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The Finnish population is a unique example of a genetic isolate affected by a recent founder event. Previous studies have suggested that the ancestors of Finnic-speaking Finns and Estonians reached the circum-Baltic region by the 1(st) millennium BC. However, high linguistic similarity points to a more recent split of their languages. To study genetic connectedness between Finns and Estonians directly, we first assessed the efficacy of imputation of low-coverage ancient genomes by sequencing a medieval Estonian genome to high depth (23×) and evaluated the performance of its down-sampled replicas. We find that ancient genomes imputed from >0.1× coverage can be reliably used in principal-component analyses without projection. By searching for long shared allele intervals (LSAIs; similar to identity-by-descent segments) in unphased data for >143,000 present-day Estonians, 99 Finns, and 14 imputed ancient genomes from Estonia, we find unexpectedly high levels of individual connectedness between Estonians and Finns for the last eight centuries in contrast to their clear differentiation by allele frequencies. High levels of sharing of these segments between Estonians and Finns predate the demographic expansion and late settlement process of Finland. One plausible source of this extensive sharing is the 8(th)–10(th) centuries AD migration event from North Estonia to Finland that has been proposed to explain uniquely shared linguistic features between the Finnish language and the northern dialect of Estonian and shared Christianity-related loanwords from Slavic. These results suggest that LSAI detection provides a computationally tractable way to detect fine-scale structure in large cohorts.

Population genetic analyses of local ancestry tracts routinely assume that the ancestral admixture process is identical for both parents of an individual, an assumption that may be invalid when considering recent admixture. Here we present Parental Admixture Proportion Inference (PAPI), a Bayesian tool for inferring the admixture proportions and admixture times for each parent of a single admixed individual. PAPI analyzes unphased local ancestry tracts and has two components models: a binomial model that exploits the informativeness of homozygous ancestry regions to infer parental admixture proportions, and a hidden Markov model (HMM) that infers admixture times from tract lengths. Crucially, the HMM employs an approximation to the pedigree crossover dynamics that accounts for unobserved within-ancestry recombination, enabling inference of parental admixture times. We compared the accuracy of PAPI’s admixture proportion estimates with those of ANCESTOR in simulated admixed individuals and found that PAPI outperforms ANCESTOR by an average of 46% in a representative set of simulation scenarios, with PAPI’s estimates deviating from the ground truth by 0.047 on average. Moreover, PAPI’s admixture time estimates were strongly correlated with the ground truth in these simulations (R = 0.76), but have an average downward bias of 1.01 generations that is partly attributable to inaccuracies in local ancestry inference. As an illustration of its utility, we ran PAPI on real African Americans from the PAGE study (N = 5, 786) and found strong evidence of assortative mating by ancestry proportion: couples’ ancestry proportions are closer to each other than expected by chance (P < 10−6), and are highly correlated (R = 0.87). We anticipate that PAPI will be useful in studying the population dynamics of admixture and will also be of interest to individuals seeking to learn about their personal genealogies.

Despite decades of methods development for classifying relatives in genetic studies, pairwise relatedness methods’ recalls are above 90% only for first through third-degree relatives. The top-performing approaches, which leverage identity-by-descent segments, often use only kinship coefficients, while others, including estimation of recent shared ancestry (ERSA), use the number of segments relatives share. To quantify the potential for using segment numbers in relatedness inference, we leveraged information theory measures to analyze exact (i.e. produced by a simulator) identity-by-descent segments from simulated relatives. Over a range of settings, we found that the mutual information between the relatives’ degree of relatedness and a tuple of their kinship coefficient and segment number is on average 4.6% larger than between the degree and the kinship coefficient alone. We further evaluated identity-by-descent segment number utility by building a Bayes classifier to predict first through sixth-degree relationships using different feature sets. When trained and tested with exact segments, the inclusion of segment numbers improves the recall by between 0.28% and 3% for second through sixth-degree relatives. However, the recalls improve by less than 1.8% per degree when using inferred segments, suggesting limitations due to identity-by-descent detection accuracy. Last, we compared our Bayes classifier that includes segment numbers with both ERSA and IBIS and found comparable recalls, with the Bayes classifier and ERSA slightly outperforming each other across different degrees. Overall, this study shows that identity-by-descent segment numbers can improve relatedness inference, but errors from current SNP array-based detection methods yield dampened signals in practice.

