Ignore Population Structure Research Articles

Regularized multi-trait multi-locus linear mixed models for genome-wide association studies and genomic selection in crops

BackgroundWe consider two key problems in genomics involving multiple traits: multi-trait genome wide association studies (GWAS), where the goal is to detect genetic variants associated with the traits; and multi-trait genomic selection (GS), where the emphasis is on accurately predicting trait values. Multi-trait linear mixed models build on the linear mixed model to jointly model multiple traits. Existing estimation methods, however, are limited to the joint analysis of a small number of genotypes; in fact, most approaches consider one SNP at a time. Estimating multi-dimensional genetic and environment effects also results in considerable computational burden. Efficient approaches that incorporate regularization into multi-trait linear models (no random effects) have been recently proposed to identify genomic loci associated with multiple traits (Yu et al. in Multitask learning using task clustering with applications to predictive modeling and GWAS of plant varieties. arXiv:1710.01788, 2017; Yu et al in Front Big Data 2:27, 2019), but these ignore population structure and familial relatedness (Yu et al in Nat Genet 38:203–208, 2006).ResultsThis work addresses this gap by proposing a novel class of regularized multi-trait linear mixed models along with scalable approaches for estimation in the presence of high-dimensional genotypes and a large number of traits. We evaluate the effectiveness of the proposed methods using datasets in maize and sorghum diversity panels, and demonstrate benefits in both achieving high prediction accuracy in GS and in identifying relevant marker-trait associations.ConclusionsThe proposed regularized multivariate linear mixed models are relevant for both GWAS and GS. We hope that they will facilitate agronomy-related research in plant biology and crop breeding endeavors.

Population genetics: coalescence rate and demographic parameters inference

Inferring the demographic history of species is a great challenge in population genetics. This history is classically represented as a history of size changes, ignoring population structure. We present here the work carried out over the last decade around the concept of IICR (Inverse Instantaneous Coalescence Rate), which makes it possible to link, on the one hand, the history of the true population size for a panmictic population, and the inferred size, sometimes called "effective size", when structure is taken into account. We show that population structure can lead to misinterpretations of some demographic history inference results, we propose a framework for inferring structure-specific demographic parameters (number and size of subpopulations, migration rates), and we analyze the link between IICR and some form of selection modeling on genetic sequences.

Inferring number of populations and changes in connectivity under the n-island model

Inferring the demographic history of species is one of the greatest challenges in populations genetics. This history is often represented as a history of size changes, ignoring population structure. Alternatively, when structure is assumed, it is defined a priori as a population tree and not inferred. Here we propose a framework based on the IICR (Inverse Instantaneous Coalescence Rate). The IICR can be estimated for a single diploid individual using the PSMC method of Li and Durbin (2011). For an isolated panmictic population, the IICR matches the population size history, and this is how the PSMC outputs are generally interpreted. However, it is increasingly acknowledged that the IICR is a function of the demographic model and sampling scheme with limited connection to population size changes. Our method fits observed IICR curves of diploid individuals with IICR curves obtained under piecewise stationary symmetrical island models. In our models we assume a fixed number of time periods during which gene flow is constant, but gene flow is allowed to change between time periods. We infer the number of islands, their sizes, the periods at which connectivity changes and the corresponding rates of connectivity. Validation with simulated data showed that the method can accurately recover most of the scenario parameters. Our application to a set of five human PSMCs yielded demographic histories that are in agreement with previous studies using similar methods and with recent research suggesting ancient human structure. They are in contrast with the view of human evolution consisting of one ancestral population branching into three large continental and panmictic populations with varying degrees of connectivity and no population structure within each continent.

Robust, flexible, and scalable tests for Hardy-Weinberg equilibrium across diverse ancestries.

