Estimating evolutionary and demographic parameters via ARG-derived IBD.

Inference of demographic and evolutionary parameters from a sample of genome sequences often proceeds by first inferring identical-by-descent (IBD) genome segments. By exploiting efficient data encoding based on the ancestral recombination graph (ARG), we obtain three major advantages over current approaches: (i) no need to impose a length threshold on IBD segments, (ii) IBD can be defined without the hard-to-verify requirement of no recombination, and (iii) computation time can be reduced with little loss of statistical efficiency using only the IBD segments from a set of sequence pairs that scales linearly with sample size. We first demonstrate powerful inferences when true IBD information is available from simulated data. For IBD inferred from real data, we propose an approximate Bayesian computation inference algorithm and use it to show that poorly-inferred short IBD segments can improve estimation precision. We show estimation precision similar to a previously-published estimator despite a 4 000-fold reduction in data used for inference. Computational cost limits model complexity in our approach, but we are able to incorporate unknown nuisance parameters and model misspecification, still finding improved parameter inference.

Four-week inhibition of the renin-angiotensin system in spontaneously hypertensive rats results in persistently lower blood pressure with reduced kidney renin and changes in expression of relevant gene networks.

AIMS: Prevention of human hypertension is an important challenge and has been achieved in experimental models. Brief treatment with renin-angiotensin system (RAS) inhibitors permanently reduces the genetic hypertension of the spontaneously hypertensive rat (SHR). The kidney is involved in this fascinating phenomenon, but relevant changes in gene expression are unknown. METHODS AND RESULTS: In SHR, we studied the effect of treatment between 10 and 14 weeks of age with the angiotensin receptor blocker, losartan, or the angiotensin-converting enzyme inhibitor, perindopril [with controls for non-specific effects of lowering blood pressure (BP)], on differential RNA expression, DNA methylation, and renin immunolabelling in the kidney at 20 weeks of age. RNA sequencing revealed a six-fold increase in renin gene (Ren) expression during losartan treatment (P < 0.0001). Six weeks after losartan, arterial pressure remained lower (P = 0.006), yet kidney Ren showed reduced expression by 23% after losartan (P = 0.03) and by 43% after perindopril (P = 1.4 × 10-6) associated with increased DNA methylation (P = 0.04). Immunolabelling confirmed reduced cortical renin after earlier RAS blockade (P = 0.002). RNA sequencing identified differential expression of mRNAs, miRNAs, and lncRNAs with evidence of networking and co-regulation. These included 13 candidate genes (Grhl1, Ammecr1l, Hs6st1, Nfil3, Fam221a, Lmo4, Adamts1, Cish, Hif3a, Bcl6, Rad54l2, Adap1, Dok4), the miRNA miR-145-3p, and the lncRNA AC115371. Gene ontogeny analyses revealed that these networks were enriched with genes relevant to BP, RAS, and the kidneys. CONCLUSION: Early RAS inhibition in SHR resets genetic pathways and networks resulting in a legacy of reduced Ren expression and BP persisting for a minimum of 6 weeks.

Fast and accurate joint inference of coancestry parameters for populations and/or individuals.

We introduce a fast, new algorithm for inferring from allele count data the FST parameters describing genetic distances among a set of populations and/or unrelated diploid individuals, and a tree with branch lengths corresponding to FST values. The tree can reflect historical processes of splitting and divergence, but seeks to represent the actual genetic variance as accurately as possible with a tree structure. We generalise two major approaches to defining FST, via correlations and mismatch probabilities of sampled allele pairs, which measure shared and non-shared components of genetic variance. A diploid individual can be treated as a population of two gametes, which allows inference of coancestry coefficients for individuals as well as for populations, or a combination of the two. A simulation study illustrates that our fast method-of-moments estimation of FST values, simultaneously for multiple populations/individuals, gains statistical efficiency over pairwise approaches when the population structure is close to tree-like. We apply our approach to genome-wide genotypes from the 26 worldwide human populations of the 1000 Genomes Project. We first analyse at the population level, then a subset of individuals and in a final analysis we pool individuals from the more homogeneous populations. This flexible analysis approach gives advantages over traditional approaches to population structure/coancestry, including visual and quantitative assessments of long-standing questions about the relative magnitudes of within- and between-population genetic differences.

LDAK-GBAT: Fast and powerful gene-based association testing using summary statistics.

