Fast two-stage phasing of large-scale sequence data

A genome-guided strategy for climate resilience in American chestnut restoration populations

American chestnut (Castanea dentata) is a deciduous tree species of eastern North America that was decimated by the introduction of the chestnut blight fungus (Cryphonectria parasitica) in the early 20th century. Although millions of American chestnuts survive as root collar sprouts, these trees rarely reproduce. Thus, the species is considered functionally extinct. American chestnuts with improved blight resistance have been developed through interspecific hybridization followed by conspecific backcrossing, and by genetic engineering. Incorporating adaptive genomic diversity into these backcross families and transgenic lines is important for restoring the species across broad climatic gradients. To develop sampling recommendations for ex situ conservation of wild adaptive genetic variation, we coupled whole-genome resequencing of 384 stump sprouts with genotype-environment association analyses and found that the species range can be subdivided into three seed zones characterized by relatively homogeneous adaptive allele frequencies. We estimated that 21 to 29 trees per seed zone will need to be conserved to capture most extant adaptive diversity. We also resequenced the genomes of 269 backcross trees to understand the extent to which the breeding program has already captured wild adaptive diversity, and to estimate optimal reintroduction sites for specific families on the basis of their adaptive portfolio and future climate projections. Taken together, these results inform the development of an ex situ germplasm conservation and breeding plan to target blight-resistant breeding populations to specific environments and provides a blueprint for developing restoration plans for other imperiled tree species.

Genomic Reconstruction of the Successful Establishment of a Feralized Bovine Population on the Subantarctic Island of Amsterdam

The feral cattle of the subantarctic island of Amsterdam provide an outstanding case study of a large mammalian population that was established by a handful of founders and thrived within a few generations in a seemingly inhospitable environment. Here, we investigated the genetic history and composition of this population using genotyping and sequencing data. Our inference showed an intense but brief founding bottleneck around the late 19th century and revealed contributions from European taurine and Indian Ocean Zebu in the founder ancestry. Comparative analysis of whole-genome sequences further revealed a moderate reduction in genetic diversity despite high levels of inbreeding. The brief and intense bottleneck was associated with high levels of drift, a flattening of the site frequency spectrum and a slight relaxation of purifying selection on mildly deleterious variants. Unlike some populations that have experienced prolonged reductions in effective population size, we did not observe any significant purging of highly deleterious variants. Interestingly, the population's success in the harsh environment can be attributed to preadaptation from their European taurine ancestry, suggesting no strong bioclimatic challenge, and also contradicting evidence for insular dwarfism. Genome scan for footprints of selection uncovered a majority of candidate genes related to nervous system function, likely reflecting rapid feralization driven by behavioral changes and complex social restructuring. The Amsterdam Island cattle offers valuable insights into rapid population establishment, feralization, and genetic adaptation in challenging environments. It also sheds light on the unique genetic legacies of feral populations, raising ethical questions according to conservation efforts.

Global and local ancestry estimation in a captive baboon colony

The last couple of decades have highlighted the importance of studying hybridization, particularly among primate species, as it allows us to better understand our own evolutionary trajectory. Here, we report on genetic ancestry estimates using dense, full genome data from 881 olive (Papio anubus), yellow (Papio cynocephalus), or olive-yellow crossed captive baboons from the Southwest National Primate Research Center. We calculated global and local ancestry information, imputed low coverage genomes (n = 830) to improve marker quality, and updated the genetic resources of baboons available to assist future studies. We found evidence of historical admixture in some putatively purebred animals and identified errors within the Southwest National Primate Research Center pedigree. We also compared the outputs between two different phasing and imputation pipelines along with two different global ancestry estimation software. There was good agreement between the global ancestry estimation software, with R2 > 0.88, while evidence of phase switch errors increased depending on what phasing and imputation pipeline was used. We also generated updated genetic maps and created a concise set of ancestry informative markers (n = 1,747) to accurately obtain global ancestry estimates.

Systematic rare variant analyses identify RAB32 as a susceptibility gene for familial Parkinson's disease

Despite substantial progress, causal variants are identified only for a minority of familial Parkinson's disease (PD) cases, leaving high-risk pathogenic variants unidentified1,2. To identify such variants, we uniformly processed exome sequencing data of 2,184 index familial PD cases and 69,775 controls. Exome-wide analyses converged on RAB32 as a novel PD gene identifying c.213C > G/p.S71R as a high-risk variant presenting in ~0.7% of familial PD cases while observed in only 0.004% of controls (odds ratio of 65.5). This variant was confirmed in all cases via Sanger sequencing and segregated with PD in three families. RAB32 encodes a small GTPase known to interact with LRRK2 (refs. 3,4). Functional analyses showed that RAB32 S71R increases LRRK2 kinase activity, as indicated by increased autophosphorylation of LRRK2 S1292. Here our results implicate mutant RAB32 in a key pathological mechanism in PD-LRRK2 kinase activity5-7-and thus provide novel insights into the mechanistic connections between RAB family biology, LRRK2 and PD risk.

Genomic prediction and genome-wide association study using combined genotypic data from different genotyping systems: application to apple fruit quality traits

With advances in next-generation sequencing technologies, various marker genotyping systems have been developed for genomics-based approaches such as genomic selection (GS) and genome-wide association study (GWAS). As new genotyping platforms are developed, data from different genotyping platforms must be combined. However, the potential use of combined data for GS and GWAS has not yet been clarified. In this study, the accuracy of genomic prediction (GP) and the detection power of GWAS increased for most fruit quality traits of apples when using combined data from different genotyping systems, Illumina Infinium single-nucleotide polymorphism array and genotyping by random amplicon sequencing-direct (GRAS-Di) systems. In addition, the GP model, which considered the inbreeding effect, further improved the accuracy of the seven fruit traits. Runs of homozygosity (ROH) islands overlapped with the significantly associated regions detected by the GWAS for several fruit traits. Breeders may have exploited these regions to select promising apples by breeders, increasing homozygosity. These results suggest that combining genotypic data from different genotyping platforms benefits the GS and GWAS of fruit quality traits in apples. Information on inbreeding could be beneficial for improving the accuracy of GS for fruit traits of apples; however, further analysis is required to elucidate the relationship between the fruit traits and inbreeding depression (e.g. decreased vigor).

Improving population scale statistical phasing with whole-genome sequencing data

Haplotype estimation, or phasing, has gained significant traction in large-scale projects due to its valuable contributions to population genetics, variant analysis, and the creation of reference panels for imputation and phasing of new samples. To scale with the growing number of samples, haplotype estimation methods designed for population scale rely on highly optimized statistical models to phase genotype data, and usually ignore read-level information. Statistical methods excel in resolving common variants, however, they still struggle at rare variants due to the lack of statistical information. In this study we introduce SAPPHIRE, a new method that leverages whole-genome sequencing data to enhance the precision of haplotype calls produced by statistical phasing. SAPPHIRE achieves this by refining haplotype estimates through the realignment of sequencing reads, particularly targeting low-confidence phase calls. Our findings demonstrate that SAPPHIRE significantly enhances the accuracy of haplotypes obtained from state of the art methods and also provides the subset of phase calls that are validated by sequencing reads. Finally, we show that our method scales to large data sets by its successful application to the extensive 3.6 Petabytes of sequencing data of the last UK Biobank 200,031 sample release.

