Trait: reticulocyte count

Experimental Factor Ontology (EFO) Information
Identifier EFO_0007986
Description The number of reticulocytes per unit volume of blood. Reticulocytes are immature red blood cells and typically compose aoubt 1% of red blood cells in the human body.
Trait category
Hematological measurement
Mapped term MedDRA:10038787

Associated Polygenic Score(s)

Filter PGS by Participant Ancestry
Individuals included in:
G - Source of Variant Associations (GWAS)
D - Score Development/Training
E - PGS Evaluation
List of ancestries includes:
Display options:
Ancestry legend
Multi-ancestry (including European)
Multi-ancestry (excluding European)
African
East Asian
South Asian
Additional Asian Ancestries
European
Greater Middle Eastern
Hispanic or Latin American
Additional Diverse Ancestries
Not Reported
Polygenic Score ID & Name PGS Publication ID (PGP) Reported Trait Mapped Trait(s) (Ontology) Number of Variants Ancestry distribution Scoring File (FTP Link)
PGS000094
(hlr)
PGP000051 |
Xu Y et al. Cell Genom (2022)
High light scatter reticulocyte count reticulocyte count 25,493
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000094/ScoringFiles/PGS000094.txt.gz
PGS000095
(hlr_p)
PGP000051 |
Xu Y et al. Cell Genom (2022)
High light scatter reticulocyte percentage of red cells reticulocyte count 21,957
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000095/ScoringFiles/PGS000095.txt.gz
PGS000096
(irf)
PGP000051 |
Xu Y et al. Cell Genom (2022)
Immature fraction of reticulocytes reticulocyte count 17,850
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000096/ScoringFiles/PGS000096.txt.gz
PGS000111
(ret)
PGP000051 |
Xu Y et al. Cell Genom (2022)
Reticulocyte count reticulocyte count 26,077
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000111/ScoringFiles/PGS000111.txt.gz
PGS000112
(ret_p)
PGP000051 |
Xu Y et al. Cell Genom (2022)
Reticulocyte fraction of red cells reticulocyte count 25,939
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000112/ScoringFiles/PGS000112.txt.gz
PGS000169
(hlr)
PGP000078 |
Vuckovic D et al. Cell (2020)
High light scatter reticulocyte count reticulocyte count 570
-
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000169/ScoringFiles/PGS000169.txt.gz
PGS000170
(hlr_p)
PGP000078 |
Vuckovic D et al. Cell (2020)
High light scatter reticulocyte percentage of red cells reticulocyte count 566
-
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000170/ScoringFiles/PGS000170.txt.gz
PGS000171
(irf)
PGP000078 |
Vuckovic D et al. Cell (2020)
Immature fraction of reticulocytes reticulocyte count 372
-
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000171/ScoringFiles/PGS000171.txt.gz
PGS000189
(ret)
PGP000078 |
Vuckovic D et al. Cell (2020)
Reticulocyte count reticulocyte count 555
-
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000189/ScoringFiles/PGS000189.txt.gz
PGS000190
(ret_p)
PGP000078 |
Vuckovic D et al. Cell (2020)
Reticulocyte fraction of red cells reticulocyte count 537
-
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS000190/ScoringFiles/PGS000190.txt.gz
PGS001528
(GBE_INI30250)
PGP000244 |
Tanigawa Y et al. PLoS Genet (2022)
Reticulocyte count reticulocyte count 6,262
-
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001528/ScoringFiles/PGS001528.txt.gz
PGS001976
(portability-PLR_log_reticulocyte)
PGP000263 |
Privé F et al. Am J Hum Genet (2022)
Reticulocyte count reticulocyte count 75,033
-
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS001976/ScoringFiles/PGS001976.txt.gz
PGS002194
(portability-ldpred2_log_reticulocyte)
PGP000263 |
Privé F et al. Am J Hum Genet (2022)
Reticulocyte count reticulocyte count 773,305
-
https://ftp.ebi.ac.uk/pub/databases/spot/pgs/scores/PGS002194/ScoringFiles/PGS002194.txt.gz

Performance Metrics

Disclaimer: The performance metrics are displayed as reported by the source studies. It is important to note that metrics are not necessarily comparable with each other. For example, metrics depend on the sample characteristics (described by the PGS Catalog Sample Set [PSS] ID), phenotyping, and statistical modelling. Please refer to the source publication for additional guidance on performance.

