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Background: Genome-wide association studies (GWASs) have identified genes influencing skin ageing and mole count in Europeans, but little is known about the relevance of these (or other genes) in non-Europeans. - Objectives: To conduct a GWAS for facial skin ageing and mole count in adults

The inclusion of ancestrally diverse participants in genetic studies can lead to new discoveries and is important to ensure equitable health care benefit from research advances. Here, members of the Ethical, Legal, Social, Implications (ELSI) committee of the International Genetic Epidemiology Society (IGES) offer perspectives on methods and analysis tools for the conduct of inclusive genetic epidemiology research, with a focus on admixed and ancestrally diverse populations in support of reproducible research practices. We emphasize the importance of distinguishing socially defined population categorizations from genetic ancestry in the design, analysis, reporting, and interpretation of genetic epidemiology research findings. Finally, we discuss the current state of genomic resources used in genetic association studies, functional interpretation, and clinical and public health translation of genomic findings with respect to diverse populations.

Genetic epilepsy with febrile seizures plus (GEFS+) is an autosomal dominant familial epilepsy syndrome characterized by distinctive phenotypic heterogeneity within families. The SCN1B c.363C>G (p.Cys121Trp) variant has been identified in independent, multi-generational families with GEFS+. Although the variant is present in population databases (at very low frequency), there is strong clinical, genetic, and functional evidence to support pathogenicity. Recurrent variants may be due to a founder event in which the variant has been inherited from a common ancestor. Here, we report evidence of a single founder event giving rise to the SCN1B c.363C>G variant in 14 independent families with epilepsy. A common haplotype was observed in all families, and the age of the most recent common ancestor was estimated to be approximately 800 years ago. Analysis of UK Biobank whole-exome-sequencing data identified 74 individuals with the same variant. All individuals carried haplotypes matching the epilepsy-affected families, suggesting all instances of the variant derive from a single mutational event. This unusual finding of a variant causing an autosomal dominant, early-onset disease in an outbred population that has persisted over many generations can be attributed to the relatively mild phenotype in most carriers and incomplete penetrance. Founder events are well established in autosomal recessive and late-onset disorders but are rarely observed in early-onset, autosomal dominant diseases. These findings suggest variants present in the population at low frequencies should be considered potentially pathogenic in mild phenotypes with incomplete penetrance and may be more important contributors to the genetic landscape than previously thought.

Advances in whole-genome genotyping and sequencing have allowed genome-wide analyses of association, prediction and heritability in many organisms. However, the application of such analyses to bacteria is still in its infancy, being limited by difficulties including the plasticity of bacterial genomes and their strong population structure. Here we propose, and validate using simulations, a suite of genome-wide analyses for bacteria. We combine methods from human genetics and previous bacterial studies, including linear mixed models, elastic net and LD-score regression, and introduce innovations such as frequency-based allele coding, testing for both insertion/deletion and nucleotide effects and partitioning heritability by genome region. We then analyse three phenotypes of a major human pathogen Streptococcus pneumoniae, including the first analyses of minimum inhibitory concentrations (MIC) for each of two antibiotics, penicillin and ceftriaxone. We show that these are highly heritable leading to high prediction accuracy, which is explained by many genetic associations identified under good control of population structure effects. In the case of ceftriaxone MIC, these results are surprising because none of the isolates was resistant according to the inhibition zone diameter threshold. We estimate that just over half of the heritability of penicillin MIC is explained by a known drug-resistance region, which also contributes around a quarter of the heritability of ceftriaxone MIC. For the within-host survival phenotype carriage duration, no reliable associations were found but we observed moderate heritability and prediction accuracy, indicating a polygenic trait. While generating important new results for S. pneumoniae, we have critically assessed existing methods and introduced innovations that will be useful for future large-scale population genomics studies to help decipher the genetic architecture of bacterial traits.Author summaryGenome-wide association, prediction and heritability analyses in bacteria are beginning to help unravel the genetic underpinnings of traits such as antimicrobial resistance, virulence, within-host survival and transmissibility. Progress to date is limited by challenges including the effects of strong population structure and variable recombination, and the many gaps in sequence alignments including the absence of entire genes in many isolates. More work is required to critically asses and develop methods for bacterial genomics. We address this task here, using a range of existing methods from bacterial and human genetics, such as linear mixed models, elastic net and LD-score regression. Using simulations, we first validate and then adapt these methods to introduce new analyses, including separate assessment of gap and nucleotide effects, a new allele coding for association analyses and a method to partition heritability into genome regions. We analyse within-host survival and two antimicrobial response traits of Streptococcus pneumoniae, identifying many novel associations while demonstrating good control of population structure and accurate prediction. We present both new results for an important pathogen and methodological advances that will be useful in guiding future studies in bacterial population genomics.

Y-chromosomal and mitochondrial DNA profiles have been used as evidence in courts for decades, yet the problem of evaluating the weight of evidence has not been adequately resolved. Both are lineage markers (inherited from just one parent), which presents different interpretation challenges compared with standard autosomal DNA profiles (inherited from both parents), for which recombination increases profile diversity and weakens the effects of relatedness. We review approaches to the evaluation of lineage marker profiles for forensic identification, focussing on the key roles of profile mutation rate and relatedness. Higher mutation rates imply fewer individuals matching the profile of an alleged contributor, but they will be more closely related. This makes it challenging to evaluate the possibility that one of these matching individuals could be the true source, because relatedness may make them more plausible alternative contributors than less-related individuals, and they may not be well mixed in the population. These issues reduce the usefulness of profile databases drawn from a broad population: the larger the population, the lower the profile relative frequency because of lower relatedness with the alleged contributor. Many evaluation methods do not adequately take account of relatedness, but its effects have become more pronounced with the latest generation of high-mutation-rate Y profiles.

