Association - Revision history

Isin: /* Output */

2023-06-07T13:32:26Z

Output

← Older revision		Revision as of 15:32, 7 June 2023
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	<pre>		<pre>
	./angsd -doMaf 4 -beagle beagleOut.impute.beagle.gz.gprobs.gz -fai ref.fai -yBin pheno.ybin -doAsso 6		./angsd -doMaf 4 -beagle beagleOut.impute.beagle.gz.gprobs.gz -fai ref.fai -yBin pheno.ybin -doAsso 6
			</pre>

			==Printing mafs files==

			By default, -doAsso does not print the mafs file. To print the mafs file, use -forceMaf 1.

			<pre>
			-forceMaf 0 (Write .mafs file when running -doAsso (by default does not output .mafs file with -doAsso))
	</pre>		</pre>

Isin at 12:48, 22 May 2023

2023-05-22T12:48:51Z

← Older revision		Revision as of 14:48, 22 May 2023
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	\|-		\|-
	! scope="row"\| high_WT/HE/HO*		! scope="row"\| high_WT/HE/HO*
	\| Number of ~~sites~~ with a WE/HE/HO genotype posterior probability above 0.9. WT=major/major,HE=major/minor,HO=minor/minor.		\| Number of individuals with a WE/HE/HO genotype posterior probability above 0.9. WT=major/major,HE=major/minor,HO=minor/minor.
	\|-		\|-
	! scope="row"\| emIter~		! scope="row"\| emIter~

Emil: /* Score statistic */

2021-06-03T13:32:05Z

Score statistic

← Older revision		Revision as of 15:32, 3 June 2021
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	A fasta index file (.fai). For human data either on hg19 or hg38 one can just use the file, test/hg19.fa.fai that is in the ANGSD repository and is therefore downloaded when cloning ANGSD from its github. Otherwise the .fai file can be obtained by indexing the reference genome or by using the header of a bam file.		A fasta index file (.fai). For human data either on hg19 or hg38 one can just use the file, test/hg19.fa.fai that is in the ANGSD repository and is therefore downloaded when cloning ANGSD from its github. Otherwise the .fai file can be obtained by indexing the reference genome or by using the header of a bam file.
	;-sampleFile [Filename]		;-sampleFile [Filename]
	A .sample File containing phenotypes and covariates for doing the analysis. It is the Oxford sample information file (.sample) format described ~~here~~ [https://www.well.ox.ac.uk/~gav/qctool_v2/documentation/sample_file_formats.html here].		A .sample File containing phenotypes and covariates for doing the analysis. It is the Oxford sample information file (.sample) format described [https://www.well.ox.ac.uk/~gav/qctool_v2/documentation/sample_file_formats.html here].
	;-whichPhe [phe1,phe2,...]		;-whichPhe [phe1,phe2,...]
	Use this option to select which phenotypes to analyse, write phenos comma seperated ('phe1,phe2,...'), only works with a .sample file.		Use this option to select which phenotypes to analyse, write phenos comma seperated ('phe1,phe2,...'), only works with a .sample file.

Emil: /* Score statistic */

2021-06-03T13:30:57Z

Score statistic

← Older revision		Revision as of 15:30, 3 June 2021
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	</pre>		</pre>
	</div>		</div>
			;-yCount [Filename]
			A file containing the count phenotype data, for doing poission based regression. -999 being missing phenotypes.


	;-cov [Filename]		;-cov [Filename]
	Files containing additional covariates in the analysis. Each lines should contain the additional covariates for a single individuals. Thus the number of lines should match the number of individuals and the number of coloums should match the number of additional covariates.		Files containing additional covariates in the analysis. Each lines should contain the additional covariates for a single individuals. Thus the number of lines should match the number of individuals and the number of coloums should match the number of additional covariates.
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	;-fai [Filename]		;-fai [Filename]
	A fasta index file (.fai). For human data either on hg19 or hg38 one can just use the file, test/hg19.fa.fai that is in the ANGSD repository and is therefore downloaded when cloning ANGSD from its github. Otherwise the .fai file can be obtained by indexing the reference genome or by using the header of a bam file.		A fasta index file (.fai). For human data either on hg19 or hg38 one can just use the file, test/hg19.fa.fai that is in the ANGSD repository and is therefore downloaded when cloning ANGSD from its github. Otherwise the .fai file can be obtained by indexing the reference genome or by using the header of a bam file.
			;-sampleFile [Filename]
			A .sample File containing phenotypes and covariates for doing the analysis. It is the Oxford sample information file (.sample) format described here [https://www.well.ox.ac.uk/~gav/qctool_v2/documentation/sample_file_formats.html here].
			;-whichPhe [phe1,phe2,...]
			Use this option to select which phenotypes to analyse, write phenos comma seperated ('phe1,phe2,...'), only works with a .sample file.
			; -whichCov [cov1,cov2,...]
			Use this option to select which covariates to include, write covs comma seperated ('cov1,cov2,...'), only works with a .sample file.

