Clinical relevance of genome‐wide polygenic score may be less than claimed

AbstractIdentification of individuals at increased genetic risk for a complex disorder such as coronary disease can facilitate treatments or enhanced screening strategies. A rare monogenic mutation associated with increased cholesterol is present in ~1:250 carriers and confers an up to 4-fold increase in coronary risk when compared with non-carriers. Although individual common polymorphisms have modest predictive capacity, their cumulative impact can be aggregated into a polygenic score. Here, we develop a new, genome-wide polygenic score that aggregates information from 6.6 million common polymorphisms and show that this score can similarly identify individuals with a 4-fold increased risk for coronary disease. In >400,000 participants from UK Biobank, the score conforms to a normal distribution and those in the top 2.5% of the distribution are at 4-fold increased risk compared to the remaining 97.5%. Similar patterns are observed with genome-wide polygenic scores for two additional diseases – breast cancer and severe obesity.One Sentence SummaryA genome-wide polygenic score identifies 2.5% of the population born with a 4-fold increased risk for coronary artery disease.

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Validation of a Genome-Wide Polygenic Score for Coronary Artery Disease in South Asians

Journal of the American College of Cardiology ◽

10.1016/j.jacc.2020.06.024 ◽

2020 ◽

Vol 76 (6) ◽

pp. 703-714 ◽

Cited By ~ 2

Author(s):

Minxian Wang ◽

Ramesh Menon ◽

Sanghamitra Mishra ◽

Aniruddh P. Patel ◽

Mark Chaffin ◽

...

Keyword(s):

Coronary Artery Disease ◽

Coronary Artery ◽

South Asians ◽

Polygenic Score ◽

Genome Wide ◽

A Genome ◽

Artery Disease

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The genetic association between personality and major depression or bipolar disorder. A polygenic score analysis using genome-wide association data

Translational Psychiatry ◽

10.1038/tp.2011.45 ◽

2011 ◽

Vol 1 (10) ◽

pp. e50-e50 ◽

Cited By ~ 38

Author(s):

C M Middeldorp ◽

M H M de Moor ◽

L M McGrath ◽

S D Gordon ◽

D H Blackwood ◽

...

Keyword(s):

Bipolar Disorder ◽

Major Depression ◽

Genetic Association ◽

Genome Wide Association ◽

Polygenic Score ◽

Genome Wide ◽

Score Analysis ◽

Association Data

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Polygenic score accuracy in ancient samples: quantifying the effects of allelic turnover

10.1101/2021.09.21.461259 ◽

2021 ◽

Author(s):

Maryn O. Carlson ◽

Daniel P. Rice ◽

Jeremy J. Berg ◽

Matthias Steinrücken

Keyword(s):

Human Genetics ◽

Transition Density ◽

Complex Trait ◽

Weak Selection ◽

Trait Variation ◽

Polygenic Score ◽

Error Metrics ◽

Genome Wide ◽

Polygenic Scores ◽

Evolutionary Context

AbstractPolygenic scores link the genotypes of ancient individuals to their phenotypes, which are often unobservable, offering a tantalizing opportunity to reconstruct complex trait evolution. In practice, however, interpretation of ancient polygenic scores is subject to numerous assumptions. For one, the genome-wide association (GWA) studies from which polygenic scores are derived, can only estimate effect sizes for loci segregating in contemporary populations. Therefore, a GWA study may not correctly identify all loci relevant to trait variation in the ancient population. In addition, the frequencies of trait-associated loci may have changed in the intervening years. Here, we devise a theoretical framework to quantify the effect of this allelic turnover on the statistical properties of polygenic scores as functions of population genetic dynamics, trait architecture, power to detect significant loci, and the age of the ancient sample. We model the allele frequencies of loci underlying trait variation using the Wright-Fisher diffusion, and employ the spectral representation of its transition density to find analytical expressions for several error metrics, including the correlation between an ancient individual’s polygenic score and true phenotype, referred to as polygenic score accuracy. Our theory also applies to a two-population scenario and demonstrates that allelic turnover alone may explain a substantial percentage of the reduced accuracy observed in cross-population predictions, akin to those performed in human genetics. Finally, we use simulations to explore the effects of recent directional selection, a bias-inducing process, on the statistics of interest. We find that even in the presence of bias, weak selection induces minimal deviations from our neutral expectations for the decay of polygenic score accuracy. By quantifying the limitations of polygenic scores in an explicit evolutionary context, our work lays the foundation for the development of more sophisticated statistical procedures to analyze both temporally and geographically resolved polygenic scores.

