Multivariate analysis reveals shared genetic architecture of brain morphology and human behavior

Overcoming attenuation bias in regressions using polygenic indices

Measurement error in polygenic indices (PGIs) attenuates the estimation of their effects in regression models. We analyze and compare two approaches addressing this attenuation bias: Obviously Related Instrumental Variables (ORIV) and the PGI Repository Correction (PGI-RC). Through simulations, we show that the PGI-RC performs slightly better than ORIV, unless the prediction sample is very small (N < 1000) or when there is considerable assortative mating. Within families, ORIV is the best choice since the PGI-RC correction factor is generally not available. We verify the empirical validity of the simulations by predicting educational attainment and height in a sample of siblings from the UK Biobank. We show that applying ORIV between families increases the standardized effect of the PGI by 12% (height) and by 22% (educational attainment) compared to a meta-analysis-based PGI, yet estimates remain slightly below the PGI-RC estimates. Furthermore, within-family ORIV regression provides the tightest lower bound for the direct genetic effect, increasing the lower bound for the standardized direct genetic effect on educational attainment from 0.14 to 0.18 (+29%), and for height from 0.54 to 0.61 (+13%) compared to a meta-analysis-based PGI.

General dimensions of human brain morphometry inferred from genome-wide association data

Understanding the neurodegenerative mechanisms underlying cognitive decline in the general population may facilitate early detection of adverse health outcomes in late life. This study investigates genetic links between brain morphometry, ageing and cognitive ability. We develop Genomic Principal Components Analysis (Genomic PCA) to model general dimensions of brain-wide morphometry at the level of their underlying genetic architecture. Genomic PCA is applied to genome-wide association data for 83 brain-wide volumes (36,778 UK Biobank participants) and we extract genomic principal components (PCs) to capture global dimensions of genetic covariance across brain regions (unlike ancestral PCs that index genetic similarity between participants). Using linkage disequilibrium score regression, we estimate genetic overlap between those general brain dimensions and cognitive ageing. The first genetic PCs underlying the morphometric organisation of 83 brain-wide regions accounted for substantial genetic variance (R2  = 40%) with the pattern of component loadings corresponding closely to those obtained from phenotypic analyses. Genetically more central regions to overall brain structure - specifically frontal and parietal volumes thought to be part of the central executive network - tended to be somewhat more susceptible towards age (r = -0.27). We demonstrate the moderate genetic overlap between the first PC underlying each of several structural brain networks and general cognitive ability (rg  = 0.17-0.21), which was not specific to a particular subset of the canonical networks examined. We provide a multivariate framework integrating covariance across multiple brain regions and the genome, revealing moderate shared genetic etiology between brain-wide morphometry and cognitive ageing.

The identification of mediating effects using genome-based restricted maximum likelihood estimation

Mediation analysis is commonly used to identify mechanisms and intermediate factors between causes and outcomes. Studies drawing on polygenic scores (PGSs) can readily employ traditional regression-based procedures to assess whether trait M mediates the relationship between the genetic component of outcome Y and outcome Y itself. However, this approach suffers from attenuation bias, as PGSs capture only a (small) part of the genetic variance of a given trait. To overcome this limitation, we developed MA-GREML: a method for Mediation Analysis using Genome-based Restricted Maximum Likelihood (GREML) estimation. Using MA-GREML to assess mediation between genetic factors and traits comes with two main advantages. First, we circumvent the limited predictive accuracy of PGSs that regression-based mediation approaches suffer from. Second, compared to methods employing summary statistics from genome-wide association studies, the individual-level data approach of GREML allows to directly control for confounders of the association between M and Y. In addition to typical GREML parameters (e.g., the genetic correlation), MA-GREML estimates (i) the effect of M on Y, (ii) the direct effect (i.e., the genetic variance of Y that is not mediated by M), and (iii) the indirect effect (i.e., the genetic variance of Y that is mediated by M). MA-GREML also provides standard errors of these estimates and assesses the significance of the indirect effect. We use analytical derivations and simulations to show the validity of our approach under two main assumptions, viz., that M precedes Y and that environmental confounders of the association between M and Y are controlled for. We conclude that MA-GREML is an appropriate tool to assess the mediating role of trait M in the relationship between the genetic component of Y and outcome Y. Using data from the US Health and Retirement Study, we provide evidence that genetic effects on Body Mass Index (BMI), cognitive functioning and self-reported health in later life run partially through educational attainment. For mental health, we do not find significant evidence for an indirect effect through educational attainment. Further analyses show that the additive genetic factors of these four outcomes do partially (cognition and mental health) and fully (BMI and self-reported health) run through an earlier realization of these traits.

