Jesse: CFAR + COGA QC & GWAS #8

jaamarks · 2019-10-01T19:26:08Z

See parent GitHub Issue 133.

QC these dbGaP studies and combine for GWAS and eventual inclusion in the HIV acquisition meta-analysis.

Age Distributions

Age distributions for CFAR and COGA

CFAR	COGA

CFAR dbGaP Ternary Plot

COGA dbGaP Ternary Plot

The text was updated successfully, but these errors were encountered:

jaamarks · 2019-10-01T19:27:53Z

Ternary Plots

CFAR unfiltered (N=4,761)

CFAR: HAPMAP as reference Panel

I also performed the Structure analysis on the CFAR subjects using a more homogeneous reference panel. In particular, I used a subset of the three superpopulations—AFR, EUR, and EAS—that we generally use. The subset populations I used as references were YRI for AFR, CHB for EAS, and CEU for EUR. Here is the triangle plot for that analysis.

CFAR: include 1000G subjects in plot

I have included the 1,668 1KG subjects in the triangle plot below. The tight grouping at the vertices comes to no surprise however, since in Structure these were specified as the reference populations.

COGA unfiltered (N=5,415)

CFAR+COGA EA

Here are the merged CFAR+COGA samples on a triangle plot generated from the STRUCTURE analysis. We took a 10K random sample (as per usual) from the post-QC genotype data. The cases (CFAR) are blue and the controls (COGA) are red. Both plots are the same except that the controls were plotted first on the top plot and the controls were plotted second on the bottom plot.

jaamarks · 2019-10-07T14:33:15Z

We were a bit concerned that the CFAR subjects were shifted to the left. After double checking everything and reperforming the Structure analysis, it appears that this is correct though. We used the same code to process the COGA subjects and also the WIHS3 subjects and those data appear fine, so it must just be something inherent with the CFAR sample.

jaamarks · 2019-12-11T14:41:00Z

GWAS Results

Genotype PC Plots

GWAS Plots: with top 3 PCs

GWAS Plots: with top10 PCs

Genotyped SNPs only: RSQ (0.30) and MAF (0.01) filters

Only the genotyped SNPs that also passed the RSQ (0.30) and MAF (0.01) filters.

Genotyped SNPs only: RSQ (0.90) and MAF (0.01) filters

Only the genotyped SNPs that also passed the RSQ (0.90) and MAF (0.01) filters.

Top SNPs (p<0.001) from the original GWAS model:
HIV_ACQ~SNP+Sex+Alc_Dep+PC1+PC3+PC7
with the expected & observed heterozygote frequencies appended.

cfar_cogo.ea.1df.1000G_p3.HIV_ACQ~SNP+AGE+SEX+ALCOHOL+PC1+PC3+PC7.maf_gt_0.01_subject+eur.rsq_gt_0.30.p_lte_0.001.hwe_merged.xlsx

jaamarks · 2020-01-28T21:32:16Z

The results from three different Structure analyses.

The Post-dbGaP analysis was performed on the CFAR data with no processing (other than converting affymetrix ids to rsids)
Post-QC data was performed with the genotype CFAR genotype data that had passed QC
The Imputed SNPs were the set that actually got imputed. This set had 36,503 SNPs removed after applying the processing steps:
- remove 1000G discordant alleles
- remove monomorphic variants
- SNP intersection with COGA

Post-dbGaP
Post-QC
Imputed SNPs

jaamarks · 2020-02-18T00:58:29Z

jaamarks · 2020-03-19T17:24:17Z

Revisit: QC CFAR+COGA together

jaamarks · 2020-03-19T18:29:56Z

QC COGA+CFAR combined

We are going to combine the CFAR and COGA genotype data and QC them together. Here are the results from the STRUCTURE analysis thus far.

Action Description	Thresholding Criteria
For EA retainment	(AFR < 25%)∧(EAS < 25%)
For AA retainment	(AFR > 25%)∧(EAS < 25%)
For HA retainment	(AFR < 25%)∧(EAS > 25%)

Ternary Plots

CFAR subjects are blue.

