Genetic study homes in on height’s heritability mystery

Geddes, Linda

doi:10.1038/d41586-019-01157-y

Cited by 26 publications

(7 citation statements)

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“…In order to increase the matching of our populations, we employed summary statistics exclusively derived from European cohorts and European aDNA samples, which in turn increased the predictive power of our results. However, it is important to note that the absolute values of the PRS will not be accurate enough to make any direct conclusions due to the limited heritability of many complex traits, for instance, the heritability of height is 79% and BMI:40% ( Geddes, 2019 ) ( Supplementary Table S2 ). Therefore, we focused on the direction of the changes observed in the PRS within the entire dataset to model the changes in the genetic regulation of these traits within these specific populations.…”

Section: Resultsmentioning

confidence: 99%

Evolutionary Trajectories of Complex Traits in European Populations of Modern Humans

et al. 2022

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Humans have a great diversity in phenotypes, influenced by genetic, environmental, nutritional, cultural, and social factors. Understanding the historical trends of physiological traits can shed light on human physiology, as well as elucidate the factors that influence human diseases. Here we built genome-wide polygenic scores for heritable traits, including height, body mass index, lipoprotein concentrations, cardiovascular disease, and intelligence, using summary statistics of genome-wide association studies in Europeans. Subsequently, we applied these scores to the genomes of ancient European populations. Our results revealed that after the Neolithic, European populations experienced an increase in height and intelligence scores, decreased their skin pigmentation, while the risk for coronary artery disease increased through a genetic trajectory favoring low HDL concentrations. These results are a reflection of the continuous evolutionary processes in humans and highlight the impact that the Neolithic revolution had on our lifestyle and health.

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Section: Resultsmentioning

confidence: 99%

Evolutionary Trajectories of Complex Traits in European Populations of Modern Humans

et al. 2022

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“…Dotted line is the trend line, and R 2 is from the bivariate decile mean-decile model to illustrate trend linearity between the PA decile means and the PRS deciles. proposed explanations for this missing (hidden) heritability is the existence of many unidentified common variants with very small effect sizes, rare variants not captured by current genotyping platforms, structural variants, epistatic interactions, gene-environment interactions, or parent-of-origin effects (37,42,43). Interestingly, new research (43) showed that pedigree heritability for height and body mass index seemed to be fully recovered from whole-genome sequence data in the analysis including also rare variants.…”

Section: Discussionmentioning

confidence: 99%

“…proposed explanations for this missing (hidden) heritability is the existence of many unidentified common variants with very small effect sizes, rare variants not captured by current genotyping platforms, structural variants, epistatic interactions, gene-environment interactions, or parent-of-origin effects (37,42,43). Interestingly, new research (43) showed that pedigree heritability for height and body mass index seemed to be fully recovered from whole-genome sequence data in the analysis including also rare variants. These hidden genetic factors may also contribute to the differences between the pedigree and SNP heritabilities for PA in our study, as well as the low explanation rates of the constructed PRS to explain variation in PA. SNP heritabilities obtained from UK Biobank association results using only the effect sizes were statistically significant but low compared with pedigree heritabilities obtained from the FTC subjects using the whole genome and phenotypic information together.…”