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Letter by Zheng Regarding Article, “Genome-Wide Polygenic Score and the Risk of Ischemic Stroke in a Prospective Cohort: the Hisayama Study”

Stroke ◽

10.1161/strokeaha.120.029522 ◽

2020 ◽

Vol 51 (7) ◽

Author(s):

Bing-Kun Zheng

Keyword(s):

Ischemic Stroke ◽

Prospective Cohort ◽

Polygenic Score ◽

Genome Wide

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Polygenic Score of Intelligence is More Predictive of Crystallized than Fluid Performance Among Children

10.1101/637512 ◽

2019 ◽

Cited By ~ 2

Author(s):

Robert J. Loughnan ◽

Clare E. Palmer ◽

Wesley K. Thompson ◽

Anders M. Dale ◽

Terry L. Jernigan ◽

...

Keyword(s):

Surface Area ◽

Association Studies ◽

Brain Morphology ◽

Genome Wide Association Studies ◽

Polygenic Score ◽

Genome Wide ◽

Mri Scans ◽

Intelligence Scores ◽

Stratified Analysis ◽

Variance Explained

AbstractScores on intelligence tests have been reported to correlate significantly with educational, occupational and health outcomes. Twin and genome wide association studies in adults have revealed that intelligence scores are moderately heritable. We aimed to better understand the relationship between genetic variation and intelligence in the context of the developing brain. Specifically, we questioned if a genetic predictor of intelligence derived from a large GWAS dataset a) loaded on specific factors of cognition (i.e. fluid vs. crystallized) and b) were related to differences in cortical brain morphology measured using MRI scans. To do this we calculated an intelligence polygenic score (IPS) for the Adolescent Brain Cognitive Development (ABCD) baseline data, which consists of 11,875 nine- and ten-year old children across the US. We found that the IPS was a highly significant predictor of estimates of both fluid (t=8.7, p=3.0×10−18, 0.8% variance explained) and crystallized (t=17.1, p=2.0×10−64, 3.1% variance explained) cognition. Critically we found greater predictive power for crystallized than fluid (z=5.1, p=3.1×10−7), this result replicated in ancestry stratified analysis: for Europeans (z=4.7, 3.2×10−8) and non-Europeans (z=2.6, p=9.4×10−3). This indicates a stronger loading of IPS on crystallized cognition. IPS was significantly related to total cortical surface area (t=5.5, p=2.5×10−8, 0.4% variance explained), but not mean thickness (t=2.0, p=0.045) – after Bonferroni correction. These results replicated in the European subsample (area: t=5.4, p=6.3×10−8, mean thickness: t=2.3, p=0.021), but not in the non-European subsample (area: t=2.4, p=0.016, mean thickness: t=-0.41, p=0.68). Vertex-wise analyses within the European group showed that the surface area association is largely global across the cortex. The stronger association of IPS with crystallized compared to fluid measures is consistent with recent results that more culturally dependent measures of cognition are more heritable. These findings in children provide new evidence relevant to the developmental origins of previously observed cognitive loadings and brain morphology patterns associated with polygenic predictors of intelligence.

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The molecular genetics of participation in the Avon Longitudinal Study of Parents and Children

10.1101/206698 ◽

2017 ◽

Cited By ~ 3

Author(s):

Amy E. Taylor ◽

Hannah J. Jones ◽

Hannah Sallis ◽

Jack Euesden ◽

Evie Stergiakouli ◽

...

