Reciprocal causation mixture model for robust Mendelian randomization analysis using genome-scale summary data.

Mendelian randomization using GWAS summary statistics has become a popular method to infer causal relationships across complex diseases. However, the widespread pleiotropy observed in GWAS has made the selection of valid instrumental variables problematic, leading to possible violations of Mendelian randomization assumptions and thus potentially invalid inferences concerning causation. Furthermore, current MR methods can examine causation in only one direction, so that two separate analyses are required for bi-directional analysis. In this study, we propose a ststistical framework, MRCI (Mixture model Reciprocal Causation Inference), to estimate reciprocal causation between two phenotypes simultaneously using the genome-scale summary statistics of the two phenotypes and reference linkage disequilibrium information. Simulation studies, including strong correlated pleiotropy, showed that MRCI obtained nearly unbiased estimates of causation in both directions, and correct Type I error rates under the null hypothesis. In applications to real GWAS data, MRCI detected significant bi-directional and uni-directional causal influences between common diseases and putative risk factors.

Whole genome sequencing reveals epistasis effects within RET for Hirschsprung disease.

Common variants in RET and NRG1 have been associated with Hirschsprung disease (HSCR), a congenital disorder characterised by incomplete innervation of distal gut, in East Asian (EA) populations. However, the allelic effects so far identified do not fully explain its heritability, suggesting the presence of epistasis, where effect of one genetic variant differs depending on other (modifier) variants. Few instances of epistasis have been documented in complex diseases due to modelling complexity and data challenges. We proposed four epistasis models to comprehensively capture epistasis for HSCR between and within RET and NRG1 loci using whole genome sequencing (WGS) data in EA samples. 65 variants within the Topologically Associating Domain (TAD) of RET demonstrated significant epistasis with the lead enhancer variant (RET+3; rs2435357). These epistatic variants formed two linkage disequilibrium (LD) clusters represented by rs2506026 and rs2506028 that differed in minor allele frequency and the best-supported epistatic model. Intriguingly, rs2506028 is in high LD with one cis-regulatory variant (rs2506030) highlighted previously, suggesting that detected epistasis might be mediated through synergistic effects on transcription regulation of RET. Our findings demonstrated the advantages of WGS data for detecting epistasis, and support the presence of interactive effects of regulatory variants in RET for HSCR.

Polygenic power calculator: Statistical power and polygenic prediction accuracy of genome-wide association studies of complex traits.

Power calculation is a necessary step when planning genome-wide association studies (GWAS) to ensure meaningful findings. Statistical power of GWAS depends on the genetic architecture of phenotype, sample size, and study design. While several computer programs have been developed to perform power calculation for single SNP association testing, it might be more appropriate for GWAS power calculation to address the probability of detecting any number of associated SNPs. In this paper, we derive the statistical power distribution across causal SNPs under the assumption of a point-normal effect size distribution. We demonstrate how key outcome indices of GWAS are related to the genetic architecture (heritability and polygenicity) of the phenotype through the power distribution. We also provide a fast, flexible and interactive power calculation tool which generates predictions for key GWAS outcomes including the number of independent significant SNPs, the phenotypic variance explained by these SNPs, and the predictive accuracy of resulting polygenic scores. These results could also be used to explore the future behaviour of GWAS as sample sizes increase further. Moreover, we present results from simulation studies to validate our derivation and evaluate the agreement between our predictions and reported GWAS results.

EraSOR: a software tool to eliminate inflation caused by sample overlap in polygenic score analyses.

BACKGROUND: Polygenic risk score (PRS) analyses are now routinely applied across biomedical research. However, as PRS studies grow in size, there is an increased risk of sample overlap between the genome-wide association study (GWAS) from which the PRS is derived and the "target sample," in which PRSs are computed and hypotheses are tested. Despite the wide recognition of the sample overlap problem, its potential impact on the results from PRS studies has not yet been quantified, and no analytical solution has been provided. FINDINGS: Here, we first conduct a comprehensive investigation into the scale of the sample overlap problem, finding that PRS results can be substantially inflated even in the presence of minimal overlap. Next, we introduce a method and software, EraSOR (Erase Sample Overlap and Relatedness), which eliminates the inflation caused by sample overlap (and close relatedness) in almost all settings tested here. CONCLUSIONS: EraSOR could be useful in PRS studies (with target sample >1,000) similar to those investigated here, either (i) to mitigate the potential effects of known or unknown intercohort overlap and close relatedness or (ii) as a sensitivity tool to highlight the possible presence of sample overlap before its direct removal, when possible, or else to provide a lower bound on PRS analysis results after accounting for potential sample overlap.

Tutorial: a guide to performing polygenic risk score analyses.