Additive and Dominance Genome-Wide Association Studies Reveal the Genetic Basis of Heterosis Related to Growth Traits of Duhua Hybrid Pigs

Heterosis has been extensively used for pig genetic breeding and production, but the genetic basis of heterosis remains largely elusive. Crossbreeding between commercial and native breeds provides a good model to parse the genetic basis of heterosis. This study uses Duhua hybrid pigs, a crossbreed of Duroc and Liangguang small spotted pigs, as materials to explore the genetic basis underlying heterosis related to growth traits at the genomic level. The mid-parent heterosis (MPH) analysis showed heterosis of this Duhua offspring on growth traits. In this study, we examined the impact of additive and dominance effects on 100 AGE (age adjusted to 100 kg) and 100 BF (backfat thickness adjusted to 100 kg) of Duhua hybrid pigs. Meanwhile, we successfully identified SNPs associated with growth traits through both additive and dominance GWASs (genome-wide association studies). These findings will facilitate the subsequent in-depth studies of heterosis in the growth traits of Duhua pigs.

Unraveling metabolic stress response in dairy cows: genetic control of plasma biomarkers throughout lactation and the transition period

Breeding animals able to effectively respond to stress could be a long-term, sustainable, and affordable strategy to improve resilience and welfare in livestock systems. In the present study, the concentrations of 29 plasma biomarkers were used as candidate endophenotypes for metabolic stress response in single-SNP, gene- and haplotype-based GWAS using 739 healthy lactating Italian Holstein cows and 88,271 variants. Significant genetic associations were found in all the 3 GWAS approaches for plasma γ-glutamyl transferase concentration on BTA17, for paraoxonase on BTA4, and for alkaline phosphatase and zinc on BTA2. On these chromosomes, single-SNP and gene-based chromosome-wide association studies were performed, confirming GWAS findings. The signals identified for paraoxonase, γ-glutamyl transferase, and alkaline phosphatase were in proximity of the genes coding for them. The heritability of these 4 biomarkers ranged from moderate to high (from 0.39 to 0.54). Plasma biomarkers are known to undergo large changes in concentration during metabolic stress in the transition period, with an inter-individual variability in the rate of change and recovery time. Genetics may account in part for these differences. To assess this, we studied a subset of 139 periparturient cows homozygous at 3 SNPs known to be respectively associated with concentration of plasma ceruloplasmin, paraoxonase and γ-glutamyl transferase. We compared the immune-metabolic profile measured in plasma at -7, +5 and +30 d relative to calving between groups of opposite homozygotes. A significant effect of the genotype was found on paraoxonase and γ-glutamyl transferase plasma concentration at all the 3 time points. No evidence for genotype effect was detected for ceruloplasmin. Understanding the genetic control underlying metabolic stress response may suggest new approaches to foster resilience in dairy cows.

Runs of homozygosity and selection signature analyses reveal putative genomic regions for artificial selection in layer breeding

BACKGROUND

The breeding of layers emphasizes the continual selection of egg-related traits, such as egg production, egg quality and eggshell, which enhance their productivity and meet the demand of market. As the breeding process continued, the genomic homozygosity of layers gradually increased, resulting in the emergence of runs of homozygosity (ROH). Therefore, ROH analysis can be used in conjunction with other methods to detect selection signatures and identify candidate genes associated with various important traits in layer breeding.

RESULTS

In this study, we generated whole-genome sequencing data from 686 hens in a Rhode Island Red population that had undergone fifteen consecutive generations of intensive artificial selection. We performed a genome-wide ROH analysis and utilized multiple methods to detect signatures of selection. A total of 141,720 ROH segments were discovered in whole population, and most of them (97.35%) were less than 3 Mb in length. Twenty-three ROH islands were identified, and they overlapped with some regions bearing selection signatures, which were detected by the De-correlated composite of multiple signals methods (DCMS). Sixty genes were discovered and functional annotation analysis revealed the possible roles of them in growth, development, immunity and signaling in layers. Additionally, two-tailed analyses including DCMS and ROH for 44 phenotypes of layers were conducted to find out the genomic differences between subgroups of top and bottom 10% phenotype of individuals. Combining the results of GWAS, we observed that regions significantly associated with traits also exhibited selection signatures between the high and low subgroups. We identified a region significantly associated with egg weight near the 25 Mb region of GGA 1, which exhibited selection signatures and has higher genomic homozygosity in the low egg weight subpopulation. This suggests that the region may be play a role in the decline in egg weight.

CONCLUSIONS

In summary, through the combined analysis of ROH, selection signatures, and GWAS, we identified several genomic regions that associated with the production traits of layers, providing reference for the study of layer genome.

Whole-genome sequencing of two captive black soldier fly populations: Implications for commercial production

Black soldier fly (BSF; Hermetia illucens) is a promising insect species for food and feed production as its larvae can convert different organic waste to high-value protein. Selective breeding is one way to optimize production, but the potential of breeding is only starting to be explored and not yet utilized for BSF. To assist in monitoring a captive population and implementing a breeding program, genomics tools are imperative. We conducted whole genome sequencing of two captive populations separated by geographical distance - Denmark (DK) and Texas, USA (TX). Various population genetics analyses revealed a moderate genetic differentiation between two populations. Moreover, we observed higher inbreeding in the DK population, and the detection of a subpopulation within DK population aligned well with the recent foundation of the DK population from two captive populations. Additionally, we generated gene ontology annotation and variants annotation for wider potential applications. Our findings establish a robust marker set for research in population genetics, facilitating the monitoring of inbreeding and laying the groundwork for practical breeding programs for BSF.

Analysis-ready VCF at Biobank scale using Zarr

Background

Variant Call Format (VCF) is the standard file format for interchanging genetic variation data and associated quality control metrics. The usual row-wise encoding of the VCF data model (either as text or packed binary) emphasises efficient retrieval of all data for a given variant, but accessing data on a field or sample basis is inefficient. Biobank scale datasets currently available consist of hundreds of thousands of whole genomes and hundreds of terabytes of compressed VCF. Row-wise data storage is fundamentally unsuitable and a more scalable approach is needed.

Results

We present the VCF Zarr specification, an encoding of the VCF data model using Zarr which makes retrieving subsets of the data much more efficient. Zarr is a cloud-native format for storing multi-dimensional data, widely used in scientific computing. We show how this format is far more efficient than standard VCF based approaches, and competitive with specialised methods for storing genotype data in terms of compression ratios and calculation performance. We demonstrate the VCF Zarr format (and the vcf2zarr conversion utility) on a subset of the Genomics England aggV2 dataset comprising 78,195 samples and 59,880,903 variants, with a 5X reduction in storage and greater than 300X reduction in CPU usage in some representative benchmarks.

Conclusions

Large row-encoded VCF files are a major bottleneck for current research, and storing and processing these files incurs a substantial cost. The VCF Zarr specification, building on widely-used, open-source technologies has the potential to greatly reduce these costs, and may enable a diverse ecosystem of next-generation tools for analysing genetic variation data directly from cloud-based object stores.

