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American Journal of Human Genetics Dec 2022Family history is the standard indirect measure of inherited susceptibility in clinical care, whereas polygenic risk scores (PRSs) have more recently demonstrated...
Family history is the standard indirect measure of inherited susceptibility in clinical care, whereas polygenic risk scores (PRSs) have more recently demonstrated potential for more directly capturing genetic risk in many diseases. Few studies have systematically compared how these overlap and complement each other across common diseases. Within FinnGen (N = 306,418), we leverage family relationships, up to 50 years of nationwide registries, and genome-wide genotyping to examine the interplay of family history and genome-wide PRSs. We explore the dynamic for three types of family history across 24 common diseases: first- and second-degree family history and parental causes of death. Covering a large proportion of the burden of non-communicable diseases in adults, we show that family history and PRS are independent and not interchangeable measures, but instead provide complementary information on inherited disease susceptibility. The PRSs explained on average 10% of the effect of first-degree family history, and first-degree family history 3% of PRSs, and PRS effects were independent of both early- and late-onset family history. The PRS stratified the risk similarly in individuals with and without family history. In most diseases, including coronary artery disease, glaucoma, and type 2 diabetes, a positive family history with a high PRS was associated with a considerably elevated risk, whereas a low PRS compensated completely for the risk implied by positive family history. This study provides a catalogue of risk estimates for both family history of disease and PRSs and highlights opportunities for a more comprehensive way of assessing inherited disease risk across common diseases.
Topics: Adult; Humans; Genome-Wide Association Study; Diabetes Mellitus, Type 2; Multifactorial Inheritance; Genetic Predisposition to Disease; Medical History Taking; Risk Factors
PubMed: 36347255
DOI: 10.1016/j.ajhg.2022.10.009 -
Annals of Human Genetics Sep 2023Polygenic risk scores (PRS) are a method increasingly used to capture the combined effect of genome-wide significant variants and those which individually do not show... (Review)
Review
Polygenic risk scores (PRS) are a method increasingly used to capture the combined effect of genome-wide significant variants and those which individually do not show genome-wide significant association but are likely to contribute to the risk of developing diseases. However, their practical use incurs complications and inconsistencies that so far limit their clinical applicability. The aims of the present review are to discuss the PRS for age-related diseases and to highlight pitfalls and limitations of PRS prediction accuracy due to ageing and mortality effects. We argue that the PRS is widely used but the individual's PRS values differ substantially depending on the number of genetic variants included, the discovery GWAS and the method employed to generate them. Moreover, for neurodegenerative disorders, although an individual's genetics do not change with age, the actual score depends on the age of the sample used in the discovery GWAS and is likely to reflect the individual's disease risk at this particular age. Improvement of PRS prediction accuracy for neurodegenerative disorders will come from two sides, both the precision of clinical diagnoses, and a careful attention to the age distribution in the underlying samples and validation of the prediction in longitudinal studies.
Topics: Risk Factors; Phenotype; Multifactorial Inheritance; Aging; Humans; Genome-Wide Association Study; Allergens
PubMed: 37416935
DOI: 10.1111/ahg.12520 -
Nature Communications Dec 2020The human proteome is a crucial intermediate between complex diseases and their genetic and environmental components, and an important source of drug development targets...
The human proteome is a crucial intermediate between complex diseases and their genetic and environmental components, and an important source of drug development targets and biomarkers. Here, we comprehensively assess the genetic architecture of 257 circulating protein biomarkers of cardiometabolic relevance through high-depth (22.5×) whole-genome sequencing (WGS) in 1328 individuals. We discover 131 independent sequence variant associations (P < 7.45 × 10) across the allele frequency spectrum, all of which replicate in an independent cohort (n = 1605, 18.4x WGS). We identify for the first time replicating evidence for rare-variant cis-acting protein quantitative trait loci for five genes, involving both coding and noncoding variation. We construct and validate polygenic scores that explain up to 45% of protein level variation. We find causal links between protein levels and disease risk, identifying high-value biomarkers and drug development targets.
Topics: Gene Expression Regulation; Gene Regulatory Networks; Genetic Predisposition to Disease; Humans; Multifactorial Inheritance; Myocardium; Proteome; Quantitative Trait Loci; Risk Factors; Whole Genome Sequencing
PubMed: 33303764
DOI: 10.1038/s41467-020-20079-2 -
The Lancet. Psychiatry Feb 2023Cardiovascular disease is a major cause of excess mortality in people with schizophrenia. Several factors are responsible, including lifestyle and metabolic effects of... (Observational Study)
Observational Study
BACKGROUND
Cardiovascular disease is a major cause of excess mortality in people with schizophrenia. Several factors are responsible, including lifestyle and metabolic effects of antipsychotics. However, variations in cardiac structure and function are seen in people with schizophrenia in the absence of cardiovascular disease risk factors and after accounting for lifestyle and medication. Therefore, we aimed to explore whether shared genetic causes contribute to these cardiac variations.
