Schizophrenia is a debilitating psychiatric condition often associated with poor quality of life and decreased life expectancy. Lack of progress in improving treatment outcomes has been attributed to limited knowledge of the underlying biology, although large-scale genomic studies have begun to provide insights. We report a new genome-wide association study of schizophrenia (11,260 cases and 24,542 controls), and through meta-analysis with existing data we identify 50 novel associated loci and 145 loci in total. Through integrating genomic fine-mapping with brain expression and chromosome conformation data, we identify candidate causal genes within 33 loci. We also show for the first time that the common variant association signal is highly enriched among genes that are under strong selective pressures. These findings provide new insights into the biology and genetic architecture of schizophrenia, highlight the importance of mutation-intolerant genes and suggest a mechanism by which common risk variants persist in the population.

Genetic parameters widely used to monitor genetic variation in conservation programmes, such as effective number of founders, founder genome equivalents and effective population size, are interrelated in terms of coancestries and variances of contributions from ancestors to descendants. A new parameter, the effective number of non-founders, is introduced to describe the relation between effective number of founders and founder genome equivalents. Practical recommendations for the maintenance of genetic variation in small captive populations are discussed. To maintain genetic diversity, minimum coancestry among individuals should be sought. This minimizes the variances of contributions from ancestors to descendants in all previous generations. The method of choice of parents and the system of mating should be independent of each other because a clear-cut recommendation cannot be given on the latter.

Effective population size is a key parameter in evolutionary and quantitative genetics because it measures the rate of genetic drift and inbreeding. Predictive equations of effective size under a range of circumstances and some of their implications are reviewed in this paper. Derivations are made for the simplest cases, and the inter-relations between different formulae and methods are discussed.

Effective population size (N e ) is a key parameter in population genetics. It has important applications in evolutionary biology, conservation genetics and plant and animal breeding, because it measures the rates of genetic drift and inbreeding and affects the efficacy of systematic evolutionary forces, such as mutation, selection and migration. We review the developments in predictive equations and estimation methodologies of effective size. In the prediction part, we focus on the equations for populations with different modes of reproduction, for populations under selection for unlinked or linked loci and for the specific applications to conservation genetics. In the estimation part, we focus on methods developed for estimating the current or recent effective size from molecular marker or sequence data. We discuss some underdeveloped areas in predicting and estimating N e for future research.Heredity ( INTRODUCTIONThe concept of effective population size, introduced by Sewall Wright (1931, 1933), is central to plant and animal breeding (Falconer and Mackay, 1996), conservation genetics (Frankham et al., 2010;Allendorf et al., 2013) and molecular variation and evolution (Charlesworth and Charlesworth, 2010), as it quantifies the magnitude of genetic drift and inbreeding in real-world populations. A substantial number of extensions to the basic theory and predictions were made since the seminal work of Wright, with main early developments by James Crow and Motoo Kimura (Kimura and Crow, 1963a;Crow and Kimura, 1970) and later by a list of contributors. Several review papers (Crow and Denniston, 1988;Caballero, 1994;Wang and Caballero, 1999;Nomura, 2005a) and population genetic books (Fisher, 1965;Wright, 1969;Ewens, 1979;Nagylaki, 1992) have summarised the existing theory in predicting the effective size of a population at different spatial and timescales under various inheritance modes and demographies. Comparatively, methodological developments (reviewed by Schwartz et al., 1999;Beaumont, 2003a;Wang, 2005;Palstra and Ruzzante, 2008;Luikart et al., 2010;Gilbert and Whitlock, 2015) in estimating the effective size of natural populations from genetic data lag behind but are accelerating in the past decade, thanks to the rapid developments of molecular biology.The classical developments of effective population size theory are based on the rate of change in gene frequency variance (genetic drift) or the rate of inbreeding. The effective population size is defined in reference to the Wright-Fisher idealised population, that is, a hypothetical population with very simplifying characteristics where genetic drift is the only factor in operation, and the dynamics of allelic and genotypic frequencies across generations merely depend on the population census (N) size. The effective size of a real population is then defined as the size of an idealised population, which would give rise to the rate of inbreeding and the rate of change in variance of gene frequencies actually observed in the population under consideration,

Abstract Inferring changes in effective population size (Ne) in the recent past is of special interest for conservation of endangered species and for human history research. Current methods for estimating the very recent historical Ne are unable to detect complex demographic trajectories involving multiple episodes of bottlenecks, drops and expansions. We develop a theoretical and computational framework to infer the demographic history of a population within the past 100 generations from the observed spectrum of linkage disequilibrium (LD) of pairs of loci over a wide range of recombination rates in a sample of contemporary individuals. The cumulative contributions of all of the previous generations to the observed LD are included in our model, and a genetic algorithm is used to search for the sequence of historical Ne values that best explains the observed LD spectrum. The method can be applied from large samples to samples of fewer than 10 individuals using a variety of genotyping and DNA sequencing data: haploid, diploid with phased or unphased genotypes and pseudo-haploid data from low-coverage sequencing. The method was tested by computer simulation for sensitivity to genotyping errors, temporal heterogeneity of samples, population admixture and structural division into subpopulations, showing high tolerance to deviations from the assumptions of the model. Computer simulations also show that the proposed method outperforms other leading approaches when the inference concerns recent timeframes. Analysis of data from a variety of human and animal populations gave results in agreement with previous estimations by other methods or with records of historical events.