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. Second, it can correct for ascertainment of cases or controls. Third, it can accommodate covariates. Fourth, it can model the contribution of common environment. Fifth, it produces a likelihood that can be used for significance testing. We first demonstrate the validity of TetraHer on simulated data. We then use TetraHer to estimate liability heritability for 229 codes from the tenth International Classification of Diseases (ICD-10). We identify 107 codes with significant heritability (p < 0.05/229), which can be used in future analyses for investigating the genetic architecture of human diseases.

Using the Bayesian variational spike and slab model in a genome-wide association study for finding associated loci with bipolar disorder.

OBJECTIVE: The genome-wide association studies (GWAS) analysis, the most successful technique for discovering disease-related genetic variation, has some statistical concerns, including multiple testing, the correlation among variants (single-nucleotide polymorphisms) based on linkage disequilibrium and omitting the important variants when fitting the model with just one variant. To eliminate these problems in a small sample-size study, we used a sparse Bayesian learning model for finding bipolar disorder (BD) genetic variants. METHODS: This study used the Wellcome Trust Case Control Consortium data set, including 1998 BD cases and 1500 control samples, and after quality control, 380,628 variants were analysed. In this GWAS, a Bayesian logistic model with hierarchical shrinkage spike and slab priors was used, with all variants considered simultaneously in one model. In order to decrease the computational burden, an alternative inferential method, Bayesian variational inference, has been used. RESULTS: Thirteen variants were selected as associated with BD. The three of them (rs7572953, rs1378850 and rs4148944) were reported in previous GWAS. Eight of which were related to hemogram parameters, such as lymphocyte percentage, plateletcrit and haemoglobin concentration. Among selected related genes, GABPA, ELF3 and JAM2 were enriched in the platelet-derived growth factor pathway. These three genes, along with APP, ARL8A, CDH23 and GPR37L1, could be differential diagnostic variants for BD. CONCLUSIONS: By reducing the statistical restrictions of GWAS analysis, the application of the Bayesian variational spike and slab models can offer insight into the genetic link with BD even with a small sample size. To uncover related variations with other traits, this model needs to be further examined.

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. METHODS: We calculated polygenic risk scores (PRS) for ever smoking, pack-years of smoking as a proportion of adult life, and neuroticism in 337,140 UK Biobank participants of white British ancestry. These PRS and self-reported smoking status were entered as explanatory variables in survival models for hospitalization. RESULTS: The estimated single nucleotide polymorphisms heritabilities (h2 ) were 23%, 5.7%, and 5.7% for pack-years, ever smoking, and neuroticism respectively. PRS pack-years and PRS neuroticism were associated with higher hospitalization risk for mental disorders in all smoking status groups. The hazard for mental health hospitalization was higher in both previous (HR: 1.50, CI: 1.35-1.67) and current (HR: 3.58, 2.97-4.31) compared to never smokers, after adjusting for confounders. CONCLUSION: Since genetic liabilities for smoking and neuroticism are fixed at conception and smoking initiation generally started before age 20, our results show that preventing smoking in adolescents probably prevents the development of mental disorders.

Participation bias in the UK Biobank distorts genetic associations and downstream analyses.

While volunteer-based studies such as the UK Biobank have become the cornerstone of genetic epidemiology, the participating individuals are rarely representative of their target population. To evaluate the impact of selective participation, here we derived UK Biobank participation probabilities on the basis of 14 variables harmonized across the UK Biobank and a representative sample. We then conducted weighted genome-wide association analyses on 19 traits. Comparing the output from weighted genome-wide association analyses (neffective = 94,643 to 102,215) with that from standard genome-wide association analyses (n = 263,464 to 283,749), we found that increasing representativeness led to changes in SNP effect sizes and identified novel SNP associations for 12 traits. While heritability estimates were less impacted by weighting (maximum change in h2, 5%), we found substantial discrepancies for genetic correlations (maximum change in rg, 0.31) and Mendelian randomization estimates (maximum change in ßSTD, 0.15) for socio-behavioural traits. We urge the field to increase representativeness in biobank samples, especially when studying genetic correlates of behaviour, lifestyles and social outcomes.

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. It shows good control of type 1 error given an appropriate reference panel. Across 109 phenotypes (82 from the UK Biobank, 18 from the Million Veteran Program, and nine from the Psychiatric Genetics Consortium), LDAK-GBAT finds on average 19% (SE: 1%) more significant genes than the existing tool sumFREGAT-ACAT, with even greater gains in comparison with MAGMA, GCTA-fastBAT, sumFREGAT-SKAT-O, and sumFREGAT-PCA.

