Uncovering the Genetic Architectures of Quantitative Traits

Causal inference and GWAS: Rubin, Pearl, and Mendelian randomization

Although Genome Wide Analysis (GWAS) have been widely used to understand the genetic architecture of complex quantitative traits, interpreting their results in terms of the biological processes that determine those traits has been difficult or even lacking, because of the variability in responses to the tests of hypotheses within a trait, species, and breed or cross, and the lack of follow-up studies. It is then essential employing appropriate statistical tests that point out to the causal genes responsible of the relevant fraction of the genetic variability observed. We briefly review the main theoretical aspects of the two schools of causal inference (Rubin's Causal Model, RCM, and Pearl's causal inference, PCI). RCM approachs the hypothesis testing from a randomization perspective by considering a wider space of the observation, i.e. the "potential outcomes", rather than the narrower space that results from defining "treatment" effects after observing the data. Next, we discuss the assumptions involved to meet the requirements of randomization for RCM with observational data (non-designed experiments) with special emphasis on the Stable Unit Treatment Analysis (SUTVA). Due to the presence of "confounders" (i.e. systematic fixed effects, environmental permanent effects, interaction among genes, etc.), causal average treatment effects are viewed through the familiar lens of normal linear (or mixed) models. To overcome the difficulties of association analyses, a tests of causal effects is introduced using independent predicted residual breeding values from animal models of genetic evaluation that avoids the effects of population structure and confounder effects. An independent section discusses the issue of whether the additive effects defined at the "gene" level by R. A. Fisher and popularized in D. S. Falconer's textbook of quantitative genetics can be termed causal from either RCM or PCI.

The genetics-morphology-behavior trifecta: Unraveling the single greatest limitation affecting our understanding of chondrichthyan evolution

Sharks, rays, and chimaera form the clade Chondrichthyes, an ancient group of morphologically and ecologically diverse vertebrates that has played an important role in our understanding of gnathostome evolution. Increasingly, studies seek to investigate evolutionary processes operating within the chondrichthyan crown group, with the broad aim of understanding the driving forces behind the vast phenotypic diversity observed among its constituent taxa. Genetic, morphological, and behavioral studies have all contributed to our understanding of phenotypic evolution yet are typically considered in isolation in the context of Chondrichthyes. In this viewpoint, I discuss why such isolation is prevalent in the literature, how it constrains our understanding of evolution, and how it might be overcome. I argue that integrating these core fields of organismal biology is vital if we are to understand the evolutionary processes operating in contemporary chondrichthyan taxa and how such processes have contributed to past phenotypic evolution. Despite this, the necessary tools to overcome this major limitation already exist and have been applied to other taxa.

The association between saving disposition and financial distress: A genetically informed approach

Saving disposition, the tendency to save rather than consume, has been found to be associated with economic outcomes. People lacking the disposition to save are more likely to experience financial distress. This association could be driven by other economic factors, behavioral traits, or even genetic effects. Using a sample of 3,920 American twins, we develop scales to measure saving disposition and financial distress. We find genetic influences on both traits, but also a large effect of the rearing family environment on saving disposition. We estimate that 44% of the covariance between the two traits is due to genetic effects. Saving disposition remains strongly associated with lower financial distress, even after controlling for family income, cognitive ability, and personality traits. The association persists within families and monozygotic twin pairs; the twin who saves more tends to be the twin who experiences less financial distress. This result suggest that there is a direct association between saving disposition and financial distress, although the direction of causation remains unclear.

From Genotype to Phenotype: Polygenic Prediction of Complex Human Traits

Decoding the genome confers the capability to predict characteristics of the organism (phenotype) from DNA (genotype). We describe the present status and future prospects of genomic prediction of complex traits in humans. Some highly heritable complex phenotypes such as height and other quantitative traits can already be predicted with reasonable accuracy from DNA alone. For many diseases, including important common conditions such as coronary artery disease, breast cancer, type I and II diabetes, individuals with outlier polygenic scores (e.g., top few percent) have been shown to have 5 or even 10 times higher risk than average. Several psychiatric conditions such as schizophrenia and autism also fall into this category. We discuss related topics such as the genetic architecture of complex traits, sibling validation of polygenic scores, and applications to adult health, in vitro fertilization (embryo selection), and genetic engineering.