Multivariate estimation of factor structures of complex traits using SNP-based genomic relationships

Background

Heritability and genetic correlation can be estimated from genome-wide single-nucleotide polymorphism (SNP) data using various methods. We recently developed multivariate genomic-relatedness-based restricted maximum likelihood (MGREML) for statistically and computationally efficient estimation of SNP-based heritability (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$h^2_{\text{SNP}}$$\end{document}hSNP2) and genetic correlation (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\rho _G$$\end{document}ρG) across many traits in large datasets. Here, we extend MGREML by allowing it to fit and perform tests on user-specified factor models, while preserving the low computational complexity.

Results

Using simulations, we show that MGREML yields consistent estimates and valid inferences for such factor models at low computational cost (e.g., for data on 50 traits and 20,000 individuals, a saturated model involving 50 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$h^2_{\text{SNP}}$$\end{document}hSNP2’s, 1225 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\rho _G$$\end{document}ρG’s, and 50 fixed effects is estimated and compared to a restricted model in less than one hour on a single notebook with two 2.7 GHz cores and 16 GB of RAM). Using repeated measures of height and body mass index from the US Health and Retirement Study, we illustrate the ability of MGREML to estimate a factor model and test whether it fits the data better than a nested model. The MGREML tool, the simulation code, and an extensive tutorial are freely available at https://github.com/devlaming/mgreml/.

Conclusion

MGREML can now be used to estimate multivariate factor structures and perform inferences on such factor models at low computational cost. This new feature enables simple structural equation modeling using MGREML, allowing researchers to specify, estimate, and compare genetic factor models of their choosing using SNP data.

From Mendel to quantitative genetics in the genome era: the scientific legacy of W. G. Hill

The quantitative geneticist W. G. ('Bill') Hill, awardee of the 2018 Darwin Medal of the Royal Society and the 2019 Mendel Medal of the Genetics Society (United Kingdom), died on 17 December 2021 at the age of 81 years. Here, we pay tribute to his multiple key scientific contributions, which span population and evolutionary genetics, animal and plant breeding and human genetics. We discuss his theoretical research on the role of linkage disequilibrium (LD) and mutational variance in the response to selection, the origin of the widely used LD metric r² in genomic association studies, the genetic architecture of complex traits, the quantification of the variation in realized relationships given a pedigree relationship and much more. We demonstrate that basic theoretical research in quantitative and statistical genetics has led to profound insights into the genetics and evolution of complex traits and made predictions that were subsequently empirically validated, often decades later.

High-throughput characterization, correlation, and mapping of leaf photosynthetic and functional traits in the soybean (Glycine max) nested association mapping population

Photosynthesis is a key target to improve crop production in many species including soybean [Glycine max (L.) Merr.]. A challenge is that phenotyping photosynthetic traits by traditional approaches is slow and destructive. There is proof-of-concept for leaf hyperspectral reflectance as a rapid method to model photosynthetic traits. However, the crucial step of demonstrating that hyperspectral approaches can be used to advance understanding of the genetic architecture of photosynthetic traits is untested. To address this challenge, we used full-range (500-2,400 nm) leaf reflectance spectroscopy to build partial least squares regression models to estimate leaf traits, including the rate-limiting processes of photosynthesis, maximum Rubisco carboxylation rate, and maximum electron transport. In total, 11 models were produced from a diverse population of soybean sampled over multiple field seasons to estimate photosynthetic parameters, chlorophyll content, leaf carbon and leaf nitrogen percentage, and specific leaf area (with R2 from 0.56 to 0.96 and root mean square error approximately <10% of the range of calibration data). We explore the utility of these models by applying them to the soybean nested association mapping population, which showed variability in photosynthetic and leaf traits. Genetic mapping provided insights into the underlying genetic architecture of photosynthetic traits and potential improvement in soybean. Notably, the maximum Rubisco carboxylation rate mapped to a region of chromosome 19 containing genes encoding multiple small subunits of Rubisco. We also mapped the maximum electron transport rate to a region of chromosome 10 containing a fructose 1,6-bisphosphatase gene, encoding an important enzyme in the regeneration of ribulose 1,5-bisphosphate and the sucrose biosynthetic pathway. The estimated rate-limiting steps of photosynthesis were low or negatively correlated with yield suggesting that these traits are not influenced by the same genetic mechanisms and are not limiting yield in the soybean NAM population. Leaf carbon percentage, leaf nitrogen percentage, and specific leaf area showed strong correlations with yield and may be of interest in breeding programs as a proxy for yield. This work is among the first to use hyperspectral reflectance to model and map the genetic architecture of the rate-limiting steps of photosynthesis.