Temporal genomics in Hawaiian crickets reveals compensatory intragenomic coadaptation during adaptive evolution

Theory predicts that compensatory genetic changes reduce negative indirect effects of selected variants during adaptive evolution, but evidence is scarce. Here, we test this in a wild population of Hawaiian crickets using temporal genomics and a high-quality chromosome-level cricket genome. In this population, a mutation, flatwing, silences males and rapidly spread due to an acoustically-orienting parasitoid. Our sampling spanned a social transition during which flatwing fixed and the population went silent. We find long-range linkage disequilibrium around the putative flatwing locus was maintained over time, and hitchhiking genes had functions related to negative flatwing-associated effects. We develop a combinatorial enrichment approach using transcriptome data to test for compensatory, intragenomic coevolution. Temporal changes in genomic selection were distributed genome-wide and functionally associated with the population's transition to silence, particularly behavioural responses to silent environments. Our results demonstrate how 'adaptation begets adaptation'; changes to the sociogenetic environment accompanying rapid trait evolution can generate selection provoking further, compensatory adaptation.

Identity-by-descent segments in large samples

If two haplotypes share the same alleles for an extended gene tract, these haplotypes are likely to derive identical-by-descent from a recent common ancestor. Identity-by-descent segment lengths are correlated via unobserved tree and recombination processes, which commonly presents challenges to the derivation of theoretical results in population genetics. Under interpretable regularity conditions, we show that the proportion of detectable identity-by-descent segments at a locus is normally distributed for large sample size and large scaled population size. We use efficient and exact simulations to study the distributional behavior of the detectable identity-by-descent rate in finite samples. One consequence of non-normality in finite samples is that genome-wide scans based on identity-by-descent rates may be subject to anti-conservative Type 1 error control.

Highlights

We show the asymptotic normality of the identity-by-descent rate, a mean of correlated binary random variables that arises in population genetics studies.We describe an efficient algorithm capable of simulating long identity-by-descent segments around a locus in large sample sizes.In enormous simulation studies, we use this algorithm to characterize the distributional properties of the identity-by-descent rate.In finite samples, we reject the null hypothesis of normality more often than the nominal significance level, indicating that genome-wide scans based on identity-by-descent rates may be anti-conservative.

Severe Bottleneck Impacted the Genomic Structure of Egg-Eating Cichlids in Lake Victoria

Within 15,000 years, the explosive adaptive radiation of haplochromine cichlids in Lake Victoria, East Africa, generated 500 endemic species. In the 1980s, the upsurge of Nile perch, a carnivorous fish artificially introduced to the lake, drove the extinction of more than 200 endemic cichlids. The Nile perch predation particularly harmed piscivorous cichlids, including paedophages, cichlids eat eggs and fries, which is an example of the unique trophic adaptation seen in African cichlids. Here, aiming to investigate past demographic events possibly triggered by the invasion of Nile perch and the subsequent impacts on the genetic structure of cichlids, we conducted large-scale comparative genomics. We discovered evidence of recent bottleneck events in 4 species, including 2 paedophages, which began during the 1970s to 1980s, and population size rebounded during the 1990s to 2000s. The timing of the bottleneck corresponded to the historical records of endemic haplochromines" disappearance and later resurgence, which is likely associated with the introduction of Nile perch by commercial demand to Lake Victoria in the 1950s. Interestingly, among the 4 species that likely experienced bottleneck, Haplochromis sp. "matumbi hunter," a paedophagous cichlid, showed the most severe bottleneck signatures. The components of shared ancestry inferred by ADMIXTURE suggested a high genetic differentiation between matumbi hunter and other species. In contrast, our phylogenetic analyses highly supported the monophyly of the 5 paedophages, consistent with the results of previous studies. We conclude that high genetic differentiation of matumbi hunter occurred due to the loss of shared genetic components among haplochromines in Lake Victoria caused by the recent severe bottleneck.

Reconstruct recent multi-population migration history by using identical-by-descent sharing

Identical-by-descent (IBD) is a fundamental genomic characteristic in population genetics and has been widely used for population history reconstruction. However, limited by the nature of IBD, which could only capture the relationship between two individuals/haplotypes, existing IBD-based history inference is constrained to two populations. In this study, we propose a framework by leveraging IBD sharing in multi-population and develop a method, MatrixIBD, to reconstruct recent multi-population migration history. Specifically, we employ the structured coalescent theory to precisely model the genealogical process and then estimate the IBD sharing across multiple populations. Within our model, we establish a theoretical connection between migration history and IBD sharing. Our method is rigorously evaluated through simulations, revealing its remarkable accuracy and robustness. Furthermore, we apply MatrixIBD to Central and South Asia in the Human Genome Diversity Project and successfully reconstruct the recent migration history of three closely related populations in South Asia. By taking into account the IBD sharing across multiple populations simultaneously, MatrixIBD enables us to attain clearer and more comprehensive insights into the history of regions characterized by complex migration dynamics, providing a holistic perspective on intricate patterns embedded within the recent population migration history.

Identifying novel sources of resistance to wheat stem sawfly in five wild wheat species

BACKGROUND

The wheat stem sawfly (WSS, Cephus cinctus) is a major pest of wheat (Triticum aestivum) and can cause significant yield losses. WSS damage results from stem boring and/or cutting, leading to the lodging of wheat plants. Although solid-stem wheat genotypes can effectively reduce larval survival, they may have lower yields than hollow-stem genotypes and show inconsistent solidness expression. Because of limited resistance sources to WSS, evaluating diverse wheat germplasm for novel resistance genes is crucial. We evaluated 91 accessions across five wild wheat species (Triticum monococcum, T. urartu, T. turgidum, T. timopheevii, and Aegilops tauschii) and common wheat cultivars (T. aestivum) for antixenosis (host selection) and antibiosis (host suitability) to WSS. Host selection was measured as the number of eggs after adult oviposition, and host suitability was determined by examining the presence or absence of larval infestation within the stem. The plants were grown in the greenhouse and brought to the field for WSS infestation. In addition, a phylogenetic analysis was performed to determine the relationship between the WSS traits and phylogenetic clustering.

RESULTS

Overall, Ae. tauschii, T. turgidum and T. urartu had lower egg counts and larval infestation than T. monococcum, and T. timopheevii. T. monococcum, T. timopheevii, T. turgidum, and T. urartu had lower larval weights compared with T. aestivum.