A polygenic score (PGS) or polygenic risk score (PRS) is an estimate of an individual's genetic liability to a trait or disease, calculated according to their genotype profile and relevant genome-wide association study (GWAS) data. While present PRSs typically explain only a small fraction of trait variance, their correlation with the single largest contributor to phenotypic variation-genetic liability-has led to the routine application of PRSs across biomedical research. Among a range of applications, PRSs are exploited to assess shared etiology between phenotypes, to evaluate the clinical utility of genetic data for complex disease and as part of experimental studies in which, for example, experiments are performed that compare outcomes (e.g., gene expression and cellular response to treatment) between individuals with low and high PRS values. As GWAS sample sizes increase and PRSs become more powerful, PRSs are set to play a key role in research and stratified medicine. However, despite the importance and growing application of PRSs, there are limited guidelines for performing PRS analyses, which can lead to inconsistency between studies and misinterpretation of results. Here, we provide detailed guidelines for performing and interpreting PRS analyses. We outline standard quality control steps, discuss different methods for the calculation of PRSs, provide an introductory online tutorial, highlight common misconceptions relating to PRS results, offer recommendations for best practice and discuss future challenges.

A powerful conditional gene-based association approach implicated functionally important genes for schizophrenia.

MOTIVATION: It remains challenging to unravel new susceptibility genes of complex diseases and the mechanisms in genome-wide association studies. There are at least two difficulties, isolation of the genuine susceptibility genes from many indirectly associated genes and functional validation of these genes. RESULTS: We first proposed a novel conditional gene-based association test which can use only summary statistics to isolate independently associated genes of a disease. Applying this method, we detected 185 genes of independent association with schizophrenia. We then designed an in-silico experiment based on expression/co-expression to systematically validate pathogenic potential of these genes. We found that genes of independent association with schizophrenia formed more co-expression pairs in normal post-natal but not pre-natal human brain regions than expected. Interestingly, no co-expression enrichment was found in the brain regions of schizophrenia patients. The genes with independent association also had more significant P-values for differential expression between schizophrenia patients and controls in the brain regions. In contrast, indirectly associated genes or associated genes by other widely-used gene-based tests had no such differential expression and co-expression patterns. In summary, this conditional gene-based association test is effective for isolating directly associated genes from indirectly associated genes, and the results insightfully suggest that common variants might contribute to schizophrenia largely by distorting expression and co-expression in post-natal brains. AVAILABILITY AND IMPLEMENTATION: The conditional gene-based association test has been implemented in a platform 'KGG' in Java and is publicly available at http://grass.cgs.hku.hk/limx/kgg/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Trans-Ethnic Polygenic Analysis Supports Genetic Overlaps of Lumbar Disc Degeneration With Height, Body Mass Index, and Bone Mineral Density.

Lumbar disc degeneration (LDD) is age-related break-down in the fibrocartilaginous joints between lumbar vertebrae. It is a major cause of low back pain and is conventionally assessed by magnetic resonance imaging (MRI). Like most other complex traits, LDD is likely polygenic and influenced by both genetic and environmental factors. However, genome-wide association studies (GWASs) of LDD have uncovered few susceptibility loci due to the limited sample size. Previous epidemiology studies of LDD also reported multiple heritable risk factors, including height, body mass index (BMI), bone mineral density (BMD), lipid levels, etc. Genetics can help elucidate causality between traits and suggest loci with pleiotropic effects. One such approach is polygenic score (PGS) which summarizes the effect of multiple variants by the summation of alleles weighted by estimated effects from GWAS. To investigate genetic overlaps of LDD and related heritable risk factors, we calculated the PGS of height, BMI, BMD and lipid levels in a Chinese population-based cohort with spine MRI examination and a Japanese case-control cohort of lumbar disc herniation (LDH) requiring surgery. Because most large-scale GWASs were done in European populations, PGS of corresponding traits were created using weights from European GWASs. We calibrated their prediction performance in independent Chinese samples, then tested associations with MRI-derived LDD scores and LDH affection status. The PGS of height, BMI, BMD and lipid levels were strongly associated with respective phenotypes in Chinese, but phenotype variances explained were lower than in Europeans which would reduce the power to detect genetic overlaps. Despite of this, the PGS of BMI and lumbar spine BMD were significantly associated with LDD scores; and the PGS of height was associated with the increased the liability of LDH. Furthermore, linkage disequilibrium score regression suggested that, osteoarthritis, another degenerative disorder that shares common features with LDD, also showed genetic correlations with height, BMI and BMD. The findings suggest a common key contribution of biomechanical stress to the pathogenesis of LDD and will direct the future search for pleiotropic genes.

Coverage and diagnostic yield of Whole Exome Sequencing for the Evaluation of Cases with Dilated and Hypertrophic Cardiomyopathy.