Identity-by-descent (IBD) segments are a useful tool for applications ranging from demographic inference to relationship classification, but most detection methods rely on phasing information and therefore require substantial computation time. As genetic datasets grow, methods for inferring IBD segments that scale well will be critical. We developed IBIS, an IBD detector that locates long regions of allele sharing between unphased individuals, and benchmarked it with Refined IBD, GERMLINE, and TRUFFLE on 3,000 simulated individuals. Phasing these with Beagle 5 takes 4.3 CPU days, followed by either Refined IBD or GERMLINE segment detection in 2.9 or 1.1 h, respectively. By comparison, IBIS finishes in 6.8 min or 7.8 min with IBD2 functionality enabled: speedups of 805-946× including phasing time. TRUFFLE takes 2.6 h, corresponding to IBIS speedups of 20.2-23.3×. IBIS is also accurate, inferring ≥7 cM IBD segments at quality comparable to Refined IBD and GERMLINE. With these segments, IBIS classifies first through third degree relatives in real Mexican American samples at rates meeting or exceeding other methods tested and identifies fourth through sixth degree pairs at rates within 0.0%-2.0% of the top method. While allele frequency-based approaches that do not detect segments can infer relationship degrees faster than IBIS, the fastest are biased in admixed samples, with KING inferring 30.8% fewer fifth degree Mexican American relatives correctly compared with IBIS. Finally, we ran IBIS on chromosome 2 of the UK Biobank dataset and estimate its runtime on the autosomes to be 3.3 days parallelized across 128 cores.

Summary The proportion of samples with one or more close relatives in a genetic dataset increases rapidly with sample size, necessitating relatedness modeling and enabling pedigree-based analyses. Despite this, relatives are generally unreported and current inference methods typically detect only the degree of relatedness of sample pairs and not pedigree relationships. We developed CREST, an accurate and fast method that identifies the pedigree relationships of close relatives. CREST utilizes identity by descent (IBD) segments shared between a pair of samples and their mutual relatives, leveraging the fact that sharing rates among these individuals differ across pedigree configurations. Furthermore, CREST exploits the profound differences in sex-specific genetic maps to classify pairs as maternally or paternally related—e.g., paternal half-siblings—using the locations of autosomal IBD segments shared between the pair. In simulated data, CREST correctly classifies 91.5%–100% of grandparent-grandchild (GP) pairs, 80.0%–97.5% of avuncular (AV) pairs, and 75.5%–98.5% of half-siblings (HS) pairs compared to PADRE’s rates of 38.5%–76.0% of GP, 60.5%–92.0% of AV, 73.0%–95.0% of HS pairs. Turning to the real 20,032 sample Generation Scotland (GS) dataset, CREST identified seven pedigrees with incorrect relationship types or maternal/paternal parent sexes, five of which we confirmed as mistakes, and two with uncertain relationships. After correcting these, CREST correctly determines relationship types for 93.5% of GP, 97.7% of AV, and 92.2% of HS pairs that have sufficient mutual relative data; the parent sex in 100% of HS and 99.6% of GP pairs; and it completes this analysis in 2.8 h including IBD detection in eight threads.

Simulations of close relatives and identical by descent (IBD) segments are common in genetic studies, yet most past efforts have utilized sex averaged genetic maps and ignored crossover interference, thus omitting features known to affect the breakpoints of IBD segments. We developed Ped-sim, a method for simulating relatives that can utilize either sex-specific or sex averaged genetic maps and also either a model of crossover interference or the traditional Poisson model for inter-crossover distances. To characterize the impact of previously ignored mechanisms, we simulated data for all four combinations of these factors. We found that modeling crossover interference decreases the standard deviation of pairwise IBD proportions by 10.4% on average in full siblings through second cousins. By contrast, sex-specific maps increase this standard deviation by 4.2% on average, and also impact the number of segments relatives share. Most notably, using sex-specific maps, the number of segments half-siblings share is bimodal; and when combined with interference modeling, the probability that sixth cousins have non-zero IBD sharing ranges from 9.0 to 13.1%, depending on the sexes of the individuals through which they are related. We present new analytical results for the distributions of IBD segments under these models and show they match results from simulations. Finally, we compared IBD sharing rates between simulated and real relatives and find that the combination of sex-specific maps and interference modeling most accurately captures IBD rates in real data. Ped-sim is open source and available from https://github.com/williamslab/ped-sim., Author summary Simulations are ubiquitous throughout statistical genetics in order to generate data with known properties, enabling tests of inference methods and analyses of real world processes in settings where experimental data are challenging to collect. Simulating genetic data for relatives in a pedigree requires the synthesis of chromosomes parents transmit to their children. These chromosomes form as a mosaic of a given parent’s two chromosomes, with the location of switches between the two parental chromosomes known as crossovers. Detailed information about crossover generation based on real data from humans now exists, including the fact that men and women have overall different rates (women produce ∼1.6 times more crossovers) and that real crossovers are subject to interference—whereby crossovers are further apart from one another than expected under a model that selects their locations randomly. Our new method, Ped-sim, can simulate pedigree data using these less commonly modeled crossover features, and we used it to evaluate the impact of sex-specific rates and interference compared to real data. These comparisons show that both factors shape the amount of DNA two relatives share, and that their inclusion in models of crossover better fit data from real relatives.

