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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. We show that these studies tended to substantially over-correct for confounding, and as a result the number of genome-wide significant loci was under-reported by about a quarter. We also estimate enrichments for 24 categories of SNPs defined by functional annotations. A previous study using LDSC reported that conserved regions were 13-fold enriched, and found a further six categories with above threefold enrichment. By contrast, our analysis using SumHer finds that none of the categories have enrichment above twofold. SumHer provides an improved understanding of the genetic architecture of complex traits, which enables more efficient analysis of future genetic data. SumHer is a software for estimating SNP heritability from summary statistics using heritability models. Applying SumHer to publicly available results for 24 GWAS provides an improved understanding of the genetic architecture of complex traits.

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. We compare individual-level data prediction tools using 14 UK Biobank phenotypes; our new tool LDAK-Bolt-Predict outperforms the existing tools Lasso, BLUP, Bolt-LMM and BayesR for all 14 phenotypes. We compare summary statistic prediction tools using 225 UK Biobank phenotypes; our new tool LDAK-BayesR-SS outperforms the existing tools lassosum, sBLUP, LDpred and SBayesR for 223 of the 225 phenotypes. When we improve the heritability model, the proportion of phenotypic variance explained increases by on average 14%, which is equivalent to increasing the sample size by a quarter. Existing genetic prediction tools typically assume that genetic variants contribute equally towards the phenotype. The authors develop eight prediction tools that allow the user to specify the heritability model, and show that these tools enable substantially improved prediction of complex traits.

By analyzing imputed genetic data for 42 human traits, Doug Speed and colleagues derive a model that describes how heritability varies with minor allele frequency, linkage disequilibrium and genotype certainty. Using this model, they show that common SNPs contribute substantially more heritability than previously thought. SNP heritability, the proportion of phenotypic variance explained by SNPs, has been reported for many hundreds of traits. Its estimation requires strong prior assumptions about the distribution of heritability across the genome, but current assumptions have not been thoroughly tested. By analyzing imputed data for a large number of human traits, we empirically derive a model that more accurately describes how heritability varies with minor allele frequency (MAF), linkage disequilibrium (LD) and genotype certainty. Across 19 traits, our improved model leads to estimates of common SNP heritability on average 43% (s.d. 3%) higher than those obtained from the widely used software GCTA and 25% (s.d. 2%) higher than those from the recently proposed extension GCTA-LDMS. Previously, DNase I hypersensitivity sites were reported to explain 79% of SNP heritability; using our improved heritability model, their estimated contribution is only 24%.

Large structural variations (SVs) within genomes are more challenging to identify than smaller genetic variants but may substantially contribute to phenotypic diversity and evolution. We analyse the effects of SVs on gene expression, quantitative traits and intrinsic reproductive isolation in the yeast Schizosaccharomyces pombe . We establish a high-quality curated catalogue of SVs in the genomes of a worldwide library of S. pombe strains, including duplications, deletions, inversions and translocations. We show that copy number variants (CNVs) show a variety of genetic signals consistent with rapid turnover. These transient CNVs produce stoichiometric effects on gene expression both within and outside the duplicated regions. CNVs make substantial contributions to quantitative traits, most notably intracellular amino acid concentrations, growth under stress and sugar utilization in winemaking, whereas rearrangements are strongly associated with reproductive isolation. Collectively, these findings have broad implications for evolution and for our understanding of quantitative traits including complex human diseases. Fission yeast Schizosaccharomyces pombe has diverse traits. Jeffares et al . characterize large copy number variations (CNVs) and rearrangements in S. pombe , and show that CNVs are transient with effects on quantitative traits and gene expression, whereas rearrangements influence intrinsic reproductive isolation.

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. Most studies of the obesity–depression association have used body mass index as a measure of obesity. Body mass index is defined as weight (measured in kilograms) divided by the square of height (meters) and therefore does not distinguish between the contributions of fat and nonfat to body weight. To better understand the obesity–depression association, we conduct a Mendelian randomization study of the relationship between fat mass, nonfat mass, height, and depression, using genome-wide association study results from the UK Biobank (n = 332,000) and the Psychiatric Genomics Consortium (n = 480,000). Our findings suggest that both fat mass and height (short stature) are causal risk factors for depression, while nonfat mass is not. These results represent important new knowledge on the role of anthropometric measures in the etiology of depression. They also suggest that reducing fat mass will decrease the risk of depression, which lends further support to public health measures aimed at reducing the obesity epidemic.

Background Although many epidemiological studies show an association of cigarette smoking with suicide the path to the latter is not well understood. Objective Using causal inference methodology with observational data, to examine if smoking leads indirectly to suicide through mental health hospitalization. Design The study used 11 waves of a cross-sectional national health survey that was linked with hospitalization and death registers. Methods The data came from Canadian Community Health Survey respondents (n = 723 665) between the years 2000 and 2014. These respondents agreed to link their data with hospitalization and death registers and were followed for an average of 9.18 (SD: 4.42; range: 3 to 17) years. Mediation models, one each for men and women, were created in which lifetime daily smoking was the exposure, mental health hospitalization was the mediator, and death by suicide was the outcome, adjusting for 11 covariates reported at survey participation. Results In both men and women, the direct effect of daily smoking was larger than the indirect effect through hospitalization for mental conditions. The direct effect of smoking was 1.76 (95% CI: 1.47-2.10) for men and 2.60 (95% CI: 1.90-3.57) for women. The indirect effect through mental health hospitalization was 1.07 (95% CI: 1.05-1.09) for men and 1.04 (95% CI: 0.99-1.09) for women. Conclusion A relatively smaller proportion of the daily smoking-suicide association is transmitted indirectly through mental health hospitalizations compared to a direct effect. Suicide interventions focusing on people hospitalized for mental disorders may miss many suicidal people, so primary prevention and secondary prevention of smoking are crucial.

