Inference of relationships in population data using identity-by-descent and identity-by-state

Genetic characteristics and selection signatures between Southern Chinese local and commercial chickens

The introduction of exotic breeds and the cultivation of new lines by breeding companies have posed challenges to native chickens in South China, including loss of breed characteristics, decreased genetic diversity, and declining purity. Understanding the population genetic structure and genetic diversity of native chickens in South China is crucial for further advancements in breeding efforts. In this study, we analyzed the population genetic structure and genetic diversity of 321 individuals from 10 different breeds in South China. By comparing commercial chickens with native ones, we identified selection signatures occurring between local chickens and commercial breeds. The analysis of population genetic structure revealed that the native chicken populations in South China exhibited a considerable level of genetic diversity. Moreover, the commercial lines of Xiaobai chicken and Huangma chicken displayed even higher levels of genetic diversity, which distinguished them from other native varieties at the clustering level. However, certain individuals within these commercial varieties showed a discernible genetic relationship with the native populations. Notably, both commercial varieties also retained a significant degree of genetic similarity to their respective native counterparts. In order to investigate the genomic changes occurring during the commercialization of native chickens, we employed 4 methods (Fst, ROD, XPCLR, and XPEHH) to identify potential candidate regions displaying selective signatures in Southern Chinese native chicken population. A total of 168 (identified by Fst and ROD) and 86 (identified by XPCLR and XPEHH) overlapping genes were discovered. Functional annotation analysis revealed that these genes may be associated with reproduction and growth (SAMSN1, HYLS1, ROBO3, FGF14, PRSS23), musculoskeletal development (DNER, MYBPC1, DGKB, ORC1, KLF10), disease resistance and environmental adaptability (PUS3, CRB2, CALD1, USP15, SGCD, LTBP1), as well as egg production (ADGRB3, ACSF3). Overall, native chickens in South China harbor numerous selective sweep regions compared to commercial chickens, enriching valuable genomic resources for future genetic research and breeding conservation.

The genetic history of the Southern Andes from present-day Mapuche ancestry

The southernmost regions of South America harbor some of the earliest evidence of human presence in the Americas. However, connections with the rest of the continent and the contextualization of present-day indigenous ancestries remain poorly resolved. In this study, we analyze the genetic ancestry of one of the largest indigenous groups in South America: the Mapuche. We generate genome-wide data from 64 participants from three Mapuche populations in Southern Chile: Pehuenche, Lafkenche, and Huilliche. Broadly, we describe three main ancestry blocks with a common origin, which characterize the Southern Cone, the Central Andes, and Amazonia. Within the Southern Cone, ancestors of the Mapuche lineages differentiated from those of the Far South during the Middle Holocene and did not experience further migration waves from the north. We find that the deep genetic split between the Central and Southern Andes is followed by instances of gene flow, which may have accompanied the southward spread of cultural traits from the Central Andes, including crops and loanwords from Quechua into Mapudungun (the language of the Mapuche). Finally, we report close genetic relatedness between the three populations analyzed, with the Huilliche characterized additionally by intense recent exchanges with the Far South. Our findings add new perspectives on the genetic (pre)history of South America, from the first settlement through to the present-day indigenous presence. Follow-up fieldwork took these results back to the indigenous communities to contextualize the genetic narrative alongside indigenous knowledge and perspectives.

Evaluating the use of statistical and machine learning methods for estimating breed composition of purebred and crossbred animals in thirteen cattle breeds using genomic information

Introduction: The ability to accurately predict breed composition using genomic information has many potential uses including increasing the accuracy of genetic evaluations, optimising mating plans and as a parameter for genotype quality control. The objective of the present study was to use a database of genotyped purebred and crossbred cattle to compare breed composition predictions using a freely available software, Admixture, with those from a single nucleotide polymorphism Best Linear Unbiased Prediction (SNP-BLUP) approach; a supplementary objective was to determine the accuracy and general robustness of low-density genotype panels for predicting breed composition. Methods: All animals had genotype information on 49,213 autosomal single nucleotide polymorphism (SNPs). Thirteen breeds were included in the analysis and 500 purebred animals per breed were used to establish the breed training populations. Accuracy of breed composition prediction was determined using a separate validation population of 3,146 verified purebred and 4,330 two and three-way crossbred cattle. Results: When all 49,213 autosomal SNPs were used for breed prediction, a minimal absolute mean difference of 0.04 between Admixture vs. SNP-BLUP breed predictions was evident. For crossbreds, the average absolute difference in breed prediction estimates generated using SNP-BLUP and Admixture was 0.068 with a root mean square error of 0.08. Breed predictions from low-density SNP panels were generated using both SNP-BLUP and Admixture and compared to breed prediction estimates using all 49,213 SNPs (representing the gold standard). Breed composition estimates of crossbreds required more SNPs than predicting the breed composition of purebreds. SNP-BLUP required ≥3,000 SNPs to predict crossbred breed composition, but only 2,000 SNPs were required to predict purebred breed status. The absolute mean (standard deviation) difference across all panels <2,000 SNPs was 0.091 (0.054) and 0.315 (0.316) when predicting the breed composition of all animals using Admixture and SNP-BLUP, respectively compared to the gold standard prediction. Discussion: Nevertheless, a negligible absolute mean (standard deviation) difference of 0.009 (0.123) in breed prediction existed between SNP-BLUP and Admixture once ≥3,000 SNPs were considered, indicating that the prediction of breed composition could be readily integrated into SNP-BLUP pipelines used for genomic evaluations thereby avoiding the necessity for a stand-alone software.

Dual domestications and origin of traits in grapevine evolution

We elucidate grapevine evolution and domestication histories with 3525 cultivated and wild accessions worldwide. In the Pleistocene, harsh climate drove the separation of wild grape ecotypes caused by continuous habitat fragmentation. Then, domestication occurred concurrently about 11,000 years ago in Western Asia and the Caucasus to yield table and wine grapevines. The Western Asia domesticates dispersed into Europe with early farmers, introgressed with ancient wild western ecotypes, and subsequently diversified along human migration trails into muscat and unique western wine grape ancestries by the late Neolithic. Analyses of domestication traits also reveal new insights into selection for berry palatability, hermaphroditism, muscat flavor, and berry skin color. These data demonstrate the role of the grapevines in the early inception of agriculture across Eurasia.

Exploring how space, time, and sampling impact our ability to measure genetic structure across Plasmodium falciparum populations

A primary use of malaria parasite genomics is identifying highly related infections to quantify epidemiological, spatial, or temporal factors associated with patterns of transmission. For example, spatial clustering of highly related parasites can indicate foci of transmission and temporal differences in relatedness can serve as evidence for changes in transmission over time. However, for infections in settings of moderate to high endemicity, understanding patterns of relatedness is compromised by complex infections, overall high forces of infection, and a highly diverse parasite population. It is not clear how much these factors limit the utility of using genomic data to better understand transmission in these settings. In particular, further investigation is required to determine which patterns of relatedness we expect to see with high quality, densely sampled genomic data in a high transmission setting and how these observations change under different study designs, missingness, and biases in sample collection. Here we investigate two identity-by-state measures of relatedness and apply them to amplicon deep sequencing data collected as part of a longitudinal cohort in Western Kenya that has previously been analysed to identify individual-factors associated with sharing parasites with infected mosquitoes. With these data we use permutation tests, to evaluate several hypotheses about spatiotemporal patterns of relatedness compared to a null distribution. We observe evidence of temporal structure, but not of fine-scale spatial structure in the cohort data. To explore factors associated with the lack of spatial structure in these data, we construct a series of simplified simulation scenarios using an agent based model calibrated to entomological, epidemiological and genomic data from this cohort study to investigate whether the lack of spatial structure observed in the cohort could be due to inherent power limitations of this analytical method. We further investigate how our hypothesis testing behaves under different sampling schemes, levels of completely random and systematic missingness, and different transmission intensities.