PGS Performance
Metric ID (PPM)
Evaluated Score PGS Sample Set ID
(PSS)
Performance Source Trait PGS Effect Sizes
(per SD change)
Classification Metrics Other Metrics Covariates Included in the Model PGS Performance:
Other Relevant Information
PPM000220 PGS000094
(hlr)
PSS000159|
European Ancestry|
80,067 individuals
PGP000051 |
Xu Y et al. Cell Genom (2022)
Reported Trait: High light scatter reticulocyte count Pearson correlation coefficent (r): 0.4559 age, sex, 10 PCs of ancestry, lifestyle factors (diet, smoking, alcohol consumption), technical covariates (the time between venepuncture and full blood cell analysis, seasonal effects, centre of sample collection, time dependent drift of equipment, systematic differences in equipment) UKB Testing Set - these samples were held out from Score Development (regression training) but overlap with the samples used for variant selection (GWAS)
PPM000221 PGS000094
(hlr)
PSS000133|
European Ancestry|
40,244 individuals
PGP000051 |
Xu Y et al. Cell Genom (2022)
Reported Trait: High light scatter reticulocyte count Pearson correlation coefficent (r): 0.40097 age, sex, 10 PCs of ancestry, lifestyle factors (diet, smoking, alcohol consumption), technical covariates (the time between venepuncture and full blood cell analysis, seasonal effects, centre of sample collection, time dependent drift of equipment, systematic differences in equipment)
PPM000222 PGS000095
(hlr_p)
PSS000160|
European Ancestry|
80,088 individuals
PGP000051 |
Xu Y et al. Cell Genom (2022)
Reported Trait: High light scatter reticulocyte percentage of red cells Pearson correlation coefficent (r): 0.46291 age, sex, 10 PCs of ancestry, lifestyle factors (diet, smoking, alcohol consumption), technical covariates (the time between venepuncture and full blood cell analysis, seasonal effects, centre of sample collection, time dependent drift of equipment, systematic differences in equipment) UKB Testing Set - these samples were held out from Score Development (regression training) but overlap with the samples used for variant selection (GWAS)
PPM000223 PGS000095
(hlr_p)
PSS000134|
European Ancestry|
40,225 individuals
PGP000051 |
Xu Y et al. Cell Genom (2022)
Reported Trait: High light scatter reticulocyte percentage of red cells Pearson correlation coefficent (r): 0.40544 age, sex, 10 PCs of ancestry, lifestyle factors (diet, smoking, alcohol consumption), technical covariates (the time between venepuncture and full blood cell analysis, seasonal effects, centre of sample collection, time dependent drift of equipment, systematic differences in equipment)
PPM000224 PGS000096
(irf)
PSS000161|
European Ancestry|
79,282 individuals
PGP000051 |
Xu Y et al. Cell Genom (2022)
Reported Trait: Immature fraction of reticulocytes Pearson correlation coefficent (r): 0.35972 age, sex, 10 PCs of ancestry, lifestyle factors (diet, smoking, alcohol consumption), technical covariates (the time between venepuncture and full blood cell analysis, seasonal effects, centre of sample collection, time dependent drift of equipment, systematic differences in equipment) UKB Testing Set - these samples were held out from Score Development (regression training) but overlap with the samples used for variant selection (GWAS)
PPM000225 PGS000096
(irf)
PSS000135|
European Ancestry|
40,227 individuals
PGP000051 |
Xu Y et al. Cell Genom (2022)
Reported Trait: Immature fraction of reticulocytes Pearson correlation coefficent (r): 0.36441 age, sex, 10 PCs of ancestry, lifestyle factors (diet, smoking, alcohol consumption), technical covariates (the time between venepuncture and full blood cell analysis, seasonal effects, centre of sample collection, time dependent drift of equipment, systematic differences in equipment)
PPM000254 PGS000111
(ret)
PSS000176|
European Ancestry|
79,344 individuals
PGP000051 |
Xu Y et al. Cell Genom (2022)