New insights into genetics underlying of plumage color

Plumage color can be considered as a social signal in chickens and a breeding identification tool among breeders. The relationship between plumage color and trait groups of immunity, growth and fertility is still a controversial issue. This research aimed to determine the genome-wide additive and epistatic variants affecting plumage color variation in chickens using the chicken Illumina 60k high-density SNP array. Two scenarios of genome-wide additive association studies using all SNPs and independent SNPs were carried out. To perform epistatic association analysis, the LD pruning approach was used to reduce the complexity of the analysis. We detected seven novel significant loci using all of the SNPs in the model and 14 SNPs using the LD pruning approach associated with plumage color. Moreover, 89 significantly associated SNP-SNP interactions (P-value <10^-6 ) distributed in 25 chromosomes were identified, indicating that all of the signals together putatively influence the quantitative variation of plumage color. By annotating genes relevant to top SNPs, we have distinguished 18 potential candidate genes comprising HNF4beta, CKMT1B, TBC1D22A, RPL8, CACNA2D1, FZD4, SGMS1, IRF8, OPTN, LOC420362, TRABD, OvoDA1, DAD1, USP6, RBM12B, MIR1772, MIR1709 and MIR6696 and also 89 putative gene-gene combinations responsible for plumage color variation in chickens. Furthermore, several KEGG pathways including metabolic pathway, cytokine-cytokine receptor interaction, focal adhesion, melanogenesis, glycosaminoglycan biosynthesis-keratan sulfate and sphingolipid metabolism were enriched in the gene-set analysis. The results indicated that plumage color is a highly polygenic trait which, in turn, can be affected by multiple coding genes, regulatory genes and gene-gene epistasis interactions. In addition to genes with additive effects, epistatic genes with tiny individual effect sizes but significant effects in a pair have the potential to control plumage coloration in chickens.

The causal influence of brain size on human intelligence: Evidence from within-family phenotypic associations and GWAS modeling

There exists a moderate correlation between MRI-measured brain size and the general factor of IQ performance (g), but the question of whether the association reflects a theoretically important causal relationship or spurious confounding remains somewhat open. Previous small studies (n < 100) looking for the persistence of this correlation within families failed to find a tendency for the sibling with the larger brain to obtain a higher test score. We studied the within-family relationship between brain volume and intelligence in the much larger sample provided by the Human Connectome Project (n = 1,022) and found a highly significant correlation (disattenuated ρ = 0.18, p < .001). We replicated this result in the Minnesota Center for Twin and Family Research (n = 2,698), finding a highly significant within-family correlation between head circumference and intelligence (disattenuated ρ = 0.19, p < .001). We also employed novel methods of causal inference relying on summary statistics from genome-wide association studies (GWAS) of head size (n ≈ 10,000) and measures of cognition (257,000 < n < 767,000). Using bivariate LD Score regression, we found a genetic correlation between intracranial volume (ICV) and years of education (EduYears) of 0.41 (p < .001). Using the Latent Causal Variable method, we found a genetic causality proportion of 0.72 (p < .001); thus the genetic correlation arises from an asymmetric pattern, extending to sub-significant loci, of genetic variants associated with ICV also being associated with EduYears but many genetic variants associated with EduYears not being associated with ICV. This is the pattern of genetic results expected from a causal effect of brain size on intelligence. These findings give reason to take up the hypothesis that the dramatic increase in brain volume over the course of human evolution has been the result of natural selection favoring general intelligence.