Anxiety and depression commonly occur together resulting in worse health outcomes than when they occur in isolation. We aimed to determine whether the genetic liability for comorbid anxiety and depression was greater than when anxiety or depression occurred alone. Data from 12,792 genotyped twins (ages 38–85) were analysed, including 1,986 complete monozygotic and 1,594 complete dizygotic pairs. Outcomes were prescription of antidepressant and anxiolytic drugs, as defined by the World Health Organization Anatomical Therapeutic Chemical Classification System (ATC) convention, for comorbid anxiety and depression ( n  = 1028), anxiety only ( n  = 718), and depression only ( n  = 484). Heritability of each outcome was estimated using twin modelling, and the influence of common genetic variation was assessed from polygenic scores (PGS) for depressive symptoms, anxiety, and 40 other traits. Heritability of comorbid anxiety and depression was 79% compared with 41% for anxiety and 50% for depression alone. The PGS for depressive symptoms likewise predicted more variation in comorbid anxiety and depression (adjusted odds ratio per SD PGS = 1.53, 95% CI = 1.43–1.63; Δ R 2  = 0.031, ΔAUC = 0.044) than the other outcomes, with nearly identical results when comorbid anxiety and depression was defined by International Classification of Diseases (ICD) diagnoses (adjusted odds ratio per SD PGS = 1.70, 95% CI = 1.53–1.90; Δ R 2  = 0.036, ΔAUC = 0.051). Individuals in the highest decile of PGS for depressive symptoms had over 5 times higher odds of being prescribed medication for comorbid anxiety and depression compared to those in the lowest decile. While results on a predominant role of depressive symptoms may have been biased by the size and heterogeneity of available data bases, they are consistent with the conclusion that genetic factors explain substantially more variation in comorbid anxiety and depression than anxiety or depression alone.

Multiple factors contribute to the risk of mortality from visceral leishmaniasis (VL), including, patient genotype, comorbidities, and nutrition. Many of these factors are influenced by socioeconomic biases. 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. We collected and sequenced the genomes of 109 L. infantum isolates from patients in northeastern Brazil and retrieved matching patient clinical data from medical records, including mortality, sex, HIV coinfection, and laboratory data (creatinine, hemoglobin, and leukocyte and platelet counts). We identified genetic differences between parasite isolates, including single nucleotide polymorphisms (SNPs), small insertions/deletions (indels), and variations in genic, intergenic, and chromosome copy numbers (copy number variants [CNVs]). To describe associations between the parasite genotypes and clinical outcomes, we applied quantitative genetics methods of heritability and genome-wide association studies (GWAS), treating clinical outcomes as traits that may be influenced by parasite genotype. Multiple aspects of the genetic analysis indicate that parasite genotype affects clinical outcomes. We estimate that parasite genotype explains 83% chance of mortality (narrow-sense heritability [ h 2 ] = 0.83 ± 0.17) and has a significant relationship with patient sex ( h 2 = 0.60 ± 0.27). Impacts of parasite genotype on other clinical traits are lower ( h 2 ≤ 0.34). GWAS analysis identified multiple parasite genetic loci that were significantly associated with clinical outcomes; 17 CNVs were significantly associated with mortality, two with creatinine, and one with bacterial coinfection, jaundice, and HIV coinfection, and two SNPs/indels and six CNVs were associated with age, jaundice, HIV and bacterial coinfections, creatinine, and/or bleeding sites. Parasite genotype is an important factor in VL disease severity in Brazil. Our analysis indicates that specific genetic differences between parasites act as virulence factors, enhancing risks of severe disease and mortality. More detailed understanding of these virulence factors could be exploited for novel therapies. IMPORTANCE Multiple factors contribute to the risk of mortality from visceral leishmaniasis (VL), including, patient genotype, comorbidities, and nutrition. Many of these factors are influenced by socioeconomic biases. Our work suggests that the virulence of the infecting parasite is an important risk factor for mortality. We pinpoint some specific genomic markers that are associated with mortality, which can lead to a greater understanding of the molecular mechanisms that cause severe VL disease, to the identification of genetic markers for virulent parasites, and to the development of drug and vaccine therapies.

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. Based on 31 studies of complex human traits (average sample size 136,000), we show that the Baseline LD model is more realistic than other existing heritability models, but that it can be improved by incorporating features from the LDAK model. Our framework also provides a method for estimating the selection-related parameter α from summary statistics. We find strong evidence ( P  < 1 × 10 −6 ) of negative genome-wide selection for traits, including height, systolic blood pressure and college education, and that the impact of selection is stronger inside functional categories, such as coding SNPs and promoter regions. Assessing heritability models using summary statistics from genome-wide association studies of 31 human traits shows that the Baseline LD model is realistic and can be improved by incorporating features from the LDAK model.

The extent to which sex-specific genetic effects contribute to phenotypic variation is largely unknown. We applied a novel Bayesian method, sparse partitioning, to detect gene by sex (GxS) and gene by gene (GxG) quantitative loci (QTLs) in 1,900 outbred heterogeneous stock mice. In an analysis of 55 phenotypes, we detected 16 GxS and 6 GxG QTLs. The increase in the amount of phenotypic variance explained by models including GxS was small, ranging from 0.14% to 4.30%. We conclude that GxS rarely make a large overall contribution to the heritability of phenotypes, however there are cases where these will be individually important.