Samuel Pattillo Smith
@sampatsmith.bsky.social
Postdoc with @arbelharpak.bsky.social.
Popgen and complex traits.
All views and opinions are my own. he/him
Popgen and complex traits.
All views and opinions are my own. he/him
We thank co-authors @oliviarxiv.bsky.social @hakha.bsky.social DanDan Peng and @jeremyjberg.bsky.social; also @gcbias.bsky.social for guidance and advice in developing this approach.
Finally, big thanks to some very generous colleagues for their feedback; we’d love to get yours as well! [n/n]
Finally, big thanks to some very generous colleagues for their feedback; we’d love to get yours as well! [n/n]
February 4, 2025 at 6:04 PM
We thank co-authors @oliviarxiv.bsky.social @hakha.bsky.social DanDan Peng and @jeremyjberg.bsky.social; also @gcbias.bsky.social for guidance and advice in developing this approach.
Finally, big thanks to some very generous colleagues for their feedback; we’d love to get yours as well! [n/n]
Finally, big thanks to some very generous colleagues for their feedback; we’d love to get yours as well! [n/n]
…given how they integrate many small statistical associations with subtle potential biases. Even now, it feels like we are only scratching the surface! We need tools to better interpret genomic predictors.
February 4, 2025 at 6:04 PM
…given how they integrate many small statistical associations with subtle potential biases. Even now, it feels like we are only scratching the surface! We need tools to better interpret genomic predictors.
We were also able to see that different approaches for adjustment for population structure in GWASs (e.g., PCs as fixed effect covariates, LMMs) have distinct advantages with respect to mitigation of ancestry-axis-specific and isotropic SAD variance in PGS. [15/n]
February 4, 2025 at 6:04 PM
We were also able to see that different approaches for adjustment for population structure in GWASs (e.g., PCs as fixed effect covariates, LMMs) have distinct advantages with respect to mitigation of ancestry-axis-specific and isotropic SAD variance in PGS. [15/n]
In some instances, a given PGS appears to be stratified along a major axis of ancestry in one prediction sample but not in another (for example, in comparisons of prediction in samples from different countries, or in ancient DNA vs.~contemporary samples). [14/n]
February 4, 2025 at 6:04 PM
In some instances, a given PGS appears to be stratified along a major axis of ancestry in one prediction sample but not in another (for example, in comparisons of prediction in samples from different countries, or in ancient DNA vs.~contemporary samples). [14/n]
[13/n] We also found evidence of stratification and isotropic inflation in PGSs constructed using the UK Biobank.
February 4, 2025 at 6:04 PM
[13/n] We also found evidence of stratification and isotropic inflation in PGSs constructed using the UK Biobank.
Applying PGSUS, we found evidence of stratification in PGSs constructed using large meta-analyses of height and educational attainment. [12/n]
February 4, 2025 at 6:04 PM
Applying PGSUS, we found evidence of stratification in PGSs constructed using large meta-analyses of height and educational attainment. [12/n]
In particular, PGSUS can detect stratification along major axes of ancestry as well as SAD variance that is “isotropic'' with respect to axes of ancestry. [11/n]
February 4, 2025 at 6:04 PM
In particular, PGSUS can detect stratification along major axes of ancestry as well as SAD variance that is “isotropic'' with respect to axes of ancestry. [11/n]
Our method, Partitioning Genetic Scores Using Siblings (PGSUS, pronounced ``Pegasus''), breaks down variance components further by axes of genetic ancestry, allowing for a nuanced interpretation of SAD effects.
github.com/harpak-lab/P... [10/n]
github.com/harpak-lab/P... [10/n]
February 4, 2025 at 6:04 PM
Our method, Partitioning Genetic Scores Using Siblings (PGSUS, pronounced ``Pegasus''), breaks down variance components further by axes of genetic ancestry, allowing for a nuanced interpretation of SAD effects.
github.com/harpak-lab/P... [10/n]
github.com/harpak-lab/P... [10/n]
We leverage a comparison of a PGS of interest based on a standard GWAS with a PGS based on a sibling GWAS---which is largely immune to SAD effects---to quantify the relative contribution of each type of effect to variance in the PGS of interest. [9/n]
February 4, 2025 at 6:04 PM
We leverage a comparison of a PGS of interest based on a standard GWAS with a PGS based on a sibling GWAS---which is largely immune to SAD effects---to quantify the relative contribution of each type of effect to variance in the PGS of interest. [9/n]
However, because PGSs are constructed from population-level associations, they are influenced by factors other than direct genetic effects, including Stratification, Assortative mating, and Dynastic effects (“SAD effects'').
www.science.org/doi/10.1126/... [5/n]
www.science.org/doi/10.1126/... [5/n]
February 4, 2025 at 6:04 PM
However, because PGSs are constructed from population-level associations, they are influenced by factors other than direct genetic effects, including Stratification, Assortative mating, and Dynastic effects (“SAD effects'').
www.science.org/doi/10.1126/... [5/n]
www.science.org/doi/10.1126/... [5/n]
Following these observations, attention has turned toward the construction of genomic predictors of traits, so-called “polygenic scores” (PGSs). [3/n]
February 4, 2025 at 6:04 PM
Following these observations, attention has turned toward the construction of genomic predictors of traits, so-called “polygenic scores” (PGSs). [3/n]
[n/n] We thank @gcbias.bsky.social for guidance and advice in developing this approach. We also thank generous colleagues for their input and feedback; we’d love to get yours as well!