CONCLUSION

This study shows that wild relatives of wheat could be a valuable source of alleles for enhancing resistance to WSS and identifies specific germplasm resources that may be useful for breeding. © 2024 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Elucidation of population stratifying markers and selective sweeps in crossbred Landlly pig population using genome-wide SNP data

The present study was aimed at the identification of population stratifying markers from the commercial porcine SNP 60K array and elucidate the genome-wide selective sweeps in the crossbred Landlly pig population. Original genotyping data, generated on Landlly pigs, was merged in various combinations with global suid breeds that were grouped as exotic (global pig breeds excluding Indian and Chinese), Chinese (Chinese pig breeds only), and outgroup pig populations. Post quality control, the genome-wide SNPs were ranked for their stratifying power within each dataset in TRES (using three different criteria) and FIFS programs and top-ranked SNPs (0.5K, 1K, 2K, 3K, and 4K densities) were selected. PCA plots were used to assess the stratification power of low-density panels. Selective sweeps were elucidated in the Landlly population using intra- and inter-population haplotype statistics. Additionally, Tajima's D-statistics were calculated to determine the status of balancing selection in the Landlly population. PCA plots showed 0.5K marker density to effectively stratify Landlly from other pig populations. The A-score in DAPC program revealed the Delta statistic of marker selection to outperform other methods (informativeness and FST methods) and that 3000-marker density was suitable for stratification of Landlly animals from exotic pig populations. The results from selective sweep analysis revealed the Landlly population to be under selection for mammary (NAV2), reproductive efficiency (JMY, SERGEF, and MAP3K20), body conformation (FHIT, WNT2, ASRB, DMGDH, and BHMT), feed efficiency (CSRNP1 and ADRA1A), and immunity (U6, MYO3B, RBMS3, and FAM78B) traits. More than two methods suggested sweeps for immunity and feed efficiency traits, thus giving a strong indication for selection in this direction. The study is the first of its kind in Indian pig breeds with a comparison against global breeds. In conclusion, 500 markers were able to effectively stratify the breeds. Different traits under selective sweeps (natural or artificial selection) can be exploited for further improvement.

Deep history of cultural and linguistic evolution among Central African hunter-gatherers

Human evolutionary history in Central Africa reflects a deep history of population connectivity. However, Central African hunter-gatherers (CAHGs) currently speak languages acquired from their neighbouring farmers. Hence it remains unclear which aspects of CAHG cultural diversity results from long-term evolution preceding agriculture and which reflect borrowing from farmers. On the basis of musical instruments, foraging tools, specialized vocabulary and genome-wide data from ten CAHG populations, we reveal evidence of large-scale cultural interconnectivity among CAHGs before and after the Bantu expansion. We also show that the distribution of hunter-gatherer musical instruments correlates with the oldest genomic segments in our sample predating farming. Music-related words are widely shared between western and eastern groups and likely precede the borrowing of Bantu languages. In contrast, subsistence tools are less frequently exchanged and may result from adaptation to local ecologies. We conclude that CAHG material culture and specialized lexicon reflect a long evolutionary history in Central Africa.

Integrating Bioinformatics and Machine Learning for Genomic Prediction in Chickens

Genomic prediction plays an increasingly important role in modern animal breeding, with predictive accuracy being a crucial aspect. The classical linear mixed model is gradually unable to accommodate the growing number of target traits and the increasingly intricate genetic regulatory patterns. Hence, novel approaches are necessary for future genomic prediction. In this study, we used an illumina 50K SNP chip to genotype 4190 egg-type female Rhode Island Red chickens. Machine learning (ML) and classical bioinformatics methods were integrated to fit genotypes with 10 economic traits in chickens. We evaluated the effectiveness of ML methods using Pearson correlation coefficients and the RMSE between predicted and actual phenotypic values and compared them with rrBLUP and BayesA. Our results indicated that ML algorithms exhibit significantly superior performance to rrBLUP and BayesA in predicting body weight and eggshell strength traits. Conversely, rrBLUP and BayesA demonstrated 2-58% higher predictive accuracy in predicting egg numbers. Additionally, the incorporation of suggestively significant SNPs obtained through the GWAS into the ML models resulted in an increase in the predictive accuracy of 0.1-27% across nearly all traits. These findings suggest the potential of combining classical bioinformatics methods with ML techniques to improve genomic prediction in the future.

Identification of genetic markers associated with hyperketonemia patterns in early lactation Holstein cows

Ketosis, evidenced by hyperketonemia with elevated blood β-hydroxybutyrate (BHB) levels, is a significant metabolic disorder of dairy cattle, typically diagnosed within the first 6 weeks post-calving when high energy levels are essential to milk production. Our study aimed to identify genetic markers linked to hyperketonemia (HYK) patterns in Holstein cows during early lactation and compare these to HYK-negative cows. We screened 964 cows for HYK using a threshold of BHB ≥1.2 mmol/L during the first 2 weeks postpartum (screening period, SP). Cows that tested negative initially were retested the following week. Cows were deemed HYK-negative (CON group) if BHB levels were below 1.2 mmol/L in both tests, while those with BHB levels exceeding this threshold at any test were treated and classified as HYK-positive (HYK+). Post-treatment, HYK+ cows were monitored for two-week follow-up period (FP) and classified based on their recovery: cured (CUR; consistently low BHB), recurrent (REC; fluctuating BHB levels), severe (SEV; high initial BHB that decreased), or chronic (CHR; persistently high BHB). Using 489 cows that were genotyped, a GWAS was conducted using GCTA software, revealing significant associations of several SNPs across different HYK patterns when compared to the CON group. These SNPs were primarily linked to genes affecting milk traits and were enriched in biological pathways relevant to protein glycosylation, inflammatory response, glucose homeostasis, and fatty acid synthesis. Our findings highlight genomic regions, potential candidate genes, and biological pathways related to ketosis, underscoring potential targets for improving health management in dairy cattle. These insights could lead to better strategies for managing ketosis through genetic selection, ultimately enhancing dairy cattle welfare and productivity. Further research with a larger number of cows is recommended to validate these findings and help confirm the implicated SNPs and genes.

Genome-wide association study identifies genomic regions associated with key reproductive traits in Korean Hanwoo cows

BACKGROUND

Conducting genome-wide association studies (GWAS) for reproductive traits in Hanwoo cattle, including age at first calving (AFC), calving interval (CI), gestation length (GL), and number of artificial inseminations per conception (NAIPC), is of paramount significance. These analyses provided a thorough exploration of the genetic basis of these traits, facilitating the identification of key markers for targeted trait improvement. Breeders can optimize their selection strategies, leading to more efficient and sustainable breeding programs, by incorporating genetic insights. This impact extends beyond individual traits and contributes to the overall productivity and profitability of the Hanwoo beef cattle industry. Ultimately, GWAS is essential in ensuring the long-term genetic resilience and adaptability of Hanwoo cattle populations. The primary goal of this study was to identify significant single nucleotide polymorphisms (SNPs) or quantitative trait loci (QTLs) associated with the studied reproductive traits and subsequently map the underlying genes that hold promise for trait improvement.

RESULTS

A genome-wide association study of reproductive traits identified 68 significant single nucleotide polymorphisms (SNPs) distributed across 29 Bos taurus autosomes (BTA). Among them, BTA14 exhibited the highest number of identified SNPs (25), whereas BTA6, BTA7, BTA8, BTA10, BTA13, BTA17, and BTA20 exhibited 8, 5, 5, 3, 8, 2, and 12 significant SNPs, respectively. Annotation of candidate genes within a 500 kb region surrounding the significant SNPs led to the identification of ten candidate genes relevant to age at first calving. These genes were: FANCG, UNC13B, TESK1, TLN1, and CREB3 on BTA8; FAM110B, UBXN2B, SDCBP, and TOX on BTA14; and MAP3K1 on BTA20. Additionally, APBA3, TCF12, and ZFR2, located on BTA7 and BTA10, were associated with the calving interval; PAX1, SGCD, and HAND1, located on BTA7 and BTA13, were linked to gestation length; and RBM47, UBE2K, and GPX8, located on BTA6 and BTA20, were linked to the number of artificial inseminations per conception in Hanwoo cows.