Here we evaluate the accuracy of prediction for eye, hair and skin pigmentation in a dataset of > 6500 individuals from Mexico, Colombia, Peru, Chile and Brazil (including genome-wide SNP data and quantitative/categorical pigmentation phenotypes - the CANDELA dataset CAN). We evaluated accuracy in relation to different analytical methods and various phenotypic predictors. As expected from statistical principles, we observe that quantitative traits are more sensitive to changes in the prediction models than categorical traits. We find that Random Forest or Linear Regression are generally the best performing methods. We also compare the prediction accuracy of SNP sets defined in the CAN dataset (including 56, 101 and 120 SNPs for eye, hair and skin colour prediction, respectively) to the well-established HIrisPlex-S SNP set (including 6, 22 and 36 SNPs for eye, hair and skin colour prediction respectively). When training prediction models on the CAN data, we observe remarkably similar performances for HIrisPlex-S and the larger CAN SNP sets for the prediction of hair (categorical) and eye (both categorical and quantitative), while the CAN sets outperform HIrisPlex-S for quantitative, but not for categorical skin pigmentation prediction. The performance of HIrisPlex-S, when models are trained in a world-wide sample (although consisting of 80% Europeans, https://hirisplex.erasmusmc.nl), is lower relative to training in the CAN data (particularly for hair and skin colour). Altogether, our observations are consistent with common variation of eye and hair colour having a relatively simple genetic architecture, which is well captured by HIrisPlex-S, even in admixed Latin Americans (with partial European ancestry). By contrast, since skin pigmentation is a more polygenic trait, accuracy is more sensitive to prediction SNP set size, although here this effect was only apparent for a quantitative measure of skin pigmentation. Our results support the use of HIrisPlex-S in the prediction of categorical pigmentation traits for forensic purposes in Latin America, while illustrating the impact of training datasets on its accuracy. Fil: Palmal, Sagnik. Centre National de la Recherche Scientifique; Francia. Aix-Marseille Université; Francia Fil: Kaustubh Adhikari. The Open University; Reino Unido. University College London; Estados Unidos Fil: Mendoza Revilla, Javier. Universidad Peruana Cayetano Heredia; Perú. Institut Pasteur de Paris.; Francia Fil: Fuentes Guajardo, Macarena. Universidad de Tarapacá; Chile Fil: Silva de Cerqueira, Caio Cesar. Scientific Police of São Paulo State; Brasil Fil: Bonfante, Betty. Aix-Marseille Université; Francia. Centre National de la Recherche Scientifique; Francia Fil: Chacón Duque, Juan Camilo. Natural History Museum; Reino Unido Fil: Sohail, Anood. Kinnaird College for Women. Department of Biotechnology; Pakistán Fil: Hurtadeo, Malena. Universidad Peruana Cayetano Heredia; Perú Fil: Villegas, Valeria. Universidad Peruana Cayetano Heredia; Perú Fil: Granja, Vanessa. Universidad Peruana Cayetano Heredia; Perú Fil: Jaramillo, Claudia. Kinnaird College for Women. Department of Biotechnology; Pakistán. Universidad de Antioquia; Colombia Fil: Arias, Williams. Universidad de Antioquia; Colombia Fil: Barquera Lozano, Rodrigo. Instituto Nacional de Antropología e Historia; México. Max Planck Institute for the Science of Human History. Department of Archaeogenetics; Alemania Fil: Everardo Martínez, Paola. Instituto Nacional de Antropología e Historia; México Fil: Gómez Valdés, Jorge. Instituto Nacional de Antropología e Historia; México Fil: Villamil Ramirez, Hugo. Universidad Nacional Autónoma de México; México Fil: Hünemeier, Tábita. Universidade de Sao Paulo; Brasil Fil: Ramallo, Virginia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico. Instituto Patagónico de Ciencias Sociales y Humanas; Argentina. Universidade Federal do Rio Grande do Sul; Brasil Fil: Parolin, María Laura. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico. Instituto de Diversidad y Evolución Austral; Argentina Fil: Gonzalez, Rolando Jose. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico. Instituto Patagónico de Ciencias Sociales y Humanas; Argentina Fil: Schüler-Faccini, Lavinia. Universidade Federal do Rio Grande do Sul, Porto Alegre; Brasil Fil: Bortolini, María Cátira. Universidade Federal do Rio Grande do Sul; Brasil Fil: Acuña Alonzo, Victor. Instituto Nacional de Antropología e Historia; México. Universidade Federal do Rio Grande do Sul; Brasil Fil: Canizales Quinteros, Samuel. Universidad Nacional Autónoma de México; México Fil: Gallo, Carla. Universidad Peruana Cayetano Heredia; Perú Fil: Poletti, Giovanni. Universidad Peruana Cayetano Heredia; Perú Fil: Bedoya, Gabriel. Universidad de Antioquia; Colombia Fil: Rothhammer, Francisco. Universidad de Tarapacá; Chile. Universidad de Chile; Chile Fil: Balding, David. University College London; Reino Unido. University of Melbourne; Australia Fil: Faux, Pierre. Aix-Marseille Université; Francia. Centre National de la Recherche Scientifique; Francia Fil: Ruiz Linares, Andrés. Aix-Marseille Université; Francia. Centre National de la Recherche Scientifique; Francia. University College London; Reino Unido. Fudan University; República de China

