Efficient estimation of SNP heritability using Gaussian predictive process in large scale cohort studies

Heritability, phenotypic, and genetic correlations across dimensional and categorical models of bipolar disorder in a family sample

BACKGROUND

Bipolar disorder (BD) presents with a wide range of symptoms that vary among relatives, casting doubt on categorical illness models. To address this uncertainty, we investigated the heritability and genetic relationships between categorical and dimensional models of BD in a family sample.

METHODS

This retrospective study included participants (n = 397 Females, n = 329 Males, mean age 47 yr) in the Amish-Mennonite Bipolar Genetics (AMBiGen) study from North and South America that were assigned categorical mood disorder diagnoses ("narrow" or "broad") by structured psychiatric interview and completed the Mood Disorder Questionnaire (MDQ), which assesses lifetime history of manic symptoms and associated impairment. MDQ-dimensions were analyzed by Principal Component Analysis (PCA). Heritability and genetic overlaps between categorical diagnoses and MDQ-dimensions were estimated with SOLAR-ECLIPSE within 432 genotyped participants.

RESULTS

Individuals diagnosed with BD (n = 124) endorsed more MDQ items (61 %) than those with other mood disorders (26 %) or with no mood disorder (9 %), as expected. PCA suggested a three-component model for the MDQ, capturing 60 % of the variance. Heritability of the MDQ and its principal components was significant but modest (20-30 %, p < 0.001). Genetic correlations between MDQ measures and categorical diagnoses (ρG = 0.62-1.0; p < 0.001) were stronger than phenotypic correlations (ρP = 0.11-0.58; p < 0.001).

LIMITATIONS

Recruitment through probands with BD resulted in increased prevalence of BD in this sample, limiting generalizability. Unavailable genetic data reduced sample size for some analyses.

CONCLUSION

Findings support a genetic continuity between dimensional and categorical models of BD and suggest that the MDQ is a useful phenotype measure for genetic studies of BD.

SMASH: Scalable Method for Analyzing Spatial Heterogeneity of genes in spatial transcriptomics data

In high-throughput spatial transcriptomics (ST) studies, it is of great interest to identify the genes whose level of expression in a tissue covaries with the spatial location of cells/spots. Such genes, also known as spatially variable genes (SVGs), can be crucial to the biological understanding of both structural and functional characteristics of complex tissues. Existing methods for detecting SVGs either suffer from huge computational demand or significantly lack statistical power. We propose a non-parametric method termed SMASH that achieves a balance between the above two problems. We compare SMASH with other existing methods in varying simulation scenarios demonstrating its superior statistical power and robustness. We apply the method to four ST datasets from different platforms uncovering interesting biological insights.

SMASH: Scalable Method for Analyzing Spatial Heterogeneity of genes in spatial transcriptomics data

In high-throughput spatial transcriptomics (ST) studies, it is of great interest to identify the genes whose level of expression in a tissue covaries with the spatial location of cells/spots. Such genes, also known as spatially variable genes (SVGs), can be crucial to the biological understanding of both structural and functional characteristics of complex tissues. Existing methods for detecting SVGs either suffer from huge computational demand or significantly lack statistical power. We propose a non-parametric method termed SMASH that achieves a balance between the above two problems. We compare SMASH with other existing methods in varying simulation scenarios demonstrating its superior statistical power and robustness. We apply the method to four ST datasets from different platforms revealing interesting biological insights.

Comparing heritability estimators under alternative structures of linkage disequilibrium

The single nucleotide polymorphism heritability of a trait is the proportion of its variance explained by the additive effects of the genome-wide single nucleotide polymorphisms. The existing approaches to estimate single nucleotide polymorphism heritability can be broadly classified into 2 categories. One set of approaches models the single nucleotide polymorphism effects as fixed effects and the other treats the single nucleotide polymorphism effects as random effects. These methods make certain assumptions about the dependency among individuals (familial relationship) as well as the dependency among markers (linkage disequilibrium) to provide consistent estimates of single nucleotide polymorphism heritability as the number of individuals increases. While various approaches have been proposed to account for such dependencies, it remains unclear which estimates reported in the literature are more robust against various model misspecifications. Here, we investigate the impact of different structures of linkage disequilibrium and familial relatedness on heritability estimation. We show that the performance of different methods for heritability estimation depends heavily on the structure of the underlying pattern of linkage disequilibrium and the degree of relatedness among sampled individuals. Moreover, we establish the equivalence between the 2 method-of-moments estimators, one using a fixed-single nucleotide polymorphism-effects approach, and another using a random-single nucleotide polymorphism-effects approach.

DenVar: density-based variation analysis of multiplex imaging data

Summary

Multiplex imaging platforms have become popular for studying complex single-cell biology in the tumor microenvironment (TME) of cancer subjects. Studying the intensity of the proteins that regulate important cell-functions becomes extremely crucial for subject-specific assessment of risks. The conventional approach requires selection of two thresholds, one to define the cells of the TME as positive or negative for a particular protein, and the other to classify the subjects based on the proportion of the positive cells. We present a threshold-free approach in which distance between a pair of subjects is computed based on the probability density of the protein in their TMEs. The distance matrix can either be used to classify the subjects into meaningful groups or can directly be used in a kernel machine regression framework for testing association with clinical outcomes. The method gets rid of the subjectivity bias of the thresholding-based approach, enabling easier but interpretable analysis. We analyze a lung cancer dataset, finding the difference in the density of protein HLA-DR to be significantly associated with the overall survival and a triple-negative breast cancer dataset, analyzing the effects of multiple proteins on survival and recurrence. The reliability of our method is demonstrated through extensive simulation studies.

Availability and implementation

The associated R package can be found here, https://github.com/sealx017/DenVar.

Supplementary information

Supplementary data are available at Bioinformatics Advances online.