Development of a new screening method for faster kinship analyses in mass disasters: a proof of concept study

Kinship analysis in forensics is based on the calculation of the respective kinship indices. However, this calculation is only possible when the subject under identification has been associated with a particular population, whose allele frequency data is available for the particular set of STR markers used in the forensic practices. In the case of mass disasters, where a large number of individuals are to be identified, gathering the population frequency data and calculating the kinship indices can be an intricate process which requires a lot of time and huge resources. The new method of allele matching cut off score (AMCOS) developed in this study is based on the allele sharing approach. This approach simply refers to the number of shared alleles (1 or 2) between the two individuals; also known as identical by state (IBS) alleles which might have been inherited from a recent common ancestor in which the alleles are identical by descendent (IBD). In case of mass disasters, this method can be used to narrow down the number of pairs (dead and alive) to be matched for kinship without using the allele frequency data. The results obtained from this method could further be confirmed by LR based method, which uses the allele frequency data of the respective population of the pairs being tested for kinship. AMCOS method has been tested for its sensitivity, specificity and various other statistical parameters and has shown promising values for the same in various types of kinship analyses. This ascertains the authenticity and potential use of this method in forensic practice but only after its validation in a larger sample size. AMCOS method has been tested on siblings and grandparent-grandchildren by using autosomal and X-STR markers both, as the reference samples from the parents cannot always be available for the identification. The present study also compared the results shown by the autosomal and X-STR markers in siblings and grandparent-grandchildren identification, thereby suggesting the use of better set of markers on the basis of obtained values of various statistical parameters.

Parent-offspring inference in inbred populations

Genealogical relationships are fundamental components of genetic studies. However, it is often challenging to infer correct and complete pedigrees even when genome-wide information is available. For example, inbreeding can obscure genetic differences between individuals, making it difficult to even distinguish first-degree relatives such as parent-offspring from full siblings. Similarly, genotyping errors can interfere with the detection of genetic similarity between parents and their offspring. Inbreeding is common in natural, domesticated, and experimental populations and genotyping of these populations often has more errors than in human data sets, so efficient methods for building pedigrees under these conditions are necessary. Here, we present a new method for parent-offspring inference in inbred pedigrees called specific parent-offspring relationship estimation (spore). spore is vastly superior to existing pedigree-inference methods at detecting parent-offspring relationships, in particular when inbreeding is high or in the presence of genotyping errors, or both. spore therefore fills an important void in the arsenal of pedigree inference tools.

A machine learning approach for missing persons cases with high genotyping errors

Estimating the relationships between individuals is one of the fundamental challenges in many fields. In particular, relationship.ip estimation could provide valuable information for missing persons cases. The recently developed investigative genetic genealogy approach uses high-density single nucleotide polymorphisms (SNPs) to determine close and more distant relationships, in which hundreds of thousands to tens of millions of SNPs are generated either by microarray genotyping or whole-genome sequencing. The current studies usually assume the SNP profiles were generated with minimum errors. However, in the missing person cases, the DNA samples can be highly degraded, and the SNP profiles generated from these samples usually contain lots of errors. In this study, a machine learning approach was developed for estimating the relationships with high error SNP profiles. In this approach, a hierarchical classification strategy was employed first to classify the relationships by degree and then the relationship types within each degree separately. As for each classification, feature selection was implemented to gain better performance. Both simulated and real data sets with various genotyping error rates were utilized in evaluating this approach, and the accuracies of this approach were higher than individual measures; namely, this approach was more accurate and robust than the individual measures for SNP profiles with genotyping errors. In addition, the highest accuracy could be obtained by providing the same genotyping error rates in train and test sets, and thus estimating genotyping errors of the SNP profiles is critical to obtaining high accuracy of relationship estimation.

A reference-quality, fully annotated genome from a Puerto Rican individual

Until 2019, the human genome was available in only one fully annotated version, GRCh38, which was the result of 18 years of continuous improvement and revision. Despite dramatic improvements in sequencing technology, no other genome was available as an annotated reference until 2019, when the genome of an Ashkenazi individual, Ash1, was released. In this study, we describe the assembly and annotation of a second individual genome, from a Puerto Rican individual whose DNA was collected as part of the Human Pangenome project. The new genome, called PR1, is the first true reference genome created from an individual of African descent. Due to recent improvements in both sequencing and assembly technology, and particularly to the use of the recently completed CHM13 human genome as a guide to assembly, PR1 is more complete and more contiguous than either GRCh38 or Ash1. Annotation revealed 37,755 genes (of which 19,999 are protein coding), including 12 additional gene copies that are present in PR1 and missing from CHM13. Fifty-seven genes have fewer copies in PR1 than in CHM13, 9 map only partially, and 3 genes (all noncoding) from CHM13 are entirely missing from PR1.

Genetic analysis of the early bud flush trait of tea plants (Camellia sinensis) in the cultivar 'Emei Wenchun' and its open-pollinated offspring

The timing of bud flush (TBF) in the spring is one of the most important agronomic traits of tea plants (Camellia sinensis). In this study, we designed an open-pollination breeding program using 'Emei Wenchun' (EW, a clonal tea cultivar with extra-early TBF) as a female parent. A half-sib population (n = 388) was selected for genotyping using specific-locus amplified fragment sequencing. The results enabled the identification of paternity for 294 (75.8%) of the offspring, including 11 (2.8%) from EW selfing and 217 (55.9%) assigned to a common father, 'Chuanmu 217' (CM). The putative EW × CM full-sib population was used to construct a linkage map. The map has 4244 markers distributed in 15 linkage groups, with an average marker distance of 0.34 cM. A high degree of collinearity between the linkage map and physical map was observed. Sprouting index, a trait closely related to TBF, was recorded for the offspring population in 2020 and 2021. The trait had moderate variation, with coefficients of variation of 18.5 and 17.6% in 2020 and 2021, respectively. Quantitative trait locus (QTL) mapping that was performed using the linkage map identified two major QTLs and three minor QTLs related to the sprouting index. These QTLs are distributed on Chr3, Chr4, Chr5, Chr9, and Chr14 of the reference genome. A total of 1960 predicted genes were found within the confidence intervals of QTLs, and 22 key candidate genes that underlie these QTLs were preliminarily screened. These results are important for breeding and understanding the genetic base of the TBF trait of tea plants.

A 2b-RAD parentage analysis pipeline for complex and mixed DNA samples

Next-generation sequencing technology has revolutionized genotyping in many fields of study, yet parentage analysis often still relies on microsatellite markers that are costly to generate and are currently available only for a limited number of species. 2b-RAD sequencing (2b-RAD) is a DNA sequencing technique developed for ecological population genomics that utilizes type IIB restriction enzymes to generate consistent, uniform fragments across samples. This technology is inexpensive, effective with low DNA inputs, and robust to DNA degradation. Here, we developed a probabilistic genotyping-by-sequencing genetic testing pipeline for parentage analysis by using 2b-RAD for inferring familial relationships from mixed DNA samples and populations. Our approach to partial paternity assignment utilizes a novel weighted outlier paternity index (WOPI) adapted for next-generation sequencing data and an identity-by-state (IBS) matrix-based clustering method for pedigree reconstruction. The combination of these two parentage assignment methods overcomes two major obstacles faced by other genetic testing methods: 1) It allows detection of parentage when closely related or inbred individuals are in the alleged parent population (e.g., in laboratory strains); and 2) it resolves mixed DNA samples. We successfully demonstrate this novel approach by correctly inferring paternity for samples pooled from multiple offspring (i.e., entire clutches) in a highly inbred population of an East African cichlid fish. The unique advantages of 2b-RAD in combination with our bioinformatics pipeline enable straightforward and cost-effective parentage analysis in any species regardless of genomic resources available.

A pipeline for RNA-seq based eQTL analysis with automated quality control procedures

BACKGROUND

Advances in the expression quantitative trait loci (eQTL) studies have provided valuable insights into the mechanism of diseases and traits-associated genetic variants. However, it remains challenging to evaluate and control the quality of multi-source heterogeneous eQTL raw data for researchers with limited computational background. There is an urgent need to develop a powerful and user-friendly tool to automatically process the raw datasets in various formats and perform the eQTL mapping afterward.

RESULTS

In this work, we present a pipeline for eQTL analysis, termed eQTLQC, featured with automated data preprocessing for both genotype data and gene expression data. Our pipeline provides a set of quality control and normalization approaches, and utilizes automated techniques to reduce manual intervention. We demonstrate the utility and robustness of this pipeline by performing eQTL case studies using multiple independent real-world datasets with RNA-seq data and whole genome sequencing (WGS) based genotype data.