METHODS
For this observational study, we used data from the UK Biobank and included White British or Irish individuals without diagnosed schizophrenia with variable polygenic risk scores for the condition. To test the association between polygenic risk score for schizophrenia and cardiac phenotype, we used principal component analysis and regression. Robust regression was then used to explore the association between the polygenic risk score for schizophrenia and individual cardiac phenotypes. We repeated analyses with fibro-inflammatory pathway-specific polygenic risk scores for schizophrenia. Last, we investigated genome-wide sharing of common variants between schizophrenia and cardiac phenotypes using linkage disequilibrium score regression. The primary outcome was principal component regression.
FINDINGS
Of 33 353 individuals recruited, 32 279 participants had complete cardiac MRI data and were included in the analysis, of whom 16 625 (51·5%) were female and 15 654 (48·5%) were male. 1074 participants were excluded on the basis of incomplete cardiac MRI data (for all phenotypes). A model regressing polygenic risk scores for schizophrenia onto the first five cardiac principal components of the principal components analysis was significant (F=5·09; p=0·00012). Principal component 1 captured a pattern of increased cardiac volumes, increased absolute peak diastolic strain rates, and reduced ejection fractions; polygenic risk scores for schizophrenia and principal component 1 were negatively associated (β=-0·01 [SE 0·003]; p=0·017). Similar to the principal component analysis results, for individual cardiac phenotypes, we observed negative associations between polygenic risk scores for schizophrenia and indexed right ventricular end-systolic volume (β=-0·14 [0·04]; p=0·0013, p=0·015), indexed right ventricular end-diastolic volume (β=-0·17 [0·08]); p=0·025; p=0·082), and absolute longitudinal peak diastolic strain rates (β=-0·01 [0·003]; p=0·0024, p=0·015), and a positive association between polygenic risk scores for schizophrenia and right ventricular ejection fraction (β=0·09 [0·03]; p=0·0041, p=0·015). Models examining the transforming growth factor-β (TGF-β)-specific and acute inflammation-specific polygenic risk scores for schizophrenia found significant associations with the first five principal components (F=2·62, p=0·022; F=2·54, p=0·026). Using linkage disequilibrium score regression, we observed genetic overlap with schizophrenia for right ventricular end-systolic volume and right ventricular ejection fraction (p=0·0090, p=0·0077).
INTERPRETATION
High polygenic risk scores for schizophrenia are associated with decreased cardiac volumes, increased ejection fractions, and decreased absolute peak diastolic strain rates. TGF-β and inflammatory pathways might be implicated, and there is evidence of genetic overlap for some cardiac phenotypes. Reduced absolute peak diastolic strain rates indicate increased myocardial stiffness and diastolic dysfunction, which increases risk of cardiac disease. Thus, genetic risk for schizophrenia is associated with cardiac structural changes that can worsen cardiac outcomes. Further work is required to determine whether these associations are specific to schizophrenia or are also seen in other psychiatric conditions.
FUNDING
National Institute for Health Research, Maudsley Charity, Wellcome Trust, Medical Research Council, Academy of Medical Sciences, Edmond J Safra Foundation, British Heart Foundation.
Topics: Male; Female; Humans; Schizophrenia; Stroke Volume; Cardiovascular Diseases; Biological Specimen Banks; Ventricular Function, Right; Multifactorial Inheritance; United Kingdom; Genome-Wide Association Study
PubMed: 36632818
DOI: 10.1016/S2215-0366(22)00403-5 -
Nature Aug 2020Clonally expanded blood cells that contain somatic mutations (clonal haematopoiesis) are commonly acquired with age and increase the risk of blood cancer. The blood...
Clonally expanded blood cells that contain somatic mutations (clonal haematopoiesis) are commonly acquired with age and increase the risk of blood cancer. The blood clones identified so far contain diverse large-scale mosaic chromosomal alterations (deletions, duplications and copy-neutral loss of heterozygosity (CN-LOH)) on all chromosomes, but the sources of selective advantage that drive the expansion of most clones remain unknown. Here, to identify genes, mutations and biological processes that give selective advantage to mutant clones, we analysed genotyping data from the blood-derived DNA of 482,789 participants from the UK Biobank. We identified 19,632 autosomal mosaic chromosomal alterations and analysed these for relationships to inherited genetic variation. We found 52 inherited, rare, large-effect coding or splice variants in 7 genes that were associated with greatly increased vulnerability to clonal haematopoiesis with specific acquired CN-LOH mutations. Acquired mutations systematically replaced the inherited risk alleles (at MPL) or duplicated them to the homologous chromosome (at FH, NBN, MRE11, ATM, SH2B3 and TM2D3). Three of the genes (MRE11, NBN and ATM) encode components of the MRN-ATM pathway, which limits cell division after DNA damage and telomere attrition; another two (MPL and SH2B3) encode proteins that regulate the self-renewal of stem cells. In addition, we found that CN-LOH mutations across the genome tended to cause chromosomal segments with alleles that promote the expansion of haematopoietic cells to replace their homologous (allelic) counterparts, increasing polygenic drive for blood-cell proliferation traits. Readily acquired mutations that replace chromosomal segments with their homologous counterparts seem to interact with pervasive inherited variation to create a challenge for lifelong cytopoiesis.