EUR Ancestry (N=7,461)

AFR Ancestry (N=2,065)

AMR Ancestry (N=642)

CFAR_COGA Genotype QC Summary Stats

Below are the genotype summary stats for the merged CFAR & COGA data.

Initial Summary Stats

QC procedure	Variants removed	Variants retained	Subjects removed	Subjects retained
Initial CFAR dbGaP dataset	-	581,817	-	4,761
Initial COGA dbGaP dataset	-	581,036	-	5,415
Merge data	-	611,069	-	10,176
Convert Markernames to rsid	14,511	596,558	-	-
Duplicate rsID filtering	24,062	572,496	-	-
Genome build 37 and dbGaP 138 update	277	572,219	-	-

EUR Summary Stats Tables

Autosomes

This table includes autosome filtering statistics prior to merging with chrX.

QC procedure	Variants removed	Variants retained	Subjects removed	Subjects retained
STRUCTURE analysis (all chr)	-	572,219	2,715	7,461
Partitioning to only autosomes	14,112	558,107	-	-
Remove subjects missing whole autosome data	-	-	0	-
Remove variants with missing call rate > 3%	82,612	475,495	-	-
Remove variants with HWE p < 0.0001	3,832	471,663	-	-

chrX

This table includes chrX filtering statistics prior to merging with autosomes.

QC procedure	Variants removed	Variants retained	Subjects removed	Subjects retained
Partitioning to only chrX	558,107	14,112	-	-
Remove subjects missing whole chrX data	-	-	0	-
Remove variants with missing call rate > 3%	2,406	11,706	-	-
Remove variants with HWE p < 0.0001	15	11,691	-	-

Merged

QC procedure	Variants removed	Variants retained	Subjects removed	Subjects retained
Excessive homozygosity filter	-	483,354	0	-
Remove Subjects with IBS > 0.9	-	-	1,145	6,316
Remove Subjects with IBD > 0.4	-	471,663	1,408	4,908
Genotype Call Rate Subject Filter (3%)	-	-	78	4,830
Sex discordance filter	-	483,354	12	4,818

Pre-imputation filtering

QC procedure	Variants removed	Variants retained	Subjects retained
remove 1000G discordant alleles	5,444	477,910	-
remove monomorphic variants	10,750	467,160	-
remove individuals missing whole chr	-	467,160	4,818

AFR Summary Stats Tables

Autosomes

This table includes autosome filtering statistics prior to merging with chrX.

QC procedure	Variants removed	Variants retained	Subjects removed	Subjects retained
STRUCTURE analysis (all chr)	-	572,219	8,113	2,063
Partitioning to only autosomes	14,112	558,107	-	-
Remove subjects missing whole autosome data	-	-	0	-
Remove variants with missing call rate > 3%	94,597	463,510	-	-
Remove variants with HWE p < 0.0001	5,052	458,458	-	-

chrX

This table includes chrX filtering statistics prior to merging with autosomes.

QC procedure	Variants removed	Variants retained	Subjects removed	Subjects retained
Partitioning to only chrX	572,219	14,112	-	-
Remove subjects missing whole chrX data	-	-	0	-
Remove variants with missing call rate > 3%	2,627	11,485	-	-
Remove variants with HWE p < 0.0001	44	11,441	-	-

Merged

QC procedure	Variants removed	Variants retained	Subjects removed	Subjects retained
Merged	-	469,899	-	2,063
Remove Subjects with IBS > 0.9	-	-	3	2,060
Remove Subjects with IBD > 0.4	-	-	32	2,028
Genotype Call Rate Subject Filter (3%)	-	-	54	1,974
Sex discordance filter	-	-	4	1,970
Excessive homozygosity filter	-	-	0	-

Pre-imputation filtering

QC procedure	Variants removed	Variants retained	Subjects removed	Subjects retained
remove 1000G discordant alleles	5,384	464,515	-	-
remove monomorphic variants	11,464	453,051	-	-
remove individuals missing whole chr	-	453,051	-	1,970

AMR Summary Stats Tables

Autosomes

This table includes autosome filtering statistics prior to merging with chrX.