Keyword(s):

Longitudinal Study ◽

Association Studies ◽

Genetic Data ◽

Polygenic Score ◽

Parents And Children ◽

Genome Wide ◽

Common Genetic Variants ◽

Polygenic Scores ◽

Avon Longitudinal Study

AbstractBackgroundIt is often assumed that selection (including participation and dropout) does not represent an important source of bias in genetic studies. However, there is little evidence to date on the effect of genetic factors on participation.MethodsUsing data on mothers (N=7,486) and children (N=7,508) from the Avon Longitudinal Study of Parents and Children, we 1) examined the association of polygenic risk scores for a range of socio-demographic, lifestyle characteristics and health conditions related to continued participation, 2) investigated whether associations of polygenic scores with body mass index (BMI; derived from self-reported weight and height) and self-reported smoking differed in the largest sample with genetic data and a sub-sample who participated in a recent follow-up and 3) determined the proportion of variation in participation explained by common genetic variants using genome-wide data.ResultsWe found evidence that polygenic scores for higher education, agreeableness and openness were associated with higher participation and polygenic scores for smoking initiation, higher BMI, neuroticism, schizophrenia, ADHD and depression were associated with lower participation. Associations between the polygenic score for education and self-reported smoking differed between the largest sample with genetic data (OR for ever smoking per SD increase in polygenic score:0.85, 95% CI:0.81,0.89) and sub-sample (OR:0.95, 95% CI:0.88,1.02). In genome-wide analysis, single nucleotide polymorphism based heritability explained 17-31% of variability in participation.ConclusionsGenetic association studies, including Mendelian randomization, can be biased by selection, including loss to follow-up. Genetic risk for dropout should be considered in all analyses of studies with selective participation.

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The frequency of druggable targets in localized prostate cancer: Initial analysis from the Decipher GRID.

Journal of Clinical Oncology ◽

10.1200/jco.2016.34.2_suppl.98 ◽

2016 ◽

Vol 34 (2_suppl) ◽

pp. 98-98

Author(s):

Nicholas Erho ◽

Mohammed Alshalalfa ◽

Penelope J. Wood ◽

Mandeep Takhar ◽

Hussam Al-Deen Ashab ◽

...

Keyword(s):

Gene Expression ◽

Prostate Cancer ◽

Clinical Relevance ◽

Expression Patterns ◽

High Expression ◽

Genome Wide ◽

A Genome ◽

Genomic Resource ◽

Druggable Targets ◽

Low Expression

98 Background: Prostate cancers patient management has been enhanced with commercially available genomic prognostic tests such as the Decipher prostate cancer classifier that are useful for making treatment decision-making. In addition to being the most validated predictor of metastasis in prostate cancer, Decipher is also a genome-wide assay that measures the expression of many druggable targets. Methods: Decipher GRID (Genomic Resource Information Database), was queried to assess the expression patterns of 14 genes from 5 biological pathways (Table) in 1,850 patients from previously published Decipher validation studies. The frequency of high (or low) expression of each gene was ascertained using a standard and more conservative thresholds based on the median absolute deviation (MAD) metric. For the standard threshold, genes whose high expression is of clinical relevance, patients with gene expression above the median + 1.48*MAD were annotated as high expression and for genes whose low expression is of clinical relevance, patients with gene expression below the median - 1.48*MAD were annotated as low expression. For the conservative threshold, median +/- 2*1.48*MAD was used. Results: See Table. Conclusions: Since every patient receiving the Decipher test also has a genome-wide expression profile, the Decipher GRID will allow researchers to evaluate on a systematic population-level the expression of genes that may be targeted with existing therapies. Such information may be useful for selection of optimal systemic therapy and inclusion into clinical trials of novel targeted agents. This rich genomic resource is being made available on a research use only basis to prostate cancer researchers and to clinicians seeking to uncover individualized genomic insights for patients to advance precision medicine. [Table: see text]

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