Familial cortical myoclonic tremor with epilepsy is an autosomal dominant neurodegenerative disease, characterized by cortical tremor and epileptic seizures. Although four subtypes (types 1-4) mapped on different chromosomes (8q24, 2p11.1-q12.2, 5p15.31-p15.1 and 3q26.32-3q28) have been reported, the causative gene has not yet been identified. Here, we report the genetic study in a cohort of 20 Chinese pedigrees with familial cortical myoclonic tremor with epilepsy. Linkage and haplotype analysis in 11 pedigrees revealed maximum two-point logarithm of the odds (LOD) scores from 1.64 to 3.77 (LOD scores in five pedigrees were >3.0) in chromosomal region 8q24 and narrowed the candidate region to an interval of 4.9 Mb. Using whole-genome sequencing, long-range polymerase chain reaction and repeat-primed polymerase chain reaction, we identified an intronic pentanucleotide (TTTCA)n insertion in the SAMD12 gene as the cause, which co-segregated with the disease among the 11 pedigrees mapped on 8q24 and additional seven unmapped pedigrees. Only two pedigrees did not contain the (TTTCA)n insertion. Repeat-primed polymerase chain reaction revealed that the sizes of (TTTCA)n insertion in all affected members were larger than 105 repeats. The same pentanucleotide insertion (ATTTCATTTC)58 has been reported to form RNA foci resulting in neurotoxicity in spinocerebellar ataxia type 37, which suggests the similar pathogenic process in familial cortical myoclonic tremor with epilepsy type 1.

Recent advances in DNA sequencing techniques have made it possible to monitor genomes in great detail over time. This improvement provides an opportunity for us to study natural selection based on time serial samples of genomes while accounting for genetic recombination effect and local linkage information. Such time series genomic data allow for more accurate estimation of population genetic parameters and hypothesis testing on the recent action of natural selection. In this work, we develop a novel Bayesian statistical framework for inferring natural selection at a pair of linked loci by capitalising on the temporal aspect of DNA data with the additional flexibility of modelling the sampled chromosomes that contain unknown alleles. Our approach is built on a hidden Markov model where the underlying process is a two-locus Wright-Fisher diffusion with selection, which enables us to explicitly model genetic recombination and local linkage. The posterior probability distribution for selection coefficients is computed by applying the particle marginal Metropolis-Hastings algorithm, which allows us to efficiently calculate the likelihood. We evaluate the performance of our Bayesian inference procedure through extensive simulations, showing that our approach can deliver accurate estimates of selection coefficients, and the addition of genetic recombination and local linkage brings about significant improvement in the inference of natural selection. We also illustrate the utility of our method on real data with an application to ancient DNA data associated with white spotting patterns in horses.

Novel technologies for recovering DNA information from archaeological and historical specimens have made available an ever-increasing amount of temporally spaced genetic samples from natural populations. These genetic time series permit the direct assessment of patterns of temporal changes in allele frequencies, and hold the promise of improving power for the inference of selection. Increased time resolution can further facilitate testing hypotheses regarding the drivers of past selection events such as the incidence of plant and animal domestication. However, studying past selection processes through ancient DNA (aDNA) still involves considerable obstacles such as postmortem damage, high fragmentation, low coverage and small samples. To circumvent these challenges, we introduce a novel Bayesian framework for the inference of temporally variable selection based on genotype likelihoods instead of allele frequencies, thereby enabling us to model sample uncertainties resulting from the damage and fragmentation of aDNA molecules. Also, our approach permits the reconstruction of the underlying allele frequency trajectories of the population through time, which allows for a better understanding of the drivers of selection. We evaluate its performance through extensive simulations and demonstrate its utility with an application to the ancient horse samples genotyped at the loci for coat colouration. Our results reveal that incorporating sample uncertainties can further improve the inference of selection.

Temporally spaced genetic data allow for more accurate inference of population genetic parameters and hypothesis testing on the recent action of natural selection. In this work, we develop a novel likelihood-based method for jointly estimating selection coefficient and allele age from time series data of allele frequencies. Our approach is based on a hidden Markov model where the underlying process is a Wright-Fisher diffusion conditioned to survive until the time of the most recent sample. This formulation circumvents the assumption required in existing methods that the allele is created by mutation at a certain low frequency. We calculate the likelihood by numerically solving the resulting Kolmogorov backward equation backwards in time while re-weighting the solution with the emission probabilities of the observation at each sampling time point. This procedure reduces the two-dimensional numerical search for the maximum of the likelihood surface for both the selection coefficient and the allele age to a one-dimensional search over the selection coefficient only. We illustrate through extensive simulations that our method can produce accurate estimates of the selection coefficient and the allele age under both constant and non-constant demographic histories. We apply our approach to re-analyse ancient DNA data associated with horse base coat colours. We find that ignoring demographic histories or grouping raw samples can significantly bias the inference results.

We consider the spatial Λ-Fleming-Viot process model for frequencies of genetic types in a population living in R d , with two types of individuals (0 and 1) and natural selection favouring individuals of type 1. We first prove that the model is well-defined and provide a measure-valued dual process encoding the locations of the "potential ancestors" of a sample taken from such a population, in the same spirit as the dual process for the SLFV without natural selection [7]. We then consider two cases, one in which the dynamics of the process are driven by purely "local" events (that is, reproduction events of bounded radii) and one incorporating large-scale extinctionrecolonisation events whose radii have a polynomial tail distribution. In both cases, we consider a sequence of spatial Λ-Fleming-Viot processes indexed by n, and we assume that the fraction of individuals replaced during a reproduction event and the relative frequency of events during which natural selection acts tend to 0 as n tends to infinity. We choose the decay of these parameters in such a way that