As genetic datasets increase in size, the fraction of samples with one or more close relatives grows rapidly, resulting in sets of mutually related individuals. We present DRUID—deep relatedness utilizing identity by descent—a method that works by inferring the identical-by-descent (IBD) sharing profile of an ungenotyped ancestor of a set of close relatives. Using this IBD profile, DRUID infers relatedness between unobserved ancestors and more distant relatives, thereby combining information from multiple samples to remove one or more generations between the deep relationships to be identified. DRUID constructs sets of close relatives by detecting full siblings and also uses an approach to identify the aunts/uncles of two or more siblings, recovering 92.2% of real aunts/uncles with zero false positives. In real and simulated data, DRUID correctly infers up to 10.5% more relatives than PADRE when using data from two sets of distantly related siblings, and 10.7%–31.3% more relatives given two sets of siblings and their aunts/uncles. DRUID frequently infers relationships either correctly or within one degree of the truth, with PADRE classifying 43.3%–58.3% of tenth degree relatives in this way compared to 79.6%–96.7% using DRUID.

Recombination has essential functions in evolution, meiosis, and breeding. Here, we use the multi-generational pedigree, consisting of 7,165 informative meioses (3,679 female; 3,486 male), and genotyping-by-sequencing (GBS) data from the International Institute of Tropical Agriculture (IITA) to study recombination in cassava (Manihot esculenta). We detected recombination events using SHAPEIT2 and duoHMM, examined the recombination landscape across the 18 chromosomes of cassava and in regions with known introgressed segments from cassava’s wild relative Manihot glaziovii, constructed a genetic map and compared it to an existing map constructed by the International Cassava Genetic Map Consortium (ICGMC), and inspected patterns of recombination placement in male and female meioses to see if there is evidence of sexual dimorphism in crossover distribution and frequency. We found that the placement of crossovers along chromosomes did not vary between the two sexes but that females undergo more meiotic recombination than males. We also observed that introgressions from M. glaziovii decreased recombination in the introgressed region and, in the case of chromosome 4, along the entire length of the chromosome that the introgression is on. We observed a dosage effect on chromosome 1, possibly suggesting the presence of a variant on the M. glaziovii haplotype that leads to lower overall recombination in the introgressed region.

Type 2 diabetes (T2D) affects more than 415 million people worldwide, and its costs to the health care system continue to rise. To identify common or rare genetic variation with potential therapeutic implications for T2D, we analyzed and replicated genome-wide protein coding variation in a total of 8,227 individuals with T2D and 12,966 individuals without T2D of Latino descent. We identified a novel genetic variant in the IGF2 gene associated with ∼20% reduced risk for T2D. This variant, which has an allele frequency of 17% in the Mexican population but is rare in Europe, prevents splicing between IGF2 exons 1 and 2. We show in vitro and in human liver and adipose tissue that the variant is associated with a specific, allele-dosage–dependent reduction in the expression of IGF2 isoform 2. In individuals who do not carry the protective allele, expression of IGF2 isoform 2 in adipose is positively correlated with both incidence of T2D and increased plasma glycated hemoglobin in individuals without T2D, providing support that the protective effects are mediated by reductions in IGF2 isoform 2. Broad phenotypic examination of carriers of the protective variant revealed no association with other disease states or impaired reproductive health. These findings suggest that reducing IGF2 isoform 2 expression in relevant tissues has potential as a new therapeutic strategy for T2D, even beyond the Latin American population, with no major adverse effects on health or reproduction.

Relatedness inference is an essential component of many genetic analyses and popular in consumer genetic testing. Ramstetter et al. evaluate twelve....., Inferring relatedness from genomic data is an essential component of genetic association studies, population genetics, forensics, and genealogy. While numerous methods exist for inferring relatedness, thorough evaluation of these approaches in real data has been lacking. Here, we report an assessment of 12 state-of-the-art pairwise relatedness inference methods using a data set with 2485 individuals contained in several large pedigrees that span up to six generations. We find that all methods have high accuracy (92–99%) when detecting first- and second-degree relationships, but their accuracy dwindles to 76% of relative pairs. Overall, the most accurate methods are Estimation of Recent Shared Ancestry (ERSA) and approaches that compute total IBD sharing using the output from GERMLINE and Refined IBD to infer relatedness. Combining information from the most accurate methods provides little accuracy improvement, indicating that novel approaches, such as new methods that leverage relatedness signals from multiple samples, are needed to achieve a sizeable jump in performance.

The proportion of samples with one or more close relatives in a genetic dataset increases rapidly with sample size, necessitating relatedness modeling and enabling pedigree-based analyses. Despite this, relatives are generally unreported and current inference methods typically detect only the degree of relatedness of sample pairs and not pedigree relationships. We developed CREST, an accurate and fast method that identifies the pedigree relationships of close relatives. CREST utilizes identical by descent (IBD) segments shared between a pair of samples and their mutual relatives, leveraging the fact that sharing rates among these individuals differ across pedigree configurations. Furthermore, CREST exploits the profound differences in sex-specific genetic maps to classify pairs as maternally or paternally related—e.g., paternal half-siblings—using the locations of autosomal IBD segments shared between the pair. In simulated data, CREST correctly classifies 91.5-99.5% of grandparent-grandchild (GP) pairs, 70.5-97.0% of avuncular (AV) pairs, and 79.0-98.0% of half-siblings (HS) pairs compared to PADRE’s rates of 38.5-76.0% of GP, 60.5-92.0% of AV, 73.0-95.0% of HS pairs. Turning to the real 20,032 sample Generation Scotland (GS) dataset, CREST correctly determines the relationship of 99.0% of GP, 85.7% of AV, and 95.0% of HS pairs that have sufficient mutual relative data, completing this analysis in 10.1 CPU hours including IBD detection. CREST’s maternal and paternal relationship inference is also accurate, as it flagged five pairs as incorrectly labeled in the GS pedigrees— three of which we confirmed as mistakes, and two with an uncertain relationship—yielding 99.7% of HS and 93.5% of GP pairs correctly classified.

