Ian Wilson

Spinal muscular atrophy linked to chromosome 5q (SMA) is a recessive, progressive, neuromuscular disorder caused by bi-allelic mutations in the SMN1 gene, resulting in motor neuron degeneration and variable presentation in relation to onset and severity. A prevalence of approximately 1-2 per 100,000 persons and incidence around 1 in 10,000 live births have been estimated with SMA type I accounting for around 60% of all cases. Since SMA is a relatively rare condition, studies of its prevalence and incidence are challenging. Most published studies are outdated and therefore rely on clinical rather than genetic diagnosis. Furthermore they are performed in small cohorts in small geographical regions and only study European populations. In addition, the heterogeneity of the condition can lead to delays and difficulties in diagnosing the condition, especially outside of specialist clinics, and contributes to the challenges in understanding the epidemiology of the disease. The frequency of unaffected, heterozygous carriers of the SMN1 mutations appears to be higher among Caucasian and Asian populations compared to the Black (Sub-Saharan African ancestry) population. However, carrier frequencies cannot directly be translated into incidence and prevalence, as very severe (death in utero) and very mild (symptom free in adults) phenotypes carrying bi-allelic SMN1 mutations exist, and their frequency is unknown. More robust epidemiological data on SMA covering larger populations based on accurate genetic diagnosis or newborn screening would be helpful to support planning of clinical studies, provision of care and therapies and evaluation of outcomes.

Mammalian cells contain thousands of copies of mitochondrial DNA (mtDNA). At birth, these are thought to be identical in most humans. Here, we use long read length ultra-deep resequencing-by-synthesis to interrogate regions of the mtDNA genome from related and unrelated individuals at unprecedented resolution. We show that very low-level heteroplasmic variance is present in all tested healthy individuals, and is likely to be due to both inherited and somatic single base substitutions. Using this approach, we demonstrate an increase in mtDNA mutations in the skeletal muscle of patients with a proofreading-deficient mtDNA polymerase γ due to POLG mutations. In contrast, we show that OPA1 mutations, which indirectly affect mtDNA maintenance, do not increase point mutation load. The demonstration of universal mtDNA heteroplasmy has fundamental implications for our understanding of mtDNA inheritance and evolution. Ostensibly de novo somatic mtDNA mutations, seen in mtDNA maintenance disorders and neurodegenerative disease and aging, will partly be due to the clonal expansion of low-level inherited variants.

Ease and accuracy of typing, together with high levels of polymorphism and widespread distribution in the genome, make microsatellite (or short tandem repeat) loci an attractive potential source of information about both population histories and evolutionary processes. However, microsatellite data are difficult to interpret, in particular because of the frequency of back-mutations. Stochastic models for the underlying genetic processes can be specified, but in the past they have been too complicated for direct analysis. Recent developments in stochastic simulation methodology now allow direct inference about both historical events, such as genealogical coalescence times, and evolutionary parameters, such as mutation rates. A feature of the Markov chain Monte Carlo (MCMC) algorithm that we propose here is that the likelihood computations are simplified by treating the (unknown) ancestral allelic states as auxiliary parameters. We illustrate the algorithm by analyzing microsatellite samples simulated under the model. Our results suggest that a single microsatellite usually does not provide enough information for useful inferences, but that several completely linked microsatellites can be informative about some aspects of genealogical history and evolutionary processes. We also reanalyze data from a previously published human Y chromosome microsatellite study, finding evidence for an effective population size for human Y chromosomes in the low thousands and a recent time since their most recent common ancestor: the 95% interval runs from approximately 15, 000 to 130,000 years, with most likely values around 30,000 years.

Summary We develop a flexible class of Metropolis–Hastings algorithms for drawing inferences about population histories and mutation rates from deoxyribonucleic acid (DNA) sequence data. Match probabilities for use in forensic identification are also obtained, which is particularly useful for mitochondrial DNA profiles. Our data augmentation approach, in which the ancestral DNA data are inferred at each node of the genealogical tree, simplifies likelihood calculations and permits a wide class of mutation models to be employed, so that many different types of DNA sequence data can be analysed within our framework. Moreover, simpler likelihood calculations imply greater freedom for generating tree proposals, so that algorithms with good mixing properties can be implemented. We incorporate the effects of demography by means of simple mechanisms for changes in population size and structure, and we estimate the corresponding demographic parameters, but we do not here allow for the effects of either recombination or selection. We illustrate our methods by application to four human DNA data sets, consisting of DNA sequences, short tandem repeat loci, single-nucleotide polymorphism sites and insertion sites. Two of the data sets are drawn from the male-specific Y-chromosome, one from maternally inherited mitochondrial DNA and one from the β-globin locus on chromosome 11.