CONCLUSIONS

The findings of this study enhance our knowledge of the genetic factors that influence reproductive traits in Hanwoo cattle populations and provide a foundation for future breeding strategies focused on improving desirable traits in beef cattle. This research offers new evidence and insights into the genetic variants and genome regions associated with reproductive traits and contributes valuable information to guide future efforts in cattle breeding.

The genetic admixture and assimilation of Ahom: a historic migrant from Thailand to India

The Northeastern region of India is considered a gateway for modern humans' dispersal throughout Asia. This region is a mixture of various ethnic and indigenous populations amalgamating multiple ancestries. One reason for such amalgamation is that, South Asia experienced multiple historic migrations from various parts of the world. A few examples explored genetically are Jews, Parsis and Siddis. Ahom is a dynasty that historically migrated to India during the 12th century. However, this putative migration has not been studied genetically at high resolution. Therefore, to validate this historical evidence, we genotyped autosomal data of the Modern Ahom population residing in seven sister states of India. Principal Component and Admixture analyses haave suggested a substantial admixture of the Ahom population with the local Tibeto-Burman populations. Moreover, the haplotype-based analysis has linked these Ahom individuals mainly with the Kusunda (a language isolated from Nepal) and Khasi (an Austroasiatic population of Meghalaya). Such unexpected presence of widespread population affinities suggests that Ahom mixed and assimilated a wide variety of Trans-Himalayan populations inhabiting this region after the migration. In summary, we observed a significant deviation of Ahom from their ancestral homeland (Thailand) and extensive admixture and assimilation with the local South Asian populations.

Population dynamics of potentially harmful haplotypes: a pedigree analysis

BACKGROUND

The identification of low-frequency haplotypes, never observed in homozygous state in a population, is considered informative on the presence of potentially harmful alleles (candidate alleles), putatively involved in inbreeding depression. Although identification of candidate alleles is challenging, studies analyzing the dynamics of potentially harmful alleles are lacking. A pedigree of the highly endangered Gochu Asturcelta pig breed, including 471 individuals belonging to 51 different families with at least 5 offspring each, was genotyped using the Axiom PigHDv1 Array (658,692 SNPs). Analyses were carried out on four different cohorts defined according to pedigree depth and at the whole population (WP) level.

RESULTS

The 4,470 Linkage Blocks (LB) identified in the Base Population (10 individuals), gathered a total of 16,981 alleles in the WP. Up to 5,466 (32%) haplotypes were statistically considered candidate alleles, 3,995 of them (73%) having one copy only. The number of alleles and candidate alleles varied across cohorts according to sample size. Up to 4,610 of the alleles identified in the WP (27% of the total) were present in one cohort only. Parentage analysis identified a total of 67,742 parent-offspring incompatibilities. The number of mismatches varied according to family size. Parent-offspring inconsistencies were identified in 98.2% of the candidate alleles and 100% of the LB in which they were located. Segregation analyses informed that most potential candidate alleles appeared de novo in the pedigree. Only 17 candidate alleles were identified in the boar, sow, and paternal and maternal grandparents and were considered segregants.

CONCLUSIONS

Our results suggest that neither mutation nor recombination are the major forces causing the apparition of candidate alleles. Their occurrence is more likely caused by Allele-Drop-In events due to SNP calling errors. New alleles appear when wrongly called SNPs are used to construct haplotypes. The presence of candidate alleles in either parents or grandparents of the carrier individuals does not ensure that they are true alleles. Minimum Allele Frequency thresholds may remove informative alleles. Only fully segregant candidate alleles should be considered potentially harmful alleles. A set of 16 candidate genes, potentially involved in inbreeding depression, is described.

Reconstructing parent genomes using siblings and other relatives

Reconstructing the DNA of ancestors from their descendants has the potential to empower phenotypic analyses (including association and genetic nurture studies), improve pedigree reconstruction, and shed light on the ancestral population and phenotypes of ancestors. We developed HAPI-RECAP, a method that reconstructs the DNA of parents from full siblings and their relatives. This tool leverages HAPI2's output, a new phasing approach that applies to siblings (and optionally one or both parents) and reliably infers parent haplotypes but does not link the ungenotyped parents' DNA across chromosomes or between segments flanking ambiguities. By combining IBD between the reconstructed parents and the relatives, HAPI-RECAP resolves the source parent of these segments. Moreover, the method exploits crossovers the children inherited and sex-specific genetic maps to infer the reconstructed parents' sexes. We validated these methods on research participants from both 23andMe, Inc. and the San Antonio Mexican American Family Studies. Given data for one parent, HAPI2 reconstructs large fractions of the missing parent's DNA, between 77.6% and 99.97% among all families, and 90.3% on average in three- and four-child families. When reconstructing both parents, HAPI-RECAP inferred between 33.2% and 96.6% of the parents' genotypes, averaging 70.6% in four-child families. Reconstructed genotypes have average error rates < 10-3, or comparable to those from direct genotyping. HAPI-RECAP inferred the parent sexes 100% correctly given IBD-linked segments and can also reconstruct parents without any IBD. As datasets grow in size, more families will be implicitly collected; HAPI-RECAP holds promise to enable high quality parent genotype reconstruction.

Phasing millions of samples achieves near perfect accuracy, enabling parent-of-origin classification of variants

Haplotype phasing, the process of determining which genetic variants are physically located on the same chromosome, is crucial for various genetic analyses. In this study, we first benchmark SHAPEIT and Beagle, two state-of-the-art phasing methods, on two large datasets: > 8 million diverse, research-consented 23andMe, Inc. customers and the UK Biobank (UKB). We find that both perform exceptionally well. Beagle's median switch error rate (SER) (after excluding single SNP switches) in white British trios from UKB is 0.026% compared to 0.00% for European ancestry 23andMe research participants; 55.6% of European ancestry 23andMe research participants have zero non-single SNP switches, compared to 42.4% of white British trios. South Asian ancestry 23andMe research participants have the highest median SER amongst the 23andMe populations, but it is still remarkably low at 0.46%. We also investigate the relationship between identity-by-descent (IBD) and SER, finding that switch errors tend to occur in regions of little or no IBD segment coverage. SHAPEIT and Beagle excel at 'intra-chromosomal' phasing, but lack the ability to phase across chromosomes, motivating us to develop an inter-chromosomal phasing method, called HAPTIC ( HAP lotype TI ling and C lustering), that assigns paternal and maternal variants discretely genome-wide. Our approach uses identity-by-descent (IBD) segments to phase blocks of variants on different chromosomes. HAPTIC represents the segments a focal individual shares with their relatives as nodes in a signed graph and performs bipartite clustering on the signed graph using spectral clustering. We test HAPTIC on 1022 UKB trios, yielding a median phase error of 0.08% in regions covered by IBD segments (33.5% of sites). We also ran HAPTIC in the 23andMe database and found a median phase error rate (the rate of mismatching alleles between the inferred and true phase) of 0.92% in Europeans (93.8% of sites) and 0.09% in admixed Africans (92.7% of sites). HAPTIC's precision depends heavily on data from relatives, so will increase as datasets grow larger and more diverse. HAPTIC enables analyses that require the parent-of-origin of variants, such as association studies and ancestry inference of untyped parents.