Simulations of close relatives and identical by descent (IBD) segments are common in genetic studies, yet most past efforts have utilized sex averaged genetic maps and ignored crossover interference, thus omitting features known to affect the breakpoints of IBD segments. We developed Ped-sim, a method for simulating relatives that can utilize either sex-specific or sex averaged genetic maps and also either a model of crossover interference or the traditional Poisson model for inter-crossover distances. To characterize the impact of previously ignored mechanisms, we simulated data for all four combinations of these factors. We found that modeling crossover interference decreases the standard deviation of the IBD proportion by 10.4% on average in full siblings through second cousins. By contrast, sex-specific maps increase this standard deviation by 4.2% on average, and also impact the number of segments relatives share. Most notably, using sex-specific maps, the number of segments half-siblings share is bimodal; and when combined with interference modeling, the probability that sixth cousins have non-zero IBD ranges from 9.0 to 13.1%, depending on the sexes of the individuals through which they are related. We present new analytical results for the distributions of IBD segments under these models and show they match results from simulations. Finally, we compared IBD sharing rates between simulated and real relatives and find that the combination of sex-specific maps and interference modeling most accurately captures IBD rates in real data. Ped-sim is open source and available fromhttps://github.com/williamslab/ped-sim.Author summarySimulations are ubiquitous throughout statistical genetics in order to generate data with known properties, enabling tests of inference methods and analyses of real world processes in settings where experimental data are challenging to collect. Simulating genetic data for relatives in a pedigree requires the synthesis of chromosomes parents transmit to their children. These chromosomes form as a mosaic of a given parent’s two chromosomes, with the location of switches between the two parental chromosomes known as crossovers. Detailed information about crossover generation based on real data from humans now exists, including the fact that men and women have overall different rates (women produce ~1.6 times more crossovers) and that real crossovers are subject tointerference—whereby crossovers are further apart from one another than expected under a model that selects their locations randomly. Our new method, Ped-sim, can simulate pedigree data using these less commonly modeled crossover features, and we used it to evaluate the importance of sex-specific rates and interference in real data. These comparisons show that both factors shape the amount of DNA two relatives share identically, and that their inclusion in models of crossover better fit data from real relatives.

The sequencing of Neanderthal and Denisovan genomes has yielded many new insights about interbreeding events between extinct hominins and the ancestors of modern humans. While much attention has been paid to the relatively recent gene flow from Neanderthals and Denisovans into modern humans, other instances of introgression leave more subtle genomic evidence and have received less attention. Here, we present a major extension of the ARGweaver algorithm, called ARGweaver-D, which can infer local genetic relationships under a user-defined demographic model that includes population splits and migration events. This Bayesian algorithm probabilistically samples ancestral recombination graphs (ARGs) that specify not only tree topologies and branch lengths along the genome, but also indicate migrant lineages. The sampled ARGs can therefore be parsed to produce probabilities of introgression along the genome. We show that this method is well powered to detect the archaic migration into modern humans, even with only a few samples. We then show that the method can also detect introgressed regions stemming from older migration events, or from unsampled populations. We apply it to human, Neanderthal, and Denisovan genomes, looking for signatures of older proposed migration events, including ancient humans into Neanderthal, and unknown archaic hominins into Denisovans. We identify 3% of the Neanderthal genome that is putatively introgressed from ancient humans, and estimate that the gene flow occurred between 200-300kya. We find no convincing evidence that negative selection acted against these regions. Finally, we predict that 1% of the Denisovan genome was introgressed from an unsequenced, but highly diverged, archaic hominin ancestor. About 15% of these “super-archaic” regions—comprising at least about 4Mb—were, in turn, introgressed into modern humans and continue to exist in the genomes of people alive today., Author summary We present ARGweaver-D, an extension of the ARGweaver algorithm which can be applied under a user-defined demographic model including population splits and migration events. Given genome sequence data from a collection of individuals across multiple closely related populations or subspecies, ARGweaver-D can infer trees describing the genetic relationships among these individuals at every location along the genome, conditional on the demographic model. Like ARGweaver, ARGweaver-D is a Bayesian method, sampling trees from the posterior distribution in order to account for uncertainty. Using simulations, we show that ARGweaver-D can successfully identify regions introgressed from Neanderthals and Denisovans into modern humans. It is also well-powered to detect introgressed regions stemming from older gene-flow events. We apply ARGweaver-D to the genomes of two Neanderthals, a Denisovan, and two African humans. We identify 3% of the Neanderthal genome which is likely derived from gene flow from ancient humans. We also identify about 1% of the Denisovan genome that may be traced to an unsequenced archaic hominin; 15% of these regions were subsequently passed to modern humans. We find no convincing evidence that selection acted against any of these introgressed regions.

