Profiling allele-specific gene expression in brains from individuals with autism spectrum disorder reveals preferential minor allele usage.

One fundamental but understudied mechanism of gene regulation in disease is allele-specific expression (ASE), the preferential expression of one allele. We leveraged RNA-sequencing data from human brain to assess ASE in autism spectrum disorder (ASD). When ASE is observed in ASD, the allele with lower population frequency (minor allele) is preferentially more highly expressed than the major allele, opposite to the canonical pattern. Importantly, genes showing ASE in ASD are enriched in those downregulated in ASD postmortem brains and in genes harboring de novo mutations in ASD. Two regions, 14q32 and 15q11, containing all known orphan C/D box small nucleolar RNAs (snoRNAs), are particularly enriched in shifts to higher minor allele expression. We demonstrate that this allele shifting enhances snoRNA-targeted splicing changes in ASD-related target genes in idiopathic ASD and 15q11-q13 duplication syndrome. Together, these results implicate allelic imbalance and dysregulation of orphan C/D box snoRNAs in ASD pathogenesis

Bayesian inference of natural selection from allele frequency time series

The advent of accessible ancient DNA technology now allows the direct ascertainment of allele frequencies in ancestral populations, thereby enabling the use of allele frequency time series to detect and estimate natural selection. Such direct observations of allele frequency dynamics are expected to be more powerful than inferences made using patterns of linked neutral variation obtained from modern individuals. We develop a Bayesian method to make use of allele frequency time series data and infer the parameters of general diploid selection, along with allele age, in non-equilibrium populations. We introduce a novel path augmentation approach, in which we use Markov chain Monte Carlo to integrate over the space of allele frequency trajectories consistent with the observed data. Using simulations, we show that this approach has good power to estimate selection coefficients and allele age. Moreover, when applying our approach to data on horse coat color, we find that ignoring a relevant demographic history can significantly bias the results of inference. Our approach is made available in a C++ software package.Comment: 27 page

Robust identification of local adaptation from allele frequencies

Comparing allele frequencies among populations that differ in environment has long been a tool for detecting loci involved in local adaptation. However, such analyses are complicated by an imperfect knowledge of population allele frequencies and neutral correlations of allele frequencies among populations due to shared population history and gene flow. Here we develop a set of methods to robustly test for unusual allele frequency patterns, and correlations between environmental variables and allele frequencies while accounting for these complications based on a Bayesian model previously implemented in the software Bayenv. Using this model, we calculate a set of `standardized allele frequencies' that allows investigators to apply tests of their choice to multiple populations, while accounting for sampling and covariance due to population history. We illustrate this first by showing that these standardized frequencies can be used to calculate powerful tests to detect non-parametric correlations with environmental variables, which are also less prone to spurious results due to outlier populations. We then demonstrate how these standardized allele frequencies can be used to construct a test to detect SNPs that deviate strongly from neutral population structure. This test is conceptually related to FST but should be more powerful as we account for population history. We also extend the model to next-generation sequencing of population pools, which is a cost-efficient way to estimate population allele frequencies, but it implies an additional level of sampling noise. The utility of these methods is demonstrated in simulations and by re-analyzing human SNP data from the HGDP populations. An implementation of our method will be available from http://gcbias.org.Comment: 27 pages, 7 figure

Assortative human pair-bonding for partner ancestry and allelic variation of the dopamine receptor D4 (DRD4) gene

The 7R allele of the dopamine receptor D4 gene has been associated with attention-deficit hyperactivity disorder and risk taking. On the cross-population scale, 7R allele frequencies have been shown to be higher in populations with more of a history of long-term migrations. It has also been shown that the 7R allele is associated with individuals having multiple-ancestries. Here we conduct a replication of this latter finding with two independent samples. Measures of subjects’ ancestry are used to examine past reproductive bonds. The individuals’ history of inter-racial/ancestral dating and their feelings about this are also assessed. Tentative support for an association between multiple ancestries and the 7R allele were found. These results are dependent upon the method of questioning subjects about their ancestries. Inter-racial dating and feelings about inter-racial pairing were not related to the presence of the 7R allele. This might be accounted for by secular trends that might have substantively altered the decision-making process employed when considering relationships with individuals from different groups. This study provides continued support for the 7R allele playing a role in migration and/or mate choice patterns. However, replications and extensions of this study are needed and must carefully consider how ancestry/race is assessed

Allele mining strategies: principles and utilisation for blast resistance genes in rice (Oryza sativa L.)

Allele mining is a promising way to dissect naturally occurring allelic variants of candidate genes with essential agronomic qualities. With the identification, isolation and characterisation of blast resistance genes in rice, it is now possible to dissect the actual allelic variants of these genes within an array of rice cultivars via allele mining. Multiple alleles from the complex locus serve as a reservoir of variation to generate functional genes. The routine sequence exchange is one of the main mechanisms of R gene evolution and development. Allele mining for resistance genes can be an important method to identify additional resistance alleles and new haplotypes along with the development of allele-specific markers for use in marker-assisted selection. Allele mining can be visualised as a vital link between effective utilisation of genetic and genomic resources in genomics-driven modern plant breeding. This review studies the actual concepts and potential of mining approaches for the discovery of alleles and their utilisation for blast resistance genes in rice. The details provided here will be important to provide the rice breeder with a worthwhile introduction to allele mining and its methodology for breakthrough discovery of fresh alleles hidden in hereditary diversity, which is vital for crop improvement

Invasive Allele Spread under Preemptive Competition

We study a discrete spatial model for invasive allele spread in which two alleles compete preemptively, initially only the "residents" (weaker competitors) being present. We find that the spread of the advantageous mutation is well described by homogeneous nucleation; in particular, in large systems the time-dependent global density of the resident allele is well approximated by Avrami's law.Comment: Computer Simulation Studies in Condensed Matter Physics XVIII, edited by D.P. Landau, S.P. Lewis, and H.-B. Schuttler, (Springer, Heidelberg, Berlin, in press