0		\| beta_1=beta_2=0
	\| no effect in any population		\| no effect in any population
	\| 0		\| 0
	\|}		\|}

	\| model ~~\| parameters \| notes \| #effect Parameters \|~~		For the recessive model there are 8 different models:
	~~\| --- \| --- \| --- \| --- \|~~
	~~\| M0 \| (\beta_1,\beta_2,\delta_1)\in R^3 \| effect of non-assumed effect allele \| 1 \|~~
	~~\| M1 \| (\beta_1,\beta_2)\in R^2 \| population specific effects \| 2 \|~~
	~~\| M2 \| \beta_1=0,\beta_2\in R \| no effect in population \| 1 \|~~
	~~\| M3 \| \beta_1\in R, \beta_2=0 \| no effect in population 2 \| 1 \|~~
	~~\| M4 \| \beta_1=\beta_2\in R \| same effect in both populations \| 1 \|~~
	~~\| M5 \| \beta_1=\beta_2=0 \| no effect in any population \| 0 \|~~

	\| ~~model~~ \| ~~parameters~~ \| ~~notes~~ \| ~~#effect~~ Parameters \|		The output from the association analysis is a list of files called '''prefix.lrt'''. These are tab separated plain text files, with nine columns.
	\| ~~--- \| ---~~ \| -~~-- \| --- \|~~		{\| class="wikitable"
	\| R0 \| (\beta_1,\beta_m,\beta_2,\delta_1,\delta_2)\in R^5 \| recessive effect of non-assumed effect alleles \| 2 \|		\|-
	\| R1 \| (\beta_1,\beta_m,\beta_2)\in R^3 \| population specific effects \| 3 \|		! scope="col"\| Model
	\| R2 \| \beta_1\in R,\beta_m=\beta_2\in R \| same effect when one or both variant alleles are from pop 2 \| 2 \|		! scope="col"\| Parameters
	\| R3 \| \beta_1=\beta_m\in R,\beta_2\in R \| same effect when one or both variant alleles are from pop 1 \| 2 \|		! scope="col"\| Notes
	\| R4 \| \beta_1\in R,\beta_m=\beta_2=0 \| only an effect when both variant alleles are from pop 1 \| 1 \|		! scope="col"\| Effect Parameters
	\| R5 \| \beta_1=\beta_m=0, \beta_2\in R \| only an effect when both variant alleles are from pop 2 \| 1 \|		\|-
	\| R6 \| \beta_1=\beta_m=\beta_2\in R \| same effect regardless of ancestry \| 1 \|		\| R0
	\| R7 \| \beta_1=\beta_m=\beta_2=0 \| no effect in any population \| 0 \|		\| (beta_1, beta_m, beta_2, delta_1, delta_2) in R^5
			\| recessive effect of non-assumed effect alleles
			\| 2
			\|-
			\| R1
			\| (beta_1, beta_m, beta_2) in R^3
			\| population specific effects
			\| 3
			\|-
			\| R2
			\| beta_1 in R, beta_m=beta_2 in R
			\| same effect when one or both variant alleles are from pop 2
			\| 2
			\|-
			\| R3
			\| beta_1=beta_m in R, beta_2 in R
			\| same effect when one or both variant alleles are from pop 1
			\| 2
			\|-
			\| R4
			\| beta_1 in R, beta_m=beta_2=0
			\| only an effect when both variant alleles are from pop 1
			\| 1
			\|-
			\| R5
			\| beta_1=beta_m=0, beta_2 in R
			\| only an effect when both variant alleles are from pop 2
			\| 1
			\|-
			\| R6
			\| beta_1=beta_m=beta_2 in R
			\| same effect regardless of ancestry
			\| 1
			\|-
			\| R7
			\| beta_1=beta_m=beta_2=0
			\| no effect in any population
			\| 0
			\|}

			beta_1 and beta_2 are the effect of the assumed effect-allele in population 1 and 2 respectively. beta_m is the recessive effect of being recessive for an allele with one copy from population 1 and one copy from population 2. delta_1 and delta_2 are the effect of the assumed non-effect-allele in population 1 and 2 respectively.

	=Citation=		=Citation=

Emil: /* Models */

2019-03-03T18:58:49Z

Models

@@ Line 176: / Line 176: @@
 ! scope="col"| Effect Parameters
 |-
-| Chromosome
+| M0
-| Chromosome
+| (\beta_1,\beta_2,\delta_1)\in R^3
-| Chromosome
+| effect of non-assumed effect allele
-| Chromosome
+| 1
 |}

Emil: /* Models */

2019-03-03T18:56:05Z

Models

← Older revision		Revision as of 20:56, 3 March 2019
Line 176:		Line 176:
	! scope="col"\| Effect Parameters		! scope="col"\| Effect Parameters
	\|-		\|-
	~~! \| \| \|~~ \|		\| Chromosome
	\| Chromosome \| Chromosome ~~\| Chromosome \| Chromosome~~ \| Chromosome		\| Chromosome
			\| Chromosome
			\| Chromosome
	\|}		\|}