Relatedness mapping and tracts of relatedness for genome-wide data in the presence of linkage disequilibrium

Extensive Population Structure Highlights an Apparent Paradox of Stasis in the Impala (Aepyceros melampus)

Impalas are unusual among bovids because they have remained morphologically similar over millions of years-a phenomenon referred to as evolutionary stasis. Here, we sequenced 119 whole genomes from the two extant subspecies of impala, the common (Aepyceros melampus melampus) and black-faced (A. m. petersi) impala. We investigated the evolutionary forces working within the species to explore how they might be associated with its evolutionary stasis as a taxon. Despite being one of the most abundant bovid species, we found low genetic diversity overall, and a phylogeographic signal of spatial expansion from southern to eastern Africa. Contrary to expectations under a scenario of evolutionary stasis, we found pronounced genetic structure between and within the two subspecies with indications of ancient, but not recent, gene flow. Black-faced impala and eastern African common impala populations had more runs of homozygosity than common impala in southern Africa, and, using a proxy for genetic load, we found that natural selection is working less efficiently in these populations compared to the southern African populations. Together with the fossil record, our results are consistent with a fixed-optimum model of evolutionary stasis, in which impalas in the southern African core of the range are able to stay near their evolutionary fitness optimum as a generalist ecotone species, whereas eastern African impalas may struggle to do so due to the effects of genetic drift and reduced adaptation to the local habitat, leading to recurrent local extinction in eastern Africa and re-colonisation from the South.

Introgression and disruption of migration routes have shaped the genetic integrity of wildebeest populations

The blue wildebeest (Connochaetes taurinus) is a keystone species in savanna ecosystems from southern to eastern Africa, and is well known for its spectacular migrations and locally extreme abundance. In contrast, the black wildebeest (C. gnou) is endemic to southern Africa, barely escaped extinction in the 1900s and is feared to be in danger of genetic swamping from the blue wildebeest. Despite the ecological importance of the wildebeest, there is a lack of understanding of how its unique migratory ecology has affected its gene flow, genetic structure and phylogeography. Here, we analyze whole genomes from 121 blue and 22 black wildebeest across the genus' range. We find discrete genetic structure consistent with the morphologically defined subspecies. Unexpectedly, our analyses reveal no signs of recent interspecific admixture, but rather a late Pleistocene introgression of black wildebeest into the southern blue wildebeest populations. Finally, we find that migratory blue wildebeest populations exhibit a combination of long-range panmixia, higher genetic diversity and lower inbreeding levels compared to neighboring populations whose migration has recently been disrupted. These findings provide crucial insights into the evolutionary history of the wildebeest, and tangible genetic evidence for the negative effects of anthropogenic activities on highly migratory ungulates.

Relatedness coefficients and their applications for triplets and quartets of genetic markers

Relatedness coefficients which seek the identity-by-descent of genetic markers are described. The markers are in groups of two, three or four, and if four, can consist of two pairs. It is essential to use cumulants (not moments) for four-marker-gene probabilities, as the covariance of homozygosity, used in four-marker applications, can only be described with cumulants. A covariance of homozygosity between pairs of markers arises when populations follow a mixture distribution. Also, the probability of four markers all identical-by-descent equals the normalized fourth cumulant. In this article, a "genetic marker" generally represents either a gene locus or an allele at a locus. Applications of three marker coefficients mainly involve conditional regression, and applications of four marker coefficients can involve identity disequilibrium. Estimation of relatedness using genetic marker data is discussed. However, three- and four-marker estimators suffer from statistical and numerical problems, including higher statistical variance, complexity of estimation formula, and singularity at some intermediate allele frequencies.

Integrated analysis strategy of genome-wide functional gene mining reveals DKK2 gene underlying meat quality in Shaziling synthesized pigs

BACKGROUND

Shaziling pig is a well-known indigenous breed in China who has superior meat quality traits. However, the genetic mechanism and genomic evidence underlying meat quality characteristics of Shaziling pigs are still unclear. To explore and investigate the germplasm characteristics of Shaziling pigs, we totally analyzed 67 individual's whole genome sequencing data for the first time (20 Shaziling pigs [S], 20 Dabasha pigs [DBS], 11 Yorkshire pigs [Y], 10 Berkshire pigs [BKX], 5 Basha pigs [BS] and 1 Warthog).

RESULTS

A total of 2,538,577 SNPs with high quality were detected and 9 candidate genes which was specifically selected in S and shared in S to DBS were precisely mined and screened using an integrated analysis strategy of identity-by-descent (IBD) and selective sweep. Of them, dickkopf WNT signaling pathway inhibitor 2 (DKK2), the antagonist of Wnt signaling pathway, was the most promising candidate gene which was not only identified an association of palmitic acid and palmitoleic acid quantitative trait locus in PigQTLdb, but also specifically selected in S compared to other 48 Chinese local pigs of 12 populations and 39 foreign pigs of 4 populations. Subsequently, a mutation at 12,726-bp of DKK2 intron 1 (g.114874954 A > C) was identified associated with intramuscular fat content using method of PCR-RFLP in 21 different pig populations. We observed DKK2 specifically expressed in adipose tissues. Overexpression of DKK2 decreased the content of triglyceride, fatty acid synthase and expression of relevant genes of adipogenic and Wnt signaling pathway, while interference of DKK2 got contrary effect during adipogenesis differentiation of porcine preadipocytes and 3T3-L1 cells.

CONCLUSIONS

Our findings provide an analysis strategy for mining functional genes of important economic traits and provide fundamental data and molecular evidence for improving pig meat quality traits and molecular breeding.

African bushpigs exhibit porous species boundaries and appeared in Madagascar concurrently with human arrival

Several African mammals exhibit a phylogeographic pattern where closely related taxa are split between West/Central and East/Southern Africa, but their evolutionary relationships and histories remain controversial. Bushpigs (Potamochoerus larvatus) and red river hogs (P. porcus) are recognised as separate species due to morphological distinctions, a perceived lack of interbreeding at contact, and putatively old divergence times, but historically, they were considered conspecific. Moreover, the presence of Malagasy bushpigs as the sole large terrestrial mammal shared with the African mainland raises intriguing questions about its origin and arrival in Madagascar. Analyses of 67 whole genomes revealed a genetic continuum between the two species, with putative signatures of historical gene flow, variable FST values, and a recent divergence time (<500,000 years). Thus, our study challenges key arguments for splitting Potamochoerus into two species and suggests their speciation might be incomplete. Our findings also indicate that Malagasy bushpigs diverged from southern African populations and underwent a limited bottleneck 1000-5000 years ago, concurrent with human arrival in Madagascar. These results shed light on the evolutionary history of an iconic and widespread African mammal and provide insight into the longstanding biogeographic puzzle surrounding the bushpig's presence in Madagascar.

Tree-based QTL mapping with expected local genetic relatedness matrices

Understanding the genetic basis of complex phenotypes is a central pursuit of genetics. Genome-wide association studies (GWASs) are a powerful way to find genetic loci associated with phenotypes. GWASs are widely and successfully used, but they face challenges related to the fact that variants are tested for association with a phenotype independently, whereas in reality variants at different sites are correlated because of their shared evolutionary history. One way to model this shared history is through the ancestral recombination graph (ARG), which encodes a series of local coalescent trees. Recent computational and methodological breakthroughs have made it feasible to estimate approximate ARGs from large-scale samples. Here, we explore the potential of an ARG-based approach to quantitative-trait locus (QTL) mapping, echoing existing variance-components approaches. We propose a framework that relies on the conditional expectation of a local genetic relatedness matrix (local eGRM) given the ARG. Simulations show that our method is especially beneficial for finding QTLs in the presence of allelic heterogeneity. By framing QTL mapping in terms of the estimated ARG, we can also facilitate the detection of QTLs in understudied populations. We use local eGRM to analyze two chromosomes containing known body size loci in a sample of Native Hawaiians. Our investigations can provide intuition about the benefits of using estimated ARGs in population- and statistical-genetic methods in general.