As genetic datasets increase in size, the fraction of samples with one or more close relatives grows rapidly, resulting in sets of mutually related individuals. We present DRUID—Deep Relatedness Utilizing Identity by Descent—a method that works by inferring the identical by descent (IBD) sharing profile of an ungenotyped ancestor of a set of close relatives. Using this IBD profile, DRUID infers relatedness between unobserved ancestors and more distant relatives, thereby combining information from multiple samples to remove one or more generations between the deep relationships to be identified. DRUID constructs sets of close relatives by detecting full siblings and also uses a novel approach to identify the aunts/uncles of two or more siblings, recovering 92.2% of real aunts/uncles with zero false positives. In real and simulated data, DRUID correctly infers up to 10.5% more relatives than PADRE when using data from two sets of distantly related siblings, and 10.7–31.3% more relatives given two sets of siblings and their aunts/uncles. DRUID frequently infers relationships either correctly or within one degree of the truth, with PADRE classifying 43.3–58.3% of tenth degree relatives in this way compared to 79.6–96.7% using DRUID.

© 2015 by the American Diabetes Association. Insulin sensitivity, insulin secretion, insulin clearance, and glucose effectiveness exhibit strong genetic components, although few studies have examined their genetic architecture or influence on type 2 diabetes (T2D) risk. We hypothesized that loci affecting variation in these quantitative traits influence T2D. We completed a multicohort genome-wide association study to search for loci influencing T2D-related quantitative traits in 4,176 Mexican Americans. Quantitative traits were measured by the frequently sampled intravenous glucose tolerance test (four cohorts) or euglycemic clamp (three cohorts), and random-effects models were used to test the association between loci and quantitative traits, adjusting for age, sex, and admixture proportions (Discovery). Analysis revealed a significant (P < 5.00 3 1028) association at 11q14.3 (MTNR1B) with acute insulin response. Loci with P < 0.0001 among the quantitative traits were examined for translation to T2D risk in 6,463 T2D case and 9,232 control subjects of Mexican ancestry (Translation). Nonparametric meta- Analysis of the Discovery and Translation cohorts identified significant associations at 6p24 (SLC35B3/TFAP2A) with glucose effectiveness/T2D, 11p15 (KCNQ1) with disposition index/T2D, and 6p22 (CDKAL1) and 11q14 (MTNR1B) with acute insulin response/T2D. These results suggest that T2D and insulin secretion and sensitivity have both shared and distinct genetic factors, potentially delineating genomic components of these quantitative traits that drive the risk for T2D.

Type 2 diabetes (T2D) affects more than 415 million people worldwide, and its costs to the health care system continue to rise. To identify common or rare genetic variation with potential therapeutic implications for T2D, we analyzed and replicated genome-wide protein coding variation in a total of 8,227 individuals with T2D and 12,966 individuals without T2D of Latino descent. We identified a novel genetic variant in the IGF2 gene associated with ∼20% reduced risk for T2D. This variant, which has an allele frequency of 17% in the Mexican population but is rare in Europe, prevents splicing between IGF2 exons 1 and 2. We show in vitro and in human liver and adipose tissue that the variant is associated with a specific, allele-dosage–dependent reduction in the expression of IGF2 isoform 2. In individuals who do not carry the protective allele, expression of IGF2 isoform 2 in adipose is positively correlated with both incidence of T2D and increased plasma glycated hemoglobin in individuals without T2D, providing support that the protective effects are mediated by reductions in IGF2 isoform 2. Broad phenotypic examination of carriers of the protective variant revealed no association with other disease states or impaired reproductive health. These findings suggest that reducing IGF2 isoform 2 expression in relevant tissues has potential as a new therapeutic strategy for T2D, even beyond the Latin American population, with no major adverse effects on health or reproduction.

Inferring relatedness from genomic data is an essential component of genetic association studies, population genetics, forensics, and genealogy. While numerous methods exist for inferring relatedness, thorough evaluation of these methods in real data has been lacking. Here, we report an assessment of 11 state-ofthe-art relatedness inference methods using a dataset with 2,485 individuals contained in several large pedigrees that span up to six generations. We find that all methods have high accuracy (∼93%−99%) when reporting first and second degree relationships, but their accuracy dwindles to less than 60% for fifth degree relationships. However, the inferred relationships were correct to within one relatedness degree at a rate of 83%−99% across all methods and considered relationship degrees. Furthermore, most methods infer unrelated individuals correctly at a rate of ∼99%, suggesting a low rate of false positives. Overall, the most accurate methods were ERSA 2.0 and approaches that classify relationships using the IBD segments inferred by Refined IBD and IBDseq. Combining results from the most accurate methods provides little accuracy improvement, indicating that novel approaches for relatedness inference may be needed to achieve a sizeable jump in performance.

