Prospects for identifying functional variation across the genome

Gene as a dynamical notion: An extensive and integrative vision. Redefining the gene concept, from traditional to genic-interaction, as a new dynamical version

The current concept of gene has been very useful during the 20th and 21st centuries. However, recent advances in molecular biology and bioinformatics, which have further diversified the functional and adaptive profile of genetic information and its integration with cell physiology and environmental response, have contributed to focusing on additional new gene properties besides the traditional definition. Considering the inherent complexity of gene expression, whose adaptive objective must be referred to the Tortoise-Hare model, in which two tendencies converge, one focused on rapid adaptation to achieve survival, and the other that prevents an over-adaptation effect. In this context, a revision of the gene concept must be made, which must include these new mechanisms and approaches. In this paper, we propose a new conception of the idea of a gene that moves from a static and defined version of hereditary information to a dynamic idea that preponderates gene interaction (circumscribed to that established between protein-protein, protein-nucleic acid, and nucleic acid-nucleic acid) and the selection it exerts, as the irreducible element that works in a coordinated way in a genomic regulatory network (GRN).

The Neutral Theory in Light of Natural Selection

In this perspective, we evaluate the explanatory power of the neutral theory of molecular evolution, 50 years after its introduction by Kimura. We argue that the neutral theory was supported by unreliable theoretical and empirical evidence from the beginning, and that in light of modern, genome-scale data, we can firmly reject its universality. The ubiquity of adaptive variation both within and between species means that a more comprehensive theory of molecular evolution must be sought.

Gene regulation underlies environmental adaptation in house mice

Changes in cis-regulatory regions are thought to play a major role in the genetic basis of adaptation. However, few studies have linked cis-regulatory variation with adaptation in natural populations. Here, using a combination of exome and RNA-seq data, we performed expression quantitative trait locus (eQTL) mapping and allele-specific expression analyses to study the genetic architecture of regulatory variation in wild house mice (Mus musculus domesticus) using individuals from five populations collected along a latitudinal cline in eastern North America. Mice in this transect showed clinal patterns of variation in several traits, including body mass. Mice were larger in more northern latitudes, in accordance with Bergmann's rule. We identified 17 genes where cis-eQTLs were clinal outliers and for which expression level was correlated with latitude. Among these clinal outliers, we identified two genes (Adam17 and Bcat2) with cis-eQTLs that were associated with adaptive body mass variation and for which expression is correlated with body mass both within and between populations. Finally, we performed a weighted gene co-expression network analysis (WGCNA) to identify expression modules associated with measures of body size variation in these mice. These findings demonstrate the power of combining gene expression data with scans for selection to identify genes involved in adaptive phenotypic evolution, and also provide strong evidence for cis-regulatory elements as essential loci of environmental adaptation in natural populations.

Functional classification of 15 million SNPs detected from diverse chicken populations

Next-generation sequencing has prompted a surge of discovery of millions of genetic variants from vertebrate genomes. Besides applications in genetic association and linkage studies, a fraction of these variants will have functional consequences. This study describes detection and characterization of 15 million SNPs from chicken genome with the goal to predict variants with potential functional implications (pfVars) from both coding and non-coding regions. The study reports: 183K amino acid-altering SNPs of which 48% predicted as evolutionary intolerant, 13K splicing variants, 51K likely to alter RNA secondary structures, 500K within most conserved elements and 3K from non-coding RNAs. Regions of local fixation within commercial broiler and layer lines were investigated as potential selective sweeps using genome-wide SNP data. Relationships with phenotypes, if any, of the pfVars were explored by overlaying the sweep regions with known QTLs. Based on this, the candidate genes and/or causal mutations for a number of important traits are discussed. Although the fixed variants within sweep regions were enriched with non-coding SNPs, some non-synonymous-intolerant mutations reached fixation, suggesting their possible adaptive advantage. The results presented in this study are expected to have important implications for future genomic research to identify candidate causal mutations and in poultry breeding.

Rank-based genome-wide analysis reveals the association of ryanodine receptor-2 gene variants with childhood asthma among human populations

BACKGROUND

The standard approach to determine unique or shared genetic factors across populations is to identify risk alleles in one population and investigate replication in others. However, since populations differ in DNA sequence information, allele frequencies, effect sizes, and linkage disequilibrium patterns, SNP association using a uniform stringent threshold on p values may not be reproducible across populations. Here, we developed rank-based methods to investigate shared or population-specific loci and pathways for childhood asthma across individuals of diverse ancestry. We performed genome-wide association studies on 859,790 SNPs genotyped in 527 affected offspring trios of European, African, and Hispanic ancestry using publically available asthma database in the Genotypes and Phenotypes database.

RESULTS

Rank-based analyses showed that there are shared genetic factors for asthma across populations, more at the gene and pathway levels than at the SNP level. Although the top 1,000 SNPs were not shared, 11 genes (RYR2, PDE4D, CSMD1, CDH13, ROBO2, RBFOX1, PTPRD, NPAS3, PDE1C, SEMA5A, and CTNNA2) mapped by these SNPs were shared across populations. Ryanodine receptor 2 (RYR2, a statin response-related gene) showed the strongest association in European (p value=2.55×10(-7)) and was replicated in African (2.57×10(-4)) and Hispanic (1.18 × 10(-3)) Americans. Imputation analyses based on the 1000 Genomes Project uncovered additional RYR2 variants associated with asthma. Network and functional ontology analyses revealed that RYR2 is an integral part of dermatological or allergic disorder biological networks, specifically in the functional classes involving inflammatory, eosinophilic, and respiratory diseases.