Whole-genome surveillance identifies markers of Plasmodium falciparum drug resistance and novel genomic regions under selection in Mozambique

IMPORTANCE

Malaria is a devastating disease caused by Plasmodium parasites. The evolution of parasite drug resistance continues to hamper progress toward malaria elimination, and despite extensive efforts to control malaria, it remains a leading cause of death in Mozambique and other countries in the region. The development of successful vaccines and identification of molecular markers to track drug efficacy are essential for managing the disease burden. We present an analysis of the parasite genome in Mozambique, a country with one of the highest malaria burdens globally and limited available genomic data, revealing current selection pressure. We contribute additional evidence to limited prior studies supporting the effectiveness of SWGA in producing reliable genomic data from complex clinical samples. Our results provide the identity of genomic loci that may be associated with current antimalarial drug use, including artemisinin and lumefantrine, and reveal selection pressure predicted to compromise the efficacy of current vaccine candidates.

Identification of a shared, common haplotype segregating with an SGCB c.544 T > G mutation in Indian patients affected with sarcoglycanopathy

Sarcoglycanopathy is the most frequent form of autosomal recessive limb-girdle muscular dystrophies caused by mutations in SGCB gene encoding beta-sarcoglycan proteins. In this study, we describe a shared, common haplotype co-segregating in 14 sarcoglycanopathy cases from 13 unrelated families from south Indian region with the likely pathogenic homozygous mutation c.544 T > G (p.Thr182Pro) in SGCB. Haplotype was reconstructed based on 10 polymorphic markers surrounding the c.544 T > G mutation in the cases and related family members as well as 150 unrelated controls from Indian populations using PLINK1.9. We identified haplotype H1 = G, A, G, T, G, G, A, C, T, G, T at a significantly higher frequency in cases compared to related controls and unrelated control Indian population. Upon segregation analysis within the family pedigrees, H1 is observed to co-segregate with c.544 T > G in a homozygous state in all the pedigrees of cases except one indicating a probable event of founder effect. Furthermore, Identical-by-descent and inbreeding coefficient analysis revealed relatedness among 33 new pairs of seemingly unrelated individuals from sarcoglycanopathy cohort and a higher proportion of homozygous markers, thereby indicating common ancestry. Since all these patients are from the south Indian region, we suggest this region to be a primary target of mutation screening in patients diagnosed with sarcoglycanopathy.

Tree-based QTL mapping with expected local genetic relatedness matrices

Understanding the genetic basis of complex phenotypes is a central pursuit of genetics. Genome-wide Association Studies (GWAS) are a powerful way to find genetic loci associated with phenotypes. GWAS are widely and successfully used, but they face challenges related to the fact that variants are tested for association with a phenotype independently, whereas in reality variants at different sites are correlated because of their shared evolutionary history. One way to model this shared history is through the ancestral recombination graph (ARG), which encodes a series of local coalescent trees. Recent computational and methodological breakthroughs have made it feasible to estimate approximate ARGs from large-scale samples. Here, we explore the potential of an ARG-based approach to quantitative-trait locus (QTL) mapping, echoing existing variance-components approaches. We propose a framework that relies on the conditional expectation of a local genetic relatedness matrix given the ARG (local eGRM). Simulations show that our method is especially beneficial for finding QTLs in the presence of allelic heterogeneity. By framing QTL mapping in terms of the estimated ARG, we can also facilitate the detection of QTLs in understudied populations. We use local eGRM to identify a large-effect BMI locus, the CREBRF gene, in a sample of Native Hawaiians in which it was not previously detectable by GWAS because of a lack of population-specific imputation resources. Our investigations can provide intuition about the benefits of using estimated ARGs in population- and statistical-genetic methods in general.

SALAI-Net: species-agnostic local ancestry inference network

MOTIVATION

Local ancestry inference (LAI) is the high resolution prediction of ancestry labels along a DNA sequence. LAI is important in the study of human history and migrations, and it is beginning to play a role in precision medicine applications including ancestry-adjusted genome-wide association studies (GWASs) and polygenic risk scores (PRSs). Existing LAI models do not generalize well between species, chromosomes or even ancestry groups, requiring re-training for each different setting. Furthermore, such methods can lack interpretability, which is an important element in each of these applications.

RESULTS

We present SALAI-Net, a portable statistical LAI method that can be applied on any set of species and ancestries (species-agnostic), requiring only haplotype data and no other biological parameters. Inspired by identity by descent methods, SALAI-Net estimates population labels for each segment of DNA by performing a reference matching approach, which leads to an interpretable and fast technique. We benchmark our models on whole-genome data of humans and we test these models' ability to generalize to dog breeds when trained on human data. SALAI-Net outperforms previous methods in terms of balanced accuracy, while generalizing between different settings, species and datasets. Moreover, it is up to two orders of magnitude faster and uses considerably less RAM memory than competing methods.

AVAILABILITY AND IMPLEMENTATION

We provide an open source implementation and links to publicly available data at github.com/AI-sandbox/SALAI-Net. Data is publicly available as follows: https://www.internationalgenome.org (1000 Genomes), https://www.simonsfoundation.org/simons-genome-diversity-project (Simons Genome Diversity Project), https://www.sanger.ac.uk/resources/downloads/human/hapmap3.html (HapMap), ftp://ngs.sanger.ac.uk/production/hgdp/hgdp_wgs.20190516 (Human Genome Diversity Project) and https://www.ncbi.nlm.nih.gov/bioproject/PRJNA448733 (Canid genomes).

SUPPLEMENTARY INFORMATION

Supplementary data are available from Bioinformatics online.

The evolutionary history and mechanistic basis of female ornamentation in a tropical songbird

Ornamentation, such as the showy plumage of birds, is widespread among female vertebrates, yet the evolutionary pressures shaping female ornamentation remain uncertain. In part this is due to a poor understanding of the mechanistic route to ornamentation in females. To address this issue, we evaluated the evolutionary history of ornament expression in a tropical passerine bird, the White-shouldered Fairywren, whose females, but not males, strongly vary between populations in occurrence of ornamented black-and-white plumage. We first use phylogenomic analysis to demonstrate that female ornamentation is derived and that female ornamentation evolves independently of changes in male plumage. We then use exogenous testosterone in a field experiment to induce partial ornamentation in naturally unornamented females. By sequencing the transcriptome of experimentally induced ornamented and natural feathers, we identify genes expressed during ornament production and evaluate the degree to which female ornamentation in this system is associated with elevated testosterone, as is common in males. We reveal that some ornamentation in females is linked to testosterone and that sexes differ in ornament-linked gene expression. Lastly, using genomic outlier analysis we identify a candidate melanogenesis gene that lies in a region of high genomic divergence among populations that is also differentially expressed in feather follicles of different female plumages. Taken together, these findings are consistent with sex-specific selection favoring the evolution of female ornaments and demonstrate a key role for testosterone in generating population divergence in female ornamentation through gene regulation. More broadly, our work highlights similarities and differences in how ornamentation evolves in the sexes.