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

OBJECTIVES: 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 0.5% of phenotypic variance, while the genetic instrument for BMI contained 517 SNPs and explained 7.1% of phenotypic variance RESULTS: Our findings suggest that genetic liability to bipolar disorder reduces BMI (slope from Egger regression = -0.195, p = 0.004). It follows that a twofold increase in the genetic liability to bipolar disorder leads to a 0.6 (kg/m2 ) reduction in BMI, predominantly driven by reduced fat mass. Conversely, we found no evidence that BMI causes changes in the risk of developing bipolar disorder CONCLUSION: The results of this study suggest that the increased BMI observed among individuals with bipolar disorder is not a direct consequence of genetic liability to bipolar disorder, but may more likely represent the sum of downstream correlates of manifest bipolar disorder, such as side effects of pharmacological treatment, poor diet, and sedentary lifestyle. As these factors are all modifiable, they can be targeted as part of clinical management.

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. 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 [h2] = 0.83 ± 0.17) and has a significant relationship with patient sex (h2 = 0.60 ± 0.27). Impacts of parasite genotype on other clinical traits are lower (h2 ≤ 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.

Heritability of insidious uveitis in Appaloosa horses.

Equine recurrent uveitis (ERU) is a blinding ocular disorder among horses, and the Appaloosa horse breed is disproportionally affected by a chronic form of this intraocular inflammatory disease known as insidious uveitis. Strong breed predisposition and previous investigations suggest that there is a genetic component to the pathology of insidious uveitis among Appaloosa horses; however, no estimates of the heritability of the disease have previously been determined. This study aimed to characterize the genetic underpinning of the disease by estimating the heritability for insidious uveitis among Appaloosas. After combining two genotyping array datasets from the Illumina Equine SNP70 BeadChip and the Axiom Equine 670 K Genotyping Array, heritability was estimated for 59 affected and 83 unaffected horses using both restricted maximum likelihood (REML) and phenotype correlation - genotype correlation solvers from the linkage disequilibrium adjusted kinship software. Based on previous research, age and sex were used as covariates, and the locus responsible for the characteristic Appaloosa coat pattern (LP), previously associated with ERU risk, was included as a fixed effect ('top predictor'). Using prevalence values from 0.05 to 0.42, the heritability estimate for insidious uveitis ranged from 0.95 (SE = 0.14) to 1.74 (SE = 0.25) with LP contributing 0.16-0.33 to the estimate. This study suggests that insidious uveitis is highly heritable (REML 95% CI, h2  = 0.68-1.0) and additional loci outside of LP are contributing to the genetic risk for insidious uveitis for Appaloosas. Once identified, these other genetic factors may lead to new disease mitigation efforts in veterinary care and breeding practices.

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 traits1,2. The solution to this problem is to identify all causal genetic variants and to measure their individual contributions3,4. 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. This graph pangenome was used for genome-wide association study analyses and heritability estimation of 20,323 gene-expression and metabolite traits. The average estimated trait heritability is 0.41 compared with 0.33 when using the single linear reference genome. This 24% increase in estimated heritability is largely due to resolving incomplete linkage disequilibrium through the inclusion of additional causal structural variants identified using the graph pangenome. Moreover, by resolving allelic and locus heterogeneity, structural variants improve the power to identify genetic factors underlying agronomically important traits leading to, for example, the identification of two new genes potentially contributing to soluble solid content. The newly identified structural variants will facilitate genetic improvement of tomato through both marker-assisted selection and genomic selection. Our study advances the understanding of the heritability of complex traits and demonstrates the power of the graph pangenome in crop breeding.

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. METHODS: SNPs in or within 10 kb of 96 TH regulating genes were included (30 031 TSH SNPs, and 29 962 FT4 SNPs). Associations were studied in 54 288 individuals from the ThyroidOmics Consortium. Linkage disequilibrium-based clumping was used to identify independently associated SNPs. SNP-based explained variances were calculated using SumHer software. RESULTS: We identified 23 novel TSH-associated SNPs in predominantly hypothalamic-pituitary-thyroid axis genes and 25 novel FT4-associated SNPs in mainly peripheral metabolism and transport genes. Genome-wide SNP variation explained ~21% (SD 1.7) of the total variation in both TSH and FT4 concentrations, whereas SNPs in the 96 TH regulating genes explained 1.9% to 2.6% (SD 0.4). CONCLUSION: Here we report the largest candidate gene analysis on thyroid function, resulting in a substantial increase in the number of genetic variants determining TSH and FT4 concentrations. Interestingly, these candidate gene SNPs explain only a minor part of the variation in TSH and FT4 concentrations, which substantiates the need for large genetic studies including common and rare variants to unravel novel, yet unknown, pathways in TH regulation.

