Polygenic and clinical risk scores and their impact on age at onset and prediction of cardiometabolic diseases and common cancers

Mars, Nina; Koskela, Jukka; Ripatti, Pietari; Kiiskinen, Tuomo; Havulinna, Aki S.; Lindbohm, Joni V; Ahola‐Olli, Ari; Kurki, Mitja; Karjalainen, Juha; Palta, Priit; FinnGen,; Neale, Benjamin M.; Daly, Mark J.; Salomaa, Veikko; Palotie, Aarno; Widén, Elisabeth; Ripatti, Samuli

doi:10.1038/s41591-020-0800-0

Cited by 310 publications

(376 citation statements)

References 44 publications

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“…Studies of the human metabolome and its relevance to human health has also clearly increased the last five years, 76,77 and is very likely to be one of the cornerstones in an implementation of personalised medicine. With the emergence of large human cohort projects, like the UK Biobank 78 , FinnGenn 79 and BioBank Japan 80 etc., the data samples are reaching a sample level that could provide ground-breaking research if full metabolomic profiles were obtained.…”

Section: Discussionmentioning

confidence: 99%

Prediction of complex phenotypes using theDrosophilametabolome

Rohde

Kristensen

Sarup

et al. 2020

Preprint

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Understanding the genotype -phenotype map and how variation at different levels of biological organization are associated are central topics in modern biology. Fast developments in sequencing technologies and other molecular omic tools enable researchers to obtain detailed information on variation at DNA level and on intermediate endophenotypes, such as RNA, proteins, metabolites. This facilitates our understanding of the link between genotypes and molecular and functional organismal phenotypes. Here, we use the Drosophila Genetic Reference Panel and nuclear magnetic resonance (NMR) metabolomics to investigate the ability of the metabolome to predict organismal phenotypes. We do this by performing NMR metabolomics on four replicate pools of male flies from each of 170 isogenic lines. We show first that metabolite profiles are variable among the investigated lines and that this variation is highly heritable. Second, we identify genes associated with metabolome variation. Third, we show that for four of five traits related to behaviour and stress resistance the metabolome predicts phenotypes more accurately than genomic data, and that the metabolites driving the increased accuracy was trait specific. Our comprehensive characterization of population-scale diversity of metabolomes and its genetic basis illustrates that metabolites have large potential as predictors of organismal phenotypes. This finding has strong applied importance e.g. in human medicine and animal and plant breeding.1 is a design matrix linking the genomic ( 1 ) and metabolomic ( 1 ) effects to the phenotypes. The random genomic effects are 1~( , [1,1] ! " ), and the metabolomic effects are 1~( , [1,1] 5 " ), where is the additive genomic relationship matrix as specified previously, and is the metabolomic relationship matrix.The metabolomic relationship matrix was computed as = ′ NMR ⁄ , where is a × NMR matrix of *77 18* T 6 , N 6 ), and was compared (using paired t-test corrected for multiple testing by a false discovery rate (FDR) of <0.05) within and across 9/ clusters to identify the NMR features resulting in the largest prediction accuracy. We only considered Drosophila melanogaster Genetic Reference Panel lines.

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Section: Discussionmentioning

confidence: 99%

Prediction of complex phenotypes using theDrosophilametabolome

Rohde

Kristensen

Sarup

et al. 2020

Preprint

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“…A separate study using the FinnGen dataset compared the overall lifetime risk of coronary heart disease (CHD), T2D, AF, breast cancer and prostate cancer between individuals having an average compared to a high PGS score. A high PGS score was associated with a 21% to 38% increase in overall lifetime risk with age of onset 4 to 9 years earlier than in the average group 31 . Prophylactic interventions can reduce both the physical and economic stress on health systems by entering individuals on measures to prevent and manage disease earlier.…”

Section: Discussionmentioning

confidence: 83%

Controlling for background genetic effects using polygenic scores improves the power of genome-wide association studies