In population genetics, the Dirichlet (also called the Balding--Nichols) model has for 20 years been considered the key model to approximate the distribution of allele fractions within populations in a multi-allelic setting. It has often been noted that the Dirichlet assumption is approximate because positive correlations among alleles cannot be accommodated under the Dirichlet model. However, the validity of the Dirichlet distribution has never been systematically investigated in a general framework. This paper attempts to address this problem by providing a general overview of how allele fraction data under the most common multi-allelic mutational structures should be modeled. The Dirichlet and alternative models are investigated by simulating allele fractions from a diffusion approximation of the multi-allelic Wright--Fisher process with mutation, and applying a moment-based analysis method. The study shows that the optimal modeling strategy for the distribution of allele fractions depends on the specific mutation process. The Dirichlet model is only an exceptionally good approximation for the pure drift, Jukes--Cantor and parent-independent mutation processes with small mutation rates. Alternative models are required and proposed for the other mutation processes, such as a Beta--Dirichlet model for the infinite alleles mutation process, and a Hierarchical Beta model for the Kimura, Hasegawa--Kishino--Yano and Tamura--Nei processes. Finally, a novel Hierarchical Beta approximation is developed, a Pyramidal Hierarchical Beta model, for the generalized time-reversible and single-step mutation processes.

Mapping the genes underlying ecologically-relevant traits in natural populations is fundamental to develop a molecular understanding of species adaptation. Current sequencing technologies enable the characterisation of a species’ genetic diversity across the landscape or even over its whole range. The relevant capture of the genetic diversity across the landscape is critical for a successful genetic mapping of traits and there are no clear guidelines on how to achieve an optimal sampling and which sequencing strategy to implement. Here we determine through simulation, the sampling scheme that maximises the power to map the genetic basis of a complex trait in an outbreeding species across an idealised landscape and draw genomic predictions for the trait, comparing individual and pool sequencing strategies. Our results show that QTL detection power and prediction accuracy are higher when more populations over the landscape are sampled and this is more cost-effectively done with pool sequencing than with individual sequencing. Additionally, we recommend sampling populations from areas of high genetic diversity. As progress in sequencing enables the integration of trait-based functional ecology into landscape genomics studies, these findings will guide study designs allowing direct measures of genetic effects in natural populations across the environment.

We carried out a genome-wide association study in Latin Americans and identified novel face morphology loci.., To characterize the genetic basis of facial features in Latin Americans, we performed a genome-wide association study (GWAS) of more than 6000 individuals using 59 landmark-based measurements from two-dimensional profile photographs and ~9,000,000 genotyped or imputed single-nucleotide polymorphisms. We detected significant association of 32 traits with at least 1 (and up to 6) of 32 different genomic regions, more than doubling the number of robustly associated face morphology loci reported until now (from 11 to 23). These GWAS hits are strongly enriched in regulatory sequences active specifically during craniofacial development. The associated region in 1p12 includes a tract of archaic adaptive introgression, with a Denisovan haplotype common in Native Americans affecting particularly lip thickness. Among the nine previously unidentified face morphology loci we identified is the VPS13B gene region, and we show that variants in this region also affect midfacial morphology in mice.

We describe events arising from the case of Joby Rowe, convicted of the homicide of his three month old daughter, and explore what they illustrate about systemic problems in the forensic science community in Australia. A peer reviewed journal article that scrutinized the forensic evidence presented in the Rowe case was retracted by a forensic science journal for reasons unrelated to quality or accuracy, under pressure from forensic medical experts criticized in the article. Details of the retraction obtained through freedom of information mechanisms reveal improper pressure and subversion of publishing processes in order to avoid scrutiny. The retraction was supported by the editorial board and two Australian forensic science societies, which is indicative of serious deficiencies in the leadership of forensic science in Australia. We propose paths forward including blind peer review, publication of expert reports, and a criminal cases review authority, that would help stimulate a culture that encourages scrutiny, and relies on evidence-based rather than eminence-based knowledge.

We report an evaluation of prediction accuracy for eye, hair and skin pigmentation based on genomic and phenotypic data for over 6,500 admixed Latin Americans (the CANDELA dataset). We examined the impact on prediction accuracy of three main factors: (i) The methods of prediction, including classical statistical methods and machine learning approaches, (ii) The inclusion of non-genetic predictors, continental genetic ancestry and pigmentation SNPs in the prediction models, and (iii) Compared two sets of pigmentation SNPs: the commonly-used HIrisPlex-S set (developed in Europeans) and novel SNP sets we defined here based on genome-wide association results in the CANDELA sample. We find that Random Forest or regression are globally the best performing methods. Although continental genetic ancestry has substantial power for prediction of pigmentation in Latin Americans, the inclusion of pigmentation SNPs increases prediction accuracy considerably, particularly for skin color. For hair and eye color, HIrisPlex-S has a similar performance to the CANDELA-specific prediction SNP sets. However, for skin pigmentation the performance of HIrisPlex-S is markedly lower than the SNP set defined here, including predictions in an independent dataset of Native American data. These results reflect the relatively high variation in hair and eye color among Europeans for whom HIrisPlex-S was developed, whereas their variation in skin pigmentation is comparatively lower. Furthermore, we show that the dataset used in the training of prediction models strongly impacts on the portability of these models across Europeans and Native Americans.

Mapping the genes underlying ecologically-relevant traits in natural populations is fundamental to develop a molecular understanding of species adaptation. Current sequencing technologies enable the characterisation of a species' genetic diversity across the landscape or even over its whole range. The relevant capture of the genetic diversity across the landscape is critical for a successful genetic mapping of traits and there are no clear guidelines on how to achieve an optimal sampling. Here we determine through simulation, the sampling scheme that maximises the power to map the genetic basis of a complex trait across an idealised landscape and draw genomic predictions for the trait, comparing individual and pool sequencing strategies. Our results show that QTL detection power and prediction accuracy are higher when performing a shallow sampling of more populations over the landscape which is done best using pool sequencing. Populations should be collected from areas of high genetic diversity and we recommend against sampling from the margins of the species' range. As progress in sequencing enables the integration of trait-based functional ecology into landscape genomics studies, these findings will guide study designs allowing direct measures of genetic effects in natural populations across the environment.

