Performing post-genome-wide association study analysis: overview, challenges and recommendations

Genome-wide association studies (GWAS) provide huge information on statistically significant single-nucleotide polymorphisms (SNPs) associated with various human complex traits and diseases. By performing GWAS studies, scientists have successfully identified the association of hundreds of thousands to millions of SNPs to a single phenotype. Moreover, the association of some SNPs with rare diseases has been intensively tested. However, classic GWAS studies have not yet provided solid, knowledgeable insight into functional and biological mechanisms underlying phenotypes or mechanisms of diseases. Therefore, several post-GWAS (pGWAS) methods have been recommended. Currently, there is no simple scientific document to provide a quick guide for performing pGWAS analysis. pGWAS is a crucial step for a better understanding of the biological machinery beyond the SNPs. Here, we provide an overview to performing pGWAS analysis and demonstrate the challenges behind each method. Furthermore, we direct readers to key articles for each pGWAS method and present the overall issues in pGWAS analysis. Finally, we include a custom pGWAS pipeline to guide new users when performing their research.

Repository scale classification and decomposition of tandem mass spectral data

Various studies have shown associations between molecular features and phenotypes of biological samples. These studies, however, focus on a single phenotype per study and are not applicable to repository scale metabolomics data. Here we report MetSummarizer, a method for predicting (i) the biological phenotypes of environmental and host-oriented samples, and (ii) the raw ingredient composition of complex mixtures. We show that the aggregation of various metabolomic datasets can improve the accuracy of predictions. Since these datasets have been collected using different standards at various laboratories, in order to get unbiased results it is crucial to detect and discard standard-specific features during the classification step. We further report high accuracy in prediction of the raw ingredient composition of complex foods from the Global Foodomics Project.

Assessment of genetic variability in captive capuchin monkeys (Primates: Cebidae)

Capuchin monkeys (genera Cebus and Sapajus) show a wide range distribution, from Honduras to Argentina. The aim of this work was to evaluate the genetic and phenotypic variability of captive specimens putatively belonging to S. cay (SCY) and S. nigritus (SNI) at their southernmost distribution limit. Forty-four individuals held in five captive centers from Argentina were analyzed based on external morphology, karyology and DNA sequences of mitochondrial control region (mtDNA-CR). Three morphotypes associated with their probable geographical origin in SCY and a single morphotype in SNI were found. For SCY we could associate each morphotype with the most frequent karyotype. SNI showed a single phenotype and a homogenous karyotype. Heterochromatin showed geographical patterns within species. A 515-bp mtDNA-CR fragment was sequenced, defining fourteen haplotypes at 59 polymorphic sites. A network constructed with our 14 haplotypes and other 77 from S. apella, S. macrocephalus, S. cay and S. nigritus from bibliography revealed some phylogeographic signals. Our SCY and SNI samples rendered four groups that differed in multiple mutational steps, with SCY being more similar to S. apella than to S. macrocephalus. Also, we identified two genetic divergent SCY groups: samples from NOA and from NEA with high mitochondrial diversity. Our results highlight the relevance of using complementary genetic tools throughout the distribution ranges of SCY and SNI for a better assessment of their diversity.

Genome-wide pathway-based quantitative multiple phenotypes analysis

For complex diseases, genome-wide pathway association studies have become increasingly promising. Currently, however, pathway-based association analysis mainly focus on a single phenotype, which may insufficient to describe the complex diseases and physiological processes. This work proposes a combination model to evaluate the association between a pathway and multiple phenotypes and to reduce the run time based on asymptotic results. For a single phenotype, we propose a semi-supervised maximum kernel-based U-statistics (mSKU) method to assess the pathway-based association analysis. For multiple phenotypes, we propose the fisher combination function with dependent phenotypes (FC) to transform the p-values between the pathway and each marginal phenotype individually to achieve pathway-based multiple phenotypes analysis. With real data from the Alzheimer Disease Neuroimaging Initiative (ADNI) study and Human Liver Cohort (HLC) study, the FC-mSKU method allows us to specify which pathways are specific to a single phenotype or contribute to common genetic constructions of multiple phenotypes. If we only focus on single-phenotype tests, we may miss some findings for etiology studies. Through extensive simulation studies, the FC-mSKU method demonstrates its advantages compared with its counterparts.

A phenotype-specific framework for identifying the eye abnormalities causative nonsynonymous-variants

The most important role of variant pathogenicity predictors is to identify the disease-phenotype causative variant in studying monogenic diseases. In the last decade, machine-learning based predictors exhibited a relatively accurate performance for distinguishing the pathogenic variants and contributed a significant role for all disease-spectrums. Yet, few predictors can investigate the phenotypic significance of variants. Here we presented a phenotype-specific framework aimed to directly point out the phenotypic significance of predicted candidates, and showed its advancing performance in eye abnormalities. By training on eye-abnormalities causative variants, our method presented 96.2% accuracy, 96.1% precision, 93.4% recall for pathogenicity identification. Inconsistent with the modeling performance, identifying the single phenotype-causative variant from various sequencing variants is challenging for all predictors. Underlying the phenotype-oriented, our method significantly promoted the precision and reduced the cost for identifying the single causative variant from thousands of candidates. These advances highlight the significance of the phenotype-specific training method for studying disease.