Fully exploiting SNP arrays: a systematic review on the tools to extract underlying genomic structure

Single nucleotide polymorphisms (SNPs) are the most abundant type of genomic variation and the most accessible to genotype in large cohorts. However, they individually explain a small proportion of phenotypic differences between individuals. Ancestry, collective SNP effects, structural variants, somatic mutations or even differences in historic recombination can potentially explain a high percentage of genomic divergence. These genetic differences can be infrequent or laborious to characterize; however, many of them leave distinctive marks on the SNPs across the genome allowing their study in large population samples. Consequently, several methods have been developed over the last decade to detect and analyze different genomic structures using SNP arrays, to complement genome-wide association studies and determine the contribution of these structures to explain the phenotypic differences between individuals. We present an up-to-date collection of available bioinformatics tools that can be used to extract relevant genomic information from SNP array data including population structure and ancestry; polygenic risk scores; identity-by-descent fragments; linkage disequilibrium; heritability and structural variants such as inversions, copy number variants, genetic mosaicisms and recombination histories. From a systematic review of recently published applications of the methods, we describe the main characteristics of R packages, command-line tools and desktop applications, both free and commercial, to help make the most of a large amount of publicly available SNP data.

LocalNgsRelate: a software tool for inferring IBD sharing along the genome between pairs of individuals from low-depth NGS data

MOTIVATION

Inference of identity-by-descent (IBD) sharing along the genome between pairs of individuals has important uses. But all existing inference methods are based on genotypes, which is not ideal for low-depth Next Generation Sequencing (NGS) data from which genotypes can only be called with high uncertainty.

RESULTS

We present a new probabilistic software tool, LocalNgsRelate, for inferring IBD sharing along the genome between pairs of individuals from low-depth NGS data. Its inference is based on genotype likelihoods instead of genotypes, and thereby it takes the uncertainty of the genotype calling into account. Using real data from the 1000 Genomes project, we show that LocalNgsRelate provides more accurate IBD inference for low-depth NGS data than two state-of-the-art genotype-based methods, Albrechtsen et al. (2009) and hap-IBD. We also show that the method works well for NGS data down to a depth of 2×.

AVAILABILITY AND IMPLEMENTATION

LocalNgsRelate is freely available at https://github.com/idamoltke/LocalNgsRelate.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Haplotype-resolved germline and somatic alterations in renal medullary carcinomas

BACKGROUND

Renal medullary carcinomas (RMCs) are rare kidney cancers that occur in adolescents and young adults of African ancestry. Although RMC is associated with the sickle cell trait and somatic loss of the tumor suppressor, SMARCB1, the ancestral origins of RMC remain unknown. Further, characterization of structural variants (SVs) involving SMARCB1 in RMC remains limited.

METHODS

We used linked-read genome sequencing to reconstruct germline and somatic haplotypes in 15 unrelated patients with RMC registered on the Children's Oncology Group (COG) AREN03B2 study between 2006 and 2017 or from our prior study. We performed fine-mapping of the HBB locus and assessed the germline for cancer predisposition genes. Subsequently, we assessed the tumor samples for mutations outside of SMARCB1 and integrated RNA sequencing to interrogate the structural variants at the SMARCB1 locus.

RESULTS

We find that the haplotype of the sickle cell mutation in patients with RMC originated from three geographical regions in Africa. In addition, fine-mapping of the HBB locus identified the sickle cell mutation as the sole candidate variant. We further identify that the SMARCB1 structural variants are characterized by blunt or 1-bp homology events.

CONCLUSIONS

Our findings suggest that RMC does not arise from a single founder population and that the HbS allele is a strong candidate germline allele which confers risk for RMC. Furthermore, we find that the SVs that disrupt SMARCB1 function are likely repaired by non-homologous end-joining. These findings highlight how haplotype-based analyses using linked-read genome sequencing can be applied to identify potential risk variants in small and rare disease cohorts and provide nucleotide resolution to structural variants.

NGSremix: A software tool for estimating pairwise relatedness between admixed individuals from next-generation sequencing data

Estimation of relatedness between pairs of individuals is important in many genetic research areas. When estimating relatedness, it is important to account for admixture if this is present. However, the methods that can account for admixture are all based on genotype data as input, which is a problem for low-depth next-generation sequencing (NGS) data from which genotypes are called with high uncertainty. Here, we present a software tool, NGSremix, for maximum likelihood estimation of relatedness between pairs of admixed individuals from low-depth NGS data, which takes the uncertainty of the genotypes into account via genotype likelihoods. Using both simulated and real NGS data for admixed individuals with an average depth of 4x or below we show that our method works well and clearly outperforms all the commonly used state-of-the-art relatedness estimation methods PLINK, KING, relateAdmix, and ngsRelate that all perform quite poorly. Hence, NGSremix is a useful new tool for estimating relatedness in admixed populations from low-depth NGS data. NGSremix is implemented in C/C++ in a multi-threaded software and is freely available on Github https://github.com/KHanghoj/NGSremix.

Probabilistic Estimation of Identity by Descent Segment Endpoints and Detection of Recent Selection

Most methods for fast detection of identity by descent (IBD) segments report identity by state segments without any quantification of the uncertainty in the endpoints and lengths of the IBD segments. We present a method for determining the posterior probability distribution of IBD segment endpoints. Our approach accounts for genotype errors, recent mutations, and gene conversions which disrupt DNA sequence identity within IBD segments, and it can be applied to large cohorts with whole-genome sequence or SNP array data. We find that our method's estimates of uncertainty are well calibrated for homogeneous samples. We quantify endpoint uncertainty for 77.7 billion IBD segments from 408,883 individuals of white British ancestry in the UK Biobank, and we use these IBD segments to find regions showing evidence of recent natural selection. We show that many spurious selection signals are eliminated by the use of unbiased estimates of IBD segment endpoints and a pedigree-based genetic map. Eleven of the twelve regions with the greatest evidence for recent selection in our scan have been identified as selected in previous analyses using different approaches. Our computationally efficient method for quantifying IBD segment endpoint uncertainty is implemented in the open source ibd-ends software package.

Genomic and chemical evidence for local adaptation in resistance to different herbivores in Datura stramonium

Because most species are collections of genetically variable populations distributed to habitats differing in their abiotic/biotic environmental factors and community composition, the pattern and strength of natural selection imposed by species on each other's traits are also expected to be highly spatially variable. Here, we used genomic and quantitative genetic approaches to understand how spatially variable selection operates on the genetic basis of plant defenses to herbivores. To this end, an F₂ progeny was generated by crossing Datura stramonium (Solanaceae) parents from two populations differing in their level of chemical defense. This F₂ progeny was reciprocally transplanted into the parental plants' habitats and by measuring the identity by descent (IBD) relationship of each F₂ plant to each parent, we were able to elucidate how spatially variable selection imposed by herbivores operated on the genetic background (IBD) of resistance to herbivory, promoting local adaptation. The results highlight that plants possessing the highest total alkaloid concentrations (sum of all alkaloid classes) were not the most well-defended or fit. Instead, specific alkaloids and their linked loci/alleles were favored by selection imposed by different herbivores. This has led to population differentiation in plant defenses and thus, to local adaptation driven by plant-herbivore interactions.