Because hyper-excitability has been shown to be a shared pathophysiological mechanism, we used the latest and largest genome-wide studies in amyotrophic lateral sclerosis (n = 36,052) and epilepsy (n = 38,349) to determine genetic overlap between these conditions. First, we showed no significant genetic correlation, also when binned on minor allele frequency. Second, we confirmed the absence of polygenic overlap using genomic risk score analysis. Finally, we did not identify pleiotropic variants in meta-analyses of the 2 diseases. Our findings indicate that amyotrophic lateral sclerosis and epilepsy do not share common genetic risk, showing that hyper-excitability in both disorders has distinct origins. ispartof: NEUROBIOLOGY OF AGING vol:92 ispartof: location:United States status: published

There is currently much debate regarding the best model for how heritability varies across the genome. The authors of GCTA recommend the GCTA-LDMS-I model, the authors of LD Score Regression recommend the Baseline LD model, and we have recommended the LDAK model. Here we provide a statistical framework for assessing heritability models using summary statistics from genome-wide association studies. Based on 31 studies of complex human traits (average sample size 136,000), we show that the Baseline LD model is more realistic than other existing heritability models, but that it can be improved by incorporating features from the LDAK model. Our framework also provides a method for estimating the selection-related parameter α from summary statistics. We find strong evidence (P

In forensic genetics, the likelihood ratio (LR), measuring the value of DNA profile evidence, is computed from a database of allele frequencies. Here, we address the choice of database and adjustments for population structure and sample size in the context of Brazil. The Brazilian population underwent a complex process of colonization, migration and mating, which created an admixed genetic composition that makes it difficult to obtain an appropriate database for a given case. National databases are now available, as well as databases for many Brazilian states. However, those databases are not statistically random samples, and state boundaries may not accurately reflect the sub-structuring of genetic diversity. We compared the LR calculated using the relevant state-specific database with the statistics calculated when a national database and when international databases were used. We evaluated two methods of adjustment for population structure, due to Wright [13] and Balding and Nichols [14]. We also considered two adjustments for database sample size: the Balding size bias correction [15] and a minimum allele frequency [16]. Our results show that the use of a national database with the Balding and Nichols adjustment and θ = 0.002 generated lower LR values than did the state-specific database in more than 50% of the profiles simulated using the state-based allele frequencies, while θ = 0.01 produced lower LRs for more than 90% of the profiles. We conclude that the utilization of a national database for Brazilian cases can be justified in association with the appropriate adjustment for population structure.

Gene panel and exome sequencing have revealed a high rate of molecular diagnoses among diseases where the genetic architecture has proven suitable for sequencing approaches, with a large number of distinct and highly penetrant causal variants identified among a growing list of disease genes. The challenge is, given the DNA sequence of a new patient, to distinguish disease-causing from benign variants. Large samples of human standing variation data highlight regional variation in the tolerance to missense variation within the protein-coding sequence of genes. This information is not well captured by existing bioinformatic tools, but is effective in improving variant interpretation. To address this limitation in existing tools, we introduce the missense tolerance ratio (MTR), which summarizes available human standing variation data within genes to encapsulate population level genetic variation. We find that patient-ascertained pathogenic variants preferentially cluster in low MTR regions (P < 0.005) of well-informed genes. By evaluating 20 publicly available predictive tools across genes linked to epilepsy, we also highlight the importance of understanding the empirical null distribution of existing prediction tools, as these vary across genes. Subsequently integrating the MTR with the empirically selected bioinformatic tools in a gene-specific approach demonstrates a clear improvement in the ability to predict pathogenic missense variants from background missense variation in disease genes. Among an independent test sample of case and control missense variants, case variants (0.83 median score) consistently achieve higher pathogenicity prediction probabilities than control variants (0.02 median score; Mann-Whitney U test, P < 1 × 10−16). We focus on the application to epilepsy genes; however, the framework is applicable to disease genes beyond epilepsy.

SNP heritability, the proportion of phenotypic variance explained by SNPs, has been reported for many hundreds of traits. Its estimation requires strong prior assumptions about the distribution of heritability across the genome, but the assumptions in current use have not been thoroughly tested. By analyzing imputed data for a large number of human traits, we empirically derive a model that more accurately describes how heritability varies with minor allele frequency, linkage disequilibrium and genotype certainty. Across 19 traits, our improved model leads to estimates of common SNP heritability on average 43% (standard deviation 3) higher than those obtained from the widely-used software GCTA, and 25% (standard deviation 2) higher than those from the recently-proposed extension GCTA-LDMS. Previously, DNaseI hypersensitivity sites were reported to explain 79% of SNP heritability; using our improved heritability model their estimated contribution is only 24%.

Linkage disequilibrium SCore regression (LDSC) has become a popular approach to estimate confounding bias, heritability, and genetic correlation using only genome-wide association study (GWAS) test statistics. SumHer is a newly introduced alternative with similar aims. We show using theory and simulations that both approaches fail to adequately account for confounding bias, even when the assumed heritability model is correct. Consequently, these methods may estimate heritability poorly if there was an inadequate adjustment for confounding in the original GWAS analysis. We also show that the choice of a summary statistic for use in LDSC or SumHer can have a large impact on resulting inferences. Further, covariate adjustments in the original GWAS can alter the target of heritability estimation, which can be problematic for test statistics from a meta-analysis of GWAS with different covariate adjustments.