Objectives Academic and not-for-profit research funders are increasingly requiring that the research they fund must be published open access, with some insisting on publishing with a Creative Commons Attribution (CC BY) licence to allow the broadest possible use. We set out to clarify the open access variants provided by leading medical journals for research in general and industry-funded research in particular, and record the availability of the CC BY licence for commercially funded research. Methods We identified medical journals with a 2015 impact factor of at least 15.0 on 24 May 2017, then excluded from the analysis journals that only publish review articles. Between 29 June 2017 and 26 July 2017, we collected information about each journal’s open access policies from their websites and/or by email contact. We contacted the journals by email again between 6 December 2017 and 2 January 2018 to confirm our findings. Results Thirty-five medical journals publishing original research from 13 publishers were included in the analysis. All 35 journals offered some form of open access with varying embargo periods of up to 12 months. Of these journals, 21 (60%) provided immediate open access with a CC BY licence under certain circumstances (e.g. to specific research funders). Of these 21, 20 only offered a CC BY licence to authors funded by non-commercial organizations and one offered this option to funders who required it. Conclusions Most leading medical journals do not offer to authors reporting commercially funded research an open access licence that allows unrestricted sharing and adaptation of the published material. The journals’ policies are therefore not aligned with open access declarations and guidelines. Commercial research funders lag behind academic funders in the development of mandatory open access policies, and it is time for them to work with publishers to advance the dissemination of the research they fund. Strengths and limitations of this study This manuscript includes a systematic analysis of open access policies of journals with a high impact factor, including society-owned journals, from multiple publishers. The open access policies of all journals analysed were clarified, and confirmation of our findings was received by email from 97% of the contacted journals. Open access policies of the journals and publishers analysed are subject to change, so the information presented here may not be current. By selecting journals with a high impact factor, our analysis does not include prestigious journals from specialized therapy areas and regional or non-English language journals, which may have lower impact factors. Although our study covers only a small number of journals, extending such a manual analysis to a greater number of journals without loss of detail and verification of all results would be cumbersome and inefficient by relying on traditional analysis tools.

Haplotypes are an important resource for a large number of applications in human genetics, but computationally inferred haplotypes are subject to switch errors that decrease their utility. The accuracy of computationally inferred haplotypes increases with sample size, and although ever larger genotypic data sets are being generated, the fact that existing methods require substantial computational resources limits their applicability to data sets containing tens or hundreds of thousands of samples. Here, we present HAPI-UR (haplotype inference for unrelated samples), an algorithm that is designed to handle unrelated and/or trio and duo family data, that has accuracy comparable to or greater than existing methods, and that is computationally efficient and can be applied to 100,000 samples or more. We use HAPI-UR to phase a data set with 58,207 samples and show that it achieves practical runtime and that switch errors decrease with sample size even with the use of samples from multiple ethnicities. Using a data set with 16,353 samples, we compare HAPI-UR to Beagle, MaCH, IMPUTE2, and SHAPEIT and show that HAPI-UR runs 18× faster than all methods and has a lower switch-error rate than do other methods except for Beagle; with the use of consensus phasing, running HAPI-UR three times gives a slightly lower switch-error rate than Beagle does and is more than six times faster. We demonstrate results similar to those from Beagle on another data set with a higher marker density. Lastly, we show that HAPI-UR has better runtime scaling properties than does Beagle so that for larger data sets, HAPI-UR will be practical and will have an even larger runtime advantage. HAPI-UR is available online (see Web Resources).

Among US Latinas and Mexican women, those with higher European ancestry have increased risk of breast cancer. We combined an admixture mapping and genome-wide association mapping approach to search for genomic regions that may explain this observation. Latina women with breast cancer (n= 1497) and Latina controls (n= 1272) were genotyped using Affymetrix and Illumina arrays. We inferred locus-specific genetic ancestry and compared the ancestry between cases and controls. We also performed single nucleotide polymorphism (SNP) association analyses in regions of interest. Correction for multiple-hypothesis testing was conducted using permutations (P(corrected)). We identified one region where genetic ancestry was significantly associated with breast cancer risk: 6q25 [odds ratio (OR) per Indigenous American chromosome 0.75, 95% confidence interval (CI): 0.65-0.85, P= 1.1 × 10(-5), P(corrected)= 0.02]. A second region on 11p15 showed a trend towards association (OR per Indigenous American chromosome 0.77, 95% CI: 0.68-0.87, P= 4.3 × 10(-5), P(corrected)= 0.08). In both regions, breast cancer risk decreased with higher Indigenous American ancestry in concordance with observations made on global ancestry. The peak of the 6q25 signal includes the estrogen receptor 1 (ESR1) gene and 5' region, a locus previously implicated in breast cancer. Genome-wide association analysis found that a multi-SNP model explained the admixture signal in both regions. Our results confirm that the association between genetic ancestry and breast cancer risk in US Latinas is partly due to genetic differences between populations of European and Indigenous Americans origin. Fine-mapping within the 6q25 and possibly the 11p15 loci will lead to the discovery of the biologically functional variant/s behind this association.