Reported Trait: Reticulocyte count Pearson correlation coefficent (r): 0.45071 age, sex, 10 PCs of ancestry, lifestyle factors (diet, smoking, alcohol consumption), technical covariates (the time between venepuncture and full blood cell analysis, seasonal effects, centre of sample collection, time dependent drift of equipment, systematic differences in equipment) UKB Testing Set - these samples were held out from Score Development (regression training) but overlap with the samples used for variant selection (GWAS)
PPM000255 PGS000111
(ret)
PSS000150|
European Ancestry|
40,253 individuals
PGP000051 |
Xu Y et al. Cell Genom (2022)
Reported Trait: Reticulocyte count Pearson correlation coefficent (r): 0.44742 age, sex, 10 PCs of ancestry, lifestyle factors (diet, smoking, alcohol consumption), technical covariates (the time between venepuncture and full blood cell analysis, seasonal effects, centre of sample collection, time dependent drift of equipment, systematic differences in equipment)
PPM000256 PGS000112
(ret_p)
PSS000177|
European Ancestry|
79,362 individuals
PGP000051 |
Xu Y et al. Cell Genom (2022)
Reported Trait: Reticulocyte fraction of red cells Pearson correlation coefficent (r): 0.45239 age, sex, 10 PCs of ancestry, lifestyle factors (diet, smoking, alcohol consumption), technical covariates (the time between venepuncture and full blood cell analysis, seasonal effects, centre of sample collection, time dependent drift of equipment, systematic differences in equipment) UKB Testing Set - these samples were held out from Score Development (regression training) but overlap with the samples used for variant selection (GWAS)
PPM000257 PGS000112
(ret_p)
PSS000151|
European Ancestry|
40,286 individuals
PGP000051 |
Xu Y et al. Cell Genom (2022)
Reported Trait: Reticulocyte fraction of red cells Pearson correlation coefficent (r): 0.45318 age, sex, 10 PCs of ancestry, lifestyle factors (diet, smoking, alcohol consumption), technical covariates (the time between venepuncture and full blood cell analysis, seasonal effects, centre of sample collection, time dependent drift of equipment, systematic differences in equipment)
PPM000525 PGS000169
(hlr)
PSS000291|
European Ancestry|
39,260 individuals
PGP000078 |
Vuckovic D et al. Cell (2020)
Reported Trait: High light scatter reticulocyte count : 0.11896 sex, age, 10 genetic PCs
PPM000526 PGS000170
(hlr_p)
PSS000291|
European Ancestry|
39,260 individuals
PGP000078 |
Vuckovic D et al. Cell (2020)
Reported Trait: High light scatter reticulocyte percentage of red cells : 0.12799 sex, age, 10 genetic PCs
PPM000527 PGS000171
(irf)
PSS000291|
European Ancestry|
39,260 individuals
PGP000078 |
Vuckovic D et al. Cell (2020)
Reported Trait: Immature fraction of reticulocytes : 0.09164 sex, age, 10 genetic PCs
PPM000543 PGS000189
(ret)
PSS000291|
European Ancestry|
39,260 individuals
PGP000078 |
Vuckovic D et al. Cell (2020)
Reported Trait: Reticulocyte count : 0.14142 sex, age, 10 genetic PCs
PPM000544 PGS000190
(ret_p)
PSS000291|
European Ancestry|
39,260 individuals
PGP000078 |
Vuckovic D et al. Cell (2020)
Reported Trait: Reticulocyte fraction of red cells : 0.15022 sex, age, 10 genetic PCs
PPM007061 PGS001528
(GBE_INI30250)
PSS007022|
East Asian Ancestry|
1,623 individuals
PGP000244 |
Tanigawa Y et al. PLoS Genet (2022)
Reported Trait: Reticulocyte count : 0.07592 [0.05179, 0.10005]
Incremental R2 (full-covars): 0.04581
PGS R2 (no covariates): 0.04535 [0.02608, 0.06461]
age, sex, UKB array type, Genotype PCs
PPM007062 PGS001528
(GBE_INI30250)
PSS007023|
European Ancestry|
23,688 individuals
PGP000244 |
Tanigawa Y et al. PLoS Genet (2022)
Reported Trait: Reticulocyte count : 0.04398 [0.039, 0.04896]
Incremental R2 (full-covars): 0.0298
PGS R2 (no covariates): 0.03007 [0.02589, 0.03425]
age, sex, UKB array type, Genotype PCs
PPM007063 PGS001528
(GBE_INI30250)
PSS007024|
South Asian Ancestry|
7,323 individuals
PGP000244 |
Tanigawa Y et al. PLoS Genet (2022)