LD SCore regression (LDSC) has become a popular approach to estimate confounding bias, heritability and genetic correlation using only genome wide association study (GWAS) test statistics. SumHer is a newly-introduced alternative with similar aims. We show using theory and simulations that both approaches fail to adequately account for confounding bias, even when the assumed heritability model is correct. Consequently, these methods may estimate heritability poorly if there was inadequate adjustment for confounding in the original GWAS analysis. We also show that choice of summary statistic for use in LDSC or SumHer can have a large impact on resulting inferences. Further, covariate adjustments in the original GWAS can alter the target of heritability estimation, which can be problematic when LDSC or SumHer is applied to test statistics from a meta-analysis of GWAS with different covariate adjustments.

The epilepsies affect around 65 million people worldwide and have a substantial missing heritability component. We report a genome-wide mega-analysis involving 15,212 individuals with epilepsy and 29,677 controls, which reveals 16 genome-wide significant loci, of which 11 are novel. Using various prioritization criteria, we pinpoint the 21 most likely epilepsy genes at these loci, with the majority in genetic generalized epilepsies. These genes have diverse biological functions, including coding for ion-channel subunits, transcription factors and a vitamin-B6 metabolism enzyme. Converging evidence shows that the common variants associated with epilepsy play a role in epigenetic regulation of gene expression in the brain. The results show an enrichment for monogenic epilepsy genes as well as known targets of antiepileptic drugs. Using SNP-based heritability analyses we disentangle both the unique and overlapping genetic basis to seven different epilepsy subtypes. Together, these findings provide leads for epilepsy therapies based on underlying pathophysiology., SCOPUS: ar.j, info:eu-repo/semantics/published

As legal practitioners and courts become more aware of scientific methods and evidence evaluation, they are demanding measures of the reliability of expert opinion. In particular, there are calls for error rates to accompany opinion evidence in comparative forensic sciences. While error rates or confidence intervals can be useful for those disciplines that claim to identify the source of a trace, the call for these statistical tools has extended to sciences that present opinions in the form of a likelihood ratio. In this article we argue against presenting both a likelihood ratio and numerical measures of its uncertainty. We explain how the LR already encapsulates uncertainty. Instead we consider how sensitivity analyses can be used to guide the presentation of LRs that are informative to the court and not unfair to defendants.

Direct-to-consumer genetic ancestry testing is a new and growing industry that has gained widespread media coverage and public interest. Its scientific base is in the fields of population and evolutionary genetics and it has benefitted considerably from recent advances in rapid and cost-effective DNA typing technologies. There is a considerable body of scientific literature on the use of genetic data to make inferences about human population history, although publications on inferring the ancestry of specific individuals are rarer. Population geneticists have questioned the scientific validity of some population history inference approaches, particularly those of a more interpretative nature. These controversies have spilled over into commercial genetic ancestry testing, with some companies making sensational claims about their products. One such company&mdash, BritainsDNA&mdash, made a number of dubious claims both directly to its customers and in the media. Here we outline our scientific concerns, document the exchanges between us, BritainsDNA and the BBC, and discuss the issues raised about media promotion of commercial enterprises, academic freedom of expression, science and pseudoscience and the genetic ancestry testing industry. We provide a detailed account of this case as a resource for historians and sociologists of science, and to shape public understanding, media reporting and scientific scrutiny of the commercial use of population and evolutionary genetics.

We compare two open-source programs for the evaluation of evidential weight arising from complex DNA profiles recovered in a crime investigation. Here, "complex" means one or more of: low-template, degraded and mixed-source. Although software for complex DNA profile analysis has made great strides in recent years, the ability of courts to effectively scrutinise and challenge the reliability of the resulting evidence remains problematic. One key step is to compare different software on the same evidence, but there are currently few published comparisons in part because of the problems posed by restricted access to commercial software. We present here an extensive comparison between two open-source software, LikeLTD and EuroForMix. We find that despite different modelling assumptions the two programs generate similar results. The differences that we do identify can inform future improvements and can provide a benchmark for acceptable discrepancies between alternative programs.

LD Score Regression (LDSC) has been widely applied to the results of genome-wide association studies. However, its estimates of SNP heritability are derived from an unrealistic model in which each SNP is expected to contribute equal heritability. As a consequence, LDSC tends to over-estimate confounding bias, under-estimate the total phenotypic variation explained by SNPs, and provide misleading estimates of the heritability enrichment of SNP categories. Therefore, we present SumHer, software for estimating SNP heritability from summary statistics using more realistic heritability models. After demonstrating its superiority over LDSC, we apply SumHer to the results of 24 large-scale association studies (average sample size 121 000). First we show that these studies have tended to substantially over-correct for confounding, and as a result the number of genome-wide significant loci has under-reported by about 20%. Next we estimate enrichment for 24 categories of SNPs defined by functional annotations. A previous study using LDSC reported that conserved regions were 13-fold enriched, and found a further twelve categories with above 2-fold enrichment. By contrast, our analysis using SumHer finds that conserved regions are only 1.6-fold (SD 0.06) enriched, and that no category has enrichment above 1.7-fold. SumHer provides an improved understanding of the genetic architecture of complex traits, which enables more efficient analysis of future genetic data.