Although the past decade has seen tremendous progress in our understanding of fine-scale recombination, little is known about non-crossover (NCO) gene conversion. We report the first genome-wide study of NCO events in humans. Using SNP array data from 98 meioses, we identified 103 sites affected by NCO, of which 50/52 were confirmed in sequence data. Overlap with double strand break (DSB) hotspots indicates that most of the events are likely of meiotic origin. We estimate that a site is involved in a NCO at a rate of 5.9 × 10−6/bp/generation, consistent with sperm-typing studies, and infer that tract lengths span at least an order of magnitude. Observed NCO events show strong allelic bias at heterozygous AT/GC SNPs, with 68% (58–78%) transmitting GC alleles (p = 5 × 10−4). Strikingly, in 4 of 15 regions with resequencing data, multiple disjoint NCO tracts cluster in close proximity (∼20–30 kb), a phenomenon not previously seen in mammals. DOI: http://dx.doi.org/10.7554/eLife.04637.001, eLife digest The genetic information inside our cells is stored in the form of chromosomes, which are carefully packaged strands of DNA. Most human cells contain a pair of each chromosome: one inherited from the mother and another from the father. Typically, when a human cell divides, it duplicates all of its chromosomes and then places one copy of each into the two new cells. However, a different process—known as ‘meiosis’—occurs when a human cell divides to make the cells involved in sexual reproduction (i.e., egg cells in females and sperm cells in males). First, the cell duplicates all of its chromosomes as before, but then it pairs the chromosomes originally from the mother with the equivalent chromosomes from the father. These paired chromosomes then swap sections of DNA. Next, the cell divides, and the resulting cells divide again; this produces four new cells that each contain a single, unique copy of every chromosome. In the process of swapping sections of DNA between chromosomes, the DNA molecule inside the chromosome is broken and different sections of DNA are then joined together. This can occur by one of two methods: ‘crossover events’ that produce a final chromosome made up of long sequences from each of the contributing chromosomes; and ‘non-crossover events’, where only a small section of DNA is swapped between the chromosomes. Research has tended to focus on DNA breaks and crossover events. Now, Williams et al. have looked at the genetic sequences transmitted by both parents to 49 humans—revealing information about a total of 98 meioses—and scoured them for evidence of non-crossover events. In addition to finding 103 sites where these events occurred, Williams et al. discovered that non-crossover events are more frequent around sites where crossover events also have a higher frequency. This suggests that the mechanism that initiates non-crossover events is shared with crossovers, and that non-crossover events primarily occur during meiosis. Unexpectedly, in some areas non-crossover events were found close to each other in ‘clusters’, which had not previously been seen in humans. Non-crossover events will only produce an observable change if the chromosomes involved have differences in the sequence of the DNA section that is swapped between them. The number of such variable genetic positions that non-crossover events affect in a generation is roughly the same number as the number of newly generated random mutations to the DNA sequence in a generation. Examining the DNA sequences transferred during non-crossover events also shows that two different types of DNA bases (cytosine and guanine) are more likely to be transmitted by a non-crossover event than are the other two bases (adenine and thymine). This bias indicates that non-crossover events are an important factor in driving genome evolution. In the future, sequencing the entire genome—the total genetic material—of many different people could provide further insights into non-crossover events in humans. DOI: http://dx.doi.org/10.7554/eLife.04637.002

Importance: Latino populations have one of the highest prevalences of type 2 diabetes worldwide. Objectives: To investigate the association between rare protein-coding genetic variants and prevalence of type 2 diabetes in a large Latino population and to explore potential molecular and physiological mechanisms for the observed relationships. Design, Setting, and Participants: Whole-exome sequencing was performed on DNA samples from 3756 Mexican and US Latino individuals (1794 with type 2 diabetes and 1962 without diabetes) recruited from 1993 to 2013. One variant was further tested for allele frequency and association with type 2 diabetes in large multiethnic data sets of 14 276 participants and characterized in experimental assays. Main Outcome and Measures: Prevalence of type 2 diabetes. Secondary outcomes included age of onset, body mass index, and effect on protein function. Results: A single rare missense variant (c.1522G>A [p.E508K]) was associated with type 2 diabetes prevalence (odds ratio [OR], 5.48; 95% CI, 2.83-10.61; P = 4.4 × 10−7) in hepatocyte nuclear factor 1-α (HNF1A), the gene responsible for maturity onset diabetes of the young type 3 (MODY3). This variant was observed in 0.36% of participants without type 2 diabetes and 2.1% of participants with it. In multiethnic replication data sets, the p.E508K variant was seen only in Latino patients (n = 1443 with type 2 diabetes and 1673 without it) and was associated with type 2 diabetes (OR, 4.16; 95% CI, 1.75-9.92; P = .0013). In experimental assays, HNF-1A protein encoding the p.E508K mutant demonstrated reduced transactivation activity of its target promoter compared with a wild-type protein. In our data, carriers and noncarriers of the p.E508K mutation with type 2 diabetes had no significant differences in compared clinical characteristics, including age at onset. The mean (SD) age for carriers was 45.3 years (11.2) vs 47.5 years (11.5) for noncarriers (P = .49) and the mean (SD) BMI for carriers was 28.2 (5.5) vs 29.3 (5.3) for noncarriers (P = .19). Conclusions and Relevance: Using whole-exome sequencing, we identified a single low-frequency variant in the MODY3-causing gene HNF1A that is associated with type 2 diabetes in Latino populations and may affect protein function. This finding may have implications for screening and therapeutic modification in this population, but additional studies are required. publishedVersion

