What is a functional locus? Understanding the genetic basis of complex phenotypic traits

Is atypical rhythm a risk factor for developmental speech and language disorders?

Although a growing literature points to substantial variation in speech/language abilities related to individual differences in musical abilities, mainstream models of communication sciences and disorders have not yet incorporated these individual differences into childhood speech/language development. This article reviews three sources of evidence in a comprehensive body of research aligning with three main themes: (a) associations between musical rhythm and speech/language processing, (b) musical rhythm in children with developmental speech/language disorders and common comorbid attentional and motor disorders, and (c) individual differences in mechanisms underlying rhythm processing in infants and their relationship with later speech/language development. In light of converging evidence on associations between musical rhythm and speech/language processing, we propose the Atypical Rhythm Risk Hypothesis, which posits that individuals with atypical rhythm are at higher risk for developmental speech/language disorders. The hypothesis is framed within the larger epidemiological literature in which recent methodological advances allow for large-scale testing of shared underlying biology across clinically distinct disorders. A series of predictions for future work testing the Atypical Rhythm Risk Hypothesis are outlined. We suggest that if a significant body of evidence is found to support this hypothesis, we can envision new risk factor models that incorporate atypical rhythm to predict the risk of developing speech/language disorders. Given the high prevalence of speech/language disorders in the population and the negative long-term social and economic consequences of gaps in identifying children at-risk, these new lines of research could potentially positively impact access to early identification and treatment. This article is categorized under: Linguistics > Language in Mind and Brain Neuroscience > Development Linguistics > Language Acquisition.

Strengthening Causal Inference for Complex Disease Using Molecular Quantitative Trait Loci

Large genome-wide association studies (GWAS) have identified loci that are associated with complex traits and diseases, but index variants are often not causal and reside in non-coding regions of the genome. To gain a better understanding of the relevant biological mechanisms, intermediate traits such as gene expression and protein levels are increasingly being investigated because these are likely mediators between genetic variants and disease outcome. Genetic variants associated with intermediate traits, termed molecular quantitative trait loci (molQTLs), can then be used as instrumental variables in a Mendelian randomization (MR) approach to identify the causal features and mechanisms of complex traits. Challenges such as pleiotropy and the non-specificity of molQTLs remain, and further approaches and methods need to be developed.

Identifying Multi-Omics Causers and Causal Pathways for Complex Traits

The central dogma of molecular biology delineates a unidirectional causal flow, i.e., DNA → RNA → protein → trait. Genome-wide association studies, next-generation sequencing association studies, and their meta-analyses have successfully identified ~12,000 susceptibility genetic variants that are associated with a broad array of human physiological traits. However, such conventional association studies ignore the mediate causers (i.e., RNA, protein) and the unidirectional causal pathway. Such studies may not be ideally powerful; and the genetic variants identified may not necessarily be genuine causal variants. In this article, we model the central dogma by a mediate causal model and analytically prove that the more remote an omics level is from a physiological trait, the smaller the magnitude of their correlation is. Under both random and extreme sampling schemes, we numerically demonstrate that the proteome-trait correlation test is more powerful than the transcriptome-trait correlation test, which in turn is more powerful than the genotype-trait association test. In conclusion, integrating RNA and protein expressions with DNA data and causal inference are necessary to gain a full understanding of how genetic causal variants contribute to phenotype variations.

Use of alternative promoters may hide genetic effects on phenotypic traits

Genome-wide association studies have identified a multitude of single-nucleotide polymorphisms (SNPs) associated with a wide spectrum of human phenotypic traits. However, the SNPs identified so far do not explain much of the expected genetic variation and they are poor predictors of the occurrence of disease. I recently advanced the hypothesis that there is person-to-person variation in the use of alternative regulatory elements (for example, gene promoters) and this new source of variation may explain in part the low genetic variation accounted for known genetic variants. In the present report a simple mathematical model is developed to explore the biological consequences of the proposed hypothesis. The model predicts that in presence of person-to-person variation in the use of alternative promoters the observable effects of genetic variants located inside promoters will be smaller than their actual effects. As a consequence, genetic variation because of those observed polymorphisms will be reduced. The present report suggests new paths of research to elucidate the genetic basis of human complex traits.