Multiple testing and power calculations in genetic association studies

Recommendations for risk allele evidence curation, classification, and reporting from the ClinGen Low Penetrance/Risk Allele Working Group

PURPOSE

Genetic variants at the low end of the penetrance spectrum have historically been challenging to interpret because their high population frequencies exceed the disease prevalence of the associated condition, leading to a lack of clear segregation between the variant and disease. There is currently substantial variation in the classification of these variants, and no formal classification framework has been widely adopted. The Clinical Genome Resource Low Penetrance/Risk Allele Working Group was formed to address these challenges and promote harmonization within the clinical community.

METHODS

The work presented here is the product of internal and community Likert-scaled surveys in combination with expert consensus within the Working Group.

RESULTS

We formally recognize risk alleles and low-penetrance variants as distinct variant classes from those causing highly penetrant disease that require special considerations regarding their clinical classification and reporting. First, we provide a preferred terminology for these variants. Second, we focus on risk alleles and detail considerations for reviewing relevant studies and present a framework for the classification these variants. Finally, we discuss considerations for clinical reporting of risk alleles.

CONCLUSION

These recommendations support harmonized interpretation, classification, and reporting of variants at the low end of the penetrance spectrum.

The history and conceptual framework of assays and screens

Since the 16th century, assays and screens have been essential for scientific investigation. However, most methods could be significantly improved, especially in accuracy, scalability, and often lack adequate comparisons to negative controls. There is a lack of consistency in distinguishing assays, in which accuracy is the main goal, from screens, in which scalability is prioritized over accuracy. We dissected and modernized the original definitions of assays and screens based upon recent developments and the conceptual framework of the original definitions. All methods have three components: design/measurement, performance, and interpretation. We propose a model of method development in which reproducible observations become new methods, initially assessed by sensitivity. Further development can proceed along a path to either screens or assays. The screen path focuses on scalability first, but can later prioritize analysis of negatives. Alternatively, the assay path first compares results to negative controls, assessing specificity and accuracy, later adding scalability. Both pathways converge on a high-accuracy and throughput (HAT) assay, like next generation sequencing, which we suggest should be the ultimate goal of all testing methods. Our model will help scientists better select among available methods, as well as improve existing methods, expanding their impact on science.

Translating GWAS findings into therapies for depression and anxiety disorders: gene-set analyses reveal enrichment of psychiatric drug classes and implications for drug repositioning

BACKGROUND

Depression and anxiety disorders (AD) are the first and sixth leading causes of disability worldwide. Despite their high prevalence and significant disability resulted, there are limited advances in new drug development. Recently, genome-wide association studies (GWAS) have greatly advanced our understanding of the genetic basis underlying psychiatric disorders.

METHODS

Here we employed gene-set analyses of GWAS summary statistics for drug repositioning. We explored five related GWAS datasets, including two on major depressive disorder (MDD2018 and MDD-CONVERGE, with the latter focusing on severe melancholic depression), one on AD, and two on depressive symptoms and neuroticism in the population. We extracted gene-sets associated with each drug from DSigDB and examined their association with each GWAS phenotype. We also performed repositioning analyses on meta-analyzed GWAS data, integrating evidence from all related phenotypes.

RESULTS

Importantly, we showed that the repositioning hits are generally enriched for known psychiatric medications or those considered in clinical trials. Enrichment was seen for antidepressants and anxiolytics but also for antipsychotics. We also revealed new candidates or drug classes for repositioning, some of which were supported by experimental or clinical studies. For example, the top repositioning hit using meta-analyzed p values was fendiline, which was shown to produce antidepressant-like effects in mouse models by inhibition of acid sphingomyelinase.

CONCLUSION

Taken together, our findings suggest that human genomic data such as GWAS are useful in guiding drug discoveries for depression and AD.

Benzo[a]pyrene is associated with dysregulated myelo-lymphoid hematopoiesis in asthmatic children

BACKGROUND

The extent to which ambient benzo[a]pyrene (B[a]P) contributes to mechanistically distinct de novo asthma remains unknown.