February 4, 2025 at 4:50 PM
[n/n] We thank @gcbias.bsky.social for guidance and advice in developing this approach. We also thank generous colleagues for their input and feedback; we’d love to get yours as well!
[17/n] ...given how they integrate many small statistical associations with subtle potential biases. Even now, it feels like we are only scratching the surface! We need tools to better interpret genomic predictors.
February 4, 2025 at 4:50 PM
[17/n] ...given how they integrate many small statistical associations with subtle potential biases. Even now, it feels like we are only scratching the surface! We need tools to better interpret genomic predictors.
[14/n] In some instances, a given PGS appears to be stratified along a major axis of ancestry in one prediction sample but not in another (for example, in comparisons of prediction in samples from different countries, or in ancient DNA vs.~contemporary samples).
February 4, 2025 at 4:50 PM
[14/n] In some instances, a given PGS appears to be stratified along a major axis of ancestry in one prediction sample but not in another (for example, in comparisons of prediction in samples from different countries, or in ancient DNA vs.~contemporary samples).
[14/n] In some instances, a given PGS appears to be stratified along a major axis of ancestry in one prediction sample but not in another (for example, in comparisons of prediction in samples from different countries, or in ancient DNA vs.~contemporary samples).
February 4, 2025 at 4:50 PM
[14/n] In some instances, a given PGS appears to be stratified along a major axis of ancestry in one prediction sample but not in another (for example, in comparisons of prediction in samples from different countries, or in ancient DNA vs.~contemporary samples).
[13/n] We also found evidence of stratification and isotropic inflation in PGSs constructed using the UK Biobank.
February 4, 2025 at 4:50 PM
[13/n] We also found evidence of stratification and isotropic inflation in PGSs constructed using the UK Biobank.
[12/n] Applying PGSUS, we found evidence of stratification in PGSs constructed using large meta-analyses of height and educational attainment.
February 4, 2025 at 4:50 PM
[12/n] Applying PGSUS, we found evidence of stratification in PGSs constructed using large meta-analyses of height and educational attainment.
[11/n] In particular, PGSUS can detect stratification along major axes of ancestry as well as SAD variance that is “isotropic'' with respect to axes of ancestry.
February 4, 2025 at 4:50 PM
[11/n] In particular, PGSUS can detect stratification along major axes of ancestry as well as SAD variance that is “isotropic'' with respect to axes of ancestry.
[10/n] Our method, Partitioning Genetic Scores Using Siblings (PGSUS, pronounced ``Pegasus''), breaks down variance components further by axes of genetic ancestry, allowing for a nuanced interpretation of SAD effects.
github.com/harpak-lab/P...
github.com/harpak-lab/P...
February 4, 2025 at 4:50 PM
[10/n] Our method, Partitioning Genetic Scores Using Siblings (PGSUS, pronounced ``Pegasus''), breaks down variance components further by axes of genetic ancestry, allowing for a nuanced interpretation of SAD effects.
github.com/harpak-lab/P...
github.com/harpak-lab/P...
[9/n] We leverage a comparison of a PGS of interest based on a standard GWAS with a PGS based on a sibling GWAS---which is largely immune to SAD effects---to quantify the relative contribution of each type of effect to variance in the PGS of interest.
February 4, 2025 at 4:50 PM
[9/n] We leverage a comparison of a PGS of interest based on a standard GWAS with a PGS based on a sibling GWAS---which is largely immune to SAD effects---to quantify the relative contribution of each type of effect to variance in the PGS of interest.
[6/n] However, because PGSs are constructed from population-level associations, they are influenced by factors other than direct genetic effects, including Stratification, Assortative mating, and Dynastic effects (“SAD effects''). www.science.org/doi/10.1126/...
February 4, 2025 at 4:50 PM
[6/n] However, because PGSs are constructed from population-level associations, they are influenced by factors other than direct genetic effects, including Stratification, Assortative mating, and Dynastic effects (“SAD effects''). www.science.org/doi/10.1126/...
[4/n] Following these observations, attention has turned toward the construction of genomic predictors of traits, so-called “polygenic scores” (PGSs).
February 4, 2025 at 4:50 PM
[4/n] Following these observations, attention has turned toward the construction of genomic predictors of traits, so-called “polygenic scores” (PGSs).