Identity by descent analysis identifies founder events and links SOD1 familial and sporadic ALS cases

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disorder characterised by the loss of upper and lower motor neurons resulting in paralysis and eventual death. Approximately 10% of ALS cases have a family history of disease, while the remainder present as apparently sporadic cases. Heritability studies suggest a significant genetic component to sporadic ALS, and although most sporadic cases have an unknown genetic aetiology, some familial ALS mutations have also been found in sporadic cases. This suggests that some sporadic cases may be unrecognised familial cases with reduced disease penetrance in their ancestors. A powerful strategy to uncover a familial link is identity-by-descent (IBD) analysis, which detects genomic regions that have been inherited from a common ancestor. IBD analysis was performed on 83 Australian familial ALS cases from 25 families and three sporadic ALS cases, each of whom carried one of three SOD1 mutations (p.I114T, p.V149G and p.E101G). We defined five unique 350-SNP haplotypes that carry these mutations in our cohort, indicative of five founder events. This included two founder haplotypes that carry SOD1 p.I114T; linking familial and sporadic cases. We found that SOD1 p.E101G arose independently in each family that carries this mutation and linked two families that carry SOD1 p.V149G. The age of disease onset varied between cases that carried each SOD1 p.I114T haplotype. Linking families with identical ALS mutations allows for larger sample sizes and increased statistical power to identify putative phenotypic modifiers.

A Fast and Simple Method for Detecting Identity-by-Descent Segments in Large-Scale Data

Segments of identity by descent (IBD) are used in many genetic analyses. We present a method for detecting identical-by-descent haplotype segments in phased genotype data. Our method, called hap-IBD, combines a compressed representation of haplotype data, the positional Burrows-Wheeler transform, and multi-threaded execution to produce very fast analysis times. An attractive feature of hap-IBD is its simplicity: the input parameters clearly and precisely define the IBD segments that are reported, so that program correctness can be confirmed by users. We evaluate hap-IBD and four state-of-the-art IBD segment detection methods (GERMLINE, iLASH, RaPID, and TRUFFLE) using UK Biobank chromosome 20 data and simulated sequence data. We show that hap-IBD detects IBD segments faster and more accurately than competing methods, and that hap-IBD is the only method that can rapidly and accurately detect short 2-4 centiMorgan (cM) IBD segments in the full UK Biobank data. Analysis of 485,346 UK Biobank samples through the use of hap-IBD with 12 computational threads detects 231.5 billion autosomal IBD segments with length ≥2 cM in 24.4 h.

Enabling population assignment from cancer genomes with SNP2pop

In many cancers, incidence, treatment efficacy and overall prognosis vary between geographic populations. Studies disentangling the contributing factors may help in both understanding cancer biology and tailoring therapeutic interventions. Ancestry estimation in such studies should preferably be driven by genomic data, due to frequently missing or erroneous self-reported or inferred metadata. While respective algorithms have been demonstrated for baseline genomes, such a strategy has not been shown for cancer genomes carrying a substantial somatic mutation load. We have developed a bioinformatics tool for the assignment of population groups from genome profiling data for both unaltered and cancer genomes. Despite extensive somatic mutations in the cancer genomes, consistency between germline and cancer data reached of 97% and 92% for assignment into 5 and 26 ancestral groups, respectively. Comparison with self-reported meta-data estimated a matching rate between 88-92%, mostly limited by interpretation of self-reported ethnicity labels compared to the standardized mapping output. Our SNP2pop application allows to assess population information from SNP arrays as well as sequencing platforms and to estimate the population structure in cancer genomics projects, to facilitate research into the interplay between ethnicity-related genetic background, environmental factors and somatic mutation patterns in cancer biology.

Allele frequency-free inference of close familial relationships from genotypes or low-depth sequencing data

Knowledge of how individuals are related is important in many areas of research, and numerous methods for inferring pairwise relatedness from genetic data have been developed. However, the majority of these methods were not developed for situations where data are limited. Specifically, most methods rely on the availability of population allele frequencies, the relative genomic position of variants and accurate genotype data. But in studies of non‐model organisms or ancient samples, such data are not always available. Motivated by this, we present a new method for pairwise relatedness inference, which requires neither allele frequency information nor information on genomic position. Furthermore, it can be applied not only to accurate genotype data but also to low‐depth sequencing data from which genotypes cannot be accurately called. We evaluate it using data from a range of human populations and show that it can be used to infer close familial relationships with a similar accuracy as a widely used method that relies on population allele frequencies. Additionally, we show that our method is robust to SNP ascertainment and applicable to low‐depth sequencing data generated using different strategies, including resequencing and RADseq, which is important for application to a diverse range of populations and species.

Joint Estimation of Pedigrees and Effective Population Size Using Markov Chain Monte Carlo

Pedigrees provide the genealogical relationships among individuals at a fine resolution and serve an important function in many areas of genetic studies. One such use of pedigree information is in the estimation of the short-term effective population size [Formula: see text], which is of great relevance in fields such as conservation genetics. Despite the usefulness of pedigrees, however, they are often an unknown parameter and must be inferred from genetic data. In this study, we present a Bayesian method to jointly estimate pedigrees and [Formula: see text] from genetic markers using Markov Chain Monte Carlo. Our method supports analysis of a large number of markers and individuals within a single generation with the use of a composite likelihood, which significantly increases computational efficiency. We show, on simulated data, that our method is able to jointly estimate relationships up to first cousins and [Formula: see text] with high accuracy. We also apply the method on a real dataset of house sparrows to reconstruct their previously unreported pedigree.

Testing for Hardy-Weinberg equilibrium in structured populations using genotype or low-depth next generation sequencing data

Testing for deviations from Hardy-Weinberg equilibrium (HWE) is a common practice for quality control in genetic studies. Variable sites violating HWE may be identified as technical errors in the sequencing or genotyping process, or they may be of particular evolutionary interest. Large-scale genetic studies based on next-generation sequencing (NGS) methods have become more prevalent as cost is decreasing but these methods are still associated with statistical uncertainty. The large-scale studies usually consist of samples from diverse ancestries that make the existence of some degree of population structure almost inevitable. Precautions are therefore needed when analysing these data set, as population structure causes deviations from HWE. Here we propose a method that takes population structure into account in the testing for HWE, such that other factors causing deviations from HWE can be detected. We show the effectiveness of PCAngsd in low-depth NGS data, as well as in genotype data, for both simulated and real data set, where the use of genotype likelihoods enables us to model the uncertainty.

Reconstructing recent population history while mapping rare variants using haplotypes

Haplotype-based methods are a cost-effective alternative to characterize unobserved rare variants and map disease-associated alleles. Moreover, they can be used to reconstruct recent population history, which shaped distribution of rare variants and thus can be used to guide gene mapping studies. In this study, we analysed Illumina 650 k genotyped dataset on three underrepresented populations from Eastern Europe, where ancestors of Russians came into contact with two indigenous ethnic groups, Bashkirs and Tatars. Using the IBD mapping approach, we identified two rare IBD haplotypes strongly enriched in asthma patients of distinct ethnic background. We reconstructed recent population history using haplotype-based methods to reconcile this contradictory finding. Our ChromoPainter analysis showed that these haplotypes each descend from a single ancestor coming from one of the ethnic groups studied. Next, we used DoRIS approach and showed that source populations for patients exchanged recent (<60 generations) asymmetric gene flow, which supported the ChromoPainter-based scenario that patients share haplotypes through inter-ethnic admixture. Finally, we show that these IBD haplotypes overlap with asthma-associated genomic regions ascertained in European population. This finding is consistent with the fact that the two donor populations for the rare IBD haplotypes: Russians and Tatars have European ancestry.