Traditional Hardy-Weinberg equilibrium (HWE) tests (the χ2 test and the exact test) have long been used as a metric for evaluating genotype quality, as technical artifacts leading to incorrect genotype calls often can be identified as deviations from HWE. However, in data sets composed of individuals from diverse ancestries, HWE can be violated even without genotyping error, complicating the use of HWE testing to assess genotype data quality. In this manuscript, we present the Robust Unified Test for HWE (RUTH) to test for HWE while accounting for population structure and genotype uncertainty, and to evaluate the impact of population heterogeneity and genotype uncertainty on the standard HWE tests and alternative methods using simulated and real sequence data sets. Our results demonstrate that ignoring population structure or genotype uncertainty in HWE tests can inflate false-positive rates by many orders of magnitude. Our evaluations demonstrate different tradeoffs between false positives and statistical power across the methods, with RUTH consistently among the best across all evaluations. RUTH is implemented as a practical and scalable software tool to rapidly perform HWE tests across millions of markers and hundreds of thousands of individuals while supporting standard VCF/BCF formats. RUTH is publicly available at https://www.github.com/statgen/ruth.

Vultures and Livestock: The Where, When, and Why of Visits to Farms.

Simple SummaryThe abandonment of carcasses around livestock farms has been recently legalized in Europe. Since little is known about how vultures use this kind of resource, we aimed to determine the main drivers of vultures’ visits to farms. We evaluated the effects of characteristics of both birds and farms regarding the way that vultures visit farms thanks to data collected from 45 GPS-tagged Egyptian Vultures and most farms on Fuerteventura Island, Spain (318 farms with >94% of insular livestock). We found that farms were more visited when they were located close to highly predictable feeding places, when they had more available food, and during the vulture breeding season, whereas farms located close to roads and vultures’ breeding territories received fewer visits. Younger territorial birds visited a farm more frequently than older territorial ones, whereas older non-territorial individuals concentrated those visits on farms closer to their main centers of activity compared with younger ones. Our findings indicate that visits to farms were determined by their spatial distribution regarding bird activity centers, availability of carcasses, seasonality, and vulture characteristics. Hence, these factors should be considered in vulture conservation, avoiding very general solutions that ignore population structure and that could be not enough to protect the biodiversity.Recent changes in European legislation have legalized the abandonment of carcasses around livestock farms, but our understanding of how vultures exploit these semi-predictable food sources is still very limited. For filling this gap, we determine the individual and ecological drivers influencing vulture visits to farms. We assessed the effects of individual characteristics of both birds and farms on the frequency of vultures’ visits to livestock facilities using data collected from 45 GPS-tagged Egyptian Vultures (Neophron percnopterus) and 318 farms (>94% of livestock) on Fuerteventura Island, Spain. Farms were more visited during the vultures’ breeding season. Farms located closer to highly predictable feeding places (i.e., vulture restaurants and garbage dumps) or with more available feeding resources were visited by more vultures, whereas those located close to roads and vultures’ breeding territories received fewer visits. Younger territorial birds visited a farm more frequently than older territorial ones, whereas older non-territorial individuals concentrated those visits on farms closer to their activity core areas compared with younger ones. Our findings indicate that visits to farms were determined by their spatial distribution in relation to the age-specific birds’ activity centers, the availability of carcasses, seasonality, and individual characteristics of vultures. These interacting factors should be considered in vulture conservation, avoiding very general solutions that ignore population structure.

Joint genome-wide prediction in several populations accounting for randomness of genotypes: A hierarchical Bayes approach. I: Multivariate Gaussian priors for marker effects and derivation of the joint probability mass function of genotypes

It is important to consider heterogeneity of marker effects and allelic frequencies in across population genome-wide prediction studies. Moreover, all regression models used in genome-wide prediction overlook randomness of genotypes. In this study, a family of hierarchical Bayesian models to perform across population genome-wide prediction modeling genotypes as random variables and allowing population-specific effects for each marker was developed. Models shared a common structure and differed in the priors used and the assumption about residual variances (homogeneous or heterogeneous). Randomness of genotypes was accounted for by deriving the joint probability mass function of marker genotypes conditional on allelic frequencies and pedigree information. As a consequence, these models incorporated kinship and genotypic information that not only permitted to account for heterogeneity of allelic frequencies, but also to include individuals with missing genotypes at some or all loci without the need for previous imputation. This was possible because the non-observed fraction of the design matrix was treated as an unknown model parameter. For each model, a simpler version ignoring population structure, but still accounting for randomness of genotypes was proposed. Implementation of these models and computation of some criteria for model comparison were illustrated using two simulated datasets. Theoretical and computational issues along with possible applications, extensions and refinements were discussed. Some features of the models developed in this study make them promising for genome-wide prediction, the use of information contained in the probability distribution of genotypes is perhaps the most appealing. Further studies to assess the performance of the models proposed here and also to compare them with conventional models used in genome-wide prediction are needed.

