SardiNIA - Revision history

Sarahgag: /* Locations of Files for Current Data Freeze of 3839 Samples */

2018-09-08T14:24:07Z

Locations of Files for Current Data Freeze of 3839 Samples

← Older revision		Revision as of 10:24, 8 September 2018
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	=== Locations of Files for Current Data Freeze of 3839 Samples===		=== Locations of Files for Current Data Freeze of 3839 Samples===

	NOTICE: We identified late in the process that two of the samples (22855 and 22358) were actually the same individual. They both should be the same individual 22855. Therefore, there are 3840 sample IDs in each of the files below, but only 22855 should move on to later processes. In future data freezes with this data, these two sequencing sets should be merged into a single 22855 individual.		NOTICE: We identified late in the process that two of the samples (22855 and 22358) were actually the same individual. They both should be the same individual 22855. Therefore, there are 3840 sample IDs in each of the files below, but only 22855 should move on to later processes. In future data freezes with this data, these two sequencing sets should be merged into a single 22855 individual. For association analyses, 22358 has been removed.

			* '''PheWAS: GWAS on 120 traits for Visit 1'''
			** Results: /net/wonderland/home/csidore/Epacts_vcfs/Michelle_panel/GOODheader/R2sameasPapers
			** PheWeb (previous release: http://pheweb.irgb.cnr.it); current release: http://sardinia-pheweb.sph.umich.edu

	* List of '''Sample Numbers'''		* List of '''Sample Numbers'''

Sarahgag: /* Status as of July 2016 */

2018-09-08T14:19:31Z

Status as of July 2016

← Older revision		Revision as of 10:19, 8 September 2018
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	We are sequencing the genomes of 1,000 individuals to learn about the genetics of blood lipid levels and personality.		We are sequencing the genomes of 1,000 individuals to learn about the genetics of blood lipid levels and personality.

	== Status as of July ~~2016~~ ==		== Status as of July 2018 ==

	=== Locations of Files for Current Data Freeze of 3839 Samples===		=== Locations of Files for Current Data Freeze of 3839 Samples===

Sarahgag: /* Future Directions */

2018-09-08T14:19:07Z

Future Directions

← Older revision		Revision as of 10:19, 8 September 2018
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	* '''Telomere Length Analysis'''		* '''Telomere Length Analysis'''
	** Investigate the associations between telomere length (an indicator of aging) and variants. Likely interesting in Sardinia population because Sardinians have longer lifespans & centenarians.		** Investigate the associations between telomere length (an indicator of aging) and variants. Likely interesting in Sardinia population because Sardinians have longer lifespans & centenarians.
	* '''Phenotype Study'''
	** Likely will not yield much because not many additional samples since Carlo's last data freeze (3,514 samples there) <b>--> GWASs on 120 Visit 1 traits Complete </b>