Identity-by-descent analyses for measuring population dynamics and selection in recombining pathogens

Identification of genomic regions that are identical by descent (IBD) has proven useful for human genetic studies where analyses have led to the discovery of familial relatedness and fine-mapping of disease critical regions. Unfortunately however, IBD analyses have been underutilized in analysis of other organisms, including human pathogens. This is in part due to the lack of statistical methodologies for non-diploid genomes in addition to the added complexity of multiclonal infections. As such, we have developed an IBD methodology, called isoRelate, for analysis of haploid recombining microorganisms in the presence of multiclonal infections. Using the inferred IBD status at genomic locations, we have also developed a novel statistic for identifying loci under positive selection and propose relatedness networks as a means of exploring shared haplotypes within populations. We evaluate the performance of our methodologies for detecting IBD and selection, including comparisons with existing tools, then perform an exploratory analysis of whole genome sequencing data from a global Plasmodium falciparum dataset of more than 2500 genomes. This analysis identifies Southeast Asia as having many highly related isolates, possibly as a result of both reduced transmission from intensified control efforts and population bottlenecks following the emergence of antimalarial drug resistance. Many signals of selection are also identified, most of which overlap genes that are known to be associated with drug resistance, in addition to two novel signals observed in multiple countries that have yet to be explored in detail. Additionally, we investigate relatedness networks over the selected loci and determine that one of these sweeps has spread between continents while the other has arisen independently in different countries. IBD analysis of microorganisms using isoRelate can be used for exploring population structure, positive selection and haplotype distributions, and will be a valuable tool for monitoring disease control and elimination efforts of many diseases.

There are growing concerns over the emergence of antimicrobial drug resistance, which threatens the efficacy of treatments for infectious diseases such as malaria. As such, it is important to understand the dynamics of resistance by investigating population structure, natural selection and disease transmission in microorganisms. The study of disease dynamics has been hampered by the lack of suitable statistical models for analysis of isolates containing multiple infections. We introduce a statistical model that uses population genomic data to identify genomic regions (loci) that are inherited from a common ancestor, in the presence of multiple infections. We demonstrate its potential for biological discovery using a global Plasmodium falciparum dataset. We identify low genetic diversity in isolates from Southeast Asia, possibly from clonal expansion following intensified control efforts after the emergence of artemisinin resistance. We also identify loci under positive selection, most of which contain genes that have been associated with antimalarial drug resistance. We discover two loci under strong selection in multiple countries throughout Southeast Asia and Africa where the selection pressure is currently unknown. We find that the selection pressure at one of these loci has originated from gene flow, while the other loci has originated from multiple independent events.

Rare Risk Variants Identification by Identity-by-Descent Mapping and Whole-Exome Sequencing Implicates Neuronal Development Pathways in Schizophrenia and Bipolar Disorder

Schizophrenia (SCZ) and bipolar disorder (BPD) are highly heritable disorders with an estimated co-heritability of 68%. Hundreds of common alleles have been implicated, but recently a role for rare, high-penetrant variants has been also suggested in both disorders. This study investigated a familial cohort of SCZ and BPD patients from a closed population sample, where the high recurrence of the disorders and the homogenous genetic background indicate a possible enrichment in rare risk alleles. A total of 230 subjects (161 cases, 22 unaffected relatives, and 47 controls) were genetically investigated through an innovative strategy that integrates identity-by-descent (IBD) mapping and whole-exome sequencing (WES). IBD analysis allowed to track high-risk haplotypes (IBD_risk) shared exclusively by multiple patients from different families and possibly carrying the most penetrant alleles. A total of 444 non-synonymous sequence variants, of which 137 disruptive, were identified in IBD_risk haplotypes by WES. Interestingly, gene sets previously implicated in SCZ (i.e., post-synaptic density (PSD) proteins, voltage-gated calcium channels (VGCCs), and fragile X mental retardation protein (FMRP) targets) were found significantly enriched in genes carrying IBD_risk variants. Further, IBD_risk variants were preferentially affecting genes involved in the extracellular matrix (ECM) biology and axon guidance processes which appeared to be functionally connected in the pathway-derived meta-network analysis. Results thus confirm rare risk variants as key factors in SCZ and BPD pathogenesis and highlight a role for the development of neuronal connectivity in the etiology of both disorders.

Founder Effect of the RETC611Y Mutation in Multiple Endocrine Neoplasia 2A in Denmark: A Nationwide Study

BACKGROUND

Multiple endocrine neoplasia (MEN) 2A and 2B are caused by REarranged during Transfection (RET) germline mutations. In a recent nationwide study, an unusually high prevalence (33%) of families with the C611Y mutation was reported, and it was hypothesized that this might be due to a founder effect. The first nationwide study of haplotypes in MEN2A families was conducted, with the aim of investigating the relatedness and occurrence of de novo mutations among Danish families carrying similar mutations.

METHODS

The study included 21 apparently unrelated MEN2A families identified from a nationwide Danish RET cohort from 1994 to 2014. Twelve, two, two, three, and two families carried the C611Y, C618F, C618Y, C620R, and C634R mutations, respectively. Single nucleotide polymorphism chip data and identity by descent analysis were used to assess relatedness.

RESULTS

A common founder mutation was found among all 12 C611Y families and between both C618Y families. No relatedness was identified in the remaining families.

CONCLUSION

The data suggest that all families with the C611Y germline mutation in Denmark originate from a recent common ancestor, probably explaining the unusually high prevalence of this mutation. Additionally, the results indicate that the C611Y mutation rarely arises de novo, thus underlining the need for thorough multigenerational genetic work up in carriers of this mutation.

Weighted likelihood inference of genomic autozygosity patterns in dense genotype data

Background

Genomic regions of autozygosity (ROA) arise when an individual is homozygous for haplotypes inherited identical-by-descent from ancestors shared by both parents. Over the past decade, they have gained importance for understanding evolutionary history and the genetic basis of complex diseases and traits. However, methods to infer ROA in dense genotype data have not evolved in step with advances in genome technology that now enable us to rapidly create large high-resolution genotype datasets, limiting our ability to investigate their constituent ROA patterns.

Methods

We report a weighted likelihood approach for inferring ROA in dense genotype data that accounts for autocorrelation among genotyped positions and the possibilities of unobserved mutation and recombination events, and variability in the confidence of individual genotype calls in whole genome sequence (WGS) data.

Results

Forward-time genetic simulations under two demographic scenarios that reflect situations where inbreeding and its effect on fitness are of interest suggest this approach is better powered than existing state-of-the-art methods to infer ROA at marker densities consistent with WGS and popular microarray genotyping platforms used in human and non-human studies. Moreover, we present evidence that suggests this approach is able to distinguish ROA arising via consanguinity from ROA arising via endogamy. Using subsets of The 1000 Genomes Project Phase 3 data we show that, relative to WGS, intermediate and long ROA are captured robustly with popular microarray platforms, while detection of short ROA is more variable and improves with marker density. Worldwide ROA patterns inferred from WGS data are found to accord well with those previously reported on the basis of microarray genotype data. Finally, we highlight the potential of this approach to detect genomic regions enriched for autozygosity signals in one group relative to another based upon comparisons of per-individual autozygosity likelihoods instead of inferred ROA frequencies.