Genome-wide association study and fine-mapping on Korean biobank to discover renal trait-associated variants

BACKGROUND

Chronic kidney disease is a significant health burden worldwide, with increasing incidence. Although several genome- wide association studies (GWAS) have investigated single nucleotide polymorphisms (SNP) associated with kidney trait, most studies were focused on European ancestry.

METHODS

We utilized clinical and genetic information collected from the Korean Genome and Epidemiology Study (KoGES).

RESULTS

More than five million SNPs from 58,406 participants were analyzed. After meta-GWAS, 1,360 loci associated with estimated glomerular filtration rate (eGFR) at a genome-wide significant level (p = 5 × 10-8) were identified. Among them, 399 loci were validated with at least one other biomarker (blood urea nitrogen [BUN] or eGFRcysC) and 149 loci were validated using both markers. Among them, 18 SNPs (nine known ones and nine novel ones) with 20 putative genes were found. The aggregated effect of genes estimated by MAGMA gene analysis showed that these significant genes were enriched in kidney-associated pathways, with the kidney and liver being the most enriched tissues.

CONCLUSION

In this study, we conducted GWAS for more than 50,000 Korean individuals and identified several variants associated with kidney traits, including eGFR, BUN, and eGFRcysC. We also investigated functions of relevant genes using computational methods to define putative causal variants.

The genetic identity of the Vedda: A language isolate of South Asia

Linguistic data from South Asia identified several language isolates in the subcontinent. The Vedda, an indigenous population of Sri Lanka, are the least studied amongst them. Therefore, to understand the initial peopling of Sri Lanka and the genetic affinity of the Vedda with other populations in Eurasia, we extensively studied the high-resolution autosomal and mitogenomes from the Vedda population of Sri Lanka. Our autosomal analyses suggest a close genetic link of Vedda with the tribal populations of India despite no evidence of close linguistic affinity, thus suggesting a deep genetic link of the Vedda with these populations. The mitogenomic analysis supports this association by pointing to an ancient link with Indian populations. We suggest that the Vedda population is a genetically drifted group with limited gene flow from neighbouring Sinhalese and Sri Lankan Tamil populations. Interestingly, the genetic ancestry sharing of Vedda neglects the isolation-by-distance model. Collectively, the demography of Sri Lanka is unique, where Sinhalese and Sri Lankan Tamil populations excessively admixed, whilst Vedda largely preserved their isolation and deep genetic association with India.

Single-nucleotide variant calling in single-cell sequencing data with Monopogen

Single-cell omics technologies enable molecular characterization of diverse cell types and states, but how the resulting transcriptional and epigenetic profiles depend on the cell's genetic background remains understudied. We describe Monopogen, a computational tool to detect single-nucleotide variants (SNVs) from single-cell sequencing data. Monopogen leverages linkage disequilibrium from external reference panels to identify germline SNVs and detects putative somatic SNVs using allele cosegregating patterns at the cell population level. It can identify 100 K to 3 M germline SNVs achieving a genotyping accuracy of 95%, together with hundreds of putative somatic SNVs. Monopogen-derived genotypes enable global and local ancestry inference and identification of admixed samples. It identifies variants associated with cardiomyocyte metabolic levels and epigenomic programs. It also improves putative somatic SNV detection that enables clonal lineage tracing in primary human clonal hematopoiesis. Monopogen brings together population genetics, cell lineage tracing and single-cell omics to uncover genetic determinants of cellular processes.

Genomic signatures of strawberry domestication and diversification

Cultivated strawberry (Fragaria × ananassa) has a brief history of less than 300 yr, beginning with the hybridization of octoploids Fragaria chiloensis and Fragaria virginiana. Here we explored the genomic signatures of early domestication and subsequent diversification for different climates using whole-genome sequences of 289 wild, heirloom, and modern varieties from two major breeding programs in the United States. Four nonadmixed wild octoploid populations were identified, with recurrent introgression among the sympatric populations. The proportion of F. virginiana ancestry increased by 20% in modern varieties over initial hybrids, and the proportion of F. chiloensis subsp. pacifica rose from 0% to 3.4%. Effective population size rapidly declined during early breeding. Meanwhile, divergent selection for distinct environments reshaped wild allelic origins in 21 out of 28 chromosomes. Overlapping divergent selective sweeps in natural and domesticated populations revealed 16 convergent genomic signatures that may be important for climatic adaptation. Despite 20 breeding cycles since initial hybridization, more than half of loci underlying yield and fruit size are still not under artificial selection. These insights add clarity to the domestication and breeding history of what is now the most widely cultivated fruit in the world.

Cis-eQTLs in seven duck tissues identify novel candidate genes for growth and carcass traits

BACKGROUND

Expression quantitative trait loci (eQTL) studies aim to understand the influence of genetic variants on gene expression. The colocalization of eQTL mapping and GWAS strategy could help identify essential candidate genes and causal DNA variants vital to complex traits in human and many farm animals. However, eQTL mapping has not been conducted in ducks. It is desirable to know whether eQTLs within GWAS signals contributed to duck economic traits.

RESULTS

In this study, we conducted an eQTL analysis using publicly available RNA sequencing data from 820 samples, focusing on liver, muscle, blood, adipose, ovary, spleen, and lung tissues. We identified 113,374 cis-eQTLs for 12,266 genes, a substantial fraction 39.1% of which were discovered in at least two tissues. The cis-eQTLs of blood were less conserved across tissues, while cis-eQTLs from any tissue exhibit a strong sharing pattern to liver tissue. Colocalization between cis-eQTLs and genome-wide association studies (GWAS) of 50 traits uncovered new associations between gene expression and potential loci influencing growth and carcass traits. SRSF4, GSS, and IGF2BP1 in liver, NDUFC2 in muscle, ELF3 in adipose, and RUNDC1 in blood could serve as the candidate genes for duck growth and carcass traits.

CONCLUSIONS

Our findings highlight substantial differences in genetic regulation of gene expression across duck primary tissues, shedding light on potential mechanisms through which candidate genes may impact growth and carcass traits. Furthermore, this availability of eQTL data offers a valuable resource for deciphering further genetic association signals that may arise from ongoing extensive endeavors aimed at enhancing duck production traits.