Section: Discussionmentioning

confidence: 99%

Polygenic Risk Scores and Physical Activity

Palviainen

Pesonen

Waller

et al. 2020

Medicine &Amp; Science in Sports &Amp; Exercise

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Purpose: Polygenic risk scores (PRS) summarize genome-wide genotype data into a single variable that produces an individual-level risk score for genetic liability. PRS has been used for prediction of chronic diseases and some risk factors. As PRS has been studied less for physical activity (PA), we constructed PRS for PA and studied how much variation in PA can be explained by this PRS in independent population samples. Methods: We calculated PRS for self-reported and objectively measured PA using UK Biobank genome-wide association study summary statistics, and analyzed how much of the variation in selfreported (MET-hours per day) and measured (steps and moderate-to-vigorous PA minutes per day) PA could be accounted for by the PRS in the Finnish Twin Cohorts (FTC; N = 759-11,528) and the Northern Finland Birth Cohort 1966 (NFBC1966; N = 3263-4061). Objective measurement of PA was done with wrist-worn accelerometer in UK Biobank and NFBC1966 studies, and with hip-worn accelerometer in the FTC. Results: The PRS accounted from 0.07% to 1.44% of the variation (R 2) in the self-reported and objectively measured PA volumes (P value range = 0.023 to <0.0001) in the FTC and NFBC1966. For both self-reported and objectively measured PA, individuals in the highest PRS deciles had significantly (11%-28%) higher PA volumes compared with the lowest PRS deciles (P value range = 0.017 to <0.0001). Conclusions: PA is a multifactorial phenotype, and the PRS constructed based on UK Biobank results accounted for statistically significant but overall small proportion of the variation in PA in the Finnish cohorts. Using identical methods to assess PA and including less common and rare variants in the construction of PRS may increase the proportion of PA explained by the PRS.

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“…Nevertheless, a small portion of the heritabilities can be explained by the discovered common variants [ 2 , 3 ]. Sequencing studies showed that some of the “missing heritability” was attributable to rare variants [ 4 , 5 ]. Complex diseases are usually influenced by genetic factors, environmental factors and the interplay between them.…”

Section: Introductionmentioning

confidence: 99%

Kernel-based gene–environment interaction tests for rare variants with multiple quantitative phenotypes

Jin

Su²

2022

PLoS ONE

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Previous studies have suggested that gene–environment interactions (GEIs) between a common variant and an environmental factor can influence multiple correlated phenotypes simultaneously, that is, GEI pleiotropy, and that analyzing multiple phenotypes jointly is more powerful than analyzing phenotypes separately by using single-phenotype GEI tests. Methods to test the GEI for rare variants with multiple phenotypes are, however, lacking. In our work, we model the correlation among the GEI effects of a variant on multiple quantitative phenotypes through four kernels and propose four multiphenotype GEI tests for rare variants, which are a test with a homogeneous kernel (Hom-GEI), a test with a heterogeneous kernel (Het-GEI), a test with a projection phenotype kernel (PPK-GEI) and a test with a linear phenotype kernel (LPK-GEI). Through numerical simulations, we show that correlation among phenotypes can enhance the statistical power except for LPK-GEI, which simply combines statistics from single-phenotype GEI tests and ignores the phenotypic correlations. Among almost all considered scenarios, Het-GEI and PPK-GEI are more powerful than Hom-GEI and LPK-GEI. We apply Het-GEI and PPK-GEI in the genome-wide GEI analysis of systolic blood pressure (SBP) and diastolic blood pressure (DBP) in the UK Biobank. We analyze 18,101 genes and find that LEUTX is associated with SBP and DBP (p = 2.20×10−6) through its interaction with hemoglobin. The single-phenotype GEI test and our multiphenotype GEI tests Het-GEI and PPK-GEI are also used to evaluate the gene–hemoglobin interactions for 22 genes that were previously reported to be associated with SBP or DBP in a meta-analysis of genetic main effects. MYO1C shows nominal significance (p < 0.05) by the Het-GEI test. NOS3 shows nominal significance in DBP and MYO1C in both SBP and DBP by the single-phenotype GEI test.

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Genetic study homes in on height’s heritability mystery

Abstract: Analysis of 20,000 genomes reveals that rare gene variants can help to explain how the trait is inherited.

Cited by 26 publications

References 1 publication

Evolutionary Trajectories of Complex Traits in European Populations of Modern Humans

Evolutionary Trajectories of Complex Traits in European Populations of Modern Humans

Polygenic Risk Scores and Physical Activity

Kernel-based gene–environment interaction tests for rare variants with multiple quantitative phenotypes

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