CONCLUSIONS

eQTLQC provides a reliable computational workflow for eQTL analysis. It provides standard quality control and normalization as well as eQTL mapping procedures for eQTL raw data in multiple formats. The source code, demo data, and instructions are freely available at https://github.com/stormlovetao/eQTLQC .

Genome-wide association study of yield components in spring wheat collection harvested under two water regimes in Northern Kazakhstan

Background

Bread wheat is the most important cereal in Kazakhstan, where it is grown on over 12 million hectares. One of the major constraints affecting wheat grain yield is drought due to the limited water supply. Hence, the development of drought-resistant cultivars is critical for ensuring food security in this country. Therefore, identifying quantitative trait loci (QTLs) associated with drought tolerance as an essential step in modern breeding activities, which rely on a marker-assisted selection approach.

Methods

A collection of 179 spring wheat accessions was tested under irrigated and rainfed conditions in Northern Kazakhstan over three years (2018, 2019, and 2020), during which data was collected on nine traits: heading date (HD), seed maturity date (SMD), plant height (PH), peduncle length (PL), number of productive spikes (NPS), spike length (SL), number of kernels per spike (NKS), thousand kernel weight (TKW), and kernels yield per m² (YM2). The collection was genotyped using a 20,000 (20K) Illumina iSelect SNP array, and 8,662 polymorphic SNP markers were selected for a genome-wide association study (GWAS) to identify QTLs for targeted agronomic traits.

Results

Out of the total of 237 discovered QTLs, 50 were identified as being stable QTLs for irrigated and rainfed conditions in the Akmola region, Northern Kazakhstan; the identified QTLs were associated with all the studied traits except PH. The results indicate that nine QTLs for HD and 11 QTLs for SMD are presumably novel genetic factors identified in the irrigated and rainfed conditions of Northern Kazakhstan. The identified SNP markers of the QTLs for targeted traits in rainfed conditions can be applied to develop new competitive spring wheat cultivars in arid zones using a marker-assisted selection approach.

Population genetic considerations for using biobanks as international resources in the pandemic era and beyond

The past years have seen the rise of genomic biobanks and mega-scale meta-analysis of genomic data, which promises to reveal the genetic underpinnings of health and disease. However, the over-representation of Europeans in genomic studies not only limits the global understanding of disease risk but also inhibits viable research into the genomic differences between carriers and patients. Whilst the community has agreed that more diverse samples are required, it is not enough to blindly increase diversity; the diversity must be quantified, compared and annotated to lead to insight. Genetic annotations from separate biobanks need to be comparable and computable and to operate without access to raw data due to privacy concerns. Comparability is key both for regular research and to allow international comparison in response to pandemics. Here, we evaluate the appropriateness of the most common genomic tools used to depict population structure in a standardized and comparable manner. The end goal is to reduce the effects of confounding and learn from genuine variation in genetic effects on phenotypes across populations, which will improve the value of biobanks (locally and internationally), increase the accuracy of association analyses and inform developmental efforts.

Identity-by-state-based haplotyping expands the application of comprehensive preimplantation genetic testing

STUDY QUESTION

Is it possible to haplotype parents using parental siblings to leverage preimplantation genetic testing (PGT) for monogenic diseases and aneuploidy (comprehensive PGT) by genome-wide haplotyping?

SUMMARY ANSWER

We imputed identity-by-state (IBS) sharing of parental siblings to phase parental genotypes.

WHAT IS KNOWN ALREADY

Genome-wide haplotyping of preimplantation embryos is being implemented as a generic approach for genetic diagnosis of inherited single-gene disorders. To enable the phasing of genotypes into haplotypes, genotyping the direct family members of the prospective parent carrying the mutation is required. Current approaches require genotypes of either (i) both or one of the parents of the affected prospective parent or (ii) an affected or an unaffected child of the couple. However, this approach cannot be used when parents or children are not attainable, prompting an investigation into alternative phasing options.

STUDY DESIGN, SIZE, DURATION

This is a retrospective validation study, which applied IBS-based phasing of parental haplotypes in 56 embryos derived from 12 PGT families. Genome-wide haplotypes and copy number profiles generated for each embryo using the new phasing approach were compared with the reference PGT method to evaluate the diagnostic concordance.

PARTICIPANTS/MATERIALS, SETTING, METHODS

This study included 12 couples with a known hereditary genetic disorder, participating in the comprehensive PGT program and with at least one parental sibling available (e.g. brother and/or sister). Genotyping data from both prospective parents and the parental sibling(s) were used to perform IBS-based phasing and to trace the disease-associated alleles. The outcome of the IBS-based PGT was compared with the results of the clinically implemented reference haplotyping-based PGT method.

MAIN RESULTS AND THE ROLE OF CHANCE

IBS-based haplotyping was performed for 12 PGT families. In accordance with the theoretical prediction of allele sharing between sibling pairs, 6 out of 12 (50%) couples or 23 out of 56 embryos could be phased using parental siblings. In families where phasing was possible, haplotype calling in the locus of interest was 100% concordant between the reference PGT method and IBS-based approach using parental siblings.

LARGE SCALE DATA

N/A.

LIMITATIONS, REASONS FOR CAUTION

Phasing of parental haplotypes will only be possible when the disease locus lies in an informative region (categorized as IBS1). Phasing prospective parents using relatives with reduced genetic relatedness as a reference (e.g. siblings) decreases the size and the occurrence of informative IBS1 regions, necessary for haplotype calling. By including more than one extended family member, the chance of obtaining IBS1 coverage in the interrogated locus can be increased. A pre-PGT work-up can define whether the carrier couple could benefit from this approach.

WIDER IMPLICATIONS OF THE FINDINGS

Phasing by relatives extends the potential of comprehensive PGT, since it allows the inclusion of couples who do not have access to the standard phasing references, such as parents or offspring.

STUDY FUNDING/COMPETING INTEREST(S)

The study was funded by the KU Leuven grant (C14/18/092), Research Foundation Flanders (FWO; GA09311N), Horizon 2020 innovation programme (WIDENLIFE, 692065) and Agilent Technologies. J.R.V., T.V. and M.Z.E. are co-inventors of a patent ZL910050-PCT/EP2011/060211-WO/2011/157846 'Methods for haplotyping single-cells' and ZL913096-PCT/EP2014/068315-WO/2015/028576 'Haplotyping and copy number typing using polymorphic variant allelic frequencies' licensed to Agilent Technologies. The other authors have no conflict of interest to declare.

Founder variants and population genomes-Toward precision medicine

Human migration and community specific cultural practices have contributed to founder events and enrichment of the variants associated with genetic diseases. While many founder events in isolated populations have remained uncharacterized, the application of genomics in clinical settings as well as for population scale studies in the recent years have provided an unprecedented push towards identification of founder variants associated with human health and disease. The discovery and characterization of founder variants could have far reaching implications not only in understanding the history or genealogy of the disease, but also in implementing evidence based policies and genetic testing frameworks. This further enables precise diagnosis and prevention in an attempt towards precision medicine. This review provides an overview of founder variants along with methods and resources cataloging them. We have also discussed the public health implications and examples of prevalent disease associated founder variants in specific populations.

Case Report: Two Monochorionic Twins With a Critically Different Course of Progressive Osseus Heteroplasia

Progressive osseous heteroplasia (POH; OMIM 166350) is a rare autosomal-dominant genetic disorder in which extra-skeletal bone forms within skin and muscle tissue. POH is one of the clinical manifestations of an inactivating mutation in the GNAS gene. GNAS gene alterations are difficult matter to address, as GNAS alleles show genetic imprinting and produce several transcript products, and the same mutation may lead to strikingly different phenotypes. Also, most of the publications concerning POH patients are either clinical depictions of a case (or a case series), descriptions of their genetic background, or a tentative correlation of both clinical and molecular findings. Treatment for POH is rarely addressed, and POH still lacks therapeutic options. We describe a unique case of POH in two monochorionic twins, who presented an almost asymptomatic vs. the severe clinical course, despite sharing the same mutation and genetic background. We also report the results of the therapeutic interventions currently available for heterotopic ossification in the patient with the severe course. This article not only critically supports the assumption that the POH course is strongly influenced by factors beyond genetic background but also remarks the lack of options for patients suffering an orphan disease, even after testing drugs with promising in vitro results.