Ability of Genomic Prediction to Bi-Parent-Derived Breeding Population Using Public Data for Soybean Oil and Protein Content

Genomic selection (GS) is a marker-based selection method used to improve the genetic gain of quantitative traits in plant breeding. A large number of breeding datasets are available in the soybean database, and the application of these public datasets in GS will improve breeding efficiency and reduce time and cost. However, the most important problem to be solved is how to improve the ability of across-population prediction. The objectives of this study were to perform genomic prediction (GP) and estimate the prediction ability (PA) for seed oil and protein contents in soybean using available public datasets to predict breeding populations in current, ongoing breeding programs. In this study, six public datasets of USDA GRIN soybean germplasm accessions with available phenotypic data of seed oil and protein contents from different experimental populations and their genotypic data of single-nucleotide polymorphisms (SNPs) were used to perform GP and to predict a bi-parent-derived breeding population in our experiment. The average PA was 0.55 and 0.50 for seed oil and protein contents within the bi-parents population according to the within-population prediction; and 0.45 for oil and 0.39 for protein content when the six USDA populations were combined and employed as training sets to predict the bi-parent-derived population. The results showed that four USDA-cultivated populations can be used as a training set individually or combined to predict oil and protein contents in GS when using 800 or more USDA germplasm accessions as a training set. The smaller the genetic distance between training population and testing population, the higher the PA. The PA increased as the population size increased. In across-population prediction, no significant difference was observed in PA for oil and protein content among different models. The PA increased as the SNP number increased until a marker set consisted of 10,000 SNPs. This study provides reasonable suggestions and methods for breeders to utilize public datasets for GS. It will aid breeders in developing GS-assisted breeding strategies to develop elite soybean cultivars with high oil and protein contents.

From sub-Saharan Africa to China: Evolutionary history and adaptation of Drosophila melanogaster revealed by population genomics

Drosophila melanogaster is a widely used model organism for studying environmental adaptation. However, the genetic diversity of populations in Asia is poorly understood, leaving a notable gap in our knowledge of the global evolution and adaptation of this species. We sequenced genomes of 292 D. melanogaster strains from various ecological settings in China and analyzed them along with previously published genome sequences. We have identified six global genetic ancestry groups, despite the presence of widespread genetic admixture. The strains from China represent a unique ancestry group, although detectable differentiation exists among populations within China. We deciphered the global migration and demography of D. melanogaster, and identified widespread signals of adaptation, including genetic changes in response to insecticides. We validated the effects of insecticide resistance variants using population cage trials and deep sequencing. This work highlights the importance of population genomics in understanding the genetic underpinnings of adaptation, an effort that is particularly relevant given the deterioration of ecosystems.

Noninvasive Prenatal Diagnosis of SEA-Thalassemia by Combining 1000 Genomes Database and Relative Haplotype Dosage

To explore a noninvasive method for diagnosis of SEA-thalassemia and to investigate whether the regional factors affect the accuracy of this method. The method involved using a public database and bioinformatics software to construct parental haplotypes for proband and predicting fetal genotypes using relative haplotype dosage. We screened and downloaded sequencing data of couples who were both SEA-thalassemia carriers from the China National Genebank public data platform, and matched the sequencing data format with that of the reference panel using Ubuntu system tools. We then used Beagle software to construct parental haplotypes, predicted fetal haplotypes by relative haplotype dosage. Finally, we used Hidden Markov Model and Viterbi algorithm to determine fetal pathogenic haplotypes. All noninvasive fetal genotype diagnosis results were compared with gold standard gap-PCR electrophoresis results. Our method was successful in diagnosing 13 families with SEA-thalassemia carriers. The best diagnostic results were obtained when Southern Chinese Han was used as the reference panel, and 10 families showed full agreement between our noninvasive diagnostic results and the gap-PCR electrophoresis results. The accuracy of our method was higher when using a Chinese Han as the reference panel for haplotype construction in the Southern Chinese Han region as opposed to Beijing Chinese region. The combined use of public databases and relative haplotype dosage for diagnosing SEA-thalassemia is a feasible approach. Our method produces the best noninvasive diagnostic results when the test samples and population reference panel are closely matched in both ethnicity and geography. When constructing parental haplotypes with our method, it is important to consider the effect of region in addition to population background alone.

Chromatin accessibility variation provides insights into missing regulation underlying immune-mediated diseases

Most genetic loci associated with complex traits and diseases through genome-wide association studies (GWAS) are noncoding, suggesting that the causal variants likely have gene regulatory effects. However, only a small number of loci have been linked to expression quantitative trait loci (eQTLs) detected currently. To better understand the potential reasons for many trait-associated loci lacking eQTL colocalization, we investigated whether chromatin accessibility QTLs (caQTLs) in lymphoblastoid cell lines (LCLs) explain immune-mediated disease associations that eQTLs in LCLs did not. The power to detect caQTLs was greater than that of eQTLs and was less affected by the distance from the transcription start site of the associated gene. Meta-analyzing LCL eQTL data to increase the sample size to over a thousand led to additional loci with eQTL colocalization, demonstrating that insufficient statistical power is still likely to be a factor. Moreover, further eQTL colocalization loci were uncovered by surveying eQTLs of other immune cell types. Altogether, insufficient power and context-specificity of eQTLs both contribute to the 'missing regulation.'

Shared genomic segments analysis identifies MHC class I and class III molecules as genetic risk factors for juvenile idiopathic arthritis

Juvenile idiopathic arthritis (JIA) is a complex rheumatic disease encompassing several clinically defined subtypes of varying severity. The etiology of JIA remains largely unknown, but genome-wide association studies (GWASs) have identified up to 22 genes associated with JIA susceptibility, including a well-established association with HLA-DRB1. Continued investigation of heritable risk factors has been hindered by disease heterogeneity and low disease prevalence. In this study, we utilized shared genomic segments (SGS) analysis on whole-genome sequencing of 40 cases from 12 multi-generational pedigrees significantly enriched for JIA. Subsets of cases are connected by a common ancestor in large extended pedigrees, increasing the power to identify disease-associated loci. SGS analysis identifies genomic segments shared among disease cases that are likely identical by descent and anchored by a disease locus. This approach revealed statistically significant signals for major histocompatibility complex (MHC) class I and class III alleles, particularly HLA-A∗02:01, which was observed at a high frequency among cases. Furthermore, we identified an additional risk locus at 12q23.2-23.3, containing genes primarily expressed by naive B cells, natural killer cells, and monocytes. The recognition of additional risk beyond HLA-DRB1 provides a new perspective on immune cell dynamics in JIA. These findings contribute to our understanding of JIA and may guide future research and therapeutic strategies.

Site-directed genotype screening for elimination of antinutritional saponins in quinoa seeds identifies TSARL1 as a master controller of saponin biosynthesis selectively in seeds

Climate change may result in a drier climate and increased salinization, threatening agricultural productivity worldwide. Quinoa (Chenopodium quinoa) produces highly nutritious seeds and tolerates abiotic stresses such as drought and high salinity, making it a promising future food source. However, the presence of antinutritional saponins in their seeds is an undesirable trait. We mapped genes controlling seed saponin content to a genomic region that includes TSARL1. We isolated desired genetic variation in this gene by producing a large mutant library of a commercial quinoa cultivar and screening the library for specific nucleotide substitutions using droplet digital PCR. We were able to rapidly isolate two independent tsarl1 mutants, which retained saponins in the leaves and roots for defence, but saponins were undetectable in the seed coat. We further could show that TSARL1 specifically controls seed saponin biosynthesis in the committed step after 2,3-oxidosqualene. Our work provides new important knowledge on the function of TSARL1 and represents a breakthrough for quinoa breeding.

