Power estimation and sample size determination for replication studies of genome-wide association studies

Tuning parameters for polygenic risk score methods using GWAS summary statistics from training data

Various polygenic risk scores (PRS) methods have been proposed to combine the estimated effects of single nucleotide polymorphisms (SNPs) to predict genetic risks for common diseases, using data collected from genome-wide association studies (GWAS). Some methods require external individual-level GWAS dataset for parameter tuning, posing privacy and security-related concerns. Leaving out partial data for parameter tuning can also reduce model prediction accuracy. In this article, we propose PRStuning, a method that tunes parameters for different PRS methods using GWAS summary statistics from the training data. PRStuning predicts the PRS performance with different parameters, and then selects the best-performing parameters. Because directly using training data effects tends to overestimate the performance in the testing data, we adopt an empirical Bayes approach to shrinking the predicted performance in accordance with the genetic architecture of the disease. Extensive simulations and real data applications demonstrate PRStuning's accuracy across PRS methods and parameters.

Ten simple rules for designing and conducting undergraduate replication projects

Conducting a replication study is a valuable way for undergraduate students to learn about the scientific process and gain research experience. By promoting the evaluation of existing studies to confirm their reliability, replications play a unique, though often underappreciated, role in the scientific enterprise. Involving students early in this process can help make replication mainstream among the new generation of scientists. Beyond their benefit to science, replications also provide an invaluable learning ground for students, from encouraging the development of critical thinking to emphasizing the importance of details and honing research skills. In this piece, we outline 10 simple rules for designing and conducting undergraduate replication projects, from conceptualization to implementation and dissemination. We hope that these guidelines can help educators provide students with a meaningful and constructive pedagogical experience, without compromising the scientific value of the replication project, therefore ensuring robust, valuable contributions to our understanding of the world.

Robustification of GWAS to explore effective SNPs addressing the challenges of hidden population stratification and polygenic effects

Genome-wide association studies (GWAS) play a vital role in identifying important genes those is associated with the phenotypic variations of living organisms. There are several statistical methods for GWAS including the linear mixed model (LMM) which is popular for addressing the challenges of hidden population stratification and polygenic effects. However, most of these methods including LMM are sensitive to phenotypic outliers that may lead the misleading results. To overcome this problem, in this paper, we proposed a way to robustify the LMM approach for reducing the influence of outlying observations using the β-divergence method. The performance of the proposed method was investigated using both synthetic and real data analysis. Simulation results showed that the proposed method performs better than both linear regression model (LRM) and LMM approaches in terms of powers and false discovery rates in presence of phenotypic outliers. On the other hand, the proposed method performed almost similar to LMM approach but much better than LRM approach in absence of outliers. In the case of real data analysis, our proposed method identified 11 SNPs that are significantly associated with the rice flowering time. Among the identified candidate SNPs, some were involved in seed development and flowering time pathways, and some were connected with flower and other developmental processes. These identified candidate SNPs could assist rice breeding programs effectively. Thus, our findings highlighted the importance of robust GWAS in identifying candidate genes.

GWAS of peptic ulcer disease implicates Helicobacter pylori infection, other gastrointestinal disorders and depression

Genetic factors are recognized to contribute to peptic ulcer disease (PUD) and other gastrointestinal diseases, such as gastro-oesophageal reflux disease (GORD), irritable bowel syndrome (IBS) and inflammatory bowel disease (IBD). Here, genome-wide association study (GWAS) analyses based on 456,327 UK Biobank (UKB) individuals identify 8 independent and significant loci for PUD at, or near, genes MUC1, MUC6, FUT2, PSCA, ABO, CDX2, GAST and CCKBR. There are previously established roles in susceptibility to Helicobacter pylori infection, response to counteract infection-related damage, gastric acid secretion or gastrointestinal motility for these genes. Only two associations have been previously reported for duodenal ulcer, here replicated trans-ancestrally. The results highlight the role of host genetic susceptibility to infection. Post-GWAS analyses for PUD, GORD, IBS and IBD add insights into relationships between these gastrointestinal diseases and their relationships with depression, a commonly comorbid disorder.

Leveraging genome-wide data to investigate differences between opioid use vs. opioid dependence in 41,176 individuals from the Psychiatric Genomics Consortium