Discovery of Genomic Characteristics and Selection Signatures in Southern Chinese Local Cattle

Chinese local cattle with a high level of genetic diversity mainly originate from two subspecies; the cattle in northern China are primarily Bos Taurus, and the cattle in southern China are primarily Bos indicus. Cattle from southern China are characterized by a specific phenotype and adapted to the local environment. This study explored the genetic diversity, degree of admixture, and selection signature in eight local cattle breeds in southern China. The lowest level of heterozygosity was found in Hainan and Nandan cattle from Hainan and Guangxi province, respectively, whereas the highest level of heterozygosity was detected in Zhaotong cattle from Yunnan province. A neighbor-joining phylogenetic tree analysis clearly separated Lufeng cattle from other breeds, whereas Leiqiong and Hainan cattle have some crossover. Based on linkage disequilibrium-filtered single nucleotide polymorphisms (SNPs), the admixture analysis revealed two clusters corresponding to the taurine and indicine cattle lineages, and the local cattle breeds from southern China showed a certain degree of admixture. When K = 4 and 9, we found a slight separation among Leiqiong, Lufeng, and Hainan cattle. Meanwhile, we performed a selection signature analysis in Hainan, Leiqiong, and Lufeng cattle distributed in the extreme south of China, using the integrated haplotype score (iHS), Rsb statistic, and BayeScan software. Using the iHS approach, we identified 251, 270, and 256 candidate regions in Lufeng, Leiqiong, and Hainan cattle, respectively. Moreover, we identified 184, 174, and 146 candidate regions in pairwise comparisons of Leiqiong vs. Lufeng, Leiqiong vs. Hainan, and Hainan vs. Lufeng cattle using the Rsb approach. In addition, we identified 76 loci with a total of 48 genes under selection, based on the F_ST approach. Several candidate genes under selection were found to be related to meat quality, immunity, and adaptation to the local environment in southern China. Our results provide significant information about the genetic differences among the cattle breeds from southern China and the possible cause of difference in breed-specific characteristics. Selection signature analysis identified a few candidate SNPs and genes related to certain important traits of these cattle. In general, our results provide valuable insights into the genetic basis of specific traits under selection in certain local cattle breeds.

Hypomethylation of the aquatic invasive plant, Ludwigia grandiflora subsp. hexapetala mimics the adaptive transition into the terrestrial morphotype

Ongoing global changes affect ecosystems and open up new opportunities for biological invasion. The ability of invasive species to rapidly adapt to new environments represents a relevant model for studying short-term adaptation mechanisms. The aquatic invasive plant, Ludwigia grandiflora subsp. hexapetala, is classified as harmful in European rivers. In French wet meadows, this species has shown a rapid transition from aquatic to terrestrial environments with emergence of two distinct morphotypes in 5 years. To understand the heritable mechanisms involved in adjustment to such a new environment, we investigate both genetic and epigenetic as possible sources of flexibility involved in this fast terrestrial transition. We found a low overall genetic differentiation between the two morphotypes arguing against the possibility that terrestrial morphotype emerged from a new adaptive genetic capacity. Artificial hypomethylation was induced on both morphotypes to assess the epigenetic hypothesis. We analyzed global DNA methylation, morphological changes, phytohormones and metabolite profiles of both morphotype responses in both aquatic and terrestrial conditions in shoot and root tissues. Hypomethylation significantly affected morphological variables, phytohormone levels and the amount of some metabolites. The effects of hypomethylation depended on morphotypes, conditions and plant tissues, which highlighted differences among the morphotypes and their plasticity. Using a correlative integrative approach, we showed that hypomethylation of the aquatic morphotype mimicked the characteristics of the terrestrial morphotype. Our data suggest that DNA methylation rather than a new adaptive genetic capacity is playing a key role in L. grandiflora subsp. hexapetala plasticity during its rapid aquatic to terrestrial transition.

Genomic signatures and candidate genes of lint yield and fibre quality improvement in Upland cotton in Xinjiang

Xinjiang has been the largest and highest yield cotton production region not only in China, but also in the world. Improvements in Upland cotton cultivars in Xinjiang have occurred via pedigree selection and/or crossing of elite alleles from the former Soviet Union and other cotton producing regions of China. But it is unclear how genomic constitutions from foundation parents have been selected and inherited. Here, we deep-sequenced seven historic foundation parents, comprising four cultivars introduced from the former Soviet Union (108Ф, C1470, 611Б and KK1543) and three from United States and Africa (DPL15, STV2B and UGDM), and re-sequenced sixty-nine Xinjiang modern cultivars. Phylogenetic analysis of more than 2 million high-quality single nucleotide polymorphisms allowed their classification two groups, suggesting that Xinjiang Upland cotton cultivars were not only spawned from 108Ф, C1470, 611Б and KK1543, but also had a close kinship with DPL15, STV2B and UGDM. Notably, identity-by-descent (IBD) tracking demonstrated that the former Soviet Union cultivars have made a huge contribution to modern cultivar improvement in Xinjiang. A total of 156 selective sweeps were identified. Among them, apoptosis-antagonizing transcription factor gene (GhAATF1) and mitochondrial transcription termination factor family protein gene (GhmTERF1) were highly involved in the determination of lint percentage. Additionally, the auxin response factor gene (GhARF3) located in inherited IBD segments from 108Ф and 611Б was highly correlated with fibre quality. These results provide an insight into the genomics of artificial selection for improving cotton production and facilitate next-generation precision breeding of cotton and other crops.

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.

Estimating Relatedness Between Malaria Parasites

Understanding the relatedness of individuals within or between populations is a common goal in biology. Increasingly, relatedness features in genetic epidemiology studies of pathogens. These studies are relatively new compared to those in humans and other organisms, but are important for designing interventions and understanding pathogen transmission. Only recently have researchers begun to routinely apply relatedness to apicomplexan eukaryotic malaria parasites, and to date have used a range of different approaches on an ad hoc basis. Therefore, it remains unclear how to compare different studies and which measures to use. Here, we systematically compare measures based on identity-by-state (IBS) and identity-by-descent (IBD) using a globally diverse data set of malaria parasites, Plasmodium falciparum and P. vivax, and provide marker requirements for estimates based on IBD. We formally show that the informativeness of polyallelic markers for relatedness inference is maximized when alleles are equifrequent. Estimates based on IBS are sensitive to allele frequencies, which vary across populations and by experimental design. For portability across studies, we thus recommend estimates based on IBD. To generate estimates with errors below an arbitrary threshold of 0.1, we recommend ∼100 polyallelic or 200 biallelic markers. Marker requirements are immediately applicable to haploid malaria parasites and other haploid eukaryotes. C.I.s facilitate comparison when different marker sets are used. This is the first attempt to provide rigorous analysis of the reliability of, and requirements for, relatedness inference in malaria genetic epidemiology. We hope it will provide a basis for statistically informed prospective study design and surveillance strategies.

Genome-wide scan reveals genetic divergence and diverse adaptive selection in Chinese local cattle

Background

Understanding the population structure and genetic bases of well-adapted cattle breeds to local environments is one of the most essential tasks to develop appropriate genetic improvement programs.

Results

We performed a comprehensive study to investigate the population structure, divergence and selection signatures at genome-wide level in diverse Chinese local cattle using Bovine HD SNPs array, including two breeds from North China, one breed from Northwest China, three breeds from Southwest China and two breeds from South China. Population genetic analyses revealed the genetic structures of these populations were mostly related to the geographic locations. Notably, we detected 294 and 1263 candidate regions under selection using the di and iHS approaches, respectively. A series of group-specific and breed-specific candidate genes were identified, which are involved in immune response, sexual maturation, stature related, birth and bone weight, embryonic development, coat colors and adaptation. Furthermore, haplotype diversity and network pattern for candidate genes, including LPGAT1, LCORL, PPP1R8, RXFP2 and FANCA, suggest that these genes have been under differential selection pressures in various environmental conditions.

Conclusions

Our results shed insights into diverse selection during breed formation in Chinese local cattle. These findings may promote the application of genome-assisted breeding for well-adapted local breeds with economic and ecological importance.

Electronic supplementary material

The online version of this article (10.1186/s12864-019-5822-y) contains supplementary material, which is available to authorized users.