We present LDAK-GBAT, a tool for gene-based association testing using summary statistics from genome-wide association studies that is computationally efficient, produces well-calibrated p values, and is significantly more powerful than existing tools. LDAK-GBAT takes approximately 30 min to analyze imputed data (2.9M common, genic SNPs), requiring less than 10 Gb memory. It shows good control of type 1 error given an appropriate reference panel. Across 109 phenotypes (82 from the UK Biobank, 18 from the Million Veteran Program, and nine from the Psychiatric Genetics Consortium), LDAK-GBAT finds on average 19% (SE: 1%) more significant genes than the existing tool sumFREGAT-ACAT, with even greater gains in comparison with MAGMA, GCTA-fastBAT, sumFREGAT-SKAT-O, and sumFREGAT-PCA.

A founder event causing a dominant childhood epilepsy survives 800 years through weak selective pressure.

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.

Genome-wide association mapping of Hagberg falling number, protein content, test weight, and grain yield in U.K. wheat.

Association mapping using crop cultivars allows identification of genetic loci of direct relevance to breeding. Here, 150 U.K. wheat (Triticum aestivum L.) cultivars genotyped with 23,288 single nucleotide polymorphisms (SNPs) were used for genome-wide association studies (GWAS) using historical phenotypic data for grain protein content, Hagberg falling number (HFN), test weight, and grain yield. Power calculations indicated experimental design would enable detection of quantitative trait loci (QTL) explaining ≥20% of the variation (PVE) at a relatively high power of >80%, falling to 40% for detection of a SNP with an R2 ≥ .5 with the same QTL. Genome-wide association studies identified marker-trait associations for all four traits. For HFN (h 2 = .89), six QTL were identified, including a major locus on chromosome 7B explaining 49% PVE and reducing HFN by 44 s. For protein content (h 2 = 0.86), 10 QTL were found on chromosomes 1A, 2A, 2B, 3A, 3B, and 6B, together explaining 48.9% PVE. For test weight, five QTL were identified (one on 1B and four on 3B; 26.3% PVE). Finally, 14 loci were identified for grain yield (h 2 = 0.95) on eight chromosomes (1A, 2A, 2B, 2D, 3A, 5B, 6A, 6B; 68.1% PVE), of which five were located within 16 Mbp of genetic regions previously identified as under breeder selection in European wheat. Our study demonstrates the utility of exploiting historical crop datasets, identifying genomic targets for independent validation, and ultimately for wheat genetic improvement.

Including diverse and admixed populations in genetic epidemiology research.

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.

Disentangling Signatures of Selection Before and After European Colonization in Latin Americans.

Throughout human evolutionary history, large-scale migrations have led to intermixing (i.e., admixture) between previously separated human groups. Although classical and recent work have shown that studying admixture can yield novel historical insights, the extent to which this process contributed to adaptation remains underexplored. Here, we introduce a novel statistical model, specific to admixed populations, that identifies loci under selection while determining whether the selection likely occurred post-admixture or prior to admixture in one of the ancestral source populations. Through extensive simulations, we show that this method is able to detect selection, even in recently formed admixed populations, and to accurately differentiate between selection occurring in the ancestral or admixed population. We apply this method to genome-wide SNP data of ∼4,000 individuals in five admixed Latin American cohorts from Brazil, Chile, Colombia, Mexico, and Peru. Our approach replicates previous reports of selection in the human leukocyte antigen region that are consistent with selection post-admixture. We also report novel signals of selection in genomic regions spanning 47 genes, reinforcing many of these signals with an alternative, commonly used local-ancestry-inference approach. These signals include several genes involved in immunity, which may reflect responses to endemic pathogens of the Americas and to the challenge of infectious disease brought by European contact. In addition, some of the strongest signals inferred to be under selection in the Native American ancestral groups of modern Latin Americans overlap with genes implicated in energy metabolism phenotypes, plausibly reflecting adaptations to novel dietary sources available in the Americas.

SNP-based heritability and selection analyses: Improved models and new results.