To provide insights into the biology of opioid dependence (OD) and opioid use (i.e., exposure, OE), we completed a genome-wide analysis comparing 4503 OD cases, 4173 opioid-exposed controls, and 32,500 opioid-unexposed controls, including participants of European and African descent (EUR and AFR, respectively). Among the variants identified, rs9291211 was associated with OE (exposed vs. unexposed controls; EUR z = -5.39, p = 7.2 × 10-8). This variant regulates the transcriptomic profiles of SLC30A9 and BEND4 in multiple brain tissues and was previously associated with depression, alcohol consumption, and neuroticism. A phenome-wide scan of rs9291211 in the UK Biobank (N > 360,000) found association of this variant with propensity to use dietary supplements (p = 1.68 × 10-8). With respect to the same OE phenotype in the gene-based analysis, we identified SDCCAG8 (EUR + AFR z = 4.69, p = 10-6), which was previously associated with educational attainment, risk-taking behaviors, and schizophrenia. In addition, rs201123820 showed a genome-wide significant difference between OD cases and unexposed controls (AFR z = 5.55, p = 2.9 × 10-8) and a significant association with musculoskeletal disorders in the UK Biobank (p = 4.88 × 10-7). A polygenic risk score (PRS) based on a GWAS of risk-tolerance (n = 466,571) was positively associated with OD (OD vs. unexposed controls, p = 8.1 × 10-5; OD cases vs. exposed controls, p = 0.054) and OE (exposed vs. unexposed controls, p = 3.6 × 10-5). A PRS based on a GWAS of neuroticism (n = 390,278) was positively associated with OD (OD vs. unexposed controls, p = 3.2 × 10-5; OD vs. exposed controls, p = 0.002) but not with OE (p = 0.67). Our analyses highlight the difference between dependence and exposure and the importance of considering the definition of controls in studies of addiction.

Leveraging effect size distributions to improve polygenic risk scores derived from summary statistics of genome-wide association studies

Genetic risk prediction is an important problem in human genetics, and accurate prediction can facilitate disease prevention and treatment. Calculating polygenic risk score (PRS) has become widely used due to its simplicity and effectiveness, where only summary statistics from genome-wide association studies are needed in the standard method. Recently, several methods have been proposed to improve standard PRS by utilizing external information, such as linkage disequilibrium and functional annotations. In this paper, we introduce EB-PRS, a novel method that leverages information for effect sizes across all the markers to improve prediction accuracy. Compared to most existing genetic risk prediction methods, our method does not need to tune parameters nor external information. Real data applications on six diseases, including asthma, breast cancer, celiac disease, Crohn's disease, Parkinson's disease and type 2 diabetes show that EB-PRS achieved 307.1%, 42.8%, 25.5%, 3.1%, 74.3% and 49.6% relative improvements in terms of predictive r2 over standard PRS method with optimally tuned parameters. Besides, compared to LDpred that makes use of LD information, EB-PRS also achieved 37.9%, 33.6%, 8.6%, 36.2%, 40.6% and 10.8% relative improvements. We note that our method is not the first method leveraging effect size distributions. Here we first justify our method by presenting theoretical optimal property over existing methods in this class of methods, and substantiate our theoretical result with extensive simulation results. The R-package EBPRS that implements our method is available on CRAN.

Statistical power in genome-wide association studies and quantitative trait locus mapping

Power calculation prior to a genetic experiment can help investigators choose the optimal sample size to detect a quantitative trait locus (QTL). Without the guidance of power analysis, an experiment may be underpowered or overpowered. Either way will result in wasted resource. QTL mapping and genome-wide association studies (GWAS) are often conducted using a linear mixed model (LMM) with controls of population structure and polygenic background using markers of the whole genome. Power analysis for such a mixed model is often conducted via Monte Carlo simulations. In this study, we derived a non-centrality parameter for the Wald test statistic for association, which allows analytical power analysis. We show that large samples are not necessary to detect a biologically meaningful QTL, say explaining 5% of the phenotypic variance. Several R functions are provided so that users can perform power analysis to determine the minimum sample size required to detect a given QTL with a certain statistical power or calculate the statistical power with given sample size and known values of other population parameters.

Enrichment of B cell receptor signaling and epidermal growth factor receptor pathways in monoclonal gammopathy of undetermined significance: a genome-wide genetic interaction study

BACKGROUND

Recent identification of 10 germline variants predisposing to monoclonal gammopathy of undetermined significance (MGUS) explicates genetic dependency of this asymptomatic precursor condition with multiple myeloma (MM). Yet much of genetic burden as well as functional links remain unexplained. We propose a workflow to expand the search for susceptibility loci with genome-wide interaction and for subsequent identification of genetic clusters and pathways.

METHODS

Polygenic interaction analysis on 243 cases/1285 controls identified 14 paired risk loci belonging to unique chromosomal bands which were then replicated in two independent sets (case only study, 82 individuals; case/control study 236 cases/ 2484 controls). Further investigation on gene-set enrichment, regulatory pathway and genetic network was carried out with stand-alone in silico tools separately for both interaction and genome-wide association study-detected risk loci.