Targeted next generation sequencing of gene panels has become a popular tool for the genetic diagnosis of hypertrophic (HCM) and dilated cardiomyopathy (DCM). However, it is uncertain whether the use of Whole Exome Sequencing (WES) represents a more effective approach for diagnosis of cases with HCM and DCM. In this study, we performed indirect comparisons of the coverage and diagnostic yield of WES on genes and variants related to HCM and DCM versus 4 different commercial gene panels using 40 HCM and DCM patients, assuming perfect coverage in those panels. We identified 6 pathogenic or likely pathogenic among 14 HCM patients (diagnostic yield 43%). 3 pathogenic or likely pathogenic were found among the 26 DCM patients (diagnostic yield 12%). The coverage was similar to that of four existing commercial gene panels due to the clustering of mutation within MYH7, MYBPC3, TPM1, TNT2, and TTN. Moreover, the coverage of WES appeared inadequate for TNNI3 and PLN. We conclude that most of the pathogenic variants for HCM and DCM can be found within a small number of genes which were covered by all the commercial gene panels, and the application of WES did not increase diagnostic yield.

Polygenic scores via penalized regression on summary statistics.

Polygenic scores (PGS) summarize the genetic contribution of a person's genotype to a disease or phenotype. They can be used to group participants into different risk categories for diseases, and are also used as covariates in epidemiological analyses. A number of possible ways of calculating PGS have been proposed, and recently there is much interest in methods that incorporate information available in published summary statistics. As there is no inherent information on linkage disequilibrium (LD) in summary statistics, a pertinent question is how we can use LD information available elsewhere to supplement such analyses. To answer this question, we propose a method for constructing PGS using summary statistics and a reference panel in a penalized regression framework, which we call lassosum. We also propose a general method for choosing the value of the tuning parameter in the absence of validation data. In our simulations, we showed that pseudovalidation often resulted in prediction accuracy that is comparable to using a dataset with validation phenotype and was clearly superior to the conservative option of setting the tuning parameter of lassosum to its lowest value. We also showed that lassosum achieved better prediction accuracy than simple clumping and P-value thresholding in almost all scenarios. It was also substantially faster and more accurate than the recently proposed LDpred.

Rapid Identification of Pathogenic Variants in Two Cases of Charcot-Marie-Tooth Disease by Gene-Panel Sequencing.

Charcot-Marie-Tooth disease (CMT) is a common inherited peripheral neuropathy affecting up to 1 in 1214 of the general population with more than 60 nuclear genes implicated in its pathogenesis. Traditional molecular diagnostic pathways based on relative prevalence and clinical phenotyping are limited by long turnaround time, population-specific prevalence of causative variants and inability to assess multiple co-existing variants. In this study, a CMT gene panel comprising 27 genes was used to uncover the pathogenic mutations in two index patients. The first patient is a 15-year-old boy, born of consanguineous parents, who has had frequent trips and falls since infancy, and was later found to have inverted champagne bottle appearance of bilateral legs and foot drop. His elder sister is similarly affected. The second patient is a 37-year-old woman referred for pre-pregnancy genetic diagnosis. During early adulthood, she developed progressive lower limb weakness, difficulties in tip-toe walking and thinning of calf muscles. Both patients are clinically compatible with CMT, have undergone multiple genetic testings and have not previously received a definitive genetic diagnosis. Patients 1 and 2 were found to have pathogenic homozygous HSPB1:NM_001540:c.250G>A (p.G84R) variant and heterozygous GDAP1:NM_018972:c.358C>T (p.R120W) variant, respectively. Advantages and limitations of the current approach are discussed.

Local True Discovery Rate Weighted Polygenic Scores Using GWAS Summary Data.

A polygenic score is commonly derived using genome-wide genotype data to summarize the genetic contribution to a particular disease at the individual level. Usually it is constructed as a linear combination of SNP genotype weighted by the SNP-wise regression coefficient of the SNP to the phenotype using SNPs with p values smaller than a particular threshold. Commonly a range of thresholds are used which can pose problems with multiple comparisons as well as over-fitting. Here, an alternative weighting scheme is proposed, making use of the local true discovery rate, estimated from summary statistics. Two methods of estimation are proposed-maximum likelihood and kernel density estimation. Simulation studies using real and artificial data suggest this new weighting scheme is highly comparable with standard polygenic scores using the best possible p value threshold in prediction, even though this threshold is not normally known in practice.

Robust bayesian sensitivity analysis for case-control studies with uncertain exposure misclassification probabilities.

Exposure misclassification in case-control studies leads to bias in odds ratio estimates. There has been considerable interest recently to account for misclassification in estimation so as to adjust for bias as well as more accurately quantify uncertainty. These methods require users to elicit suitable values or prior distributions for the misclassification probabilities. In the event where exposure misclassification is highly uncertain, these methods are of limited use, because the resulting posterior uncertainty intervals tend to be too wide to be informative. Posterior inference also becomes very dependent on the subjectively elicited prior distribution. In this paper, we propose an alternative "robust Bayesian" approach, where instead of eliciting prior distributions for the misclassification probabilities, a feasible region is given. The extrema of posterior inference within the region are sought using an inequality constrained optimization algorithm. This method enables sensitivity analyses to be conducted in a useful way as we do not need to restrict all of our unknown parameters to fixed values, but can instead consider ranges of values at a time.