Bennett

Morris

Seoighe

2020

Preprint

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Ongoing increases in the size of human genotype and phenotype collections offer the promise of improved understanding of the genetics of complex diseases.In addition to the biological insights that can be gained from the nature of the variants that contribute to the genetic component of complex trait variability, these data have brought forward the prospect of predicting complex traits and the risk of complex genetic diseases from genotype data. Optimal realization of these objectives requires ongoing methodological developments, designed to identify true trait-associated variants and accurately predict phenotype from genotype. In addition to optimal accuracy, these methods must also be computationally efficient, in order to remain tractable in the context of high variant densities and very large sample sizes. Here we show that the power of linear mixed models that are in widespread use for GWAS can be increased significantly by modeling off target genetic effects using polygenic scores derived from SNPs that are not on the same chromosome as the target SNP. Applying this method to simulated and real data we found that it can result in a substantial increase in the number of variants passing genome-wide significance thresholds. This increase in power to detect trait-associated variants also translates into an increase in the accuracy with which the resulting polygenic score predicts the phenotype from genotype data. Bachground 1 Linear mixed effects models (LMMs) are routinely applied to detect associations 2 between SNPs and phenotypes in genome-wide association studies (GWAS). The 3 Bennett et al. Page 2 of 16 contribution of a given SNP to the phenotype is modelled as a fixed effect, while 4 the contribution of all other SNPs is modelled as a random effect, with the co-5 variance structure of this random effect corresponding to the genetic relationships 6 between the individuals in the study. In order to avoid including the component 7 of the genetic relatedness contributed by variants in linkage disequilibrium (LD) 8 with the tested variant, the genetic relationship matrix (GRM) is calculated using 9 an LD-pruned set of variants [1, 2]. Originally these methods assumed that a large 10 number of SNPs may each make small contributions to the phenotypic variation, 11 such that the phenotypic contribution of the genetic background could be mod-12 elled as a multivariate normal (with covariance matrix proportional to the GRM) 13 [2, 3, 4, 5]. Newer implementations of these models have allowed for the possibility 14 of a combination of SNPs with effect sizes close to zero as well as some SNPs with 15 large effect sizes. This can be done by modelling the effect sizes of background SNPs 16 as a mixture of normal distributions, with a proportion, p << 1, of SNPs with large 17 effect sizes and the remainder with effect sizes close to zero. This results in a 'spike 18 and slab' mixture model for the SNP effect sizes [6] (the spike corresponds to a nor-19 mal component with small variance and the slab corresponding to a ...

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“…Therefore, future genetic studies may benefit from examining the age of onset rather than adjust for age. 53 Second, our functional findings are based on chondrogenic data sourced from several different tissues that did not include tissues from the hand joints. However, consistent results were found across the available chondrogenic source material from several different origins (primary, cell culture, developmental), indicating a more general role for the WNT9A locus in chondrocyte functional pathways.…”

Section: Discussionmentioning

confidence: 99%

Genome-wide association of phenotypes based on clustering patterns of hand osteoarthritis identifyWNT9Aas novel osteoarthritis gene

et al. 2020

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BackgroundDespite recent advances in the understanding of the genetic architecture of osteoarthritis (OA), only two genetic loci have been identified for OA of the hand, in part explained by the complexity of the different hand joints and heterogeneity of OA pathology.MethodsWe used data from the Rotterdam Study (RSI, RSII and RSIII) to create three hand OA phenotypes based on clustering patterns of radiographic OA severity to increase power in our modest discovery genome-wide association studies in the RS (n=8700), and sought replication in an independent cohort, the Framingham Heart Study (n=1203). We used multiple approaches that leverage different levels of information and functional data to further investigate the underlying biological mechanisms and candidate genes for replicated loci. We also attempted to replicate known OA loci at other joint sites, including the hips and knees.ResultsWe found two novel genome-wide significant loci for OA in the thumb joints. We identified WNT9A as a possible novel causal gene involved in OA pathogenesis. Furthermore, several previously identified genetic loci for OA seem to confer risk for OA across multiple joints: TGFa, RUNX2, COL27A1, ASTN2, IL11 and GDF5 loci.ConclusionsWe identified a robust novel genetic locus for hand OA on chromosome 1, of which WNT9A is the most likely causal gene. In addition, multiple genetic loci were identified to be associated with OA across multiple joints. Our study confirms the potential for novel insight into the genetic architecture of OA by using biologically meaningful stratified phenotypes.

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Polygenic and clinical risk scores and their impact on age at onset and prediction of cardiometabolic diseases and common cancers

Cited by 310 publications

References 44 publications

Prediction of complex phenotypes using theDrosophilametabolome

Prediction of complex phenotypes using theDrosophilametabolome

Controlling for background genetic effects using polygenic scores improves the power of genome-wide association studies

Genome-wide association of phenotypes based on clustering patterns of hand osteoarthritis identifyWNT9Aas novel osteoarthritis gene

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