OBJECTIVES

To identify molecular signatures and regulatory networks underlying childhood exposure to ambient B[a]P and asthma, using robust and unbiased systems biology approaches.

METHODS

Clinically confirmed asthmatic (n = 191) vs. control (n = 194) children (aged, 7-15) were enrolled from a polluted urban center and semi-rural region in Czech Republic. Contemporaneous B[a]P concentration, gene expressions, DNA methylation data were analyzed against asthma diagnosis, as well as a modified prognostic index of asthma, using integrative multiscale co-expression network analysis. Sample-wise cell type compositions were inferred by a machine learning approach (i.e. CIBERSORT) with reference gene expressions of purified 38 distinct hematopoietic cell states from umbilical cord (i.e. stem cell/progenitors) or peripheral blood (i.e. lymphocytes).

RESULTS

The median outdoor B[a]P was increased near the homes of the urban children with 'moderate' or 'severe' prognostic markers of asthma, but not in the urban controls. An elevated B[a]P induced epigenetic suppression of NF-κB inflammation, decreased Natural Killer T (NKT) cells and activated anti-inflammatory IL10-secreting CD8+ T effective memory cells. B[a]P was positively correlated with an increased expression of a heme biosynthesis gene, ALAS2, which in turn, appears to promote concurrent increase of neutrophilic metamyelocyte and mature CD71^low erythroid cells. Furthermore, erythroid-specific master transcription regulator gene (GATA1), glutathione transferase genes (GSTM1 and GSTM3) and Eosinophil marker (IL5RA) were simultaneously activated in the urban asthma cases.

CONCLUSIONS

B[a]P might contribute to concurrent suppression of pro-inflammatory (e.g. NF-κB mediated NKT cells), and activation of anti-inflammatory pathways (e.g. IL10-secreting CD8+ T cells) in the urban asthmatic children. In addition, B[a]P appears to elevate heme biosynthesis, which in turn, promotes neutrophilic metamyelocyte expansion and reduction of CD71⁺ erythroids.

Polymorphisms of Helicobacter pylori signaling pathway genes and gastric cancer risk in the European Prospective Investigation into Cancer-Eurgast cohort

Helicobacter pylori is a recognized causal factor of noncardia gastric cancer (GC). Lipopolysaccharide and peptidoglycan of this bacterium are recognized by CD14, TLR4 and NOD2 human proteins, while NFKB1 activates the transcription of pro-inflammatory cytokines to elicit an immune response. Single nucleotide polymorphisms (SNPs) in these genes have been associated with GC in different populations. We genotyped 30 SNPs of these genes, in 365 gastric adenocarcinomas and 1,284 matched controls from the European Prospective Investigation into Cancer cohort. The association with GC and its histological and anatomical subtypes was analyzed by logistic regression and corrected for multiple comparisons. Using a log-additive model, we found a significant association between SNPs in CD14, NOD2 and TLR4 with GC risk. However, after applying the multiple comparisons tests only the NOD2 region remained significant (p = 0.009). Analysis according to anatomical subtypes revealed NOD2 and NFKB1 SNPs associated with noncardia GC and CD14 SNPs associated with cardia GC, while analysis according to histological subtypes showed that CD14 was associated with intestinal but not diffuse GC. The multiple comparisons tests confirmed the association of NOD2 with noncardia GC (p = 0.0003) and CD14 with cardia GC (p = 0.01). Haplotype analysis was in agreement with single SNP results for NOD2 and CD14 genes. From these results, we conclude that genetic variation in NOD2 associates with noncardia GC while variation in CD14 is associated with cardia GC.