QC procedure	Variants removed	Variants retained	Subjects removed	Subjects retained
STRUCTURE analysis (all chr)	-	572,219	9,534	642
Partitioning to only autosomes	14,112	558,107	-	-
Remove subjects missing whole autosome data	-	-	0	-
Remove variants with missing call rate > 3%	84,711	473,396	-	-
Remove variants with HWE p < 0.0001	1,299	472,097	-	-

chrX

This table includes chrX filtering statistics prior to merging with autosomes.

QC procedure	Variants removed	Variants retained	Subjects removed	Subjects retained
Partitioning to only chrX	572,219	14,112	-	-
Remove subjects missing whole chrX data	-	-	0	-
Remove variants with missing call rate > 3%	2,371	11,741	-	-
Remove variants with HWE p < 0.0001	10	11,731	-	-

Merged

QC procedure	Variants removed	Variants retained	Subjects removed	Subjects retained
Merged	-	483,828	-	642
Remove Subjects with IBS > 0.9	-	-	47	595
Remove Subjects with IBD > 0.4	-	-	59	536
Genotype Call Rate Subject Filter (3%)	-	-	8	528
Sex discordance filter	-	-	0	-
Excessive homozygosity filter	-	-	0	-

Pre-imputation filtering

QC procedure	Variants removed	Variants retained	Subjects removed	Subjects retained
remove 1000G discordant alleles	5,446	478,382	-	-
remove monomorphic variants	20,635	457,747	-	-
remove individuals missing whole chr	-	457,747	-	528

Phenotype & Covariate Distributions

EUR

	N
HIV case	2,365
HIV control	2,398
Alc_Dep case	1,223
Alc_Dep contrl	3,540
Male	3,307
Female	1,456

AFR

N=1969

	N
HIV case	1,832
HIV control	137
Alc_Dep case	495
Alc_Dep contrl	1,474
Male	1,338
Female	631

AMR

N=526

	N
HIV case	414
HIV control	112
Alc_Dep case	169
Alc_Dep contrl	357
Male	100
Female	426

EUR Genotype PCs

PCs 1-10

Genotype PCs Explaining Phenotypic Variation

================ EUR group ================
Top PCs:  PC8 PC4 PC3 PC5 
PVE:      80.2

jaamarks · 2020-03-30T20:18:48Z

CFAR_COGA EUR HIV Acquisition GWAS (N=4,763)

PC3, PC4, PC5, PC8

Included as covariates: age, sex, alc_dep, PC8,PC4,PC3,PC5 (~80%)
Performed with RVTESTS.

Click buttons to expand Manhattan and QQ plots.

	RSQ 0.30	RSQ 0.80	RSQ 0.90
MAF 1%	🕳️	🕳️	🕳️
MAF 3%	🕳️	🕳️	🕳️
MAF 5%	🕳️	🕳️	🕳️

Tables P-value < 10e-4

click to expand

cfar_coga.eur.1000g_p3.hiv_acq.maf_0.01.rsq_0.30.p_lte_0.001.txt
cfar_coga.eur.1000g_p3.hiv_acq.maf_0.01.rsq_0.80.p_lte_0.001.txt
cfar_coga.eur.1000g_p3.hiv_acq.maf_0.01.rsq_0.90.p_lte_0.001.txt

cfar_coga.eur.1000g_p3.hiv_acq.maf_0.03.rsq_0.30.p_lte_0.001.txt
cfar_coga.eur.1000g_p3.hiv_acq.maf_0.03.rsq_0.80.p_lte_0.001.txt
cfar_coga.eur.1000g_p3.hiv_acq.maf_0.03.rsq_0.90.p_lte_0.001.txt

cfar_coga.eur.1000g_p3.hiv_acq.maf_0.05.rsq_0.30.p_lte_0.001.txt
cfar_coga.eur.1000g_p3.hiv_acq.maf_0.05.rsq_0.80.p_lte_0.001.txt
cfar_coga.eur.1000g_p3.hiv_acq.maf_0.05.rsq_0.90.p_lte_0.001.txt