Complex-trait genetics has advanced dramatically through methods to estimate the heritability tagged by SNPs, both genome-wide and in genomic regions of interest such as those defined by functional annotations. The models underlying many of these analyses are inadequate, and consequently many SNP-heritability results published to date are inaccurate. Here, we review the modelling issues, both for analyses based on individual genotype data and association test statistics, highlighting the role of a low-dimensional model for the heritability of each SNP. We use state-of-art models to present updated results about how heritability is distributed with respect to functional annotations in the human genome, and how it varies with allele frequency, which can reflect purifying selection. Our results give finer detail to the picture that has emerged in recent years of complex trait heritability widely dispersed across the genome. Confounding due to population structure remains a problem that summary statistic analyses cannot reliably overcome. Also see the video abstract here: https://youtu.be/WC2u03V65MQ.

Bayesian inference of ancestral recombination graphs.

We present a novel algorithm, implemented in the software ARGinfer, for probabilistic inference of the Ancestral Recombination Graph under the Coalescent with Recombination. Our Markov Chain Monte Carlo algorithm takes advantage of the Succinct Tree Sequence data structure that has allowed great advances in simulation and point estimation, but not yet probabilistic inference. Unlike previous methods, which employ the Sequentially Markov Coalescent approximation, ARGinfer uses the Coalescent with Recombination, allowing more accurate inference of key evolutionary parameters. We show using simulations that ARGinfer can accurately estimate many properties of the evolutionary history of the sample, including the topology and branch lengths of the genealogical tree at each sequence site, and the times and locations of mutation and recombination events. ARGinfer approximates posterior probability distributions for these and other quantities, providing interpretable assessments of uncertainty that we show to be well calibrated. ARGinfer is currently limited to tens of DNA sequences of several hundreds of kilobases, but has scope for further computational improvements to increase its applicability.

Genome-wide association, prediction and heritability in bacteria with application to Streptococcus pneumoniae.

Whole-genome sequencing has facilitated genome-wide analyses of association, prediction and heritability in many organisms. However, such analyses in bacteria are still in their infancy, being limited by difficulties including genome plasticity and strong population structure. Here we propose a suite of methods including linear mixed models, elastic net and LD-score regression, adapted to bacterial traits using innovations such as frequency-based allele coding, both insertion/deletion and nucleotide testing and heritability partitioning. We compare and validate our methods against the current state-of-art using simulations, and analyse three phenotypes of the major human pathogen Streptococcus pneumoniae, including the first analyses of minimum inhibitory concentrations (MIC) for penicillin and ceftriaxone. We show that the MIC traits are highly heritable with high prediction accuracy, explained by many genetic associations under good population structure control. In ceftriaxone MIC, this is surprising because none of the isolates are resistant as per the inhibition zone criteria. We estimate that half of the heritability of penicillin MIC is explained by a known drug-resistance region, which also contributes a quarter of the ceftriaxone MIC heritability. For the within-host carriage duration phenotype, no associations were observed, but the moderate heritability and prediction accuracy indicate a moderately polygenic trait.

Optimizing sampling design and sequencing strategy for the genomic analysis of quantitative traits in natural populations.

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 characterization 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 maximizes the power to map the genetic basis of a complex trait in an outbreeding species across an idealized landscape and draw genomic predictions for the trait, comparing individual and pool sequencing strategies. Our results show that quantitative trait locus 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.

Retraction of a peer reviewed article suggests ongoing problems with Australian forensic science.

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.

Assessing the Forensic Value of DNA Evidence from Y Chromosomes and Mitogenomes.

Y chromosome 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). We review approaches to the evaluation of lineage marker profiles for forensic identification, focussing on the key roles of profile mutation rate and relatedness (extending beyond known relatives). 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 relatives may be plausible alternative contributors, and may not be well mixed in the population. These issues reduce the usefulness of profile databases drawn from a broad population: larger populations can have a lower profile relative frequency because of lower relatedness with the alleged contributor. Many evaluation methods do not adequately take account of distant relatedness, but its effects have become more pronounced with the latest generation of high-mutation-rate Y profiles.

Prediction of eye, hair and skin colour in Latin Americans.

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.

A GWAS in Latin Americans identifies novel face shape loci, implicating VPS13B and a Denisovan introgressed region in facial variation.

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.

How can courts take into account the uncertainty in a likelihood ratio?

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.

Evaluating and improving heritability models using summary statistics.

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 < 1 × 10-6) of negative genome-wide selection for traits, including height, systolic blood pressure and college education, and that the impact of selection is stronger inside functional categories, such as coding SNPs and promoter regions.

Summary statistic analyses can mistake confounding bias for heritability.

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.