Emil: /* Brief Overview */

2021-06-03T13:19:54Z

Brief Overview

← Older revision		Revision as of 15:19, 3 June 2021
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	./angsd -doAsso		./angsd -doAsso
	abcAsso.cpp:		abcAsso.cpp:
	-doAsso 0		-doAsso 0
	1: Frequency Test (Known Major and Minor)		1: Frequency Test (Known Major and Minor)
	2: Score Test		2: Score Test
	4: Latent genotype model		4: Latent genotype model
	5: Score Test with latent genotype model - hybrid test		5: Score Test with latent genotype model - hybrid test
	6: Dosage regression		6: Dosage regression
			7: Latent genotype model (wald test) - NOT PROPERLY TESTED YET!
	Frequency Test Options:		Frequency Test Options:
	-yBin (null) (File containing disease status)		-yBin (null) (File containing disease status)

	Score, Latent, Hybrid and Dosage Test Options:		Score, Latent, Hybrid and Dosage Test Options:
	-yBin (null) (File containing disease status)		-yBin (null) (File containing disease status)
	-yQuant (null) (File containing phenotypes)		-yCount (null) (File containing count phenotypes)
	-cov (null) (File containing additional covariates)		-yQuant (null) (File containing phenotypes)
	-model 1		-cov (null) (File containing additional covariates)
	1: Additive/Log-Additive (Default)		-sampleFile (null) (.sample File containing phenotypes and covariates)
	2: Dominant		-whichPhe (null) Select which phenotypes to analyse, write phenos comma seperated ('phe1,phe2,...'), only works with a .sample file
	3: Recessive		-whichCov (null) Select which covariates to include, write covs comma seperated ('cov1,cov2,...'), only works with a .sample file
			-model 1
			1: Additive/Log-Additive (Default)
			2: Dominant
			3: Recessive

			-minHigh 10 (Require atleast minHigh number of high credible genotypes)
			-minCount 10 (Require this number of minor alleles, estimated from MAF)
			-assoThres 0.000001 Threshold for logistic regression
			-assoIter 100 Number of iterations for logistic regression
			-emThres 0.000100 Threshold for convergence of EM algorithm in doAsso 4 and 5
			-emIter 40 Number of max iterations for EM algorithm in doAsso 4 and 5

	~~-minHigh 10 (Require atleast minHigh number of high credible genotypes)~~
	~~-minCount 10 (Require this number of minor alleles, estimated from MAF)~~
	~~-assoThres 0.000001 Threshold for logistic regression~~
	~~-assoIter 100 Number of iterations for logistic regression~~
	~~-emThres 0.000100 Threshold for convergence of EM algorithm in doAsso 4 and 5~~
	~~-emIter 40 Number of max iterations for EM algorithm in doAsso 4 and 5~~
	-doPriming 1 Prime EM algorithm with dosage derived coefficients (0: no, 1: yes - default)		-doPriming 1 Prime EM algorithm with dosage derived coefficients (0: no, 1: yes - default)

	-Pvalue 0 Prints a P-value instead of a likelihood ratio (0: no - default, 1: yes)		-Pvalue 0 Prints a P-value instead of a likelihood ratio (0: no - default, 1: yes)

	Hybrid Test Options:		Hybrid Test Options:
	-hybridThres 0.050000 (p-value value threshold for when to perform latent genotype model)		-hybridThres 0.050000 (p-value value threshold for when to perform latent genotype model)



	</pre>		</pre>

Emil: /* BCF/VCF files */

2020-12-05T07:35:30Z

BCF/VCF files

← Older revision		Revision as of 09:35, 5 December 2020
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	And with a quantitative phenotype (using normal linear regression):		And with a quantitative phenotype (using normal linear regression):
	<pre>		<pre>
	./angsd -doMaf 4 -vcf-~~gptest~~/assotest/test.vcf -yQuant test/assotest/testQuant.phe -doAsso 4 -cov test.cov -out test.res		./angsd -doMaf 4 -vcf-gp test/assotest/test.vcf -yQuant test/assotest/testQuant.phe -doAsso 4 -cov test.cov -out test.res
	</pre>		</pre>