Meta-MultiSKAT: Multiple phenotype meta-analysis for region-based association test

The power of genetic association analyses can be increased by jointly meta-analyzing multiple correlated phenotypes. Here, we develop a meta-analysis framework, Meta-MultiSKAT, that uses summary statistics to test for association between multiple continuous phenotypes and variants in a region of interest. Our approach models the heterogeneity of effects between studies through a kernel matrix and performs a variance component test for association. Using a genotype kernel, our approach can test for rare-variants and the combined effects of both common and rare-variants. To achieve robust power, within Meta-MultiSKAT, we developed fast and accurate omnibus tests combining different models of genetic effects, functional genomic annotations, multiple correlated phenotypes and heterogeneity across studies. Additionally, Meta-MultiSKAT accommodates situations where studies do not share exactly the same set of phenotypes or have differing correlation patterns among the phenotypes. Simulation studies confirm that Meta-MultiSKAT can maintain type-I error rate at exome-wide level of 2.5×10−6. Further simulations under different models of association show that Meta-MultiSKAT can improve power of detection from 23% to 38% on average over single phenotype-based meta-analysis approaches. We demonstrate the utility and improved power of Meta-MultiSKAT in the meta-analyses of four white blood cell subtype traits from the Michigan Genomics Initiative (MGI) and SardiNIA studies.

Predictable gene expression related to behavioral variation in parenting

Differential gene expression has been associated with transitions between behavioral states for a wide variety of organisms and behaviors. Heterochrony, genetic toolkits, and predictable pathways underlying behavioral transitions have been hypothesized to explain the relationship between transcription and behavioral changes. Less studied is how variation in transcription is related to variation within a behavior, and if the genes that are associated with this variation are predictable. Here, we adopt an evolutionary systems biology perspective to address 2 hypotheses relating differential expression to changes within and between behavior. We predicted fewer genes will be associated with variation within a behavior than with transitions between states, and the genes underlying variation within a behavior will represent a narrower set of biological functions. We tested for associations with parenting variation within a state with a set of genes known a priori to be differentially expressed (DE) between parenting states in the burying beetle Nicrophorus vespilloides. As predicted, we found that far fewer genes are DE related to variation within parenting. Moreover, these were not randomly distributed among categories or pathways in the gene set we tested and primarily involved genes associated with neurotransmission. We suggest that this means candidate genes will be easier to identify for associations within a behavior, as descriptions of behavioral state may include more than a single phenotype.

Quantitative developmental analysis of two phenotypes of Hibiscus rosa-sinensis in the context of homeosis

The flowers of Hibiscus rosa-sinensis L. (Malvaceae) exist in two floral morphologies: a single phenotype, and a double phenotype. This study focused on the early stages of floral development, just before the initiation of petal primordia and up until the bifurcation of the stamen primordia. The two phenotypes were compared using logistic regression and bootstrapping techniques. Four aspects of floral development were considered: (i) organogenesis of petal and stamen primordia, and stamen bifurcation; (ii) allometry of stamen primordia; (iii) morphology of stamen primordia; and (iv) size of stamen primordia. The single and double buds initiated petal primordia at the same bud radii, but double buds initiated stamen primordia and stamen bifurcation at larger bud radii than the single phenotype. Double stamen primordia were shorter, wider, and more spherical than single stamen primordia, although the sizes of the single and double stamen primordia (defined as the sum of their length and width measurements) were not different. Results suggest that the additional space on the floral meristem of the double phenotype is linked to the divergent development of stamen primordia occupying this extra space.

Environmental unpredictability and inbreeding depression select for mixed dispersal syndromes

Mixed dispersal syndromes have historically been regarded as bet-hedging mechanisms that enhance survival in unpredictable environments, ensuring that some propagules stay in the maternal environment while others can potentially colonize new sites. However, this entails paying the costs of both dispersal and non-dispersal. Propagules that disperse are likely to encounter unfavorable conditions for establishment, while non-dispersing propagules might form populations of close relatives burdened with inbreeding. Here, we investigate the conditions under which mixed dispersal syndromes emerge and are evolutionarily stable, taking into account the risks of both environmental unpredictability and inbreeding. Using mathematical and computational modeling we show that high dispersal propensity is favored whenever temporal environmental unpredictability is low and inbreeding depression high, whereas mixed dispersal syndromes are adaptive under conditions of high environmental unpredictability, but more particularly if also inbreeding depression is small. Although pure dispersers can be selected for under some circumstances, mixed dispersal provides the optimal strategy under most parameterizations of our models, indicating that this strategy is likely to be favored under a wide variety of conditions. Furthermore, populations exhibiting any single phenotype go inevitably extinct when environmental and genetic costs are high, whilst mixed strategies can maintain viable populations even under such conditions. Our models support the hypothesis that the interplay between inbreeding depression and environmental unpredictability shapes dispersal syndromes, often resulting in mixed strategies. Moreover, mixed dispersal seems to facilitate persistence whenever conditions are critical or nearly critical for survival.