In our recent publication,1 we examined the two heritability models most widely used when estimating SNP heritability: the GCTA Model, which is used by the software GCTA2 and upon which LD Score regression (LDSC) is based,3 and the LDAK Model, which is used by our software LDAK.4 First we demonstrated the importance of choosing an appropriate heritability model, by showing that estimates of SNP heritability can be highly sensitive to which model is assumed. Then we empirically tested the GCTA and LDAK Models on GWAS data for a wide variety of complex traits. We found that the LDAK Model fits real data both significantly and substantially better than the GCTA Model, indicating that LDAK estimates more accurately describe the genetic architecture of complex traits than those from GCTA or LDSC.Some of our most striking results were our revised estimates of functional enrichments (the heritability enrichments of SNP categories defined by functional annotations). In general, estimates from LDAK were substantially more modest than previous estimates based on the GCTA Model. For example, we estimated that DNase I hypersensitive sites (DHS) were 1.4-fold (SD 0.1) enriched, whereas a study using GCTA had found they were 5.1-fold (SD 0.5) enriched,5 and we estimated that conserved SNPs were 1.3-fold (SD 0.3) enriched, whereas a study using S-LDSC (stratified LDSC) had found they were 13.3-fold (SD 1.5) enriched.6In their correspondence, Gazal et al. dispute our findings. They assert that the heritability model assumed by LDSC is more realistic than the LDAK Model, and that estimates of enrichment from S-LDSC7 are more accurate than those from LDAK. Here, we explain why their justification for preferring the model used by LDSC is incorrect, and provide a simple demonstration that S-LDSC produces unreliable estimates of enrichment.

We present SumHer, software for estimating confounding bias, SNP heritability, enrichments of heritability and genetic correlations using summary statistics from genome-wide association studies. The key difference between SumHer and the existing software LD Score Regression (LDSC) is that SumHer allows the user to specify the heritability model. We apply SumHer to results from 24 large-scale association studies (average sample size 121,000) using our recommended heritability model. We show that these studies tended to substantially over-correct for confounding, and as a result the number of genome-wide significant loci was under-reported by about a quarter. We also estimate enrichments for 24 categories of SNPs defined by functional annotations. A previous study using LDSC reported that conserved regions were 13-fold enriched, and found a further six categories with above threefold enrichment. By contrast, our analysis using SumHer finds that none of the categories have enrichment above twofold. SumHer provides an improved understanding of the genetic architecture of complex traits, which enables more efficient analysis of future genetic data.

Historical records and genetic analyses indicate that Latin Americans trace their ancestry mainly to the intermixing (admixture) of Native Americans, Europeans and Sub-Saharan Africans. Using novel haplotype-based methods, here we infer sub-continental ancestry in over 6,500 Latin Americans and evaluate the impact of regional ancestry variation on physical appearance. We find that Native American ancestry components in Latin Americans correspond geographically to the present-day genetic structure of Native groups, and that sources of non-Native ancestry, and admixture timings, match documented migratory flows. We also detect South/East Mediterranean ancestry across Latin America, probably stemming mostly from the clandestine colonial migration of Christian converts of non-European origin (Conversos). Furthermore, we find that ancestry related to highland (Central Andean) versus lowland (Mapuche) Natives is associated with variation in facial features, particularly nose morphology, and detect significant differences in allele frequencies between these groups at loci previously associated with nose morphology in this sample., Latin Americans trace their ancestry to the admixture of Native Americans, Europeans and Sub-Saharan Africans. Here, the authors develop a novel haplotype-based approach and analyse over 6,500 Latin Americans to infer the geographically-detailed genetic structure of this population.

Supplemental Digital Content is available in the text., Context In a previous work, we have shown that penalized regression approaches can allow many genetic variants to be incorporated into sophisticated pharmacokinetic (PK) models in a way that is both computationally and statistically efficient. The phenotypes were the individual model parameter estimates, obtained a posteriori of the model fit and known to be sensitive to the study design. Objective The aim of this study was to propose an integrated approach in which genetic effect sizes are estimated simultaneously with the PK model parameters, which should improve the estimate precision and reduce sensitivity to study design. Methods A total of 200 data sets were simulated under the null and each of the following three alternative scenarios: (i) a phase II study with N=300 participants and n=6 sampling times, wherein six unobserved causal variants affect the drug elimination clearance; (ii) the addition of participants with a residual concentration collected in clinical routine (N=300, n=6 plus N=700, n=1); and (iii) a phase II study (N=300, n=6) in which four unobserved causal variants affect two different model parameters. Results In all scenarios the integrated approach detected fewer false positives. In scenario (i), true-positive rates were low and the stepwise procedure outperformed the integrated approach. In scenario (ii), approaches performed similarly and rates were higher. In scenario (iii), the integrated approach outperformed the stepwise procedure. Conclusion A PK phase II study with N=300 lacks the power to detect genetic effects on PK using genetic arrays. Our approach can simultaneously analyse phase II and clinical routine data and identify when genetic variants affect multiple PK parameters.

The introduction of forensic autosomal DNA profiles was controversial, but the problems were successfully addressed, and DNA profiling has gone on to revolutionise forensic science. Y-chromosome profiles are valuable when there is a mixture of male-source and female-source DNA, and interest centres on the identity of the male source(s) of the DNA. The problem of evaluating evidential weight is even more challenging for Y profiles than for autosomal profiles. Numerous approaches have been proposed, but they fail to deal adequately with the fact that men with matching Y-profiles are related in extended patrilineal clans, many of which may not be represented in available databases. The higher mutation rates of modern profiling kits have led to increased discriminatory power but they have also exacerbated the problem of fairly conveying evidential value. Because the relevant population is difficult to define, yet the number of matching relatives is fixed as population size varies, it is typically infeasible to derive population-based match probabilities relevant to a specific crime. We propose a conceptually simple solution, based on a simulation model and software to approximate the distribution of the number of males with a matching Y profile. We show that this distribution is robust to different values for the variance in reproductive success and the population growth rate. We also use importance sampling reweighting to derive the distribution of the number of matching males conditional on a database frequency, finding that this conditioning typically has only a modest impact. We illustrate the use of our approach to quantify the value of Y profile evidence for a court in a way that is both scientifically valid and easily comprehensible by a judge or juror., Author summary Y-chromosome DNA profiles are important in forensic science, particularly when a male has been accused of assaulting a female. However, unlike for autosomal profiles, the problem of evaluating weight-of-evidence for Y profiles has not been satisfactorily resolved despite many attempts. The key idea missing from current approaches is that Y-profile matches are due to patrilineal relatedness that is typically too remote to be recognized, but close compared with the relatedness of random pairs from the population. We focus on approximating the number of males with matching Y profiles, rather than a population fraction or match probability. We describe a simulation model of Y profile evolution, implemented in open-source software, for approximating the number of males sharing a Y profile. We extend our simulation method to also model database selection. Even under the optimistic assumption that the database has been sampled randomly in the relevant population, we show that database counts don’t help much. The reason is that modern profiling kits with high profile mutation rates imply that almost all profiles are rare relative to typical database sizes. We discuss how to use our approach to present evidence in court in a way that is both fair and easy to understand.