MAF filter applied separately for cases & controls

click to expand

MAF 1% (applied separately) and RSQ 0.30

MAF 1% (applied separately) and RSQ 0.80

MAF 1% (applied separately) and RSQ 0.90

MAF 3% (applied separately) and RSQ 0.30

MAF 3% (applied separately) and RSQ 0.80

MAF 3% (applied separately) and RSQ 0.90

Tables P-value < 10e-4

click to expand

cfar_coga.eur.1000g_p3.hiv_acq.maf_0.01_both.rsq_0.30.p_lte_0.001.txt
cfar_coga.eur.1000g_p3.hiv_acq.maf_0.01_both.rsq_0.80.p_lte_0.001.txt
cfar_coga.eur.1000g_p3.hiv_acq.maf_0.01_both.rsq_0.90.p_lte_0.001.txt
cfar_coga.eur.1000g_p3.hiv_acq.maf_0.03_both.rsq_0.30.p_lte_0.001.txt
cfar_coga.eur.1000g_p3.hiv_acq.maf_0.03_both.rsq_0.80.p_lte_0.001.txt
cfar_coga.eur.1000g_p3.hiv_acq.maf_0.03_both.rsq_0.90.p_lte_0.001.txt

Top 10 PCs

Included as covariates: age, sex, alc_dep, PC1–10

Click buttons to expand Manhattan and QQ plots.

	RSQ 0.30	RSQ 0.80	RSQ 0.90
MAF 1%	🕳️	🕳️	🕳️
MAF 3%	🕳️	🕳️	🕳️
MAF 5%	🕳️	🕳️	🕳️

Tables P-value < 10e-4

click to expand

cfar_coga.eur.1000g_p3.hiv_acq.maf_0.01.rsq_0.30.p_lte_0.001.txt
cfar_coga.eur.1000g_p3.hiv_acq.maf_0.01.rsq_0.80.p_lte_0.001.txt
cfar_coga.eur.1000g_p3.hiv_acq.maf_0.01.rsq_0.90.p_lte_0.001.txt

cfar_coga.eur.1000g_p3.hiv_acq.maf_0.03.rsq_0.30.p_lte_0.001.txt
cfar_coga.eur.1000g_p3.hiv_acq.maf_0.03.rsq_0.80.p_lte_0.001.txt
cfar_coga.eur.1000g_p3.hiv_acq.maf_0.03.rsq_0.90.p_lte_0.001.txt

cfar_coga.eur.1000g_p3.hiv_acq.maf_0.05.rsq_0.30.p_lte_0.001.txt
cfar_coga.eur.1000g_p3.hiv_acq.maf_0.05.rsq_0.80.p_lte_0.001.txt
cfar_coga.eur.1000g_p3.hiv_acq.maf_0.05.rsq_0.90.p_lte_0.001.txt

MAF filter applied separately for cases & controls

click to expand

MAF 1% (applied separately) and RSQ 0.30

MAF 1% (applied separately) and RSQ 0.80

MAF 1% (applied separately) and RSQ 0.90

MAF 3% (applied separately) and RSQ 0.30

MAF 3% (applied separately) and RSQ 0.80

MAF 3% (applied separately) and RSQ 0.90

Tables P-value < 10e-4

click to expand

cfar_coga.eur.1000g_p3.hiv_acq.maf_0.01_both.rsq_0.30.p_lte_0.001.txt
cfar_coga.eur.1000g_p3.hiv_acq.maf_0.01_both.rsq_0.80.p_lte_0.001.txt
cfar_coga.eur.1000g_p3.hiv_acq.maf_0.01_both.rsq_0.90.p_lte_0.001.txt
cfar_coga.eur.1000g_p3.hiv_acq.maf_0.03_both.rsq_0.30.p_lte_0.001.txt
cfar_coga.eur.1000g_p3.hiv_acq.maf_0.03_both.rsq_0.80.p_lte_0.001.txt
cfar_coga.eur.1000g_p3.hiv_acq.maf_0.03_both.rsq_0.90.p_lte_0.001.txt

jaamarks · 2020-04-08T13:54:37Z

CFAR_COGA HIV Acquisition GWAS (N=4,373)

Removed age outliers from COGA (24 < age < 86).