Reported Trait: Reticulocyte count : 0.05148 [0.04195, 0.06101]
Incremental R2 (full-covars): 0.03077
PGS R2 (no covariates): 0.03212 [0.02444, 0.0398]
age, sex, UKB array type, Genotype PCs
PPM007060 PGS001528
(GBE_INI30250)
PSS007021|
African Ancestry|
5,974 individuals
PGP000244 |
Tanigawa Y et al. PLoS Genet (2022)
Reported Trait: Reticulocyte count : 0.03089 [0.02261, 0.03917]
Incremental R2 (full-covars): 0.01205
PGS R2 (no covariates): 0.01388 [0.00823, 0.01953]
age, sex, UKB array type, Genotype PCs
PPM007064 PGS001528
(GBE_INI30250)
PSS007025|
European Ancestry|
64,570 individuals
PGP000244 |
Tanigawa Y et al. PLoS Genet (2022)
Reported Trait: Reticulocyte count : 0.0518 [0.04854, 0.05506]
Incremental R2 (full-covars): 0.03968
PGS R2 (no covariates): 0.03966 [0.03677, 0.04254]
age, sex, UKB array type, Genotype PCs
PPM010683 PGS001976
(portability-PLR_log_reticulocyte)
PSS009465|
European Ancestry|
19,117 individuals
PGP000263 |
Privé F et al. Am J Hum Genet (2022)
Reported Trait: Reticulocyte count Partial Correlation (partial-r): 0.3967 [0.3847, 0.4086] sex, age, birth date, deprivation index, 16 PCs
PPM010684 PGS001976
(portability-PLR_log_reticulocyte)
PSS009239|
European Ancestry|
3,923 individuals
PGP000263 |
Privé F et al. Am J Hum Genet (2022)
Reported Trait: Reticulocyte count Partial Correlation (partial-r): 0.3757 [0.3485, 0.4024] sex, age, birth date, deprivation index, 16 PCs
PPM010685 PGS001976
(portability-PLR_log_reticulocyte)
PSS008793|
European Ancestry|
6,297 individuals
PGP000263 |
Privé F et al. Am J Hum Genet (2022)
Reported Trait: Reticulocyte count Partial Correlation (partial-r): 0.3753 [0.3538, 0.3963] sex, age, birth date, deprivation index, 16 PCs
PPM010686 PGS001976
(portability-PLR_log_reticulocyte)
PSS008567|
Greater Middle Eastern Ancestry|
1,127 individuals
PGP000263 |
Privé F et al. Am J Hum Genet (2022)
Reported Trait: Reticulocyte count Partial Correlation (partial-r): 0.3116 [0.2574, 0.3638] sex, age, birth date, deprivation index, 16 PCs
PPM010688 PGS001976
(portability-PLR_log_reticulocyte)
PSS008122|
East Asian Ancestry|
1,722 individuals
PGP000263 |
Privé F et al. Am J Hum Genet (2022)
Reported Trait: Reticulocyte count Partial Correlation (partial-r): 0.2708 [0.2261, 0.3142] sex, age, birth date, deprivation index, 16 PCs
PPM010690 PGS001976
(portability-PLR_log_reticulocyte)
PSS009013|
African Ancestry|
3,602 individuals
PGP000263 |
Privé F et al. Am J Hum Genet (2022)
Reported Trait: Reticulocyte count Partial Correlation (partial-r): 0.1736 [0.1416, 0.2052] sex, age, birth date, deprivation index, 16 PCs
PPM010687 PGS001976
(portability-PLR_log_reticulocyte)
PSS008345|
South Asian Ancestry|
5,935 individuals
PGP000263 |
Privé F et al. Am J Hum Genet (2022)
Reported Trait: Reticulocyte count Partial Correlation (partial-r): 0.2981 [0.2747, 0.3211] sex, age, birth date, deprivation index, 16 PCs
PPM010689 PGS001976
(portability-PLR_log_reticulocyte)
PSS007909|
African Ancestry|
2,294 individuals
PGP000263 |
Privé F et al. Am J Hum Genet (2022)
Reported Trait: Reticulocyte count Partial Correlation (partial-r): 0.1931 [0.1532, 0.2323] sex, age, birth date, deprivation index, 16 PCs
PPM012399 PGS002194
(portability-ldpred2_log_reticulocyte)
PSS009465|
European Ancestry|
19,117 individuals
PGP000263 |
Privé F et al. Am J Hum Genet (2022)
Reported Trait: Reticulocyte count Partial Correlation (partial-r): 0.391 [0.3789, 0.403] sex, age, birth date, deprivation index, 16 PCs
PPM012400 PGS002194
(portability-ldpred2_log_reticulocyte)
PSS009239|
European Ancestry|
3,923 individuals
PGP000263 |
Privé F et al. Am J Hum Genet (2022)
Reported Trait: Reticulocyte count Partial Correlation (partial-r): 0.3672 [0.3398, 0.3941] sex, age, birth date, deprivation index, 16 PCs
PPM012401 PGS002194
(portability-ldpred2_log_reticulocyte)
PSS008793|
European Ancestry|
6,297 individuals
PGP000263 |
Privé F et al. Am J Hum Genet (2022)