Emil: /* BGEN files */

2020-12-05T07:35:15Z

BGEN files

← Older revision		Revision as of 09:35, 5 December 2020
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	Also it is made according to v1.3 of the BGEN file format and only the recommended layout 2 is supported. It can also be run with a .sample file [https://www.well.ox.ac.uk/~gav/qctool/documentation/sample_file_formats.html] with both the phenotype and covariates. An example of a command with a binary phenotype:		Also it is made according to v1.3 of the BGEN file format and only the recommended layout 2 is supported. It can also be run with a .sample file [https://www.well.ox.ac.uk/~gav/qctool/documentation/sample_file_formats.html] with both the phenotype and covariates. An example of a command with a binary phenotype:
	<pre>		<pre>
	./angsd -doMaf 4 -~~bgentest~~/assotest/test.bgen -sampleFile test/assotest/test.sample -doAsso 4 -out test.res -fai test/hg19.fa.fai		./angsd -doMaf 4 -bgen test/assotest/test.bgen -sampleFile test/assotest/test.sample -doAsso 4 -out test.res -fai test/hg19.fa.fai
	</pre>		</pre>
	The column type line in the .sample will decide if a binary or normal regression model will be run, depending on the type of the phenotype (binary or quantitative).		The column type line in the .sample will decide if a binary or normal regression model will be run, depending on the type of the phenotype (binary or quantitative).

Emil: /* Brief Overview */

2020-12-02T20:47:08Z

Brief Overview

← Older revision		Revision as of 22:47, 2 December 2020
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	-emIter 40 Number of max iterations for EM algorithm in doAsso 4 and 5		-emIter 40 Number of max iterations for EM algorithm in doAsso 4 and 5
	-doPriming 1 Prime EM algorithm with dosage derived coefficients (0: no, 1: yes - default)		-doPriming 1 Prime EM algorithm with dosage derived coefficients (0: no, 1: yes - default)
	-Pvalue 0 Prints P-~~values~~ instead of likelihood ratio (0: no - ~~defailt~~, 1: yes)		-Pvalue 0 Prints a P-value instead of a likelihood ratio (0: no - default, 1: yes)

	Hybrid Test Options:		Hybrid Test Options:

Emil: /* Example */

2020-12-02T20:44:14Z

Example

← Older revision		Revision as of 22:44, 2 December 2020
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	</div>		</div>

	The LRT is the likelihood ratio statistics which is chi square distributed with one degree of freedom. The P-value can also be obtained instead (by using -Pvalue 1).		The LRT is the likelihood ratio statistics which is chi square distributed with one degree of freedom. The P-value can also be obtained instead (by using -Pvalue 1). -Pvalue is accurate upto chisq values of 70, which is equvialent to P-values of 1.1102e-16.

	==Dependency Chain==		==Dependency Chain==

Emil: /* Output format */

2020-11-30T22:11:45Z

Output format

← Older revision		Revision as of 00:11, 1 December 2020
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	! scope="col"\| Frequency		! scope="col"\| Frequency
	! scope="col"\| N*		! scope="col"\| N*
	! scope="col"\| LRT		! scope="col"\| LRT (or P)
	! scope="col"\| beta^		! scope="col"\| beta^
	! scope="col"\| SE^		! scope="col"\| SE^
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	\| Number of individuals. That is the number of samples that have both sequencing data and phenotypic data.		\| Number of individuals. That is the number of samples that have both sequencing data and phenotypic data.
	\|-		\|-
	! scope="row"\| LRT		! scope="row"\| LRT (or P)
	\| The likelihood ratio statistic. This statistic is chi square distributed with one degree of freedom. Sites that fails one of the filters are given the value -999.000000. The P-value can also be obtained instead (by using -Pvalue 1), this column will have "P" as its column name the.		\| The likelihood ratio statistic. This statistic is chi square distributed with one degree of freedom. Sites that fails one of the filters are given the value -999.000000. The P-value can also be obtained instead (by using -Pvalue 1), this column will have "P" as its column name the.
	\|-		\|-