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In Wertz et al. (2019), parents' polygenic scores of educational attainment (PGS‐EA) predicted parental sensitive responses to the child's needs for support, as observed in a dyadic task (i.e., observed sensitivity). We aimed to replicate and expand these findings by combining longitudinal data, child genotype data and several polygenic scores in the Generation R Study. Mother–child dyads participated in two developmental periods, toddlerhood (14 months old; n = 648) and early childhood (3–4 years old, n = 613). Higher maternal PGS‐EA scores predicted higher observed sensitivity in toddlerhood (b = 0.12, 95% CI 0.03, 0.20) and early childhood (b = 0.16, 95% CI 0.08, 0.24). Child PGS‐EA was significantly associated with maternal sensitivity in early childhood (b = 0.11, 95% CI 0.02, 0.21), and the effect of maternal PGS‐EA was no longer significant when correcting for child PGS‐EA. A latent factor of PGSs based on educational attainment, intelligence (IQ) and income showed similar results. These polygenic scores might be associated with maternal cognitive and behavioral skills that help shape parenting. Maternal PGSs predicted observed sensitivity over and above the maternal phenotypes, showing an additional role for PGSs in parenting research. In conclusion, we replicated the central finding of Wertz et al. (2019) that parental PGS‐EA partially explains parental sensitivity. Our findings may be consistent with evocative gene–environment correlation (rGE), emphasizing the dynamic nature of parenting behavior across time, although further research using family trios is needed to adequately test this hypothesis. We replicated the Dunedin study on the relation between the polygenic score for educational attainment and observed sensitivity but also showed that the children's genotypic make‐up has to be taken into account. Our results point to the role of evocative gene–environment correlation in the dynamic interactions between parents and children.

Polygenic scores (PGS) can be used for risk stratification by quantifying individuals’ genetic predisposition to disease, and many potentially clinically useful applications have been proposed. Here, we review the latest potential benefits of PGS in the clinic and challenges to implementation. PGS could augment risk stratification through combined use with traditional risk factors (demographics, disease-specific risk factors, family history, etc.), to support diagnostic pathways, to predict groups with therapeutic benefits, and to increase the efficiency of clinical trials. However, there exist challenges to maximizing the clinical utility of PGS, including FAIR (Findable, Accessible, Interoperable, and Reusable) use and standardized sharing of the genomic data needed to develop and recalculate PGS, the equitable performance of PGS across populations and ancestries, the generation of robust and reproducible PGS calculations, and the responsible communication and interpretation of results. We outline how these challenges may be overcome analytically and with more diverse data as well as highlight sustained community efforts to achieve equitable, impactful, and responsible use of PGS in healthcare.

Abstract Since organisms develop and thrive in the face of constant perturbations due to environmental and genetic variation, species may evolve resilient genetic architectures. We sought evidence for this process, known as canalization, through a comparison of the prevalence of phenotypes as a function of the polygenic score (PGS) across environments in the UK Biobank cohort study. Contrasting seven diseases and three categorical phenotypes with respect to 151 exposures in 408,925 people, the deviation between the prevalence–risk curves was observed to increase monotonically with the PGS percentile in one-fifth of the comparisons, suggesting extensive PGS-by-Environment (PGS×E) interaction. After adjustment for the dependency of allelic effect sizes on increased prevalence in the perturbing environment, cases where polygenic influences are greater or lesser than expected are seen to be particularly pervasive for educational attainment, obesity, and metabolic condition type-2 diabetes. Inflammatory bowel disease analysis shows fewer interactions but confirms that smoking and some aspects of diet influence risk. Notably, body mass index has more evidence for decanalization (increased genetic influence at the extremes of polygenic risk), whereas the waist-to-hip ratio shows canalization, reflecting different evolutionary pressures on the architectures of these weight-related traits. An additional 10 % of comparisons showed evidence for an additive shift of prevalence independent of PGS between exposures. These results provide the first widespread evidence for canalization protecting against disease in humans and have implications for personalized medicine as well as understanding the evolution of complex traits. The findings can be explored through an R shiny app at https://canalization-gibsonlab.shinyapps.io/rshiny/.

Background The polygenic score (PGS) is an estimate of an individual’s genetic susceptibility to a specific complex trait and has been instrumental to the development of precision medicine. As an increasing number of genome-wide association studies (GWAS) have emerged, numerous sophisticated statistical and computational methods have been developed to facilitate the PGS construction. However, both the complex statistical estimation procedure and the various data formats of summary statistics and reference panel make the PGS calculation challenging and not easily accessible to researchers with limited statistical and computational backgrounds. Results Here, we propose PGSFusion, a webserver designed to carry out PGS construction for targeting variety of analytic requirements while requiring minimal prior computational knowledge. Implemented with well-established web development technologies, PGSFusion streamlines the construction of PGS using 17 PGS methods in four categories: 11 single-trait, one multiple-trait, two annotation-based and three cross-ancestry based methods. In addition, PGSFusion also utilizes UK Biobank data to provide two kinds of in-depth analyses for 201 complex traits: i) prediction performance evaluation to display the consistency between PGS and specific traits and the effect size of PGS in different genetic risk groups; ii) joint effect analysis to investigate the interaction between PGS and covariates, as well as the effect size of covariates in different genetic subgroups. PGSFusion benchmarks the prediction performances for different methods in one summary statistics. PGSFusion automatically identifies the required parameters in different data formats of uploaded GWAS summary statistics files, provides a selection of suitable methods, and outputs calculated PGSs and their corresponding epidemiological results. Finally, we showcase three case studies in different application scenarios, highlighting its versatility and values to researchers. Conclusions Overall, PGSFusion presents an easy-to-use, effective, and extensible platform for PGS construction, promoting the accessibility and utility of PGS for researchers in the field of precision medicine. PGSFusion is freely available at http://www.pgsfusion.net/ .