On the importance of being structured: instantaneous coalescence rates and human evolution--lessons for ancestral population size inference?

Most species are structured and influenced by processes that either increased or reduced gene flow between populations. However, most population genetic inference methods assume panmixia and reconstruct a history characterized by population size changes. This is potentially problematic as population structure can generate spurious signals of population size change through time. Moreover, when the model assumed for demographic inference is misspecified, genomic data will likely increase the precision of misleading if not meaningless parameters. For instance, if data were generated under an n-island model (characterized by the number of islands and migrants exchanged) inference based on a model of population size change would produce precise estimates of a bottleneck that would be meaningless. In addition, archaeological or climatic events around the bottleneck's timing might provide a reasonable but potentially misleading scenario. In a context of model uncertainty (panmixia versus structure) genomic data may thus not necessarily lead to improved statistical inference. We consider two haploid genomes and develop a theory that explains why any demographic model with structure will necessarily be interpreted as a series of changes in population size by inference methods ignoring structure. We formalize a parameter, the inverse instantaneous coalescence rate, and show that it is equivalent to a population size only in panmictic models, and is mostly misleading for structured models. We argue that this issue affects all population genetics methods ignoring population structure which may thus infer population size changes that never took place. We apply our approach to human genomic data.

Assessment of Genetic Heterogeneity in Structured Plant Populations Using Multivariate Whole-Genome Regression Models.

Plant breeding populations exhibit varying levels of structure and admixture; these features are likely to induce heterogeneity of marker effects across subpopulations. Traditionally, structure has been dealt with as a potential confounder, and various methods exist to "correct" for population stratification. However, these methods induce a mean correction that does not account for heterogeneity of marker effects. The animal breeding literature offers a few recent studies that consider modeling genetic heterogeneity in multibreed data, using multivariate models. However, these methods have received little attention in plant breeding where population structure can have different forms. In this article we address the problem of analyzing data from heterogeneous plant breeding populations, using three approaches: (a) a model that ignores population structure [A-genome-based best linear unbiased prediction (A-GBLUP)], (b) a stratified (i.e., within-group) analysis (W-GBLUP), and (c) a multivariate approach that uses multigroup data and accounts for heterogeneity (MG-GBLUP). The performance of the three models was assessed on three different data sets: a diversity panel of rice (Oryza sativa), a maize (Zea mays L.) half-sib panel, and a wheat (Triticum aestivum L.) data set that originated from plant breeding programs. The estimated genomic correlations between subpopulations varied from null to moderate, depending on the genetic distance between subpopulations and traits. Our assessment of prediction accuracy features cases where ignoring population structure leads to a parsimonious more powerful model as well as others where the multivariate and stratified approaches have higher predictive power. In general, the multivariate approach appeared slightly more robust than either the A- or the W-GBLUP.

Consequences of a mismatch between biological and management units on our perception of Atlantic cod off New England

