Genome-wide association study meta-analysis provides insights into the etiology of heart failure and its subtypes.

Heart failure (HF) is a major contributor to global morbidity and mortality. While distinct clinical subtypes, defined by etiology and left ventricular ejection fraction, are well recognized, their genetic determinants remain inadequately understood. In this study, we report a genome-wide association study of HF and its subtypes in a sample of 1.

The heritability of fitness in a wild annual plant population with hierarchical size structure.

The relative magnitude of additive genetic vs. residual variation for fitness traits is important in models for predicting the rate of evolution and population persistence in response to changes in the environment. In many annual plants, lifetime reproductive fitness is correlated with end-of-season plant biomass, which can vary significantly from plant to plant in the same population.

Estimating disease heritability from complex pedigrees allowing for ascertainment and covariates.

We propose TetraHer, a method for estimating the liability heritability of binary phenotypes. TetraHer has five key features. First, it can be applied to data from complex pedigrees that contain multiple types of relationships.

Using polygenic risk scores to investigate the evolution of smoking and mental health outcomes in UK biobank participants.

Objective: Mendelian randomization studies report a bi-directional relation between cigarette smoking and mental disorders, yet from a clinical standpoint, mental disorders are the focus of treatment. Here, we used an event history framework to understand their evolution in the life course. Our objective was to estimate the relative contribution of genetic predispositions and self-reported smoking status (never, former, and present smoker) to hospitalizations for major depression, bipolar disorder, and schizophrenia.

LDAK-GBAT: Fast and powerful gene-based association testing using summary statistics.

We present LDAK-GBAT, a tool for gene-based association testing using summary statistics from genome-wide association studies that is computationally efficient, produces well-calibrated p values, and is significantly more powerful than existing tools. LDAK-GBAT takes approximately 30 min to analyze imputed data (2.9M common, genic SNPs), requiring less than 10 Gb memory.

Genetic liability to bipolar disorder and body mass index: A bidirectional two-sample Mendelian randomization study.

Bipolar disorder is associated with increased body mass index (BMI), but it remains undetermined if this association is causal and, if so, in which direction it goes. Here, we sought to answer these questions using bidirectional two-sample Mendelian randomization, a method from genetic epidemiology that uses data from genome-wide association studies (GWAS) to examine whether a risk factor is causal for an outcome METHODS: We used summary statistics from GWAS of bipolar disorder and BMI conducted using data collected by the Psychiatric Genomics Consortium and the UK Biobank, respectively. The genetic instrument for bipolar disorder contained 53 SNPs and explained 0.

Parasite Genotype Is a Major Predictor of Mortality from Visceral Leishmaniasis.

Visceral leishmaniasis (VL) is a potentially fatal disease caused mainly by Leishmania infantum in South America and Leishmania donovani in Asia and Africa. Disease outcomes have been associated with patient genotype, nutrition, age, sex, comorbidities, and coinfections. In this study, we examine the effects of parasite genetic variation on VL disease severity in Brazil.

Graph pangenome captures missing heritability and empowers tomato breeding.

Missing heritability in genome-wide association studies defines a major problem in genetic analyses of complex biological traits. The solution to this problem is to identify all causal genetic variants and to measure their individual contributions. Here we report a graph pangenome of tomato constructed by precisely cataloguing more than 19 million variants from 838 genomes, including 32 new reference-level genome assemblies.

SNP-based heritability and selection analyses: Improved models and new results.

Complex-trait genetics has advanced dramatically through methods to estimate the heritability tagged by SNPs, both genome-wide and in genomic regions of interest such as those defined by functional annotations. The models underlying many of these analyses are inadequate, and consequently many SNP-heritability results published to date are inaccurate. Here, we review the modelling issues, both for analyses based on individual genotype data and association test statistics, highlighting the role of a low-dimensional model for the heritability of each SNP.

The Effects of Common Genetic Variation in 96 Genes Involved in Thyroid Hormone Regulation on TSH and FT4 Concentrations.