Conclusions

This weighted likelihood ROA inference approach can assist population- and disease-geneticists working with a wide variety of data types and species to explore ROA patterns and to identify genomic regions with differential ROA signals among groups, thereby advancing our understanding of evolutionary history and the role of recessive variation in phenotypic variation and disease.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-017-4312-3) contains supplementary material, which is available to authorized users.

IPED2: Inheritance Path Based Pedigree Reconstruction Algorithm for Complicated Pedigrees

Reconstruction of family trees, or pedigree reconstruction, for a group of individuals is a fundamental problem in genetics. The problem is known to be NP-hard even for datasets known to only contain siblings. Some recent methods have been developed to accurately and efficiently reconstruct pedigrees. These methods, however, still consider relatively simple pedigrees, for example, they are not able to handle half-sibling situations where a pair of individuals only share one parent. In this work, we propose an efficient method, IPED2, based on our previous work, which specifically targets reconstruction of complicated pedigrees that include half-siblings. We note that the presence of half-siblings makes the reconstruction problem significantly more challenging which is why previous methods exclude the possibility of half-siblings. We proposed a novel model as well as an efficient graph algorithm and experiments show that our algorithm achieves relatively accurate reconstruction. To our knowledge, this is the first method that is able to handle pedigree reconstruction from genotype data when half-sibling exists in any generation of the pedigree.

Composite likelihood method for inferring local pedigrees

Pedigrees contain information about the genealogical relationships among individuals and are of fundamental importance in many areas of genetic studies. However, pedigrees are often unknown and must be inferred from genetic data. Despite the importance of pedigree inference, existing methods are limited to inferring only close relationships or analyzing a small number of individuals or loci. We present a simulated annealing method for estimating pedigrees in large samples of otherwise seemingly unrelated individuals using genome-wide SNP data. The method supports complex pedigree structures such as polygamous families, multi-generational families, and pedigrees in which many of the member individuals are missing. Computational speed is greatly enhanced by the use of a composite likelihood function which approximates the full likelihood. We validate our method on simulated data and show that it can infer distant relatives more accurately than existing methods. Furthermore, we illustrate the utility of the method on a sample of Greenlandic Inuit.

Pedigrees contain information about the genealogical relationships among individuals. This information can be used in many areas of genetic studies such as disease association studies, conservation efforts, and for inferences about the demographic history and social structure of a population. Despite their importance, pedigrees are often unknown and must be estimated from genetic information. However, pedigree inference remains a difficult problem due to the high cost of likelihood computation and the enormous number of possible pedigrees that must be considered. These difficulties limit existing methods in their ability to infer pedigrees when the sample size or the number of markers is large, or when the sample contains only distant relatives. In this report, we present a method that circumvents these computational challenges in order to infer pedigrees of complex structure for a large number of individuals. Using simulations, we find that the method can infer distant relatives much more accurately than existing methods. Furthermore, we show that even pairwise inferences of relatedness can be improved substantially by consideration of the pedigree structure with other related individuals in the sample.

Determination of genetic relatedness from low-coverage human genome sequences using pedigree simulations

We develop and evaluate methods for inferring relatedness among individuals from low-coverage DNA sequences of their genomes, with particular emphasis on sequences obtained from fossil remains. We suggest the major factors complicating the determination of relatedness among ancient individuals are sequencing depth, the number of overlapping sites, the sequencing error rate and the presence of contamination from present-day genetic sources. We develop a theoretical model that facilitates the exploration of these factors and their relative effects, via measurement of pairwise genetic distances, without calling genotypes, and determine the power to infer relatedness under various scenarios of varying sequencing depth, present-day contamination and sequencing error. The model is validated by a simulation study as well as the analysis of aligned sequences from present-day human genomes. We then apply the method to the recently published genome sequences of ancient Europeans, developing a statistical treatment to determine confidence in assigned relatedness that is, in some cases, more precise than previously reported. As the majority of ancient specimens are from animals, this method would be applicable to investigate kinship in nonhuman remains. The developed software grups (Genetic Relatedness Using Pedigree Simulations) is implemented in Python and freely available.

Genetic screening of the FLCN gene identify six novel variants and a Danish founder mutation

Pathogenic germline mutations in the folliculin (FLCN) tumor suppressor gene predispose to Birt-Hogg-Dubé (BHD) syndrome, a rare disease characterized by the development of cutaneous hamartomas (fibrofolliculomas), multiple lung cysts, spontaneous pneumothoraces and renal cell cancer. In this study, we report the identification of 13 variants and three polymorphisms in the FLCN gene in 143 Danish patients or families with suspected BHD syndrome. Functional mini-gene splicing analysis revealed that two intronic variants (c.1062+2T>G and c.1177-5_1177-3del) introduced splicing aberrations. Eleven families exhibited the c.1062+2T>G mutation. Combined single nucleotide polymorphism array-haplotype analysis showed that these families share a 3-Mb genomic fragment containing the FLCN gene, revealing that the c.1062+2T>G mutation is a Danish founder mutation. On the basis of in silico prediction and functional splicing assays, we classify the 16 identified variants in the FLCN gene as follows: nine as pathogenic, one as likely pathogenic, three as likely benign and three as polymorphisms. In conclusion, the study describes the FLCN mutation spectrum in Danish BHD patients, and contributes to a better understanding of BHD syndrome and management of BHD patients.

Loss-of-function mutations in the SIGMAR1 gene cause distal hereditary motor neuropathy by impairing ER-mitochondria tethering and Ca2+ signalling

Distal hereditary motor neuropathies (dHMNs) are clinically and genetically heterogeneous neurological conditions characterized by degeneration of the lower motor neurons. So far, 18 dHMN genes have been identified, however, about 80% of dHMN cases remain without a molecular diagnosis. By a combination of autozygosity mapping, identity-by-descent segment detection and whole-exome sequencing approaches, we identified two novel homozygous mutations in the SIGMAR1 gene (p.E138Q and p.E150K) in two distinct Italian families affected by an autosomal recessive form of HMN. Functional analyses in several neuronal cell lines strongly support the pathogenicity of the mutations and provide insights into the underlying pathomechanisms involving the regulation of ER-mitochondria tethering, Ca2+ homeostasis and autophagy. Indeed, in vitro, both mutations reduce cell viability, the formation of abnormal protein aggregates preventing the correct targeting of sigma-1R protein to the mitochondria-associated ER membrane (MAM) and thus impinging on the global Ca2+ signalling. Our data definitively demonstrate the involvement of SIGMAR1 in motor neuron maintenance and survival by correlating, for the first time in the Caucasian population, mutations in this gene to distal motor dysfunction and highlight the chaperone activity of sigma-1R at the MAM as a critical aspect in dHMN pathology.