The aryl hydrocarbon receptor interacting protein (AIP) founder mutation R304* (or p.R304*; NM_003977.3:c.910C>T, p.Arg304Ter) identified in Northern Ireland (NI) predisposes to acromegaly/gigantism; its population health impact remains unexplored. We measured R304* carrier frequency in 936 Mid Ulster, 1000 Greater Belfast (both in NI) and 2094 Republic of Ireland (ROI) volunteers and in 116 acromegaly/gigantism patients. Carrier frequencies were 0.0064 in Mid Ulster (95%CI = 0.0027-0.013; P = 0.0005 vs. ROI), 0.001 in Greater Belfast (0.00011-0.0047) and zero in ROI (0-0.0014). R304* prevalence was elevated in acromegaly/gigantism patients in NI (11/87, 12.6%, P < 0.05), but not in ROI (2/29, 6.8%) vs. non-Irish patients (0-2.41%). Haploblock conservation supported a common ancestor for all the 18 identified Irish pedigrees (81 carriers, 30 affected). Time to most recent common ancestor (tMRCA) was 2550 (1275-5000) years. tMRCA-based simulations predicted 432 (90-5175) current carriers, including 86 affected (18-1035) for 20% penetrance. In conclusion, R304* is frequent in Mid Ulster, resulting in numerous acromegaly/gigantism cases. tMRCA is consistent with historical/folklore accounts of Irish giants. Forward simulations predict many undetected carriers; geographically-targeted population screening improves asymptomatic carrier identification, complementing clinical testing of patients/relatives. We generated disease awareness locally, necessary for early diagnosis and improved outcomes of AIP-related disease. This article is protected by copyright. All rights reserved.

Objective Mutations in the aryl hydrocarbon receptor-interacting protein (AIP) gene are associated with pituitary adenoma, acromegaly and gigantism. Identical alleles in unrelated pedigrees could be inherited from a common ancestor or result from recurrent mutation events. Design and methods Observational, inferential and experimental study, including: AIP mutation testing; reconstruction of 14 AIP-region (8.3 Mbp) haplotypes; coalescent-based approximate Bayesian estimation of the time to most recent common ancestor (tMRCA) of the derived allele; forward population simulations to estimate current number of allele carriers; proposal of mutation mechanism; protein structure predictions; co-immunoprecipitation and cycloheximide chase experiments. Results Nine European-origin, unrelated c.805_825dup-positive pedigrees (four familial, five sporadic from the UK, USA and France) included 16 affected (nine gigantism/four acromegaly/two non-functioning pituitary adenoma patients and one prospectively diagnosed acromegaly patient) and nine unaffected carriers. All pedigrees shared a 2.79 Mbp haploblock around AIP with additional haploblocks privately shared between subsets of the pedigrees, indicating the existence of an evolutionarily recent common ancestor, the ‘English founder’, with an estimated median tMRCA of 47 generations (corresponding to 1175 years) with a confidence interval (9–113 generations, equivalent to 225–2825 years). The mutation occurred in a small tandem repeat region predisposed to slipped strand mispairing. The resulting seven amino-acid duplication disrupts interaction with HSP90 and leads to a marked reduction in protein stability. Conclusions The c.805_825dup allele, originating from a common ancestor, associates with a severe clinical phenotype and a high frequency of gigantism. The mutation is likely to be the result of slipped strand mispairing and affects protein–protein interactions and AIP protein stability.

We show the application of association mapping and genomic selection for key breeding targets using a large panel of elite winter wheat varieties and a large volume of agronomic data. The heightening urgency to increase wheat production in line with the needs of a growing population, and in the face of climatic uncertainty, mean new approaches, including association mapping (AM) and genomic selection (GS) need to be validated and applied in wheat breeding. Key adaptive responses are the cornerstone of regional breeding. There is evidence that new ideotypes for long-standing traits such as flowering time may be required. In order to detect targets for future marker-assisted improvement and validate the practical application of GS for wheat breeding we genotyped 376 elite wheat varieties with 3,046 DArT, single nucleotide polymorphism and gene markers and measured seven traits in replicated yield trials over 2 years in France, Germany and the UK. The scale of the phenotyping exceeds the breadth of previous AM and GS studies in these key economic wheat production regions of Northern Europe. Mixed-linear modelling (MLM) detected significant marker-trait associations across and within regions. Genomic prediction using elastic net gave low to high prediction accuracies depending on the trait, and could be experimentally increased by modifying the constituents of the training population (TP). We also tested the use of differentially penalised regression to integrate candidate gene and genome-wide markers to predict traits, demonstrating the validity and simplicity of this approach. Overall, our results suggest that whilst AM offers potential for application in both research and breeding, GS represents an exciting opportunity to select key traits, and that optimisation of the TP is crucial to its successful implementation.

