Sibling variation in polygenic traits and DNA recombination mapping with UK Biobank and IVF family data

Lello, Louis; Hsu, Maximus; Widén, Erik; Raben, Timothy G.

doi:10.1038/s41598-023-27561-z

Cited by 10 publications

(7 citation statements)

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“…every member of the set has at least one sibling also in the set) where the ancestry is self-reported, but the sibling status is determined by a genetic analysis as detailed in the Supplementary Information . This sib-set attempts to partially control for environmental effects as described in 64 , 65 . It also allows for performing sibling selection experiments as described in Section “ Methods ”.…”

Section: Resultsmentioning

confidence: 99%

“…Similar results for the other phenotypes are found in the Supplementary Information . These tests were developed in 64 , 65 . More detailed descriptions of these tests and how siblings are defined can be found in Section “ Methods ” and in the Supplementary Information .…”

Section: Resultsmentioning

confidence: 99%

“…The performance of PGS can always be confounded due to environmental factors, interactions between genes and the environment, and non-linear genetic effects (e.g., epistasis). To attempt to guard against some of these effects we can perform sibling tests similar to those described in 64 , 65 . Genetic siblings can be assumed to have, on average, a more similar environmental background than unrelated individuals.…”

Section: Resultsmentioning

confidence: 99%

“…For both types of phenotypes there are sibling specific tests that we perform. These were developed in 64 , 65 . All siblings used in these analyses are genetic siblings identified using KING 109 , 110 and filtered in a manner similar to 110 .…”

Section: Methodsmentioning

confidence: 99%

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Biobank-scale methods and projections for sparse polygenic prediction from machine learning

Raben

Lello

Widén

et al. 2023

Sci Rep

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In this paper we characterize the performance of linear models trained via widely-used sparse machine learning algorithms. We build polygenic scores and examine performance as a function of training set size, genetic ancestral background, and training method. We show that predictor performance is most strongly dependent on size of training data, with smaller gains from algorithmic improvements. We find that LASSO generally performs as well as the best methods, judged by a variety of metrics. We also investigate performance characteristics of predictors trained on one genetic ancestry group when applied to another. Using LASSO, we develop a novel method for projecting AUC and correlation as a function of data size (i.e., for new biobanks) and characterize the asymptotic limit of performance. Additionally, for LASSO (compressed sensing) we show that performance metrics and predictor sparsity are in agreement with theoretical predictions from the Donoho-Tanner phase transition. Specifically, a future predictor trained in the Taiwan Precision Medicine Initiative for asthma can achieve an AUC of $$0.63_{(0.02)}$$ 0 . 63 ( 0.02 ) and for height a correlation of $$0.648_{(0.009)}$$ 0 . 648 ( 0.009 ) for a Taiwanese population. This is above the measured values of $$0.61_{(0.01)}$$ 0 . 61 ( 0.01 ) and $$0.631_{(0.008)}$$ 0 . 631 ( 0.008 ) , respectively, for UK Biobank trained predictors applied to a European population.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Biobank-scale methods and projections for sparse polygenic prediction from machine learning

Raben

Lello

Widén

et al. 2023

Sci Rep

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“…For both types of phenotypes there are sibling specific tests that we perform. These were developed in [57, 94]. Siblings generally share a similar environment growing up which helps to control for some external factors.…”

Section: Methodsmentioning

confidence: 99%

Biobank-scale methods and projections for sparse polygenic prediction from machine learning

Raben

Lello

Widén

et al. 2023

Preprint

Self Cite

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In this paper we characterize the performance of linear models trained via widely-used sparse machine learning algorithms. We build polygenic scores and examine performance as a function of training set size, genetic ancestral background, and training method. We show that predictor performance is most strongly dependent on size of training data, with smaller gains from algorithmic improvements. We find that LASSO generally performs as well as the best methods, judged by a variety of metrics. We also investigate performance characteristics of predictors trained on one genetic ancestry group when applied to another. Using LASSO, we develop a novel method for projecting AUC and Correlation as a function of data size (i.e., for new biobanks) and characterize the asymptotic limit of performance. Additionally, for LASSO (compressed sensing) we show that performance metrics and predictor sparsity are in agreement with theoretical predictions from the Donoho-Tanner phase transition. Specifically, a predictor trained in the Taiwan Precision Medicine Initiative for asthma can achieve an AUC of 0.63(0.02) and for height a correlation of 0.648(0.009) for a Taiwanese population. This is above the measured values of 0.61(0.01) and 0.631(0.008), respectively, for UK Biobank trained predictors applied to a European population.

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Polygenic embryo screening: quo vadis?

Siermann,

Vermeesch,

Raivio

et al. 2024

J Assist Reprod Genet

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Sibling variation in polygenic traits and DNA recombination mapping with UK Biobank and IVF family data

Cited by 10 publications

References 50 publications

Biobank-scale methods and projections for sparse polygenic prediction from machine learning

Biobank-scale methods and projections for sparse polygenic prediction from machine learning

Biobank-scale methods and projections for sparse polygenic prediction from machine learning

Polygenic embryo screening: quo vadis?

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