Identity by descent fine mapping of familial adult myoclonus epilepsy (FAME) to 2p11.2-2q11.2

Familial adult myoclonus epilepsy (FAME) is a rare autosomal dominant disorder characterized by adult onset, involuntary muscle jerks, cortical myoclonus and occasional seizures. FAME is genetically heterogeneous with more than 70 families reported worldwide and five potential disease loci. The efforts to identify potential causal variants have been unsuccessful in all but three families. To date, linkage analysis has been the main approach to find and narrow FAME critical regions. We propose an alternative method, pedigree free identity-by-descent (IBD) mapping, that infers regions of the genome between individuals that have been inherited from a common ancestor. IBD mapping provides an alternative to linkage analysis in the presence of allelic and locus heterogeneity by detecting clusters of individuals who share a common allele. Succeeding IBD mapping, gene prioritization based on gene co-expression analysis can be used to identify the most promising candidate genes. We performed an IBD analysis using high-density single nucleotide polymorphism (SNP) array data followed by gene prioritization on a FAME cohort of ten European families and one Australian/New Zealander family; eight of which had known disease loci. By identifying IBD regions common to multiple families, we were able to narrow the FAME2 locus to a 9.78 megabase interval within 2p11.2-q11.2. We provide additional evidence of a founder effect in four Italian families and allelic heterogeneity with at least four distinct founders responsible for FAME at the FAME2 locus. In addition, we suggest candidate disease genes using gene prioritization based on gene co-expression analysis.

Conflation of Short Identity-by-Descent Segments Bias Their Inferred Length Distribution

Identity-by-descent (IBD) is a fundamental concept in genetics with many applications. In a common definition, two haplotypes are said to share an IBD segment if that segment is inherited from a recent shared common ancestor without intervening recombination. Segments several cM long can be efficiently detected by a number of algorithms using high-density SNP array data from a population sample, and there are currently efforts to detect shorter segments from sequencing. Here, we study a problem of identifiability: because existing approaches detect IBD based on contiguous segments of identity-by-state, inferred long segments of IBD may arise from the conflation of smaller, nearby IBD segments. We quantified this effect using coalescent simulations, finding that significant proportions of inferred segments 1–2 cM long are results of conflations of two or more shorter segments, each at least 0.2 cM or longer, under demographic scenarios typical for modern humans for all programs tested. The impact of such conflation is much smaller for longer (> 2 cM) segments. This biases the inferred IBD segment length distribution, and so can affect downstream inferences that depend on the assumption that each segment of IBD derives from a single common ancestor. As an example, we present and analyze an estimator of the de novo mutation rate using IBD segments, and demonstrate that unmodeled conflation leads to underestimates of the ages of the common ancestors on these segments, and hence a significant overestimate of the mutation rate. Understanding the conflation effect in detail will make its correction in future methods more tractable.

XIBD: software for inferring pairwise identity by descent on the X chromosome

XIBD performs pairwise relatedness mapping on the X chromosome using dense single nucleotide polymorphism (SNP) data from either SNP chips or next generation sequencing data. It correctly accounts for the difference in chromosomal numbers between males and females and estimates global relatedness as well as regions of the genome that are identical by descent (IBD). XIBD also generates novel graphical summaries of all pairwise IBD tracts for a cohort making it very useful for disease locus mapping.

AVAILABILITY AND IMPLEMENTATION

XIBD is written in R/Rcpp and executed from shell scripts that are freely available from http://bioinf.wehi.edu.au/software/XIBD along with accompanying reference datasets.

CONTACT

henden.l@wehi.edu.au

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Partial USH2A deletions contribute to Usher syndrome in Denmark

Usher syndrome is an autosomal recessive disorder characterized by congenital hearing impairment, progressive visual loss owing to retinitis pigmentosa and in some cases vestibular dysfunction. Usher syndrome is divided into three subtypes, USH1, USH2 and USH3. Twelve loci and eleven genes have so far been identified. Duplications and deletions in PCDH15 and USH2A that lead to USH1 and USH2, respectively, have previously been identified in patients from United Kingdom, Spain and Italy. In this study, we investigate the proportion of exon deletions and duplications in PCDH15 and USH2A in 20 USH1 and 30 USH2 patients from Denmark using multiplex ligation-dependent probe amplification (MLPA). Two heterozygous deletions were identified in USH2A, but no deletions or duplications were identified in PCDH15. Next-generation mate-pair sequencing was used to identify the exact breakpoints of the two deletions identified in USH2A. Our results suggest that USH2 is caused by USH2A exon deletions in a small fraction of the patients, whereas deletions or duplications in PCDH15 might be rare in Danish Usher patients.

PIGS: improved estimates of identity-by-descent probabilities by probabilistic IBD graph sampling

Identifying segments in the genome of different individuals that are identical-by-descent (IBD) is a fundamental element of genetics. IBD data is used for numerous applications including demographic inference, heritability estimation, and mapping disease loci. Simultaneous detection of IBD over multiple haplotypes has proven to be computationally difficult. To overcome this, many state of the art methods estimate the probability of IBD between each pair of haplotypes separately. While computationally efficient, these methods fail to leverage the clique structure of IBD resulting in less powerful IBD identification, especially for small IBD segments.

We develop a hybrid approach (PIGS), which combines the computational efficiency of pairwise methods with the power of multiway methods. It leverages the IBD graph structure to compute the probability of IBD conditional on all pairwise estimates simultaneously. We show via extensive simulations and analysis of real data that our method produces a substantial increase in the number of identified small IBD segments.

Joint inference of identity by descent along multiple chromosomes from population samples

There has been much interest in detecting genomic identity by descent (IBD) segments from modern dense genetic marker data and in using them to identify human disease susceptibility loci. Here we present a novel Bayesian framework using Markov chain Monte Carlo (MCMC) realizations to jointly infer IBD states among multiple individuals not known to be related, together with the allelic typing error rate and the IBD process parameters. The data are phased single nucleotide polymorphism (SNP) haplotypes. We model changes in latent IBD state along homologous chromosomes by a continuous time Markov model having the Ewens sampling formula as its stationary distribution. We show by simulation that this model for the IBD process fits quite well with the coalescent predictions. Using simulation data sets of 40 haplotypes over regions of 1 and 10 million base pairs (Mbp), we show that the jointly estimated IBD states are very close to the true values, although the presence of linkage disequilibrium decreases the accuracy. We also present comparisons with the ibd_haplo program, which estimates IBD among sets of four haplotypes. Our new IBD detection method focuses on the scale between genome-wide methods using simple IBD models and complex coalescent-based methods that are limited to short genome segments. At the scale of a few Mbp, our approach offers potentially more power for fine-scale IBD association mapping.

The genetic prehistory of the New World Arctic

The New World Arctic, the last region of the Americas to be populated by humans, has a relatively well-researched archaeology, but an understanding of its genetic history is lacking. We present genome-wide sequence data from ancient and present-day humans from Greenland, Arctic Canada, Alaska, Aleutian Islands, and Siberia. We show that Paleo-Eskimos (~3000 BCE to 1300 CE) represent a migration pulse into the Americas independent of both Native American and Inuit expansions. Furthermore, the genetic continuity characterizing the Paleo-Eskimo period was interrupted by the arrival of a new population, representing the ancestors of present-day Inuit, with evidence of past gene flow between these lineages. Despite periodic abandonment of major Arctic regions, a single Paleo-Eskimo metapopulation likely survived in near-isolation for more than 4000 years, only to vanish around 700 years ago.

Using familial information for variant filtering in high-throughput sequencing studies

High-throughput sequencing studies (HTS) have been highly successful in identifying the genetic causes of human disease, particularly those following Mendelian inheritance. Many HTS studies to date have been performed without utilizing available family relationships between samples. Here, we discuss the many merits and occasional pitfalls of using identity by descent information in conjunction with HTS studies. These methods are not only applicable to family studies but are also useful in cohorts of apparently unrelated, ‘sporadic’ cases and small families underpowered for linkage and allow inference of relationships between individuals. Incorporating familial/pedigree information not only provides powerful filtering options for the extensive variant lists that are usually produced by HTS but also allows valuable quality control checks, insights into the genetic model and the genotypic status of individuals of interest. In particular, these methods are valuable for challenging discovery scenarios in HTS analysis, such as in the study of populations poorly represented in variant databases typically used for filtering, and in the case of poor-quality HTS data.

