Heritability, the fraction of phenotypic variation explained by genetic variation, has been estimated for many phenotypes in a range of populations, organisms, and time points. The recent development of efficient genotyping and sequencing technology has led researchers to attempt to identify the genetic variants responsible for the genetic component of phenotype directly via GWAS. The gap between the phenotypic variance explained by GWAS results and those estimated from classical heritability methods has been termed the “missing heritability problem”. In this work, we examine modern methods for estimating heritability, which use the genotype and sequence data directly. We discuss them in the context of classical heritability methods, the missing heritability problem, and describe their implications for understanding the genetic architecture of complex phenotypes.

Heritability in the genome-wide association era

Barrier island evolution, middle Atlantic shelf, U.S.A. Part I: Shoreface dynamics

Shoreface morphodynamics on wave-dominated coasts

http://www.cell.com/article/S1535610822000654/pdf

Immune phenotypic linkage between colorectal cancer and liver metastasis.

Many diseases exhibit population-specific causal effect sizes with trans-ethnic genetic correlations significantly less than 1, limiting trans-ethnic polygenic risk prediction. We develop a new method, S-LDXR, for stratifying squared trans-ethnic genetic correlation across genomic annotations, and apply S-LDXR to genome-wide summary statistics for 31 diseases and complex traits in East Asians (average N = 90K) and Europeans (average N = 267K) with an average trans-ethnic genetic correlation of 0.85. We determine that squared trans-ethnic genetic correlation is 0.82× (s.e. 0.01) depleted in the top quintile of background selection statistic, implying more population-specific causal effect sizes. Accordingly, causal effect sizes are more population-specific in functionally important regions, including conserved and regulatory regions. In regions surrounding specifically expressed genes, causal effect sizes are most population-specific for skin and immune genes, and least population-specific for brain genes. Our results could potentially be explained by stronger gene-environment interaction at loci impacted by selection, particularly positive selection. Trans-ethnic genetic correlation is significantly less than 1 for many diseases. Here, the authors stratify this correlation by genomic annotations, finding that loci whose causal disease effect sizes differ between ethnicities are likely impacted by selection, particularly positive selection.

/pdf/population-specific-causal-disease-effect-sizes-in-1qnr82e0oq.pdf

Population-specific causal disease effect sizes in functionally important regions impacted by selection.

Summary The role of germline genetics in shaping the tumor immune landscape is largely unknown. Using genotypes from >9,000 individuals in The Cancer Genome Atlas, we investigated the association of common and rare variants with 139 well-defined immune traits. Our analysis of common variants identified 10 immune traits with significant heritability estimates, and an additional 23 with suggestive heritability, including estimates of T-cell subset abundance and interferon signaling. We performed genome-wide association on the 33 heritable traits and identified 23 genome-wide significant loci associated with at least one immune trait, including SNPs in the IFIH1 locus previously associated with several autoimmune diseases. We also found significant associations between immune traits and pathogenic or likely-pathogenic rare variants in BRCA1 and in genes functionally linked to telomere stabilization, and Wnt/Beta-catenin signaling. We conclude that germline genetic variants significantly impact the composition and functional orientation of the tumor immune microenvironment.

https://biorxiv.org/content/biorxiv/early/2020/01/31/2020.01.30.926527.full-text.pdf

Germline genetic contribution to the immune landscape of cancer

The genetic control of gene expression is a core component of human physiology. For the past several years, transcriptome-wide association studies have leveraged large datasets of linked genotype and RNA sequencing information to create a powerful gene-based test of association that has been used in dozens of studies. While numerous discoveries have been made, the populations in the training data are overwhelmingly of European descent, and little is known about the portability of these models to other populations. Here, we test for cross-population portability of gene expression prediction models using a dataset of African American individuals with RNA-Seq data in whole blood. We find that the default models trained in large datasets such as GTEx and DGN fare poorly in African Americans, with a notable reduction in prediction accuracy when compared to European Americans. We replicate these limitations in cross-population portability using the five populations in the GEUVADIS dataset. Via simulations of both populations and gene expression, we show that accurate cross-population portability of transcriptome imputation only arises when eQTL architecture is substantially shared across populations. In contrast, models with non-identical eQTL showed patterns similar to real-world data. Therefore, generating RNA-Seq data in diverse populations is a critical step towards multi-ethnic utility of gene expression imputation.

/pdf/on-the-cross-population-portability-of-gene-expression-3phl80qsdt.pdf

On the cross-population portability of gene expression prediction models

A comparison is made between the measured infilling of two test pits off the coastline of San Francisco and predictions using a coastal bedload transport model. The model, based on the work of Madsen and Grant (1967), relates the bedload transport to the bottom shear stress through an empirical relationship based on laboratory experiments. The bottom shear stress is estimated from the bottom currents created by waves and low frequency currents. The model applies beyond the breaker zone in contrast to littoral transport. The test pits, dredged as part of the Southwest Ocean Outfall Project for San Francisco, were located 1.6 km (1 mi) and 3.2 km (2 mi) offshore in 13' m (42 ft) and 16 m (53 ft) of water. The depth of the pits relative to the natural seabed was about 8.4 m (25 ft). The comparison was conducted for a period up to 2 months in the fall of 1978. The paper discussed the quality and scope of available data required as input to the model and shows how regional wave data were trans formed to augment local measurements. Uncertainties in model results stemming from limitations in the input data are presented. With suitable adjustment of the scale of the gravitational term in the expression for the Shields parameter, overall agreement between computed and measured bedload was accomplished within the limits of accuracy of the bathymetric surveys. A sensitivity analysis of selected input conditions and coefficients was also conducted.

https://icce-ojs-tamu.tdl.org/icce/index.php/icce/article/download/3698/3381

Scott R. Huntsman

Papers

Germline genetic contribution to the immune landscape of cancer

On the cross-population portability of gene expression prediction models

Measured and Computed Coastal Ocean Bedload Transport

Attenuation of Wave-Induced Pore Pressures in Sand