Hapi is a new dynamic programming algorithm that ignores uninformative states and state transitions in order to efficiently compute minimum-recombinant and maximum likelihood haplotypes. When applied to a dataset containing 103 families, Hapi performs 3.8 and 320 times faster than state-of-the-art algorithms. Because Hapi infers both minimum-recombinant and maximum likelihood haplotypes and applies to related individuals, the haplotypes it infers are highly accurate over extended genomic distances., National Institutes of Health (U.S.) (NIH grant 5-T90-DK070069), National Institutes of Health (U.S.) (Grant 5-P01-NS055923), National Science Foundation (U.S.) (Graduate Research Fellowship)

We undertook a two-stage genome-wide association study (GWAS) of Alzheimer's disease (AD) involving over 16,000 individuals, the most powerful AD GWAS to date. In stage 1 (3,941 cases and 7,848 controls), we replicated the established association with the apolipoprotein E (APOE) locus (most significant SNP, rs2075650, P = 1.8 × 10−157) and observed genome-wide significant association with SNPs at two loci not previously associated with the disease: at the CLU (also known as APOJ) gene (rs11136000, P = 1.4 × 10−9) and 5′ to the PICALM gene (rs3851179, P = 1.9 × 10−8). These associations were replicated in stage 2 (2,023 cases and 2,340 controls), producing compelling evidence for association with Alzheimer's disease in the combined dataset (rs11136000, P = 8.5 × 10−10, odds ratio = 0.86; rs3851179, P = 1.3 × 10−9, odds ratio = 0.86).

Library writers wish to provide a guarantee not only that each procedure in the library performs correctly in isolation, but also that the procedures perform correctly when run in conjunction. To this end, we propose a method for static detection of deadlock in Java libraries. Our goal is to determine whether client code exists that may deadlock a library, and, if so, to enable the library writer to discover the calling patterns that can lead to deadlock. Our flow-sensitive, context-sensitive analysis determines possible deadlock configurations using a lock-order graph. This graph represents the order in which locks are acquired by the library. Cycles in the graph indicate deadlock possibilities, and our tool reports all such possibilities. We implemented our analysis and evaluated it on 18 libraries comprising 1245 kLOC. We verified 13 libraries to be free from deadlock, and found 14 distinct deadlocks in 3 libraries.

The computational bottleneck in a ray tracer using bounding volume hierarchies is often the ray intersection routine with axis-aligned bounding boxes. We describe a version of this routine that uses IEEE numerical properties to ensure that those tests are both robust and efficient. Sample source code is available online.

We evaluated the sensitivity and specificity of a PCR-based qualitative test for the rapid diagnosis of Mycobacterium avium-M. intracellulare complex (MAC) bacteremia in patients with AIDS disease. Eleven subjects with newly culture-proven MAC bacteremia had the following tests performed at biweekly intervals during the first 8 weeks of therapy: blood culture, Mycobacterium -specific PCR, and quantitative human immunodeficiency virus (HIV) viral-load testing. Mycobacterium genus-specific biotinylated primers were used to amplify a sequence of approximately 582 nucleotides within the 16S rRNA genes of M. avium and M. intracellulare . Detection of the amplified product was performed with an oligonucleotide probe-coated microwell plate combined with an avidin-horseradish peroxidase–tetramethylbenzidine conjugate-substrate system. While not as sensitive as BACTEC culture, PCR detected 17 of 18 specimens which grew ≥40 organisms/ml (94.4% sensitivity) and 9 of 16 specimens which grew ≤40 organisms/ml (56.3% sensitivity). No clear change in HIV viremia occurred in response to successful treatment of patients’ MAC bacteremia. Use of the PCR test allowed detection of MAC bacteremia in 1 day, with a sensitivity similar to those of quantitative blood culture techniques, and it may prove useful for rapid screening of suspected cases. HIV viremia was unaffected by 8 weeks of MAC therapy.

Activated microglia, the resident macrophages of the brain, are a feature of Alzheimer's disease. Animal models suggest that when activated microglia are further activated by a subsequent systemic infection this results in significantly raised levels of interleukin 1beta within the CNS, which may in turn potentiate neurodegeneration. This prospective pilot study in Alzheimer's disease subjects showed that cognitive function can be impaired for at least two months after the resolution of a systemic infection and that cognitive impairment is preceded by raised serum levels of interleukin 1beta. These relations were not confounded by the presence of any subsequent systemic infection or by baseline cognitive scores. Further research is needed to determine whether recurrent systemic infections drive cognitive decline in Alzheimer's disease subjects through a cytokine mediated pathway.