Detecting genetic association of common human facial morphological variation using high density 3D image registration

Human facial morphology is a combination of many complex traits. Little is known about the genetic basis of common facial morphological variation. Existing association studies have largely used simple landmark-distances as surrogates for the complex morphological phenotypes of the face. However, this can result in decreased statistical power and unclear inference of shape changes. In this study, we applied a new image registration approach that automatically identified the salient landmarks and aligned the sample faces using high density pixel points. Based on this high density registration, three different phenotype data schemes were used to test the association between the common facial morphological variation and 10 candidate SNPs, and their performances were compared. The first scheme used traditional landmark-distances; the second relied on the geometric analysis of 15 landmarks and the third used geometric analysis of a dense registration of ∼30,000 3D points. We found that the two geometric approaches were highly consistent in their detection of morphological changes. The geometric method using dense registration further demonstrated superiority in the fine inference of shape changes and 3D face modeling. Several candidate SNPs showed potential associations with different facial features. In particular, one SNP, a known risk factor of non-syndromic cleft lips/palates, rs642961 in the IRF6 gene, was validated to strongly predict normal lip shape variation in female Han Chinese. This study further demonstrated that dense face registration may substantially improve the detection and characterization of genetic association in common facial variation.

Heritability of human facial appearance is an intriguing question to the general public and researchers. Although it is known that some facial features are highly heritable, the exact genetic basis is unknown. Previous studies used simple linear measurements such as landmark distances, to evaluate the facial shape variation. Such approaches, although easy to carry out, may lack statistical power and miss complex morphological changes. In this study, we utilized a new 3D face registration method that enables subtle differences to be detected at high resolution 3D images. Based on this, we tried to test and characterize the associations of 10 candidate genetic variants to common facial morphological variations. Different types of phenotype data were extracted and compared in the association tests. Our results show that geometry based data performed better than simple distance based data. Furthermore, high density geometric data outstood the others in capturing small shape changes and modeling the 3D face visualization. Interestingly, a genetic variant from IRF6 gene, which is also a well-known risk factor of non-syndrome cleft lip, was found to strongly predispose the mouth shape in Han Chinese females.

Special considerations in prognostic research in cancer involving genetic polymorphisms

Analysis of genetic polymorphisms may help identify putative prognostic markers and determine the biological basis of variable prognosis in patients. However, in contrast to other variables commonly used in the prognostic studies, there are special considerations when studying genetic polymorphisms. For example, variable inheritance patterns (recessive, dominant, codominant, and additive genetic models) need to be explored to identify the specific genotypes associated with the outcome. In addition, several characteristics of genetic polymorphisms, such as their minor allele frequency and linkage disequilibrium among multiple polymorphisms, and the population substructure of the cohort investigated need to be accounted for in the analyses. In addition, in cancer research due to the genomic differences between the tumor and non-tumor DNA, differences in the genetic information obtained using these tissues need to be carefully assessed in prognostic studies. In this article, we review these and other considerations specific to genetic polymorphism by focusing on genetic prognostic studies in cancer.

TMA Navigator: Network inference, patient stratification and survival analysis with tissue microarray data

Tissue microarrays (TMAs) allow multiplexed analysis of tissue samples and are frequently used to estimate biomarker protein expression in tumour biopsies. TMA Navigator (www.tmanavigator.org) is an open access web application for analysis of TMA data and related information, accommodating categorical, semi-continuous and continuous expression scores. Non-biological variation, or batch effects, can hinder data analysis and may be mitigated using the ComBat algorithm, which is incorporated with enhancements for automated application to TMA data. Unsupervised grouping of samples (patients) is provided according to Gaussian mixture modelling of marker scores, with cardinality selected by Bayesian information criterion regularization. Kaplan–Meier survival analysis is available, including comparison of groups identified by mixture modelling using the Mantel-Cox log-rank test. TMA Navigator also supports network inference approaches useful for TMA datasets, which often constitute comparatively few markers. Tissue and cell-type specific networks derived from TMA expression data offer insights into the molecular logic underlying pathophenotypes, towards more effective and personalized medicine. Output is interactive, and results may be exported for use with external programs. Private anonymous access is available, and user accounts may be generated for easier data management.

"Statistics 101"--a primer for the genetics of complex human disease

This article reviews the basis of probability and statistics used in the genetic analysis of complex human diseases and illustrates their use in several simple examples. Much of the material presented here is so fundamental to statistics that it has become common knowledge in the field and the originators are no longer cited (e.g., Gauss).