A Log-Ratio Biplot Approach for Exploring Genetic Relatedness Based on Identity by State

The detection of cryptic relatedness in large population-based cohorts is of great importance in genome research. The usual approach for detecting closely related individuals is to plot allele sharing statistics, based on identity-by-state or identity-by-descent, in a two-dimensional scatterplot. This approach ignores that allele sharing data across individuals has in reality a higher dimensionality, and neither regards the compositional nature of the underlying counts of shared genotypes. In this paper we develop biplot methodology based on log-ratio principal component analysis that overcomes these restrictions. This leads to entirely new graphics that are essentially useful for exploring relatedness in genetic databases from homogeneous populations. The proposed method can be applied in an iterative manner, acting as a looking glass for more remote relationships that are harder to classify. Datasets from the 1,000 Genomes Project and the Genomes For Life-GCAT Project are used to illustrate the proposed method. The discriminatory power of the log-ratio biplot approach is compared with the classical plots in a simulation study. In a non-inbred homogeneous population the classification rate of the log-ratio principal component approach outperforms the classical graphics across the whole allele frequency spectrum, using only identity by state. In these circumstances, simulations show that with 35,000 independent bi-allelic variants, log-ratio principal component analysis, combined with discriminant analysis, can correctly classify relationships up to and including the fourth degree.

Genetic assessment of inbred chicken lines indicates genomic signatures of resistance to Marek's disease

Background

Marek’s disease (MD) is a highly contagious pathogenic and oncogenic disease primarily affecting chickens. However, the mechanisms of genetic resistance for MD are complex and not fully understood. MD-resistant line 6₃ and MD-susceptible line 7₂ are two highly inbred progenitor lines of White Leghorn. Recombinant Congenic Strains (RCS) were developed from these two lines, which show varied susceptibility to MD.

Results

We investigated genetic structure and genomic signatures across the genome, including the line 6₃ and line 7₂, six RCSs, and two reciprocally crossed flocks between the lines 6₃ and 7₂ (F1 6₃ × 7₂ and F1 7₂ × 6₃) using Affymetrix® Axiom® HD 600 K genotyping array. We observed 18 chickens from RCS lines were specifically clustered into resistance sub-groups distributed around line 6₃. Additionally, homozygosity analysis was employed to explore potential genetic components related to MD resistance, while runs of homozygosity (ROH) are regions of the genome where the identical haplotypes are inherited from each parent. We found several genes including SIK, SOX1, LIG4, SIK1 and TNFSF13B were contained in ROH region identified in resistant group (line 6₃ and RCS), and these genes have been reported that are contribute to immunology and survival. Based on F_ST based population differential analysis, we also identified important genes related to cell death and anti-apoptosis, including AKT1, API5, CDH13, CFDP and USP15, which could be involved in divergent selection during inbreeding process.

Conclusions

Our findings offer valuable insights for understanding the genetic mechanism of resistance to MD and the identified genes could be considered as candidate biomarkers in further evaluation.

Electronic supplementary material

The online version of this article (10.1186/s40104-018-0281-x) contains supplementary material, which is available to authorized users.

Genome-wide association study identifies genes associated with neuropathy in patients with head and neck cancer

Neuropathic pain (NP), defined as pain initiated or caused by a primary lesion or dysfunction in the nervous system, is a debilitating chronic pain condition often resulting from cancer treatment. Among cancer patients, neuropathy during cancer treatment is a predisposing event for NP. To identify genetic variants influencing the development of NP, we conducted a genome-wide association study in 1,043 patients with squamous cell carcinoma of the head and neck, based on 714,494 tagging single-nucleotide polymorphisms (SNPs) (130 cases, 913 controls). About 12.5% of the patients, who previously had cancer treatment, had neuropathy-associated diagnoses, as defined using the ICD-9/ICD-10 codes. We identified four common SNPs representing four genomic regions: 7q22.3 (rs10950641; SNX8; P = 3.39 × 10⁻¹⁴), 19p13.2 (rs4804217; PCP2; P = 2.95 × 10⁻⁹), 3q27.3 (rs6796803; KNG1; P = 6.42 × 10⁻⁹) and 15q22.2 (rs4775319; RORA; P = 1.02 × 10⁻⁸), suggesting SNX8, PCP2, KNG1 and RORA might be novel target genes for NP in patients with head and neck cancer. Future experimental validation to explore physiological effects of the identified SNPs will provide a better understanding of the biological mechanisms underlying NP and may provide insights into novel therapeutic targets for treatment and management of NP.

Genomic approaches to the assessment of human spina bifida risk

Structural birth defects are a leading cause of mortality and morbidity in children world-wide, affecting as much as 6% of all live births. Among these conditions, neural tube defects (NTDs), including spina bifida and anencephaly, arise from a combination of complex gene and environment interactions that are as yet poorly understood within human populations. Rapid advances in massively parallel DNA sequencing and bioinformatics allow for analyses of the entire genome beyond the 2% of the genomic sequence covering protein coding regions. Efforts to collect and analyze these large datasets hold promise for illuminating gene network variations and eventually epigenetic events that increase individual risk for failure to close the neural tube. In this review, we discuss current challenges for DNA genome sequence analysis of NTD affected populations, and compare experience in the field with other complex genetic disorders for which large datasets are accumulating. The ultimate goal of this research is to find strategies for optimizing conditions that promote healthy birth outcomes for individual couples. Birth Defects Research 109:120-128, 2017. © 2016 Wiley Periodicals, Inc.

Nonparametric approaches for population structure analysis

The analysis of population structure has many applications in medical and population genetic research. Such analysis is used to provide clear insight into the underlying genetic population substructure and is a crucial prerequisite for any analysis of genetic data. The analysis involves grouping individuals into subpopulations based on shared genetic variations. The most widely used markers to study the variation of DNA sequences between populations are single nucleotide polymorphisms. Data preprocessing is a necessary step to assess the quality of the data and to determine which markers or individuals can reasonably be included in the analysis. After preprocessing, several methods can be utilized to uncover population substructure, which can be categorized into two broad approaches: parametric and nonparametric. Parametric approaches use statistical models to infer population structure and assign individuals into subpopulations. However, these approaches suffer from many drawbacks that make them impractical for large datasets. In contrast, nonparametric approaches do not suffer from these drawbacks, making them more viable than parametric approaches for analyzing large datasets. Consequently, nonparametric approaches are increasingly used to reveal population substructure. Thus, this paper reviews and discusses the nonparametric approaches that are available for population structure analysis along with some implications to resolve challenges.

Quickly identifying identical and closely related subjects in large databases using genotype data

Genome-wide association studies (GWAS) usually rely on the assumption that different samples are not from closely related individuals. Detection of duplicates and close relatives becomes more difficult both statistically and computationally when one wants to combine datasets that may have been genotyped on different platforms. The dbGaP repository at the National Center of Biotechnology Information (NCBI) contains datasets from hundreds of studies with over one million samples. There are many duplicates and closely related individuals both within and across studies from different submitters. Relationships between studies cannot always be identified by the submitters of individual datasets. To aid in curation of dbGaP, we developed a rapid statistical method called Genetic Relationship and Fingerprinting (GRAF) to detect duplicates and closely related samples, even when the sets of genotyped markers differ and the DNA strand orientations are unknown. GRAF extracts genotypes of 10,000 informative and independent SNPs from genotype datasets obtained using different methods, and implements quick algorithms that enable it to find all of the duplicate pairs from more than 880,000 samples within and across dbGaP studies in less than two hours. In addition, GRAF uses two statistical metrics called All Genotype Mismatch Rate (AGMR) and Homozygous Genotype Mismatch Rate (HGMR) to determine subject relationships directly from the observed genotypes, without estimating probabilities of identity by descent (IBD), or kinship coefficients, and compares the predicted relationships with those reported in the pedigree files. We implemented GRAF in a freely available C++ program of the same name. In this paper, we describe the methods in GRAF and validate the usage of GRAF on samples from the dbGaP repository. Other scientists can use GRAF on their own samples and in combination with samples downloaded from dbGaP.