Topics: Adult; Aged; Alleles; Cardiovascular Diseases; Cell Division; Chromosome Aberrations; Clonal Evolution; Clone Cells; Female; Genetic Predisposition to Disease; Hematologic Neoplasms; Hematopoiesis; Humans; Loss of Heterozygosity; Male; Middle Aged; Mosaicism; Multifactorial Inheritance; United Kingdom
PubMed: 32581363
DOI: 10.1038/s41586-020-2430-6 -
Diabetes Care Mar 2022Polygenic prediction of type 2 diabetes (T2D) in continental Africans is adversely affected by the limited number of genome-wide association studies (GWAS) of T2D from... (Meta-Analysis)
Meta-Analysis
OBJECTIVE
Polygenic prediction of type 2 diabetes (T2D) in continental Africans is adversely affected by the limited number of genome-wide association studies (GWAS) of T2D from Africa and the poor transferability of European-derived polygenic risk scores (PRSs) in diverse ethnicities. We set out to evaluate if African American, European, or multiethnic-derived PRSs would improve polygenic prediction in continental Africans.
RESEARCH DESIGN AND METHODS
Using the PRSice software, ethnic-specific PRSs were computed with weights from the T2D GWAS multiancestry meta-analysis of 228,499 case and 1,178,783 control subjects. The South African Zulu study (n = 1,602 case and 981 control subjects) was used as the target data set. Validation and assessment of the best predictive PRS association with age at diagnosis were conducted in the Africa America Diabetes Mellitus (AADM) study (n = 2,148 case and 2,161 control subjects).
RESULTS
The discriminatory ability of the African American and multiethnic PRSs was similar. However, the African American-derived PRS was more transferable in all the countries represented in the AADM cohort and predictive of T2D in the country combined analysis compared with the European and multiethnic-derived scores. Notably, participants in the 10th decile of this PRS had a 3.63-fold greater risk (odds ratio 3.63; 95% CI 2.19-4.03; P = 2.79 × 10-17) per risk allele of developing diabetes and were diagnosed 2.6 years earlier than those in the first decile.
CONCLUSIONS
African American-derived PRS enhances polygenic prediction of T2D in continental Africans. Improved representation of non-European populations (including Africans) in GWAS promises to provide better tools for precision medicine interventions in T2D.
Topics: Black People; Diabetes Mellitus, Type 2; Genetic Predisposition to Disease; Genome-Wide Association Study; Humans; Multifactorial Inheritance; Risk Factors; South Africa
PubMed: 35015074
DOI: 10.2337/dc21-0365 -
Trends in Genetics : TIG Sep 2022Most large-scale genetic studies of autism have focused on the discovery of genes by proving an enrichment of de novo mutations (DNMs) in autism probands or... (Review)
Review
Most large-scale genetic studies of autism have focused on the discovery of genes by proving an enrichment of de novo mutations (DNMs) in autism probands or characterizing polygenic risk based on the association of common variants. We present evidence in support of an oligogenic model where two or more ultrarare mutations of more modest effect are preferentially transmitted to children with autism. Such private gene-disruptive mutations are enriched in families where there are multiple affected individuals, emerged two or three generations ago, and map to genes not previously associated with autism. Although no single gene has reached statistical significance, this class of variation should be considered along with genetic and nongenetic factors to better explain the etiology of this complex trait.
Topics: Autistic Disorder; Child; Genetic Predisposition to Disease; Humans; Multifactorial Inheritance; Mutation
PubMed: 35410794
DOI: 10.1016/j.tig.2022.03.009 -
Biological Psychiatry Jan 2021Most neuropsychiatric disorders are highly polygenic, implicating hundreds to thousands of causal genetic variants that span much of the genome. This widespread... (Review)
Review
Most neuropsychiatric disorders are highly polygenic, implicating hundreds to thousands of causal genetic variants that span much of the genome. This widespread polygenicity complicates biological understanding because no single variant can explain disease etiology. A strategy to advance biological insight is to seek convergent functions among the large set of variants and map them to a smaller set of disease-relevant genes and pathways. Accordingly, functional genomic resources that provide data on intermediate molecular phenotypes, such as gene-expression and methylation status, can be leveraged to functionally annotate variants and map them to genes. Such molecular quantitative trait locus mappings can be integrated with genome-wide association studies to make sense of the polygenic signal that underlies complex disease. Other resources that provide data on the 3-dimensional structure of chromatin and functional importance of specific genomic regions can be integrated similarly. In addition, mapped genes can then be tested for convergence in biological function, tissue, cell type, or developmental stage. In this review, we provide an overview of functional genomic resources and methods that can be used to interpret results from genome-wide association studies, and we discuss current challenges for biological understanding and future requirements to overcome them.