CONCLUSION

Our rank-based genome-wide analysis revealed for the first time an association of RYR2 variants with asthma and replicated previously discovered PDE4D asthma gene across human populations. The replication of top-ranked asthma genes across populations suggests that such loci are less likely to be false positives and could indicate true associations. Variants that are associated with asthma across populations could be used to identify individuals who are at high risk for asthma regardless of genetic ancestry.

An entropy-based approach for testing genetic epistasis underlying complex diseases

The genetic basis of complex diseases is expected to be highly heterogeneous, with complex interactions among multiple disease loci and environment factors. Due to the multi-dimensional property of interactions among large number of genetic loci, efficient statistical approach has not been well developed to handle the high-order epistatic complexity. In this article, we introduce a new approach for testing genetic epistasis in multiple loci using an entropy-based statistic for a case-only design. The entropy-based statistic asymptotically follows a chi(2) distribution. Computer simulations show that the entropy-based approach has better control of type I error and higher power compared to the standard chi(2) test. Motivated by a schizophrenia data set, we propose a method for measuring and testing the relative entropy of a clinical phenotype, through which one can test the contribution or interaction of multiple disease loci to a clinical phenotype. A sequential forward selection procedure is proposed to construct a genetic interaction network which is illustrated through a tree-based diagram. The network information clearly shows the relative importance of a set of genetic loci on a clinical phenotype. To show the utility of the new entropy-based approach, it is applied to analyze two real data sets, a schizophrenia data set and a published malaria data set. Our approach provides a fast and testable framework for genetic epistasis study in a case-only design.

Joint estimates of quantitative trait locus effect and frequency using synthetic recombinant populations of Drosophila melanogaster

We develop and implement a strategy to map QTL in two synthetic populations of Drosophila melanogaster each initiated with eight inbred founder strains. These recombinant populations allow simultaneous estimates of QTL location, effect, and frequency. Five X-linked QTL influencing bristle number were resolved to intervals of approximately 1.3 cM. We confirm previous observations of bristle number QTL distal to 4A at the tip of the chromosome and identify two novel QTL in 7F-8C, an interval that does not include any classic bristle number candidate genes. If QTL at the tip of the X are biallelic they appear to be intermediate in frequency, although there is evidence that these QTL may reside in multiallelic haplotypes. Conversely, the two QTL mapping to the middle of the X chromosome are likely rare: in each case the minor allele is observed in only 1 of the 16 founders. Assuming additivity and biallelism we estimate that identified QTL contribute 1.0 and 8.7%, respectively, to total phenotypic variation in male abdominal and sternopleural bristle number in nature. Models that seek to explain the maintenance of genetic variation make different predictions about the population frequency of QTL alleles. Thus, mapping QTL in eight-way recombinant populations can distinguish between these models.

Detecting natural selection on cis-regulatory DNA

Changes in transcriptional regulation play an important role in the genetic basis for evolutionary change. Here I review a growing body of literature that seeks to determine the forces governing the non-coding regulatory sequences underlying these changes. I address the challenges present in studying natural selection without the familiar structure and regularity of protein-coding sequences, but show that most tests of neutrality that have been used for coding regions are applicable to non-coding regions, albeit with some caveats. While some experimental investment is necessary to identify heritable regulatory variation, the most basic inferences about selection require very little functional information. A growing body of research on cis-regulatory variation has uncovered all the forms of selection common to coding regions, in addition to novel forms of selection. An emerging pattern seems to be the ubiquity of local adaptation and balancing selection, possibly due to the greater freedom organisms have to fine-tune gene expression without changing protein function. It is clear from multiple single locus and whole genome studies of non-coding regulatory DNA that the effects of natural selection reach far beyond the start and stop codons.

A wing expressed sequence tag resource for Bicyclus anynana butterflies, an evo-devo model

BACKGROUND

Butterfly wing color patterns are a key model for integrating evolutionary developmental biology and the study of adaptive morphological evolution. Yet, despite the biological, economical and educational value of butterflies they are still relatively under-represented in terms of available genomic resources. Here, we describe an Expression Sequence Tag (EST) project for Bicyclus anynana that has identified the largest available collection to date of expressed genes for any butterfly.

RESULTS

By targeting cDNAs from developing wings at the stages when pattern is specified, we biased gene discovery towards genes potentially involved in pattern formation. Assembly of 9,903 ESTs from a subtracted library allowed us to identify 4,251 genes of which 2,461 were annotated based on BLAST analyses against relevant gene collections. Gene prediction software identified 2,202 peptides, of which 215 longer than 100 amino acids had no homology to any known proteins and, thus, potentially represent novel or highly diverged butterfly genes. We combined gene and Single Nucleotide Polymorphism (SNP) identification by constructing cDNA libraries from pools of outbred individuals, and by sequencing clones from the 3' end to maximize alignment depth. Alignments of multi-member contigs allowed us to identify over 14,000 putative SNPs, with 316 genes having at least one high confidence double-hit SNP. We furthermore identified 320 microsatellites in transcribed genes that can potentially be used as genetic markers.

CONCLUSION

Our project was designed to combine gene and sequence polymorphism discovery and has generated the largest gene collection available for any butterfly and many potential markers in expressed genes. These resources will be invaluable for exploring the potential of B. anynana in particular, and butterflies in general, as models in ecological, evolutionary, and developmental genetics.