EUR Phenotype & Covariate Distributions

Click to Expand

================ EUR group ================
Top PCs:  PC3 PC4 PC1 
PVE:      75.44

top 3 PCs

Included as covariates: age, sex, alc_dep, PC1,PC3,PC4

RSQ 0.30 MAF 1%

RSQ 0.80 MAF 3%

top 10 PCs

Included as covariates: age, sex, alc_dep, PC1–10

RSQ 0.30 MAF 1%

RSQ 0.80 MAF 3%

jaamarks · 2020-04-10T20:22:57Z

Details about chr23 & chr6 top hits

jmarks@RTI-103356 ~/Projects/hiv/cfar_coga/gwas/0001
awk '($2==23 && $NF< 10E-20) {print $NF}' cfar_coga.eur.1000g_p3.hiv_acq.maf_0.03.rsq_0.80.p_lte_0.001.txt

awk '($2==6 && $NF< 5.61e-8) {print $0}' cfar_coga.eur.1000g_p3.hiv_acq.maf_0.03.rsq_0.80.p_lte_0.001.txt

ID	CHROM	POS	REF	ALT	N_INFORMATIVE	AF	INFORMATIVE_ALT_AC	CALL_RATE	HWE_PVALUE	N_REF	N_HET	N_ALT	U_STAT	SQRT_V_STAT	ALT_EFFSIZE	PVALUE
rs76422484:62817386:C:T	23	62817386	C	T	4756	0.106378:0.158198:0.00995458	703.581:680.566:23.015	1:1:1	4.67019e-20:9.20735e-31:1	2578:1446:1132	729:705:24	0:0:0	193.219	12.2948	1.27823	1.18365e-55
rs78475991:62880223:A:C	23	62880223	A	C	4756	0.10861:0.161415:0.0103525	718.344:694.409:23.935	1:1:1	4.67019e-20:9.20735e-31:1	2578:1446:1132	729:705:24	0:0:0	196.814	12.6192	1.23593	7.70042e-55
rs9400531:112656072:G:C	6	112656072	G	C	4763	0.180376:0.196583:0.164392	1718.26:929.839:788.422	1:1:1	0.407277:0.400121:0.940937	3200:1528:1672	1398:738:660	165:99:66	83.2262	15.3245	0.354397	5.60578e-08
rs9384958:116112728:A:G	6	116112728	A	G	4763	0.20753:0.2334:0.182016	1976.93:1103.98:872.949	1:1:1	0.223407:0.42705:0.588751	2977:1381:1596	1559:843:716	227:141:86	86.9972	16.0186	0.339043	5.60383e-08
rs6926556:116118455:T:C	6	116118455	T	C	4763	0.214763:0.244088:0.185842	2045.83:1154.54:891.296	1:1:1	0.300898:0.575892:0.636439	2956:1362:1594	1577:859:718	230:144:86	97.7634	16.4618	0.360763	2.87105e-09

Note that the allele frequency for controls (COGA) in chr23 SNPs is near zero. Should we apply the MAF filter individually for cases and controls or overall like we currently do?

jaamarks · 2020-04-15T21:25:27Z

chrom	name	McLaren_beta	McLaren_P	CFAR_COGA_beta	CFAR_COGA_P
6	rs12210050:475489:C:T	0.2140599325811672	4.847e-09	-0.157585	0.0159497
6	rs41561016:31322611:C:T	-0.41144717978571177	9.459e-09	-0.0396366	0.750087
6	rs41557415:31323455:A:G	0.4123386770513366	9.424e-09	-0.0400005	0.74785
6	rs1140487:31322987:C:T	-0.412109650826833	9.457e-09	-0.0400005	0.74785
6	rs41543314:31322690:A:G	0.4028684822608984	2.332e-08	-0.0839558	0.507325

jaamarks · 2020-04-16T12:58:22Z

Verifying the coding for both McLaren and CFAR_COGA.