The evaluation of weight of evidence for forensic DNA profiles has been a subject of controversy since their introduction over 20 years ago. Substantial progress has been made for standard DNA profiles, but new issues have arisen in recent years with the advent of more sensitive profiling techniques, allowing profiles to be recovered from minuscule amounts of possibly degraded DNA. These low-template DNA profiles suffer from enhanced stochastic effects, including dropin, dropout, and stutter, which pose problems for DNA profile evaluation. These problems are now beginning to be overcome with the emergence of several statistical models and software. We first review the general principles of statistical evaluation of DNA profile evidence, and we then focus on low-template DNA profiles, briefly reviewing the main statistical models and software. We cover methods that use allele presence/absence and those that use electropherogram peak heights, focusing on the likelihood ratio as measure of evidential weight.

(2019). RETRACTED ARTICLE: Design of an efficient and complete elicitation decision process in contingent valuation method. Cogent Mathematics & Statistics: Vol. 6, No. 1, 1564164.

We present a new Markov chain Monte Carlo algorithm, implemented in software Arbores, for inferring the history of a sample of DNA sequences. Our principal innovation is a bridging procedure, previously applied only for simple stochastic processes, in which the local computations within a bridge can proceed independently of the rest of the DNA sequence, facilitating large-scale parallelisation., 23 pages, 9 figures, accepted for publication in Proceedings of the Royal Society A

Increases in the sensitivity of DNA profiling technology now allow profiles to be obtained from smaller and more degraded DNA samples than was previously possible. The resulting profiles can be highly informative, but the subjective elements in the interpretation make it problematic to achieve the valid and efficient evaluation of evidential strength required in criminal cases. The problems arise from stochastic phenomena such as "dropout" (absence of an allele in the profile that is present in the underlying DNA) and experimental artefacts such as "stutter" that can generate peaks of ambiguous allelic status. Currently in the UK, evidential strength evaluation uses an approach in which the complex signals in the DNA profiles are interpreted in a semi-manual fashion by trained experts aided by a set of guidelines, but also relying substantially on professional judgment. We introduce a statistical model to calculate likelihood ratios for evaluating DNA evidence arising from multiple known and unknown contributors that allows for such stochastic phenomena by incorporating peak heights. Efficient use of peak heights allows for more crime scene profiles to be reported to courts than is currently possible. The model parameters are estimated from experimental data incorporating multiple sources of variability in the profiling system. We report and analyse experimental results from the SGMPlus system, run at 28 amplification cycles with no enhancements, currently used in the UK. Our methods are readily adapted to other DNA profiling systems provided that the experimental data for the parameter estimation is available.

We present the analysis of a prospective multicentre study to investigate genetic effects on the prognosis of newly treated epilepsy. Patients with a new clinical diagnosis of epilepsy requiring medication were recruited and followed up prospectively. The clinical outcome was defined as freedom from seizures for a minimum of 12 months in accordance with the consensus statement from the International League Against Epilepsy (ILAE). Genetic effects on remission of seizures after starting treatment were analysed with and without adjustment for significant clinical prognostic factors, and the results from each cohort were combined using a fixed-effects meta-analysis. After quality control (QC), we analysed 889 newly treated epilepsy patients using 472 450 genotyped and 6.9 × 10(6) imputed single-nucleotide polymorphisms. Suggestive evidence for association (defined as Pmeta5.0 × 10(-7)) with remission of seizures after starting treatment was observed at three loci: 6p12.2 (rs492146, Pmeta = 2.1 × 10(-7), OR[G] = 0.57), 9p23 (rs72700966, Pmeta = 3.1 × 10(-7), OR[C] = 2.70) and 15q13.2 (rs143536437, Pmeta = 3.2 × 10(-7), OR[C] = 1.92). Genes of biological interest at these loci include PTPRD and ARHGAP11B (encoding functions implicated in neuronal development) and GSTA4 (a phase II biotransformation enzyme). Pathway analysis using two independent methods implicated a number of pathways in the prognosis of epilepsy, including KEGG categories 'calcium signaling pathway' and 'phosphatidylinositol signaling pathway'. Through a series of power curves, we conclude that it is unlikely any single common variant explains4.4% of the variation in the outcome of newly treated epilepsy.

Niemann–Pick disease type C (NP-C) is a rare, autosomal-recessive, progressive neurological disease caused by mutations in either the NPC1 gene (in 95% of cases) or the NPC2 gene. This observational, multicentre genetic screening study evaluated the frequency and phenotypes of NP-C in consecutive adult patients with neurological and psychiatric symptoms. Diagnostic testing for NP-C involved NPC1 and NPC2 exonic gene sequencing and gene dosage analysis. When available, results of filipin staining, plasma cholestane-3β,5α,6β-triol assays and measurements of relevant sphingolipids were also collected. NPC1 and NPC2 gene sequencing was completed in 250/256 patients from 30 psychiatric and neurological reference centres across the EU and USA [median (range) age 38 (18–90) years]. Three patients had a confirmed diagnosis of NP-C; two based on gene sequencing alone (two known causal disease alleles) and one based on gene sequencing and positive filipin staining. A further 12 patients displayed either single mutant NP-C alleles (8 with NPC1 mutations and 3 with NPC2 mutations) or a known causal disease mutation and an unclassified NPC1 allele variant (1 patient). Notably, high plasma cholestane-3β,5α,6β-triol levels were observed for all NP-C cases (n = 3). Overall, the frequency of NP-C patients in this study [1.2% (95% CI; 0.3%, 3.5%)] suggests that there may be an underdiagnosed pool of NP-C patients among adults who share common neurological and psychiatric symptoms.