Recent Historical Migrations Have Shaped the Gene Pool of Arabs and Berbers in North Africa

North Africa is characterized by its diverse cultural and linguistic groups and its genetic heterogeneity. Genomic data has shown an amalgam of components mixed since pre-Holocean times. Though no differences have been found in uniparental and classical markers between Berbers and Arabs, the two main ethnic groups in the region, the scanty genomic data available have highlighted the singularity of Berbers. We characterize the genetic heterogeneity of North African groups, focusing on the putative differences of Berbers and Arabs, and estimate migration dates. We analyze genome-wide autosomal data in five Berber and six Arab groups, and compare them to Middle Easterns, sub-Saharans, and Europeans. Haplotype-based methods show a lack of correlation between geographical and genetic populations, and a high degree of genetic heterogeneity, without strong differences between Berbers and Arabs. Berbers enclose genetically diverse groups, from isolated endogamous groups with high autochthonous component frequencies, large homozygosity runs and low effective population sizes, to admixed groups with high frequencies of sub-Saharan and Middle Eastern components. Admixture time estimates show a complex pattern of recent historical migrations, with a peak around the 7th century C.E. coincident with the Arabization of the region; sub-Saharan migrations since the 1st century B.C. in agreement with Roman slave trade; and a strong migration in the 17th century C.E., coincident with a huge impact of the trans-Atlantic and trans-Saharan trade of sub-Saharan slaves in the Modern Era. The genetic complexity found should be taken into account when selecting reference groups in population genetics and biomedical studies.

Exploring Identity-By-Descent Segments and Putative Functions Using Different Foundation Parents in Maize

Maize foundation parents (FPs) play no-alternative roles in hybrid breeding because they were widely used in the development of new lines and hybrids. The combination of different identity-by-descent (IBD) segments and genes could account for the formation patterns of different FPs, and knowledge of these IBD regions would provide an extensive foundation for the development of new candidate FP lines in future maize breeding. In this paper, a panel of 304 elite lines derived from FPs, i.e., B73, 207, Mo17, and Huangzaosi (HZS), was collected and analyzed using 43,252 single nucleotide polymorphism (SNP) markers. Most IBD segments specific to particular FP groups were identified, including 116 IBD segments in B73, 105 in Mo17, 111 in 207, and 190 in HZS. In these regions, 423 quantitative trait nucleotides (QTNs) associated with 15 agronomic traits and 804 candidate genes were identified. Some known adaptation-related genes, e.g., dwarf8 and vgt1 in HZS, zcn8 and epc in Mo17, and ZmCCT in 207, were validated as being tightly linked to particular IBD segments. In addition, numerous new candidate genes were also identified. For example, GRMZM2G154278 in HZS, which belongs to the cell cycle control family, was closely linked to a QTN of the ear height/plant height (EH/PH) trait; GRMZM2G051943 in 207, which encodes an endochitinase precursor (EP) chitinase, was closely linked to a QTN for kernel density; and GRMZM2G170586 in Mo17 was closely linked to a QTN for ear diameter. Complex correlations among these genes were also found. Many IBD segments and genes were included in the formation of FP lines, and complex regulatory networks exist among them. These results provide new insights on the genetic basis of complex traits and provide new candidate IBD regions or genes for the improvement of special traits in maize production.

Genomic continuity of Argentinean Mennonites

Mennonites are Anabaptist communities that originated in Central Europe about 500 years ago. They initially migrated to different European countries, and in the early 18^th century they established their first communities in North America, from where they moved to other American regions. We aimed to analyze an Argentinean Mennonite congregation from a genome-wide perspective by way of investigating >580.000 autosomal SNPs. Several analyses show that Argentinean Mennonites have European ancestry without signatures of admixture with other non-European American populations. Among the worldwide datasets used for population comparison, the CEU, which is the best-subrogated Central European population existing in The 1000 Genome Project, is the dataset showing the closest genome affinity to the Mennonites. When compared to other European population samples, the Mennonites show higher inbreeding coefficient values. Argentinean Mennonites show signatures of genetic continuity with no evidence of admixture with Americans of Native American or sub-Saharan African ancestry. Their genome indicates the existence of an increased endogamy compared to other Europeans most likely mirroring their lifestyle that involve small communities and historical consanguineous marriages.

Genomics-assisted characterization of a breeding collection of Apios americana, an edible tuberous legume

For species with potential as new crops, rapid improvement may be facilitated by new genomic methods. Apios (Apios americana Medik.), once a staple food source of Native American Indians, produces protein-rich tubers, tolerates a wide range of soils, and symbiotically fixes nitrogen. We report the first high-quality de novo transcriptome assembly, an expression atlas, and a set of 58,154 SNP and 39,609 gene expression markers (GEMs) for characterization of a breeding collection. Both SNPs and GEMs identify six genotypic clusters in the collection. Transcripts mapped to the Phaseolus vulgaris genome-another phaseoloid legume with the same chromosome number-provide provisional genetic locations for 46,852 SNPs. Linkage disequilibrium decays within 10 kb (based on the provisional genetic locations), consistent with outcrossing reproduction. SNPs and GEMs identify more than 21 marker-trait associations for at least 11 traits. This study demonstrates a holistic approach for mining plant collections to accelerate crop improvement.

Validation of Genome-Wide Association Studies as a Tool to Identify Virulence Factors in Parastagonospora nodorum

Parastagonospora nodorum is a necrotrophic fungal pathogen causing Septoria nodorum blotch on wheat. We have identified nine necrotrophic effector-host dominant sensitivity gene interactions, and we have cloned three of the necrotrophic effector genes, including SnToxA, SnTox1, and SnTox3. Because sexual populations of P. nodorum are difficult to develop under lab conditions, genome-wide association study (GWAS) is the best population genomic approach to identify genomic regions associated with traits using natural populations. In this article, we used a global collection of 191 P. nodorum isolates from which we identified 2,983 single-nucleotide polymorphism (SNP) markers and gene markers for SnToxA and SnTox3 to evaluate the power of GWAS on two popular wheat breeding lines that were sensitive to SnToxA and SnTox3. Strong marker trait associations (MTA) with P. nodorum virulence that mapped to SnTox3 and SnToxA were first identified using the marker set described above. A novel locus in the P. nodorum genome associated with virulence was also identified as a result of this analysis. To evaluate whether a sufficient level of marker saturation was available, we designed a set of primers every 1 kb in the genomic regions containing SnToxA and SnTox3. Polymerase chain reaction amplification was performed across the 191 isolates and the presence/absence polymorphism was scored and used as the genotype. The marker proximity necessary to identify MTA flanking SnToxA and SnTox3 ranged from 4 to 5 and 1 to 7 kb, respectively. Similar analysis was performed on the novel locus. Using a 45% missing data threshold, two more SNP were identified spanning a 4.6-kb genomic region at the novel locus. These results showed that the rate of linkage disequilibrium (LD) decay in P. nodorum and, likely, other fungi is high compared with plants and animals. The fast LD decay in P. nodorum is an advantage only if sufficient marker density is attained. Based on our results with the SnToxA and SnTox3 regions, markers are needed every 9 or 8 kb, respectively, or in every gene, to guarantee that genes associated with a quantitative trait such as virulence are not missed.

Genome-wide association study suggests common variants within RP11-634B7.4 gene influencing severe pre-treatment pain in head and neck cancer patients

Pain is often one of the first signs of squamous cell carcinoma of the head and neck (HNSCC). Pain at diagnosis is an important prognostic marker for the development of chronic pain, and importantly, for the overall survival time. To identify variants influencing severe pre-treatment pain in 1,368 patients newly diagnosed with HNSCC, we conducted a genome-wide association study based on 730,525 tagging SNPs. The patients were all previously untreated for cancer. About 15% of the patients had severe pre-treatment pain, defined as pain score ≥7 (0 = “no pain” and 10 = “worst pain”). We identified 3 common genetic variants in high linkage disequilibrium for severe pre-treatment pain, representing one genomic region at 1q44 (rs3862188, P = 3.45 × 10⁻⁸; rs880143, P = 3.45 × 10⁻⁸; and rs7526880, P = 4.92 × 10⁻⁸), which maps to the RP11-634B7.4 gene, a novel antisense gene to three olfactory receptor genes. Olfactory receptor genes, upstream effectors of the MAPK signaling cascade, might be novel target genes for pain in HNSCC patients. Future experimental validation to explore biological mechanisms will be key to defining the role of the intronic variants and non-coding RNA for pain in patients with HNSCC.

Inference of kinship using spatial distributions of SNPs for genome-wide association studies

Background

Genome-wide association studies (GWASs) are powerful in identifying genetic loci which cause complex traits of common diseases. However, it is well known that inappropriately accounting for pedigree or population structure leads to spurious associations. GWASs have often encountered increased type I error rates due to the correlated genotypes of cryptically related individuals or subgroups. Therefore, accurate pedigree information is crucial for successful GWASs.