McLaren (original)

CHR	SNP	BP	A1	A2	OR	P
6	rs12210050	475489	T	C	0.8073	4.847e-09
6	rs41561016	31322611	T	C	1.509	9.459e-09
6	rs41557415	31323455	A	G	0.6621	9.424e-09
6	rs1140487	31322987	T	C	1.51	9.457e-09
6	rs41543314	31322690	A	G	0.6684	2.332e-08

MCLAREN (converted)

chrom	name	position	REF	ALT	ALT_EFFSIZE	p
6	rs12210050:475489:C:T	475489	T	C	0.2140599325811672	4.847e-09
6	rs41561016:31322611:C:T	31322611	T	C	-0.41144717978571177	9.459e-09
6	rs41557415:31323455:A:G	31323455	A	G	0.4123386770513366	9.424e-09
6	rs1140487:31322987:C:T	31322987	T	C	-0.412109650826833	9.457e-09
6	rs41543314:31322690:A:G	31322690	A	G	0.4028684822608984	2.332e-08

CFAR_COGA

ID	CHROM	POS	REF	ALT	ALT_EFFSIZE	PVALUE
rs12210050:475489:C:T	6	475489	C	T	-0.157585	0.0159497
rs41561016:31322611:C:T	6	31322611	C	T	-0.0396366	0.750087
rs41557415:31323455:A:G	6	31323455	A	G	-0.0400005	0.74785
rs1140487:31322987:C:T	6	31322987	C	T	-0.0400005	0.74785
rs41543314:31322690:A:G	6	31322690	A	G	-0.0839558	0.507325

jaamarks · 2020-04-21T21:12:28Z

GWAS results of top SNPs on chr1 and 19

jmarks@RTI-103356 ~/Projects/hiv/cfar_coga/gwas/0001/maf_both
$ awk '$17 < 1e-15' cfar_coga.eur.1000g_p3.hiv_acq.maf_0.03_both.rsq_0.90.p_lte_0.001.txt

ID	CHROM	POS	REF	ALT	N_INFORMATIVE	AF	INFORMATIVE_ALT_AC	CALL_RATE	HWE_PVALUE	N_REF	N_HET	N_ALT	U_STAT	SQRT_V_STAT	ALT_EFFSIZE	PVALUE
rs10911132:182753673:G:A	1	182753673	G	A	4763	0.0975693:0.141029:0.0547075	929.445:667.068:262.377	1:1:1	0.75388:0.00376253:0.117543	3829:1688:2141	886:641:245	48:36:12	125.07	11.5818	0.932393	3.48702e-27
rs10911133:182753838:G:T	1	182753838	G	T	4763	0.102479:0.149187:0.0564141	976.216:705.654:270.562	1:1:1	0.587918:0.00149029:0.117543	3816:1675:2141	899:654:245	48:36:12	134.85	12.0782	0.924377	6.0628e-29
rs1064257:49993535:C:G	19	49993535	C	G	4763	0.0874761:0.118845:0.0565384	833.297:562.139:271.158	1:1:1	0.000413457:0.00120084:0.00595059	3963:1833:2130	783:516:267	17:16:1	109.352	10.96	0.910348	1.91459e-23

Dose info file

SNP	REF(0)	ALT(1)	ALT_Frq	MAF	AvgCall	Rsq	Genotyped	LooRsq	EmpR	EmpRsq	Dose0	Dose1
1:182753838:G:T	G	T	0.10209	0.10209	0.99400	0.94751	Genotyped	0.540	0.637	0.40571	0.45810	0.03929
1:182753673:G:A	G	A	0.09718	0.09718	0.99015	0.91021	Imputed	-	-	-	-	-
19:49993535:C:G	C	G	0.08712	0.08712	0.99541	0.95675	Genotyped	0.760	0.654	0.42775	0.50306	0.02054

EmpR, EmpRsq

While the LooRsq statistic completely ignores experimental genotypes, EmpR is calculated by calculating the correlation between the true genotyped values and the imputed dosages that were calculated by hiding all known genotyped for the given SNP (see LooDosage). A negative correlation between imputed and experimental genotypes can indicate allele flips. This statistic also can only be provided for genotyped sites. EmpRsq is the square of this correlation.