Simple PDMS microdevice for biomedical applications

Polydimethylsiloxane (PDMS) is a well-known biocompatible polymer employed for many applications in the biomedical field. In this study, the biocompatibility and versatility of PDMS was tested setting up a microdevice devoted to the purification and analysis of nucleic acids. The PDMS microdevice was demonstrated to successfully fulfill all requirements of genetic analyses such as genotyping and pathogen DNA identification both in multiplex and real-time PCR, suggesting the possibility to carry out a molecular test directly on-chip. Moreover, the PDMS microdevice was successfully applied to the purification and detection of disease biomarkers, such as microRNAs related to cancer or heart disease. On-chip microRNA purification was demonstrated starting from clinically relevant samples, i.e. plasma, serum, tissue biopsies. Significantly, the purification and the transcription of microRNA into cDNA occur in the same PDMS chamber, saving time and labor for the overall analysis. Again, the PDMS microdevice was confirmed as a notable candidate for compact, rapid, easy-to-use molecular tests.

The accuracy of LD Score regression as an estimator of confounding and genetic correlations in genome-wide association studies

To infer that a single-nucleotide polymorphism (SNP) either affects a phenotype or is linkage disequilibrium with a causal site, we must have some assurance that any SNP-phenotype correlation is not the result of confounding with environmental variables that also affect the trait. In this study, we study the properties of linkage disequilibrium (LD) Score regression, a recently developed method for using summary statistics from genome-wide association studies to ensure that confounding does not inflate the number of false positives. We do not treat the effects of genetic variation as a random variable and thus are able to obtain results about the unbiasedness of this method. We demonstrate that LD Score regression can produce estimates of confounding at null SNPs that are unbiased or conservative under fairly general conditions. This robustness holds in the case of the parent genotype affecting the offspring phenotype through some environmental mechanism, despite the resulting correlation over SNPs between LD Scores and the degree of confounding. Additionally, we demonstrate that LD Score regression can produce reasonably robust estimates of the genetic correlation, even when its estimates of the genetic covariance and the two univariate heritabilities are substantially biased.

Genomic data can illuminate the architecture and evolution of cognitive abilities

Does general intelligence exist across species, and has it been a target of natural selection? These questions can be addressed with genomic data, which can rule out artifacts by demonstrating that distinct cognitive abilities are genetically correlated and thus share a biological substrate. This work has begun with data from humans and can be extended to other species; it should focus not only on general intelligence but also specific capacities like language and spatial ability.

Using a multivariate model to assess the interactive effects of demographics and lifestyle on the hematological profile

AIM

To assess the extent to which a multivariate approach to modeling interrelated hematological indices provides more informative results than the traditional approach of modeling each index separately.

MATERIALS & METHODS

The effects of demographics and lifestyle on ten hematological indices collected from a Dutch population-based sample (n = 3278) were studied, jointly using multivariate distance matrix regression and separately using linear regression.

RESULTS

The multivariate approach highlighted the main effects of all predictors and several interactions; the traditional approach highlighted only main effects.

CONCLUSION

The multivariate approach provides more power than traditional methods to detect effects on interrelated biomarkers, suggesting that its use in future research may help identify subgroups that benefit from different treatment or prevention measures.

Endophenotype best practices

This review examines the current state of electrophysiological endophenotype research and recommends best practices that are based on knowledge gleaned from the last decade of molecular genetic research with complex traits. Endophenotype research is being oversold for its potential to help discover psychopathology relevant genes using the types of small samples feasible for electrophysiological research. This is largely because the genetic architecture of endophenotypes appears to be very much like that of behavioral traits and disorders: they are complex, influenced by many variants (e.g., tens of thousands) within many genes, each contributing a very small effect. Out of over 40 electrophysiological endophenotypes covered by our review, only resting heart, a measure that has received scant advocacy as an endophenotype, emerges as an electrophysiological variable with verified associations with molecular genetic variants. To move the field forward, investigations designed to discover novel variants associated with endophenotypes will need extremely large samples best obtained by forming consortia and sharing data obtained from genome wide arrays. In addition, endophenotype research can benefit from successful molecular genetic studies of psychopathology by examining the degree to which these verified psychopathology-relevant variants are also associated with an endophenotype, and by using knowledge about the functional significance of these variants to generate new endophenotypes. Even without molecular genetic associations, endophenotypes still have value in studying the development of disorders in unaffected individuals at high genetic risk, constructing animal models, and gaining insight into neural mechanisms that are relevant to clinical disorder.