	== New Updates ==		== New Updates ==

Sarahgag: /* Future Directions */

2017-04-04T19:21:35Z

Future Directions

← Older revision		Revision as of 15:21, 4 April 2017
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	=== Future Directions ===		=== Future Directions ===
	* NOTE: For sample filtering below, you will need to finish chromosome 1 for me. I have it currently running. Once it is done in a few days, you will need to run the command 'python calculateConcordance_onefile.py' while in the '/net/sardinia/progenia/SardiNIA/VariantCalling_20150330/filteringSamples/chr1/' directory. <b>--> Complete<\b>		* NOTE: For sample filtering below, you will need to finish chromosome 1 for me. I have it currently running. Once it is done in a few days, you will need to run the command 'python calculateConcordance_onefile.py' while in the '/net/sardinia/progenia/SardiNIA/VariantCalling_20150330/filteringSamples/chr1/' directory. <b>--> Complete </b>
	* '''Sample Filtering'''		* '''Sample Filtering'''
	** We did not do any filtering of samples (based on dupRate, genome coverage, mapping rate, proper paired, mean depth, or any other QPLOT stats) prior to SNP and Indel calling. Because of this, we want to do this filtering now. 3,188 or 3,839 samples have genome chip data from a few years ago. For these, we could look at the non-reference concordance between the chip genotypes and the sequencing genotypes and declare 'bad' samples to be those that fall below a certain threshold, such as 98% non-ref concordance. However, since the remaining 651 samples do not have chip data, this is not an option for them. Therefore, we decided on the following strategy instead:		** We did not do any filtering of samples (based on dupRate, genome coverage, mapping rate, proper paired, mean depth, or any other QPLOT stats) prior to SNP and Indel calling. Because of this, we want to do this filtering now. 3,188 or 3,839 samples have genome chip data from a few years ago. For these, we could look at the non-reference concordance between the chip genotypes and the sequencing genotypes and declare 'bad' samples to be those that fall below a certain threshold, such as 98% non-ref concordance. However, since the remaining 651 samples do not have chip data, this is not an option for them. Therefore, we decided on the following strategy instead:
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	** Investigate the associations between telomere length (an indicator of aging) and variants. Likely interesting in Sardinia population because Sardinians have longer lifespans & centenarians.		** Investigate the associations between telomere length (an indicator of aging) and variants. Likely interesting in Sardinia population because Sardinians have longer lifespans & centenarians.
	* '''Phenotype Study'''		* '''Phenotype Study'''
	** Likely will not yield much because not many additional samples since Carlo's last data freeze (3,514 samples there) <b>--> GWASs on 120 Visit 1 traits Complete<\b>		** Likely will not yield much because not many additional samples since Carlo's last data freeze (3,514 samples there) <b>--> GWASs on 120 Visit 1 traits Complete </b>

	== New Updates ==		== New Updates ==

Sarahgag: /* Future Directions */

2017-04-04T19:21:20Z

Future Directions

← Older revision		Revision as of 15:21, 4 April 2017
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	=== Future Directions ===		=== Future Directions ===
	* NOTE: For sample filtering below, you will need to finish chromosome 1 for me. I have it currently running. Once it is done in a few days, you will need to run the command 'python calculateConcordance_onefile.py' while in the '/net/sardinia/progenia/SardiNIA/VariantCalling_20150330/filteringSamples/chr1/' directory. /b/--> Complete/\b/		* NOTE: For sample filtering below, you will need to finish chromosome 1 for me. I have it currently running. Once it is done in a few days, you will need to run the command 'python calculateConcordance_onefile.py' while in the '/net/sardinia/progenia/SardiNIA/VariantCalling_20150330/filteringSamples/chr1/' directory. <b>--> Complete<\b>
	* '''Sample Filtering'''		* '''Sample Filtering'''
	** We did not do any filtering of samples (based on dupRate, genome coverage, mapping rate, proper paired, mean depth, or any other QPLOT stats) prior to SNP and Indel calling. Because of this, we want to do this filtering now. 3,188 or 3,839 samples have genome chip data from a few years ago. For these, we could look at the non-reference concordance between the chip genotypes and the sequencing genotypes and declare 'bad' samples to be those that fall below a certain threshold, such as 98% non-ref concordance. However, since the remaining 651 samples do not have chip data, this is not an option for them. Therefore, we decided on the following strategy instead:		** We did not do any filtering of samples (based on dupRate, genome coverage, mapping rate, proper paired, mean depth, or any other QPLOT stats) prior to SNP and Indel calling. Because of this, we want to do this filtering now. 3,188 or 3,839 samples have genome chip data from a few years ago. For these, we could look at the non-reference concordance between the chip genotypes and the sequencing genotypes and declare 'bad' samples to be those that fall below a certain threshold, such as 98% non-ref concordance. However, since the remaining 651 samples do not have chip data, this is not an option for them. Therefore, we decided on the following strategy instead:
Line 105:		Line 105:
	** Investigate the associations between telomere length (an indicator of aging) and variants. Likely interesting in Sardinia population because Sardinians have longer lifespans & centenarians.		** Investigate the associations between telomere length (an indicator of aging) and variants. Likely interesting in Sardinia population because Sardinians have longer lifespans & centenarians.
	* '''Phenotype Study'''		* '''Phenotype Study'''
	** Likely will not yield much because not many additional samples since Carlo's last data freeze (3,514 samples there) --> GWASs on 120 Visit 1 traits Complete		** Likely will not yield much because not many additional samples since Carlo's last data freeze (3,514 samples there) <b>--> GWASs on 120 Visit 1 traits Complete<\b>