RESULTS

Intronic-PREX1 (20q13.13), a reported locus predisposing to MM was confirmed to have contribution to excess MGUS risk in interaction with SETBP1, a well-established candidate predisposing to myeloid malignancies. Pathway enrichment showed B cell receptor signaling pathway (P < 5.3 × 10- 3) downstream to allograft rejection pathway (P < 5.6 × 10- 4) and autoimmune thyroid disease pathway (P < 9.3 × 10- 4) as well as epidermal growth factor receptor regulation pathway (P < 2.4 × 10- 2) to be differentially regulated. Oncogene ALK and CDH2 were also identified to be moderately interacting with rs10251201 and rs16966921, two previously reported risk loci for MGUS.

CONCLUSIONS

We described novel pathways and variants potentially causal for MGUS. The methodology thus proposed to facilitate our search streamlines risk locus-based interaction, genetic network and pathway enrichment analyses.

Advancing cancer drug development through precision medicine and innovative designs

Precision medicine has been a hot topic in drug development over the last decade. Biomarkers have been proven useful for understanding the disease progression and treatment response in precision medicine development. Advancement of high-throughput omics technologies has enabled fast identification of molecular biomarkers with low cost. Although biomarkers have brought many promises to drug development, steep challenges arise due to a large amount of data, complexity of technology, and lack of full understanding of biology. In this article, we discuss the technologies and statistical issues that are related to omics biomarker discovery. We also provide an overview of the current development of biomarker-enabled cancer clinical trial designs.

Humanity in a Dish: Population Genetics with iPSCs

Induced pluripotent stem cells (iPSCs) are powerful tools for investigating the relationship between genotype and phenotype. Recent publications have described iPSC cohort studies of common genetic variants and their effects on gene expression and cellular phenotypes. These in vitro quantitative trait locus (QTL) studies are the first experiments in a new paradigm with great potential: iPSC-based functional population genetic studies. iPSC collections from large cohorts are currently under development to facilitate the next wave of these studies, which have the potential to discover the effects of common genetic variants on cellular phenotypes and to uncover the molecular basis of common genetic diseases. Here, we describe the recent advances in this developing field, and provide a road map for future in vitro functional population genetic studies and trial-in-a-dish experiments.

KCNK3 Variants Are Associated With Hyperaldosteronism and Hypertension

Blood pressure (BP) is a complex trait that is the consequence of an interaction between genetic and environmental determinants. Previous studies have demonstrated increased BP in mice with global deletion of TASK-1 channels contemporaneous with diverse dysregulation of aldosterone production. In humans, genome-wide association studies in ≈100 000 individuals of European, East Asian, and South Asian ancestry identified a single nucleotide polymorphism (SNP) in KCNK3 (the gene encoding TASK-1) associated with mean arterial pressure. The current study was motivated by the hypotheses that (1) association of KCNK3 SNPs with BP and related traits extends to blacks and Hispanics, and (2) KCNK3 SNPs exhibit associations with plasma renin activity and aldosterone levels. We examined baseline BP measurements for 7840 participants from the Multi-Ethnic Study of Atherosclerosis (MESA), and aldosterone levels and plasma renin activity in a subset of 1653 MESA participants. We identified statistically significant association of the previously reported KCNK3 SNP (rs1275988) with mean arterial pressure in MESA blacks (P=0.024) and a nearby SNP (rs13394970) in MESA Hispanics (P=0.031). We discovered additional KCNK3 SNP associations with systolic BP, mean arterial pressure, and hypertension. We also identified statistically significant association of KCNK3 rs2586886 with plasma aldosterone level in MESA and demonstrated that global deletion of TASK-1 channels in mice produces a mild-hyperaldosteronism, not associated with a decrease in renin. Our results suggest that genetic variation in the KCNK3 gene may contribute to BP variation and less severe hypertensive disorders in which aldosterone may be one of several causative factors.

[Leukocyte count of puerperal sows]

147 blood samples of postparturient sows of a secluded housing were taken. The samples were conserved with ACD-solution. The influence of the number and week of the lactation and the health of the sow, determined by puerperal diseases was studied. Hematological values of healthy postparturient sows are: leucocytes 12.6 +/- 2.2 G/l; basophile granulocytes 0.1 +/- 0.1 G/l, eosinophile granulocytes 0.5 +/- 0.4 G/l; banded neutrophile granulocytes 1.3 +/- 0.6 G/l, segmented neutrophile granulocytes 5.2 +/- 1.4 G/l; lymphocytes 5.5 +/- 1.4 G/l, monocytes 0.3 +/- 0.3 G/l. The leucocyte number is lower in the investigated herd compared with quotations in the literature. This is based on the good health conditions in the herd. Changes due to the number and week of the lactation have no clinical relevance. Health status, here described by puerperal diseases is the significant influencing factor of the leucocyte number. The severity of puerperal diseases is significant. Due to puerperal diseases the leucocyte number rises quickly after a short drop about 2 G/l. The number of the neutrophile granulocytes increases, but the lymphocyte number is reduced at the beginning of the illness. The application of ACD-solution for stabilizing of great amounts of blood samples under practical conditions is demonstrated. It is possible to stabilize pigs blood well.