Maximum-likelihood inference of population size contractions from microsatellite data

Understanding the demographic history of populations and species is a central issue in evolutionary biology and molecular ecology. In this work, we develop a maximum-likelihood method for the inference of past changes in population size from microsatellite allelic data. Our method is based on importance sampling of gene genealogies, extended for new mutation models, notably the generalized stepwise mutation model (GSM). Using simulations, we test its performance to detect and characterize past reductions in population size. First, we test the estimation precision and confidence intervals coverage properties under ideal conditions, then we compare the accuracy of the estimation with another available method (MSVAR) and we finally test its robustness to misspecification of the mutational model and population structure. We show that our method is very competitive compared with alternative ones. Moreover, our implementation of a GSM allows more accurate analysis of microsatellite data, as we show that the violations of a single step mutation assumption induce very high bias toward false contraction detection rates. However, our simulation tests also showed some limits, which most importantly are large computation times for strong disequilibrium scenarios and a strong influence of some form of unaccounted population structure. This inference method is available in the latest implementation of the MIGRAINE software package.

Reducing pervasive false-positive identical-by-descent segments detected by large-scale pedigree analysis

Analysis of genomic segments shared identical-by-descent (IBD) between individuals is fundamental to many genetic applications, from demographic inference to estimating the heritability of diseases, but IBD detection accuracy in nonsimulated data is largely unknown. In principle, it can be evaluated using known pedigrees, as IBD segments are by definition inherited without recombination down a family tree. We extracted 25,432 genotyped European individuals containing 2,952 father-mother-child trios from the 23andMe, Inc. data set. We then used GERMLINE, a widely used IBD detection method, to detect IBD segments within this cohort. Exploiting known familial relationships, we identified a false-positive rate over 67% for 2-4 centiMorgan (cM) segments, in sharp contrast with accuracies reported in simulated data at these sizes. Nearly all false positives arose from the allowance of haplotype switch errors when detecting IBD, a necessity for retrieving long (>6 cM) segments in the presence of imperfect phasing. We introduce HaploScore, a novel, computationally efficient metric that scores IBD segments proportional to the number of switch errors they contain. Applying HaploScore filtering to the IBD data at a precision of 0.8 produced a 13-fold increase in recall when compared with length-based filtering. We replicate the false IBD findings and demonstrate the generalizability of HaploScore to alternative data sources using an independent cohort of 555 European individuals from the 1000 Genomes project. HaploScore can improve the accuracy of segments reported by any IBD detection method, provided that estimates of the genotyping error rate and switch error rate are available.

IBD-Groupon: an efficient method for detecting group-wise identity-by-descent regions simultaneously in multiple individuals based on pairwise IBD relationships

MOTIVATION

Detecting IBD tracts is an important problem in genetics. Most of the existing methods focus on detecting pairwise IBD tracts, which have relatively low power to detect short IBD tracts. Methods to detect IBD tracts among multiple individuals simultaneously, or group-wise IBD tracts, have better performance for short IBD tracts detection. Group-wise IBD tracts can be applied to a wide range of applications, such as disease mapping, pedigree reconstruction and so forth. The existing group-wise IBD tract detection method is computationally inefficient and is only able to handle small datasets, such as 20, 30 individuals with hundreds of SNPs. It also requires a previous specification of the number of IBD groups, or partitions of the individuals where all the individuals in the same partition are IBD with each other, which may not be realistic in many cases. The method can only handle a small number of IBD groups, such as two or three, because of scalability issues. What is more, it does not take LD (linkage disequilibrium) into consideration.

RESULTS

In this work, we developed an efficient method IBD-Groupon, which detects group-wise IBD tracts based on pairwise IBD relationships, and it is able to address all the drawbacks aforementioned. To our knowledge, our method is the first practical group-wise IBD tracts detection method that is scalable to very large datasets, for example, hundreds of individuals with thousands of SNPs, and in the meanwhile, it is powerful to detect short IBD tracts. Our method does not need to specify the number of IBD groups, which will be detected automatically. And our method takes LD into consideration, as it is based on pairwise IBD tracts where LD can be easily incorporated.

RelateAdmix: a software tool for estimating relatedness between admixed individuals

MOTIVATION

Pairwise relatedness plays an important role in a range of genetic research fields. However, currently only few estimators exist for individuals that are admixed, i.e. have ancestry from more than one population, and these estimators fail in some situations.

RESULTS

We present a new software tool, RelateAdmix, for obtaining maximum likelihood estimates of pairwise relatedness from genetic data between admixed individuals. We show using simulated data that it gives rise to better estimates than three state-of-the-art software tools, REAP, KING and Plink, while still being fast enough to be applicable to large datasets.

AVAILABILITY AND IMPLEMENTATION

The software tool, implemented in C and R, is freely available from www.popgen.dk/software.

Genome-wide patterns of identity-by-descent sharing in the French Canadian founder population

In genetics the ability to accurately describe the familial relationships among a group of individuals can be very useful. Recent statistical tools succeeded in assessing the degree of relatedness up to 6-7 generations with good power using dense genome-wide single-nucleotide polymorphism data to estimate the extent of identity-by-descent (IBD) sharing. It is therefore important to describe genome-wide patterns of IBD sharing for more remote and complex relatedness between individuals, such as that observed in a founder population like Quebec, Canada. Taking advantage of the extended genealogical records of the French Canadian founder population, we first compared different tools to identify regions of IBD in order to best describe genome-wide IBD sharing and its correlation with genealogical characteristics. Results showed that the extent of IBD sharing identified with FastIBD correlates best with relatedness measured using genealogical data. Total length of IBD sharing explained 85% of the genealogical kinship's variance. In addition, we observed significantly higher sharing in pairs of individuals with at least one inbred ancestor compared with those without any. Furthermore, patterns of IBD sharing and average sharing were different across regional populations, consistent with the settlement history of Quebec. Our results suggest that, as expected, the complex relatedness present in founder populations is reflected in patterns of IBD sharing. Using these patterns, it is thus possible to gain insight on the types of distant relationships in a sample from a founder population like Quebec.

IPED: inheritance path-based pedigree reconstruction algorithm using genotype data

The problem of inference of family trees, or pedigree reconstruction, for a group of individuals is a fundamental problem in genetics. Various methods have been proposed to automate the process of pedigree reconstruction given the genotypes or haplotypes of a set of individuals. Current methods, unfortunately, are very time-consuming and inaccurate for complicated pedigrees, such as pedigrees with inbreeding. In this work, we propose an efficient algorithm that is able to reconstruct large pedigrees with reasonable accuracy. Our algorithm reconstructs the pedigrees generation by generation, backward in time from the extant generation. We predict the relationships between individuals in the same generation using an inheritance path-based approach implemented with an efficient dynamic programming algorithm. Experiments show that our algorithm runs in linear time with respect to the number of reconstructed generations, and therefore, it can reconstruct pedigrees that have a large number of generations. Indeed it is the first practical method for reconstruction of large pedigrees from genotype data.