The rapid development of next-generation sequencing technologies and genomic data sharing initiatives during the post-Human Genome Project-era has catalyzed major advances in individualized medicine research. Genome-wide association studies (GWAS) have become a cornerstone of efforts towards understanding the genetic basis of complex diseases, leading to the development of polygenic scores (PGS). Despite their immense potential, the scarcity of standardized PGS development pipelines limits widespread adoption of PGS. Herein, we introduce PGSXplorer, a comprehensive Nextflow DSL2 pipeline that enables quality control of genomic data and automates the phasing, imputation, and construction of PGS models using reference GWAS data. PGSXplorer integrates various PGS development tools such as PLINK, PRSice-2, LD-Pred2, Lassosum2, MegaPRS, SBayesR-C, PRS-CSx and MUSSEL, improving the generalizability of PGS through multi-origin data integration. Tested with synthetic datasets, our fully Docker-encapsulated tool has demonstrated scalability and effectiveness for both single- and multi-population analyses. Continuously updated as an open-source tool, PGSXplorer is freely available with user tutorials at https://github.com/tutkuyaras/PGSXplorer , making it a valuable resource for advancing precision medicine in genetic research.

The prevalence of the so-called diseases of affluence, such as type 2 diabetes or hypertension, has increased dramatically in the last two generations. Although genome-wide association studies (GWAS) have discovered hundreds of genes involved in disease etiology, the sudden increase in disease incidence suggests a major role for environmental risk factors. Obesity constitutes a case example of a modern trait shaped by contemporary environment, although with considerable debates about the extent to which gene-by-environment (G×E) interactions accentuate obesity risk in individuals following obesogenic lifestyles. Although interaction effects have been robustly confirmed at the FTO locus, accumulating evidence at the genome-wide level implicates a role for polygenic risk-by-environment interactions. Through a variety of analyses using the UK Biobank, we confirm that the genomic background plays a major role in shaping the expressivity of alleles that increase body mass index (BMI).

Background: Whether the bone mineral density (BMD) T-score performs differently in osteoporosis classification in women of different genetic profiling and race background remains unclear. Methods: The genomic data in the Women’s Health Initiative study was analyzed (n = 2417). The polygenic score (PGS) was calculated from 63 BMD-associated single nucleotide polymorphisms (SNPs) for each participant. The World Health Organization′s (WHO) definition of osteoporosis (BMD T-score ≤ −2.5) was used to estimate the cumulative incidence of fracture. Results: T-score classification significantly underestimated the risk of major osteoporotic fracture (MOF) in the WHI study. An enormous underestimation was observed in African American women (POR: 0.52, 95% CI: 0.30–0.83) and in women with low PGS (predicted/observed ratio [POR]: 0.43, 95% CI: 0.28–0.64). Compared to Caucasian women, African American, African Indian, and Hispanic women respectively had a 59%, 41%, and 55% lower hazard of MOF after the T-score was adjusted for. The results were similar when used for any fractures. Conclusions: Our study suggested the BMD T-score performance varies significantly by race in postmenopausal women.

Background Polygenic scores (PGSs) combining genetic variants found to be associated with creatinine-based estimated glomerular filtration rate (eGFR crea ) have been applied in various study populations with different age ranges. This has shown that PGS explain less eGFR crea variance in the elderly. Our aim was to understand how differences in eGFR variance and the percentage explained by PGS varies between population of general adults and elderly. Results We derived a PGS for cystatin-based eGFR (eGFR cys ) from published genome-wide association studies. We used the 634 variants known for eGFR crea and the 204 variants identified for eGFR cys to calculate the PGS in two comparable studies capturing a general adult and an elderly population, KORA S4 ( n  = 2,900; age 24–69 years) and AugUR ( n  = 2,272, age ≥ 70 years). To identify potential factors determining age-dependent differences on the PGS-explained variance, we evaluated the PGS variance, the eGFR variance, and the beta estimates of PGS association on eGFR. Specifically, we compared frequencies of eGFR-lowering alleles between general adult and elderly individuals and analyzed the influence of comorbidities and medication intake. The PGS for eGFR crea explained almost twice as much (R 2  = 9.6%) of age-/sex adjusted eGFR variance in the general adults compared to the elderly (4.6%). This difference was less pronounced for the PGS for eGFR cys (4.7% or 3.6%, respectively). The beta-estimate of the PGS on eGFR crea was higher in the general adults compared to the elderly, but similar for the PGS on eGFR cys . The eGFR variance in the elderly was reduced by accounting for comorbidities and medication intake, but this did not explain the difference in R 2 -values . Allele frequencies between general adult and elderly individuals showed no significant differences except for one variant near APOE (rs429358). We found no enrichment of eGFR-protective alleles in the elderly compared to general adults. Conclusions We concluded that the difference in explained variance by PGS was due to the higher age- and sex-adjusted eGFR variance in the elderly and, for eGFR crea , also by a lower PGS association beta-estimate. Our results provide little evidence for survival or selection bias.