Reported Trait: Reticulocyte count Partial Correlation (partial-r): 0.3718 [0.3503, 0.393] sex, age, birth date, deprivation index, 16 PCs
PPM012402 PGS002194
(portability-ldpred2_log_reticulocyte)
PSS008567|
Greater Middle Eastern Ancestry|
1,127 individuals
PGP000263 |
Privé F et al. Am J Hum Genet (2022)
Reported Trait: Reticulocyte count Partial Correlation (partial-r): 0.3104 [0.2562, 0.3627] sex, age, birth date, deprivation index, 16 PCs
PPM012403 PGS002194
(portability-ldpred2_log_reticulocyte)
PSS008345|
South Asian Ancestry|
5,935 individuals
PGP000263 |
Privé F et al. Am J Hum Genet (2022)
Reported Trait: Reticulocyte count Partial Correlation (partial-r): 0.2814 [0.2577, 0.3047] sex, age, birth date, deprivation index, 16 PCs
PPM012404 PGS002194
(portability-ldpred2_log_reticulocyte)
PSS008122|
East Asian Ancestry|
1,722 individuals
PGP000263 |
Privé F et al. Am J Hum Genet (2022)
Reported Trait: Reticulocyte count Partial Correlation (partial-r): 0.2574 [0.2125, 0.3013] sex, age, birth date, deprivation index, 16 PCs
PPM012405 PGS002194
(portability-ldpred2_log_reticulocyte)
PSS007909|
African Ancestry|
2,294 individuals
PGP000263 |
Privé F et al. Am J Hum Genet (2022)
Reported Trait: Reticulocyte count Partial Correlation (partial-r): 0.21 [0.1704, 0.249] sex, age, birth date, deprivation index, 16 PCs
PPM012406 PGS002194
(portability-ldpred2_log_reticulocyte)
PSS009013|
African Ancestry|
3,602 individuals
PGP000263 |
Privé F et al. Am J Hum Genet (2022)
Reported Trait: Reticulocyte count Partial Correlation (partial-r): 0.1782 [0.1463, 0.2097] sex, age, birth date, deprivation index, 16 PCs

Evaluated Samples

PGS Sample Set ID
(PSS)
Phenotype Definitions and Methods Participant Follow-up Time Sample Numbers Age of Study Participants Sample Ancestry Additional Ancestry Description Cohort(s) Additional Sample/Cohort Information
PSS008793 6,297 individuals European Italy (South Europe) UKB
PSS007909 2,294 individuals African American or Afro-Caribbean Carribean UKB
PSS000291 39,260 individuals European INTERVAL
PSS007021 5,974 individuals African unspecified UKB
PSS007022 1,623 individuals East Asian UKB
PSS007023 23,688 individuals European non-white British ancestry UKB
PSS007024 7,323 individuals South Asian UKB
PSS007025 64,570 individuals European white British ancestry UKB Testing cohort (heldout set)
PSS009465 19,117 individuals European UK (+ Ireland) UKB
PSS008567 1,127 individuals Greater Middle Eastern (Middle Eastern, North African or Persian) Iran (Middle East) UKB
PSS000133 40,244 individuals,
49.0 % Male samples
Mean = 43.84 years
Range = [18.0, 76.4] years
European INTERVAL
PSS000134 40,225 individuals,
49.0 % Male samples
Mean = 43.84 years
Range = [18.0, 76.4] years
European INTERVAL
PSS000135 40,227 individuals,
49.0 % Male samples
Mean = 43.85 years
Range = [18.0, 76.4] years
European INTERVAL
PSS000150 40,253 individuals,
49.0 % Male samples
Mean = 43.84 years
Range = [18.0, 76.4] years
European INTERVAL
PSS000151 40,286 individuals,
49.0 % Male samples
Mean = 43.84 years
Range = [18.0, 76.4] years
European INTERVAL
PSS009239 3,923 individuals European Poland (NE Europe) UKB
PSS008345 5,935 individuals South Asian India (South Asia) UKB
PSS000159 80,067 individuals,
46.0 % Male samples
Mean = 57.2 years
Range = [39.91, 70.52] years
European UKB
PSS000160 80,088 individuals,
46.0 % Male samples
Mean = 57.19 years
Range = [39.66, 72.91] years
European UKB
PSS000161 79,282 individuals,
46.0 % Male samples
Mean = 57.28 years
Range = [39.91, 70.99] years
European UKB
PSS000176 79,344 individuals,
46.0 % Male samples
Mean = 57.29 years
Range = [39.66, 72.91] years
European UKB
PSS000177 79,362 individuals,
46.0 % Male samples
Mean = 57.22 years
Range = [39.66, 72.91] years
European UKB
PSS009013 3,602 individuals African unspecified Nigeria (West Africa) UKB
PSS008122 1,722 individuals East Asian China (East Asia) UKB