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. We use state-of-art models to present updated results about how heritability is distributed with respect to functional annotations in the human genome, and how it varies with allele frequency, which can reflect purifying selection. Our results give finer detail to the picture that has emerged in recent years of complex trait heritability widely dispersed across the genome. Confounding due to population structure remains a problem that summary statistic analyses cannot reliably overcome. Also see the video abstract here: https://youtu.be/WC2u03V65MQ.

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

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. We compare and validate our methods against the current state-of-art using simulations, and analyse three phenotypes of the major human pathogen Streptococcus pneumoniae, including the first analyses of minimum inhibitory concentrations (MIC) for penicillin and ceftriaxone. We show that the MIC traits are highly heritable with high prediction accuracy, explained by many genetic associations under good population structure control. In ceftriaxone MIC, this is surprising because none of the isolates are resistant as per the inhibition zone criteria. We estimate that half of the heritability of penicillin MIC is explained by a known drug-resistance region, which also contributes a quarter of the ceftriaxone MIC heritability. For the within-host carriage duration phenotype, no associations were observed, but the moderate heritability and prediction accuracy indicate a moderately polygenic trait.

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

Here, with the example of common copy number variation (CNV) in the TSPAN8 gene, we present an important piece of work in the field of CNV detection, that is, CNV association with complex human traits such as 1H NMR metabolomic phenotypes and an example of functional characterization of CNVs among human induced pluripotent stem cells (HipSci). We report TSPAN8 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 TSPAN8.

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. 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.

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. We conducted a two-sample bidirectional MR study on the relationship between bipolar disorder and lifetime cannabis use. Genetic instruments (SNPs) were obtained from the summary statistics of recent large genome-wide association studies (GWAS). We conducted a two-sample bidirectional MR study on the relationship between bipolar disorder and lifetime cannabis use using inverse variance weighted regression, weighted median regression, and Egger regression. Genetic liability to bipolar disorder was significantly associated with an increased risk of lifetime cannabis use; however, genetic liability to lifetime cannabis use showed no association with the risk of bipolar disorder. The sensitivity analyses showed no evidence for pleiotropic effects. The present findings support a causal effect of liability to bipolar disorder on the risk of using cannabis at least once. No evidence was found for a causal effect of liability to cannabis use on the risk of bipolar disorder. These findings add important new knowledge to the understanding of the complex relationship between cannabis use and psychiatric disorders.

Estimating narrow-sense heritability using family data from admixed populations.

Estimating total narrow-sense heritability in admixed populations remains an open question. In this work, we used extensive simulations to evaluate existing linear mixed-model frameworks for estimating total narrow-sense heritability in two population-based cohorts from Greenland, and compared the results with data from unadmixed individuals from Denmark. When our analysis focused on Greenlandic sib pairs, and under the assumption that shared environment among siblings has a negligible effect, the model with two relationship matrices, one capturing identity by descent and one capturing identity by state, returned heritability estimates close to the true simulated value, while using each of the two matrices alone led to downward biases. When phenotypes correlated with ancestry, heritability estimates were inflated. Based on these observations, we propose a PCA-based adjustment that recovers the true simulated heritability. We use this knowledge to estimate the heritability of ten quantitative traits from the two Greenlandic cohorts, and report differences such as lower heritability for height in Greenlanders compared with Europeans. In conclusion, narrow-sense heritability in admixed populations is best estimated when using a mixture of genetic relationship matrices on individuals with at least one first-degree relative included in the sample.

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. 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.

GWAS on longitudinal growth traits reveals different genetic factors influencing infant, child, and adult BMI.

Early childhood growth patterns are associated with adult health, yet the genetic factors and the developmental stages involved are not fully understood. Here, we combine genome-wide association studies with modeling of longitudinal growth traits to study the genetics of infant and child growth, followed by functional, pathway, genetic correlation, risk score, and colocalization analyses to determine how developmental timings, molecular pathways, and genetic determinants of these traits overlap with those of adult health. We found a robust overlap between the genetics of child and adult body mass index (BMI), with variants associated with adult BMI acting as early as 4 to 6 years old. However, we demonstrated a completely distinct genetic makeup for peak BMI during infancy, influenced by variation at the LEPR/LEPROT locus. These findings suggest that different genetic factors control infant and child BMI. In light of the obesity epidemic, these findings are important to inform the timing and targets of prevention strategies.

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. Consequently, these methods may estimate heritability poorly if there was an inadequate adjustment for confounding in the original GWAS analysis. We also show that the choice of a summary statistic for use in LDSC or SumHer can have a large impact on resulting inferences. Further, covariate adjustments in the original GWAS can alter the target of heritability estimation, which can be problematic for test statistics from a meta-analysis of GWAS with different covariate adjustments.

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. 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.