Results

We propose a distance-based method KIND to estimate kinship coefficients among individuals. Our method utilizes the spatial distribution of SNPs in the genome that represents how far each minor-allele variant is located from its neighboring minor-allele variants. The SNP distribution of each individual was presented in a feature vector in Euclidean space, and then the kinship coefficient was inferred from the two vectors of each individual pair. We demonstrate that the distance information can measure the similarity of genetic variants of individuals accurately and efficiently. We applied our method to a synthetic data set and two real data sets (i.e. the HapMap phase III and the 1000 genomes data). We investigated the estimation accuracy of kinship coefficients not only within homogeneous populations but also for a population with extreme stratification.

Conclusions

Our method KIND usually produces more accurate and more robust kinship coefficient estimates than existing methods especially for populations with extreme stratification. It can serve as an important and very efficient tool for GWASs.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-2696-0) contains supplementary material, which is available to authorized users.

Population-genetic properties of differentiated copy number variations in cattle

While single nucleotide polymorphism (SNP) is typically the variant of choice for population genetics, copy number variation (CNV) which comprises insertion, deletion and duplication of genomic sequence, is an informative type of genetic variation. CNVs have been shown to be both common in mammals and important for understanding the relationship between genotype and phenotype. However, CNV differentiation, selection and its population genetic properties are not well understood across diverse populations. We performed a population genetics survey based on CNVs derived from the BovineHD SNP array data of eight distinct cattle breeds. We generated high resolution results that show geographical patterns of variations and genome-wide admixture proportions within and among breeds. Similar to the previous SNP-based studies, our CNV-based results displayed a strong correlation of population structure and geographical location. By conducting three pairwise comparisons among European taurine, African taurine, and indicine groups, we further identified 78 unique CNV regions that were highly differentiated, some of which might be due to selection. These CNV regions overlapped with genes involved in traits related to parasite resistance, immunity response, body size, fertility, and milk production. Our results characterize CNV diversity among cattle populations and provide a list of lineage-differentiated CNVs.

Mapping the genomic mosaic of two 'Afro-Bolivians' from the isolated Yungas valleys

Background

Unraveling the ancestry of ‘Afro-American’ communities is hampered by the complex demographic processes that took place during the Transatlantic Slave Trade (TAST) and the (post-)colonization periods. ‘Afro-Bolivians’ from the subtropical Yungas valleys constitute small and isolated communities that live surrounded by the predominant Native American community of Bolivia. By genotyping >580,000 SNPs in two ‘Afro-Bolivians’, and comparing these genomic profiles with data compiled from more than 57 African groups and other reference ancestral populations (n = 1,161 in total), we aimed to disentangle the complex admixture processes undergone by ‘Afro-Bolivians’.

Results

The data indicate that these two genomes constitute a complex mosaic of ancestries that is approximately 80 % of recent African origin; the remaining ~20 % being European and Native American. West-Central Africa contributed most of the African ancestry to ‘Afro-Bolivians’, and this component is related to populations living along the Atlantic coast (i.e. Senegal, Ghana, Nigeria). Using tract length distribution of genomic segments attributable to distinct ancestries, we could date the time of admixture in about 400 years ago. This time coincides with the maximum importation of slaves to Bolivia to compensate the diminishing indigenous labor force needed for the development of the National Mint of Potosí.

Conclusions

Overall, the data indicate that the genome of ‘Afro-Bolivians’ was shaped by a complex process of admixture occurring in America among individuals originating in different West-Central African populations; their genomic mosaics received additional contributions of Europeans and local Native Americans (e.g. Aymaras).

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-2520-x) contains supplementary material, which is available to authorized users.

Copy Number Variants Associated with 14 Cases of Self-Injurious Behavior

Copy number variants (CNVs) were detected and analyzed in 14 probands with autism and intellectual disability with self-injurious behavior (SIB) resulting in tissue damage. For each proband we obtained a clinical history and detailed behavioral descriptions. Genetic anomalies were observed in all probands, and likely clinical significance could be established in four cases. This included two cases having novel, de novo copy number variants and two cases having variants likely to have functional significance. These cases included segmental trisomy 14, segmental monosomy 21, and variants predicted to disrupt the function of ZEB2 (encoding a transcription factor) and HTR2C (encoding a serotonin receptor). Our results identify variants in regions previously implicated in intellectual disability and suggest candidate genes that could contribute to the etiology of SIB.

R2d2 Drives Selfish Sweeps in the House Mouse

A selective sweep is the result of strong positive selection driving newly occurring or standing genetic variants to fixation, and can dramatically alter the pattern and distribution of allelic diversity in a population. Population-level sequencing data have enabled discoveries of selective sweeps associated with genes involved in recent adaptations in many species. In contrast, much debate but little evidence addresses whether “selfish” genes are capable of fixation—thereby leaving signatures identical to classical selective sweeps—despite being neutral or deleterious to organismal fitness. We previously described R2d2, a large copy-number variant that causes nonrandom segregation of mouse Chromosome 2 in females due to meiotic drive. Here we show population-genetic data consistent with a selfish sweep driven by alleles of R2d2 with high copy number (R2d2^HC) in natural populations. We replicate this finding in multiple closed breeding populations from six outbred backgrounds segregating for R2d2 alleles. We find that R2d2^HC rapidly increases in frequency, and in most cases becomes fixed in significantly fewer generations than can be explained by genetic drift. R2d2^HC is also associated with significantly reduced litter sizes in heterozygous mothers, making it a true selfish allele. Our data provide direct evidence of populations actively undergoing selfish sweeps, and demonstrate that meiotic drive can rapidly alter the genomic landscape in favor of mutations with neutral or even negative effects on overall Darwinian fitness. Further study will reveal the incidence of selfish sweeps, and will elucidate the relative contributions of selfish genes, adaptation and genetic drift to evolution.

Spatial heterogeneity as a genetic mixing mechanism in highly philopatric colonial seabirds

How genetic diversity is maintained in philopatric colonial systems remains unclear, and understanding the dynamic balance of philopatry and dispersal at all spatial scales is essential to the study of the evolution of coloniality. In the King penguin, Aptenodytes patagonicus, return rates of post-fledging chicks to their natal sub-colony are remarkably high. Empirical studies have shown that adults return year after year to their previous breeding territories within a radius of a few meters. Yet, little reliable data are available on intra- and inter-colonial dispersal in this species. Here, we present the first fine-scale study of the genetic structure in a king penguin colony in the Crozet Archipelago. Samples were collected from individual chicks and analysed at 8 microsatellite loci. Precise geolocation data of hatching sites and selective pressures associated with habitat features were recorded for all sampling locations. We found that despite strong natal and breeding site fidelity, king penguins retain a high degree of panmixia and genetic diversity. Yet, genetic structure appears markedly heterogeneous across the colony, with higher-than-expected inbreeding levels, and local inbreeding and relatedness hotspots that overlap predicted higher-quality nesting locations. This points towards heterogeneous population structure at the sub-colony level, in which fine-scale environmental features drive local philopatric behaviour, while lower-quality patches may act as genetic mixing mechanisms at the colony level. These findings show how a lack of global genetic structuring can emerge from small-scale heterogeneity in ecological parameters, as opposed to the classical model of homogeneous dispersal. Our results also emphasize the importance of sampling design for estimation of population parameters in colonial seabirds, as at high spatial resolution, basic genetic features are shown to be location-dependent. Finally, this study stresses the importance of understanding intra-colonial dispersal and genetic mixing mechanisms in order to better estimate species-wide gene flows and population dynamics.

Genomic signatures reveal new evidences for selection of important traits in domestic cattle

We investigated diverse genomic selections using high-density single nucleotide polymorphism data of five distinct cattle breeds. Based on allele frequency differences, we detected hundreds of candidate regions under positive selection across Holstein, Angus, Charolais, Brahman, and N'Dama. In addition to well-known genes such as KIT, MC1R, ASIP, GHR, LCORL, NCAPG, WIF1, and ABCA12, we found evidence for a variety of novel and less-known genes under selection in cattle, such as LAP3, SAR1B, LRIG3, FGF5, and NUDCD3. Selective sweeps near LAP3 were then validated by next-generation sequencing. Genome-wide association analysis involving 26,362 Holsteins confirmed that LAP3 and SAR1B were related to milk production traits, suggesting that our candidate regions were likely functional. In addition, haplotype network analyses further revealed distinct selective pressures and evolution patterns across these five cattle breeds. Our results provided a glimpse into diverse genomic selection during cattle domestication, breed formation, and recent genetic improvement. These findings will facilitate genome-assisted breeding to improve animal production and health.

