Prevalence, phenotype and architecture of developmental disorders caused by de novo mutation The Deciphering Developmental Disorders Study, bioRxiv, 2016-04-21

AbstractIndividuals with severe, undiagnosed developmental disorders (DDs) are enriched for damaging de novo mutations (DNMs) in developmentally important genes. We exome sequenced 4,293 families with individuals with DDs, and meta-analysed these data with published data on 3,287 individuals with similar disorders. We show that the most significant factors influencing the diagnostic yield of de novo mutations are the sex of the affected individual, the relatedness of their parents and the age of both father and mother. We identified 94 genes enriched for damaging de novo mutation at genome-wide significance (P &lt; 7 × 10−7), including 14 genes for which compelling data for causation was previously lacking. We have characterised the phenotypic diversity among these genetic disorders. We demonstrate that, at current cost differentials, exome sequencing has much greater power than genome sequencing for novel gene discovery in genetically heterogeneous disorders. We estimate that 42% of our cohort carry pathogenic DNMs (single nucleotide variants and indels) in coding sequences, with approximately half operating by a loss-of-function mechanism, and the remainder resulting in altered-function (e.g. activating, dominant negative). We established that most haplo insufficient developmental disorders have already been identified, but that many altered-function disorders remain to be discovered. Extrapolating from the DDD cohort to the general population, we estimate that developmental disorders caused by DNMs have an average birth prevalence of 1 in 213 to 1 in 448 (0.22-0.47% of live births), depending on parental age.Abbreviations<jatsdef-list><jatsdef-item>PTVProtein-Truncating Variant<jatsdef-item><jatsdef-item>DNMDe Novo Mutation<jatsdef-item><jatsdef-item>DDDevelopmental Disorder<jatsdef-item><jatsdef-item>DDDDeciphering Developmental Disorders study<jatsdef-item><jatsdef-list>

Analysis of Shared Heritability in Common Disorders of the Brain, bioRxiv, 2016-04-17

AbstractDisorders of the brain exhibit considerable epidemiological comorbidity and frequently share symptoms, provoking debate about the extent of their etiologic overlap. We quantified the genetic sharing of 25 brain disorders based on summary statistics from genome-wide association studies of 215,683 patients and 657,164 controls, and their relationship to 17 phenotypes from 1,191,588 individuals. Psychiatric disorders show substantial sharing of common variant risk, while neurological disorders appear more distinct from one another. We observe limited evidence of sharing between neurological and psychiatric disorders, but do identify robust sharing between disorders and several cognitive measures, as well as disorders and personality types. We also performed extensive simulations to explore how power, diagnostic misclassification and phenotypic heterogeneity affect genetic correlations. These results highlight the importance of common genetic variation as a source of risk for brain disorders and the value of heritability-based methods in understanding their etiology.

A reference panel of 64,976 haplotypes for genotype imputation, bioRxiv, 2015-12-24

We describe a reference panel of 64,976 human haplotypes at 39,235,157 SNPs constructed using whole genome sequence data from 20 studies of predominantly European ancestry. Using this resource leads to accurate genotype imputation at minor allele frequencies as low as 0.1%, a large increase in the number of SNPs tested in association studies and can help to discover and refine causal loci. We describe remote server resources that allow researchers to carry out imputation and phasing consistently and efficiently.

TP53 copy number expansion is associated with the evolution of increased body size and an enhanced DNA damage response in elephants, bioRxiv, 2015-10-07

SUMMARYA major constraint on the evolution of large body sizes in animals is an increased risk of developing cancer. There is no correlation, however, between body size and cancer risk. This lack of correlation is often referred to as ‘Peto’s Paradox’. Here we show that the elephant genome encodes 20 copies of the tumor suppressor gene TP53 and that the increase in TP53 copy number occurred coincident with the evolution of large body sizes, the evolution of extreme sensitivity to genotoxic stress, and a hyperactive TP53 signaling pathway in the elephant (Proboscidean) lineage. Furthermore we show that several of the TP53 retrogenes (TP53RTGs) are transcribed and likely translated. While TP53RTGs do not appear to directly function as transcription factors, they do contribute to the enhanced sensitivity of elephant cells to DNA damage and the induction of apoptosis by regulating activity of the TP53 signaling pathway. These results suggest that an increase in the copy number of TP53 may have played a direct role in the evolution of very large body sizes and the resolution of Peto’s paradox in Proboscideans.

Iron Age and Anglo-Saxon genomes from East England reveal British migration history, bioRxiv, 2015-07-18

British population history has been shaped by a series of immigrations and internal movements, including the early Anglo-Saxon migrations following the breakdown of the Roman administration after 410CE. It remains an open question how these events affected the genetic composition of the current British population. Here, we present whole-genome sequences generated from ten ancient individuals found in archaeological excavations close to Cambridge in the East of England, ranging from 2,300 until 1,200 years before present (Iron Age to Anglo-Saxon period). We use present-day genetic data to characterize the relationship of these ancient individuals to contemporary British and other European populations. By analyzing the distribution of shared rare variants across ancient and modern individuals, we find that today’s British are more similar to the Iron Age individuals than to most of the Anglo-Saxon individuals, and estimate that the contemporary East English population derives 30% of its ancestry from Anglo-Saxon migrations, with a lower fraction in Wales and Scotland. We gain further insight with a new method, rarecoal, which fits a demographic model to the distribution of shared rare variants across a large number of samples, enabling fine scale analysis of subtle genetic differences and yielding explicit estimates of population sizes and split times. Using rarecoal we find that the ancestors of the Anglo-Saxon samples are closest to modern Danish and Dutch populations, while the Iron Age samples share ancestors with multiple Northern European populations including Britain.

Meta-analysis of the heritability of human traits based on fifty years of twin studies, Nature Genetics, 2015-05-18