jaamarks changed the title ~~CFAR - QC~~ Jesse: CFAR + COGA QC & GWAS Oct 1, 2019

jaamarks added the HIV label Oct 1, 2019

jaamarks mentioned this issue Mar 19, 2020

QC IMP BioBank #2

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jaamarks closed this as completed Apr 22, 2020

jaamarks reopened this Apr 22, 2020

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Jesse: CFAR + COGA QC & GWAS #8

Jesse: CFAR + COGA QC & GWAS #8

jaamarks commented Oct 1, 2019 •

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Age distributions for CFAR and COGA

jaamarks commented Oct 1, 2019 •

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jaamarks commented Oct 7, 2019

jaamarks commented Dec 11, 2019 •

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jaamarks commented Jan 28, 2020

jaamarks commented Feb 18, 2020

jaamarks commented Mar 19, 2020 •

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jaamarks commented Mar 19, 2020 •

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Autosomes

chrX

Merged

Pre-imputation filtering

Autosomes

chrX

Merged

Pre-imputation filtering

Autosomes

chrX

Merged

Pre-imputation filtering

jaamarks commented Mar 30, 2020 •

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Tables P-value < 10e-4

Tables P-value < 10e-4

jaamarks commented Apr 8, 2020

jaamarks commented Apr 10, 2020 •

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jaamarks commented Apr 15, 2020

jaamarks commented Apr 16, 2020 •

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jaamarks commented Apr 21, 2020 •

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Jesse: CFAR + COGA QC & GWAS #8

Jesse: CFAR + COGA QC & GWAS #8

Comments

jaamarks commented Oct 1, 2019 • edited Loading

Age distributions for CFAR and COGA

jaamarks commented Oct 1, 2019 • edited Loading

Ternary Plots

jaamarks commented Oct 7, 2019

jaamarks commented Dec 11, 2019 • edited Loading

GWAS Results

jaamarks commented Jan 28, 2020

jaamarks commented Feb 18, 2020

jaamarks commented Mar 19, 2020 • edited Loading

Revisit: QC CFAR+COGA together

jaamarks commented Mar 19, 2020 • edited Loading

QC COGA+CFAR combined

Ternary Plots

CFAR_COGA Genotype QC Summary Stats

Autosomes

chrX

Merged

Pre-imputation filtering

Autosomes

chrX

Merged

Pre-imputation filtering

Autosomes

chrX

Merged

Pre-imputation filtering

Phenotype & Covariate Distributions

EUR Genotype PCs

jaamarks commented Mar 30, 2020 • edited Loading

CFAR_COGA EUR HIV Acquisition GWAS (N=4,763)

PC3, PC4, PC5, PC8

Tables P-value < 10e-4

MAF filter applied separately for cases & controls

Tables P-value < 10e-4

Top 10 PCs

Tables P-value < 10e-4

MAF filter applied separately for cases & controls

Tables P-value < 10e-4

jaamarks commented Apr 8, 2020

CFAR_COGA HIV Acquisition GWAS (N=4,373)

EUR Phenotype & Covariate Distributions

top 3 PCs

top 10 PCs

jaamarks commented Apr 10, 2020 • edited Loading

Details about chr23 & chr6 top hits

jaamarks commented Apr 15, 2020

jaamarks commented Apr 16, 2020 • edited Loading

jaamarks commented Apr 21, 2020 • edited Loading

GWAS results of top SNPs on chr1 and 19

Dose info file

jaamarks commented Oct 1, 2019 •

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jaamarks commented Oct 1, 2019 •

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jaamarks commented Dec 11, 2019 •

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jaamarks commented Mar 19, 2020 •

edited

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jaamarks commented Mar 19, 2020 •

edited

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jaamarks commented Mar 30, 2020 •

edited

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jaamarks commented Apr 10, 2020 •

edited

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jaamarks commented Apr 16, 2020 •

edited

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jaamarks commented Apr 21, 2020 •

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