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.

Privacy preserving protocol for detecting genetic relatives using rare variants

MOTIVATION

High-throughput sequencing technologies have impacted many areas of genetic research. One such area is the identification of relatives from genetic data. The standard approach for the identification of genetic relatives collects the genomic data of all individuals and stores it in a database. Then, each pair of individuals is compared to detect the set of genetic relatives, and the matched individuals are informed. The main drawback of this approach is the requirement of sharing your genetic data with a trusted third party to perform the relatedness test.

RESULTS

In this work, we propose a secure protocol to detect the genetic relatives from sequencing data while not exposing any information about their genomes. We assume that individuals have access to their genome sequences but do not want to share their genomes with anyone else. Unlike previous approaches, our approach uses both common and rare variants which provide the ability to detect much more distant relationships securely. We use a simulated data generated from the 1000 genomes data and illustrate that we can easily detect up to fifth degree cousins which was not possible using the existing methods. We also show in the 1000 genomes data with cryptic relationships that our method can detect these individuals.

AVAILABILITY

The software is freely available for download at http://genetics.cs.ucla.edu/crypto/.

Historical pedigree reconstruction from extant populations using PArtitioning of RElatives (PREPARE)

Recent technological improvements in the field of genetic data extraction give rise to the possibility of reconstructing the historical pedigrees of entire populations from the genotypes of individuals living today. Current methods are still not practical for real data scenarios as they have limited accuracy and assume unrealistic assumptions of monogamy and synchronized generations. In order to address these issues, we develop a new method for pedigree reconstruction, , which is based on formulations of the pedigree reconstruction problem as variants of graph coloring. The new formulation allows us to consider features that were overlooked by previous methods, resulting in a reconstruction of up to 5 generations back in time, with an order of magnitude improvement of false-negatives rates over the state of the art, while keeping a lower level of false positive rates. We demonstrate the accuracy of compared to previous approaches using simulation studies over a range of population sizes, including inbred and outbred populations, monogamous and polygamous mating patterns, as well as synchronous and asynchronous mating.

Learning the correct relationships between individuals from genetic data is a basic theoretical problem in the field of genetics, and has many practical consequences. A wide variety of statistical methods for genetic analysis assume the relationships between individuals are known, and can manifest relatedness information to improve inference. The current state-of-the-art methods for relationship inference consider pair-wise genetic similarity, and use it to infer the relationship between each pair of individuals. Reconstructing the pedigrees of an entire population directly has the potential to use more elaborate relationship information, and thus obtains a better prediction of the familial relationships in the population. In contrast to the full set of pair-wise relationships in a population, genetic pedigrees provide a lossless and conflict-free structure for depicting the relationships between individuals. In an effort to make pedigree reconstruction practical we developed a new method, which is an order of magnitude more accurate than previous methods, and is the first method that has the ability to reconstruct polygamous pedigrees.

Identifying large sets of unrelated individuals and unrelated markers

Background

Genetic Analyses in large sample populations are important for a better understanding of the variation between populations, for designing conservation programs, for detecting rare mutations which may be risk factors for a variety of diseases, among other reasons. However these analyses frequently assume that the participating individuals or animals are mutually unrelated which may not be the case in large samples, leading to erroneous conclusions. In order to retain as much data as possible while minimizing the risk of false positives it is useful to identify a large subset of relatively unrelated individuals in the population. This can be done using a heuristic for finding a large set of independent of nodes in an undirected graph. We describe a fast randomized heuristic for this purpose. The same methodology can also be used for identifying a suitable set of markers for analyzing population stratification, and other instances where a rapid heuristic for maximal independent sets in large graphs is needed.

Results

We present FastIndep, a fast random heuristic algorithm for finding a maximal independent set of nodes in an arbitrary undirected graph along with an efficient implementation in C++. On a 64 bit Linux or MacOS platform the execution time is a few minutes, even with a graph of several thousand nodes. The algorithm can discover multiple solutions of the same cardinality. FastIndep can be used to discover unlinked markers, and unrelated individuals in populations.

Conclusions

The methods presented here provide a quick and efficient method for identifying sets of unrelated individuals in large populations and unlinked markers in marker panels. The C++ source code and instructions along with utilities for generating the input files in the appropriate format are available at http://taurus.ansci.iastate.edu/wiki/people/jabr/Joseph_Abraham.html

GAGA: a new algorithm for genomic inference of geographic ancestry reveals fine level population substructure in Europeans

Attempts to detect genetic population substructure in humans are troubled by the fact that the vast majority of the total amount of observed genetic variation is present within populations rather than between populations. Here we introduce a new algorithm for transforming a genetic distance matrix that reduces the within-population variation considerably. Extensive computer simulations revealed that the transformed matrix captured the genetic population differentiation better than the original one which was based on the T1 statistic. In an empirical genomic data set comprising 2,457 individuals from 23 different European subpopulations, the proportion of individuals that were determined as a genetic neighbour to another individual from the same sampling location increased from 25% with the original matrix to 52% with the transformed matrix. Similarly, the percentage of genetic variation explained between populations by means of Analysis of Molecular Variance (AMOVA) increased from 1.62% to 7.98%. Furthermore, the first two dimensions of a classical multidimensional scaling (MDS) using the transformed matrix explained 15% of the variance, compared to 0.7% obtained with the original matrix. Application of MDS with Mclust, SPA with Mclust, and GemTools algorithms to the same dataset also showed that the transformed matrix gave a better association of the genetic clusters with the sampling locations, and particularly so when it was used in the AMOVA framework with a genetic algorithm. Overall, the new matrix transformation introduced here substantially reduces the within population genetic differentiation, and can be broadly applied to methods such as AMOVA to enhance their sensitivity to reveal population substructure. We herewith provide a publically available (http://www.erasmusmc.nl/fmb/resources/GAGA) model-free method for improved genetic population substructure detection that can be applied to human as well as any other species data in future studies relevant to evolutionary biology, behavioural ecology, medicine, and forensics.

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.

Indian signatures in the westernmost edge of the European Romani diaspora: new insight from mitogenomes

In agreement with historical documentation, several genetic studies have revealed ancestral links between the European Romani and India. The entire mitochondrial DNA (mtDNA) of 27 Spanish Romani was sequenced in order to shed further light on the origins of this population. The data were analyzed together with a large published dataset (mainly hypervariable region I [HVS-I] haplotypes) of Romani (N=1,353) and non-Romani worldwide populations (N>150,000). Analysis of mitogenomes allowed the characterization of various Romani-specific clades. M5a1b1a1 is the most distinctive European Romani haplogroup; it is present in all Romani groups at variable frequencies (with only sporadic findings in non-Romani) and represents 18% of their mtDNA pool. Its phylogeographic features indicate that M5a1b1a1 originated 1.5 thousand years ago (kya; 95% CI: 1.3-1.8) in a proto-Romani population living in Northwest India. U3 represents the most characteristic Romani haplogroup of European/Near Eastern origin (12.4%); it appears at dissimilar frequencies across the continent (Iberia: ≈ 31%; Eastern/Central Europe: ≈ 13%). All U3 mitogenomes of our Iberian Romani sample fall within a new sub-clade, U3b1c, which can be dated to 0.5 kya (95% CI: 0.3-0.7); therefore, signaling a lower bound for the founder event that followed admixture in Europe/Near East. Other minor European/Near Eastern haplogroups (e.g. H24, H88a) were also assimilated into the Romani by introgression with neighboring populations during their diaspora into Europe; yet some show a differentiation from the phylogenetically closest non-Romani counterpart. The phylogeny of Romani mitogenomes shows clear signatures of low effective population sizes and founder effects. Overall, these results are in good agreement with historical documentation, suggesting that cultural identity and relative isolation have allowed the Romani to preserve a distinctive mtDNA heritage, with some features linking them unequivocally to their ancestral Indian homeland.