	== New Updates ==		== New Updates ==

Sarahgag: /* Future Directions */

2017-04-04T19:20:33Z

Future Directions

← Older revision		Revision as of 15:20, 4 April 2017
Line 93:		Line 93:

	=== Future Directions ===		=== Future Directions ===
	* NOTE: For sample filtering below, you will need to finish chromosome 1 for me. I have it currently running. Once it is done in a few days, you will need to run the command 'python calculateConcordance_onefile.py' while in the '/net/sardinia/progenia/SardiNIA/VariantCalling_20150330/filteringSamples/chr1/' directory. --> Complete		* NOTE: For sample filtering below, you will need to finish chromosome 1 for me. I have it currently running. Once it is done in a few days, you will need to run the command 'python calculateConcordance_onefile.py' while in the '/net/sardinia/progenia/SardiNIA/VariantCalling_20150330/filteringSamples/chr1/' directory. /b/--> Complete/\b/
	* '''Sample Filtering'''		* '''Sample Filtering'''
	** We did not do any filtering of samples (based on dupRate, genome coverage, mapping rate, proper paired, mean depth, or any other QPLOT stats) prior to SNP and Indel calling. Because of this, we want to do this filtering now. 3,188 or 3,839 samples have genome chip data from a few years ago. For these, we could look at the non-reference concordance between the chip genotypes and the sequencing genotypes and declare 'bad' samples to be those that fall below a certain threshold, such as 98% non-ref concordance. However, since the remaining 651 samples do not have chip data, this is not an option for them. Therefore, we decided on the following strategy instead:		** We did not do any filtering of samples (based on dupRate, genome coverage, mapping rate, proper paired, mean depth, or any other QPLOT stats) prior to SNP and Indel calling. Because of this, we want to do this filtering now. 3,188 or 3,839 samples have genome chip data from a few years ago. For these, we could look at the non-reference concordance between the chip genotypes and the sequencing genotypes and declare 'bad' samples to be those that fall below a certain threshold, such as 98% non-ref concordance. However, since the remaining 651 samples do not have chip data, this is not an option for them. Therefore, we decided on the following strategy instead:

Sarahgag: /* Future Directions */

2017-04-04T19:19:58Z

Future Directions

← Older revision		Revision as of 15:19, 4 April 2017
Line 93:		Line 93:

	=== Future Directions ===		=== Future Directions ===
	* NOTE: For sample filtering below, you will need to finish chromosome 1 for me. I have it currently running. Once it is done in a few days, you will need to run the command 'python calculateConcordance_onefile.py' while in the '/net/sardinia/progenia/SardiNIA/VariantCalling_20150330/filteringSamples/chr1/' directory.		* NOTE: For sample filtering below, you will need to finish chromosome 1 for me. I have it currently running. Once it is done in a few days, you will need to run the command 'python calculateConcordance_onefile.py' while in the '/net/sardinia/progenia/SardiNIA/VariantCalling_20150330/filteringSamples/chr1/' directory. --> Complete
	* '''Sample Filtering'''		* '''Sample Filtering'''
	** We did not do any filtering of samples (based on dupRate, genome coverage, mapping rate, proper paired, mean depth, or any other QPLOT stats) prior to SNP and Indel calling. Because of this, we want to do this filtering now. 3,188 or 3,839 samples have genome chip data from a few years ago. For these, we could look at the non-reference concordance between the chip genotypes and the sequencing genotypes and declare 'bad' samples to be those that fall below a certain threshold, such as 98% non-ref concordance. However, since the remaining 651 samples do not have chip data, this is not an option for them. Therefore, we decided on the following strategy instead:		** We did not do any filtering of samples (based on dupRate, genome coverage, mapping rate, proper paired, mean depth, or any other QPLOT stats) prior to SNP and Indel calling. Because of this, we want to do this filtering now. 3,188 or 3,839 samples have genome chip data from a few years ago. For these, we could look at the non-reference concordance between the chip genotypes and the sequencing genotypes and declare 'bad' samples to be those that fall below a certain threshold, such as 98% non-ref concordance. However, since the remaining 651 samples do not have chip data, this is not an option for them. Therefore, we decided on the following strategy instead:
Line 105:		Line 105:
	** Investigate the associations between telomere length (an indicator of aging) and variants. Likely interesting in Sardinia population because Sardinians have longer lifespans & centenarians.		** Investigate the associations between telomere length (an indicator of aging) and variants. Likely interesting in Sardinia population because Sardinians have longer lifespans & centenarians.
	* '''Phenotype Study'''		* '''Phenotype Study'''
	** Likely will not yield much because not many additional samples since Carlo's last data freeze (3,514 samples there)		** Likely will not yield much because not many additional samples since Carlo's last data freeze (3,514 samples there) --> GWASs on 120 Visit 1 traits Complete

	== New Updates ==		== New Updates ==

Sarahgag at 12:51, 4 August 2016

2016-08-04T12:51:26Z

← Older revision		Revision as of 08:51, 4 August 2016
Line 110:		Line 110:
	* The duplicate person in the sequencing files was removed, and those files were used to impute the phased genotyping files.		* The duplicate person in the sequencing files was removed, and those files were used to impute the phased genotyping files.
	* Imputation was conducted using Minimac3. SNPs were imputed, and also indels were imputed.		* Imputation was conducted using Minimac3. SNPs were imputed, and also indels were imputed.
			* Merged files: /net/wonderland/home/csidore/Epacts_vcfs/Michelle_panel/newpanel.chr*.vcf.gz

	== Key References ==		== Key References ==

Sarahgag: /* Status as of July 2016 */

2016-08-03T02:55:35Z

Status as of July 2016

← Older revision		Revision as of 22:55, 2 August 2016
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	* '''Phenotype Study'''		* '''Phenotype Study'''
	** Likely will not yield much because not many additional samples since Carlo's last data freeze (3,514 samples there)		** Likely will not yield much because not many additional samples since Carlo's last data freeze (3,514 samples there)

			== New Updates ==
			* The duplicate person in the sequencing files was removed, and those files were used to impute the phased genotyping files.
			* Imputation was conducted using Minimac3. SNPs were imputed, and also indels were imputed.

	== Key References ==		== Key References ==

Kleckner: /* Locations of Files for Current Data Freeze of 3839 Samples */

2016-07-12T19:51:45Z

Locations of Files for Current Data Freeze of 3839 Samples

← Older revision		Revision as of 15:51, 12 July 2016
Line 21:		Line 21:
	=== Locations of Files for Current Data Freeze of 3839 Samples===		=== Locations of Files for Current Data Freeze of 3839 Samples===

	NOTICE: We identified late in the process that two of the samples (22855 and ~~22385~~) were actually the same individual. They both should be the same individual 22855. Therefore, there are 3840 sample IDs in each of the files below, but only 22855 should move on to later processes. In future data freezes with this data, these two sequencing sets should be merged into a single 22855 individual.		NOTICE: We identified late in the process that two of the samples (22855 and 22358) were actually the same individual. They both should be the same individual 22855. Therefore, there are 3840 sample IDs in each of the files below, but only 22855 should move on to later processes. In future data freezes with this data, these two sequencing sets should be merged into a single 22855 individual.

	* List of '''Sample Numbers'''		* List of '''Sample Numbers'''