A mismatch between the scale of fishery management units and biological population structure can potentially result in a misperception of the productivity and sustainable yield of fish stocks. We used simulation modelling as a tool to compare the perception of productivity, stability, and sustainability of Atlantic cod (Gadus morhua) off New England from an operating model based on the current US management units to a model that more closely reflects the biological complexity of the resource. Two age-structured models were compared: (i) the management unit model, wherein cod were grouped based on the current spatially defined US management areas (Gulf of Maine and Georges Bank), and (ii) the biological unit model, consisting of three genetically defined population components (northern spring spawning, southern winter/spring spawning, and eastern Georges Bank spring-spawning groups). Overall, the regional productivity and maximum sustainable yield of the biological unit model was lower compared with the management unit model. The biological unit model also provided insights on the distribution of productivity in the region, with southern and northern spawning groups being the dominant contributors to the regional spawning–stock biomass and yield and the eastern Georges Bank spawning group being the minority contributor at low to intermediate levels of fishing mortality. The comparison of models revealed that the perception of Atlantic cod derived from the management unit model was of a resource that is more resilient to fishing mortality and not as susceptible to “collapse” as indicated by the biological unit model. For Atlantic cod, one of the main risks of ignoring population structure appears the potential for overexploitation of segments of the population. Consideration of population structure of cod changed our perception of the magnitude and distribution of productivity in the region, suggesting that expectations of sustainable yield of cod in US waters should be reconsidered.

Ancient DNA from an Early Neolithic Iberian population supports a pioneer colonization by first farmers

The Neolithic transition has been widely debated particularly regarding the extent to which this revolution implied a demographic expansion from the Near East. We attempted to shed some light on this process in northeastern Iberia by combining ancient DNA (aDNA) data from Early Neolithic settlers and published DNA data from Middle Neolithic and modern samples from the same region. We successfully extracted and amplified mitochondrial DNA from 13 human specimens, found at three archaeological sites dated back to the Cardial culture in the Early Neolithic (Can Sadurní and Chaves) and to the Late Early Neolithic (Sant Pau del Camp). We found that haplogroups with a low frequency in modern populations-N* and X1-are found at higher frequencies in our Early Neolithic population (∼31%). Genetic differentiation between Early and Middle Neolithic populations was significant (F(ST) ∼0.13, P<10(-5)), suggesting that genetic drift played an important role at this time. To improve our understanding of the Neolithic demographic processes, we used a Bayesian coalescence-based simulation approach to identify the most likely of three demographic scenarios that might explain the genetic data. The three scenarios were chosen to reflect archaeological knowledge and previous genetic studies using similar inferential approaches. We found that models that ignore population structure, as previously used in aDNA studies, are unlikely to explain the data. Our results are compatible with a pioneer colonization of northeastern Iberia at the Early Neolithic characterized by the arrival of small genetically distinctive groups, showing cultural and genetic connections with the Near East.

Regression Analysis of Data from Complex Surveys

SUMMARY Three methods of carrying out a regression analysis of data collected by means of a survey of complex design are investigated. Least squares methods which ignore population structure such as clustering or stratification can give seriously misleading results. Probability weighted methods are much better and give reasonable inferences for equal probability designs. However, for designs with widely differing selection probabilities the inferences can be poor. The best results were obtained for an estimator derived from maximum likelihood theory. This estimator requires that values of a design variable be known for all units in the population. Some aspects of the robustness of this procedure are studied. REGRESSION analysis is widely used in the analysis of data derived from a sample survey of complex design. McKennell (1970) describes a survey of residents around Heathrow Airport in which a stratified design with unequal sampling fractions was employed. A regression equation was fitted to the survey data which related the respondents' subjective attitudes to noise to various measures of physical exposure. A simplified version of this equation, the Noise and Number Index, now features prominently in discussions on the siting of future airports. In a subsequent survey (HMSO, 1971) a stratified cluster sample was employed and similar regression analyses were carried out. DeMets and Halperin (1977) employ regression analysis on data from a purposive sample of patients in the Framingham Heart Study. They were interested in the effect of dietary cholesterol on serum cholesterol, both measured concurrently, and based the sample on patients with the highest and lowest values of initial serum cholesterol level. This paper is concerned with the question of what advice to give clients who wish to estimate regression parameters and to calculate the variances of their estimates using data obtained from a survey of complex design. We assume that the appropriateness of the regression model is not in question and that in practice diagnostic plots and checks would be made to confirm this. We assume further that the statistician and his client agree that the appropriate model for study is a single equation fitted to all the data rather than separate equations fitted to subsets of the data. If separate models are fitted to subsets of the data in such a way that the data may be divided into mutually exclusive groups then the analysis of each group would still fall within the scope of this paper.