Objective: While most of the variation in thyroid function is determined by genetic factors, single nucleotide polymorphisms (SNPs) identified via genome-wide association analyses have only explained ~5% to 9% of this variance so far. Most SNPs were in or nearby genes with no known role in thyroid hormone (TH) regulation. Therefore, we performed a large-scale candidate gene study investigating the effect of common genetic variation in established TH regulating genes on serum thyrotropin [thyroid-stimulating hormone (TSH)] and thyroxine (FT4) concentrations.

Genome-wide association, prediction and heritability in bacteria with application to .

Whole-genome sequencing has facilitated genome-wide analyses of association, prediction and heritability in many organisms. However, such analyses in bacteria are still in their infancy, being limited by difficulties including genome plasticity and strong population structure. Here we propose a suite of methods including linear mixed models, elastic net and LD-score regression, adapted to bacterial traits using innovations such as frequency-based allele coding, both insertion/deletion and nucleotide testing and heritability partitioning.

Signatures of TSPAN8 variants associated with human metabolic regulation and diseases.

Here, with the example of common copy number variation (CNV) in the gene, we present an important piece of work in the field of CNV detection, that is, CNV association with complex human traits such as H NMR metabolomic phenotypes and an example of functional characterization of CNVs among human induced pluripotent stem cells (HipSci). We report exon 11 (ENSE00003720745) as a pleiotropic locus associated with metabolomic regulation and show that its biology is associated with several metabolic diseases such as type 2 diabetes (T2D) and cancer. Our results further demonstrate the power of multivariate association models over univariate methods and define metabolomic signatures for variants in .

Improved genetic prediction of complex traits from individual-level data or summary statistics.

Most existing tools for constructing genetic prediction models begin with the assumption that all genetic variants contribute equally towards the phenotype. However, this represents a suboptimal model for how heritability is distributed across the genome. Therefore, we develop prediction tools that allow the user to specify the heritability model.

Bipolar disorder and cannabis use: A bidirectional two-sample Mendelian randomization study.

Cannabis use is associated with a number of psychiatric disorders; however, the causal nature of these associations has been difficult to establish. Mendelian randomization (MR) offers a way to infer causality between exposures with known genetic predictors (genome-wide significant single nucleotide polymorphisms [SNPs]) and outcomes of interest. MR has previously been applied to investigate the relationship between lifetime cannabis use (having ever used cannabis) and schizophrenia, depression, and attention deficit hyperactivity disorder (ADHD), but not bipolar disorder, representing a gap in the literature.

Evaluating and improving heritability models using summary statistics.

There is currently much debate regarding the best model for how heritability varies across the genome. The authors of GCTA recommend the GCTA-LDMS-I model, the authors of LD Score Regression recommend the Baseline LD model, and we have recommended the LDAK model. Here we provide a statistical framework for assessing heritability models using summary statistics from genome-wide association studies.

Summary statistic analyses can mistake confounding bias for heritability.

Linkage disequilibrium SCore regression (LDSC) has become a popular approach to estimate confounding bias, heritability, and genetic correlation using only genome-wide association study (GWAS) test statistics. SumHer is a newly introduced alternative with similar aims. We show using theory and simulations that both approaches fail to adequately account for confounding bias, even when the assumed heritability model is correct.

Investigating the association between body fat and depression via Mendelian randomization.

Obesity and depression are major public health concerns that are both associated with substantial morbidity and mortality. There is a considerable body of literature linking obesity to the development of depression. Recent studies using Mendelian randomization indicate that this relationship is causal.

SumHer better estimates the SNP heritability of complex traits from summary statistics.

We present SumHer, software for estimating confounding bias, SNP heritability, enrichments of heritability and genetic correlations using summary statistics from genome-wide association studies. The key difference between SumHer and the existing software LD Score Regression (LDSC) is that SumHer allows the user to specify the heritability model. We apply SumHer to results from 24 large-scale association studies (average sample size 121,000) using our recommended heritability model.

Genetic correlations among psychiatric and immune-related phenotypes based on genome-wide association data.

Individuals with psychiatric disorders have elevated rates of autoimmune comorbidity and altered immune signaling. It is unclear whether these altered immunological states have a shared genetic basis with those psychiatric disorders. The present study sought to use existing summary-level data from previous genome-wide association studies to determine if commonly varying single nucleotide polymorphisms are shared between psychiatric and immune-related phenotypes.