Topics: Genetic Predisposition to Disease; Genome; Genome-Wide Association Study; Genomics; Humans; Multifactorial Inheritance; Phenotype; Polymorphism, Single Nucleotide
PubMed: 32736792
DOI: 10.1016/j.biopsych.2020.05.022 -
Human Genomics Sep 2022A major challenge to enabling precision health at a global scale is the bias between those who enroll in state sponsored genomic research and those suffering from...
Validating and automating learning of cardiometabolic polygenic risk scores from direct-to-consumer genetic and phenotypic data: implications for scaling precision health research.
INTRODUCTION
A major challenge to enabling precision health at a global scale is the bias between those who enroll in state sponsored genomic research and those suffering from chronic disease. More than 30 million people have been genotyped by direct-to-consumer (DTC) companies such as 23andMe, Ancestry DNA, and MyHeritage, providing a potential mechanism for democratizing access to medical interventions and thus catalyzing improvements in patient outcomes as the cost of data acquisition drops. However, much of these data are sequestered in the initial provider network, without the ability for the scientific community to either access or validate. Here, we present a novel geno-pheno platform that integrates heterogeneous data sources and applies learnings to common chronic disease conditions including Type 2 diabetes (T2D) and hypertension.
METHODS
We collected genotyped data from a novel DTC platform where participants upload their genotype data files and were invited to answer general health questionnaires regarding cardiometabolic traits over a period of 6 months. Quality control, imputation, and genome-wide association studies were performed on this dataset, and polygenic risk scores were built in a case-control setting using the BASIL algorithm.
RESULTS
We collected data on N = 4,550 (389 cases / 4,161 controls) who reported being affected or previously affected for T2D and N = 4,528 (1,027 cases / 3,501 controls) for hypertension. We identified 164 out of 272 variants showing identical effect direction to previously reported genome-significant findings in Europeans. Performance metric of the PRS models was AUC = 0.68, which is comparable to previously published PRS models obtained with larger datasets including clinical biomarkers.
DISCUSSION
DTC platforms have the potential of inverting research models of genome sequencing and phenotypic data acquisition. Quality control (QC) mechanisms proved to successfully enable traditional GWAS and PRS analyses. The direct participation of individuals has shown the potential to generate rich datasets enabling the creation of PRS cardiometabolic models. More importantly, federated learning of PRS from reuse of DTC data provides a mechanism for scaling precision health care delivery beyond the small number of countries who can afford to finance these efforts directly.
CONCLUSIONS
The genetics of T2D and hypertension have been studied extensively in controlled datasets, and various polygenic risk scores (PRS) have been developed. We developed predictive tools for both phenotypes trained with heterogeneous genotypic and phenotypic data generated outside of the clinical environment and show that our methods can recapitulate prior findings with fidelity. From these observations, we conclude that it is possible to leverage DTC genetic repositories to identify individuals at risk of debilitating diseases based on their unique genetic landscape so that informed, timely clinical interventions can be incorporated.
Topics: Cardiovascular Diseases; Diabetes Mellitus, Type 2; Genetic Predisposition to Disease; Genome-Wide Association Study; Humans; Hypertension; Multifactorial Inheritance; Phenotype; Precision Medicine; Risk Factors
PubMed: 36076307
DOI: 10.1186/s40246-022-00406-y -
Journal of Medical Genetics Nov 2020The use of genomic information to better understand and prevent common complex diseases has been an ongoing goal of genetic research. Over the past few years, research... (Review)
Review
The use of genomic information to better understand and prevent common complex diseases has been an ongoing goal of genetic research. Over the past few years, research in this area has proliferated with several proposed methods of generating polygenic scores. This has been driven by the availability of larger data sets, primarily from genome-wide association studies and concomitant developments in statistical methodologies. Here we provide an overview of the methodological aspects of polygenic model construction. In addition, we consider the state of the field and implications for potential applications of polygenic scores for risk estimation within healthcare.
Topics: Delivery of Health Care; Genetic Predisposition to Disease; Genome-Wide Association Study; Genomics; Humans; Multifactorial Inheritance; Polymorphism, Single Nucleotide
PubMed: 32376789
DOI: 10.1136/jmedgenet-2019-106763