Biobank-scale inference of multi-individual identity by descent and gene conversion

We present a method for efficiently identifying clusters of identical-by-descent haplotypes in biobank-scale sequence data. Our multi-individual approach enables much more computationally efficient inference of identity by descent (IBD) than approaches that infer pairwise IBD segments and provides locus-specific IBD clusters rather than IBD segments. Our method's computation time, memory requirements, and output size scale linearly with the number of individuals in the dataset. We also present a method for using multi-individual IBD to detect alleles changed by gene conversion. Application of our methods to the autosomal sequence data for 125,361 White British individuals in the UK Biobank detects more than 9 million converted alleles. This is 2,900 times more alleles changed by gene conversion than were detected in a previous analysis of familial data. We estimate that more than 250,000 sequenced probands and a much larger number of additional genomes from multi-generational family members would be required to find a similar number of alleles changed by gene conversion using a family-based approach. Our IBD clustering method is implemented in the open-source ibd-cluster software package.

Whole-genome resequencing of Chinese indigenous sheep provides insight into the genetic basis underlying climate adaptation

BACKGROUND

Chinese indigenous sheep are valuable resources with unique features and characteristics. They are distributed across regions with different climates in mainland China; however, few reports have analyzed the environmental adaptability of sheep based on their genome. We examined the variants and signatures of selection involved in adaptation to extreme humidity, altitude, and temperature conditions in 173 sheep genomes from 41 phenotypically and geographically representative Chinese indigenous sheep breeds to characterize the genetic basis underlying environmental adaptation in these populations.

RESULTS

Based on the analysis of population structure, we inferred that Chinese indigenous sheep are divided into four groups: Kazakh (KAZ), Mongolian (MON), Tibetan (TIB), and Yunnan (YUN). We also detected a set of candidate genes that are relevant to adaptation to extreme environmental conditions, such as drought-prone regions (TBXT, TG, and HOXA1), high-altitude regions (DYSF, EPAS1, JAZF1, PDGFD, and NF1) and warm-temperature regions (TSHR, ABCD4, and TEX11). Among all these candidate genes, eight ABCD4, CNTN4, DOCK10, LOC105608545, LOC121816479, SEM3A, SVIL, and TSHR overlap between extreme environmental conditions. The TSHR gene shows a strong signature for positive selection in the warm-temperature group and harbors a single nucleotide polymorphism (SNP) missense mutation located between positions 90,600,001 and 90,650,001 on chromosome 7, which leads to a change in the protein structure of TSHR and influences its stability.

CONCLUSIONS

Analysis of the signatures of selection uncovered genes that are likely related to environmental adaptation and a SNP missense mutation in the TSHR gene that affects the protein structure and stability. It also provides information on the evolution of the phylogeographic structure of Chinese indigenous sheep populations. These results provide important genetic resources for future breeding studies and new perspectives on how animals can adapt to climate change.

Unveiling the Genetic Mechanism of Meat Color in Pigs through GWAS, Multi-Tissue, and Single-Cell Transcriptome Signatures Exploration

Meat color traits directly influence consumer acceptability and purchasing decisions. Nevertheless, there is a paucity of comprehensive investigation into the genetic mechanisms underlying meat color traits in pigs. Utilizing genome-wide association studies (GWAS) on five meat color traits and the detection of selection signatures in pig breeds exhibiting distinct meat color characteristics, we identified a promising candidate SNP, 6_69103754, exhibiting varying allele frequencies among pigs with different meat color characteristics. This SNP has the potential to affect the redness and chroma index values of pork. Moreover, transcriptome-wide association studies (TWAS) analysis revealed the expression of candidate genes associated with meat color traits in specific tissues. Notably, the largest number of candidate genes were observed from transcripts derived from adipose, liver, lung, spleen tissues, and macrophage cell type, indicating their crucial role in meat color development. Several shared genes associated with redness, yellowness, and chroma indices traits were identified, including RINL in adipose tissue, ENSSSCG00000034844 and ITIH1 in liver tissue, TPX2 and MFAP2 in lung tissue, and ZBTB17, FAM131C, KIFC3, NTPCR, and ENGSSSCG00000045605 in spleen tissue. Furthermore, single-cell enrichment analysis revealed a significant association between the immune system and meat color. This finding underscores the significance of the immune system associated with meat color. Overall, our study provides a comprehensive analysis of the genetic mechanisms underlying meat color traits, offering valuable insights for future breeding efforts aimed at improving meat quality.

Genomic analyses of nitrogen utilization efficiency, its indicator trait blood urea nitrogen and the relationship to classical growth performance and feed efficiency traits in a Landrace × Piétrain crossbred population

Improving the nutrient efficiency in pork production is required to reduce the resource competition between human food and animal feed regarding diet components edible for humans and to minimize emissions relevant to climate or the environment. Thereby, protein utilization efficiency and its equivalent nitrogen utilization efficiency (NUE) play a major role. Breeding for more nitrogen (N) efficient pigs bears a promising strategy to improve such traits, however, directly phenotyping NUE based on N balance data is neither cost-efficient nor straightforward and not applicable for routine evaluations. Blood urea nitrogen (BUN) levels in the pig are suitable to predict the NUE and, therefore, might be an indicator trait for NUE because BUN is a relatively easy-to-measure trait. This study investigated the suitability of NUE as a selection trait in future breeding programs. The relationships to classical growth performance and feed efficiency traits were analysed as well as the relationship to BUN to infer the role of BUN as an indicator trait to improve NUE via breeding. The analyzes were based on a Landrace F1 cross population consisting of 502 individuals who descended from 20 Piétrain sires. All animals were genotyped for 48,525 SNPs. They were phenotyped in two different fattening phases, i.e., FP1 and FP2, during the experiment. Uni- and bivariate analyses were run to estimate variance components and to determine the genetic correlation between different traits or between the same trait measured at different time points. Moderate heritabilities were estimated for all traits, whereby the heritability for NUE was h2 = 0.293 in FP1 and h2 = 0.163 in FP2 and BUN had the by far highest heritability (h2 = 0.415 in FP1 and h2 = 0.460 in FP2). The significant genetic correlation between NUE and BUN showed the potential of BUN to be considered an indicator trait for NUE. This was particularly pronounced when NUE was measured in FP1 (genetic correlationsr g = - 0.631 $$ {r}_g=-0.631 $$ andr g = - 0.688 $$ {r}_g=-0.688 $$ between NUE and BUN measured in FP1 and FP2, respectively). The genetic correlations of NUE and BUN with important production traits suggest selecting pigs with high growth rates and low BUN levels to breed more efficient pigs in future breeding programs.

Genome-Wide Association Study to Identify Marker-Trait Associations for Seed Color in Colored Wheat (Triticum aestivum L.)

This study conducted phenotypic evaluations on a wheat F3 population derived from 155 F2 plants. Traits related to seed color, including chlorophyll a, chlorophyll b, carotenoid, anthocyanin, L*, a*, and b*, were assessed, revealing highly significant correlations among various traits. Genotyping using 81,587 SNP markers resulted in 3969 high-quality markers, revealing a genome-wide distribution with varying densities across chromosomes. A genome-wide association study using fixed and random model circulating probability unification (FarmCPU) and Bayesian-information and linkage-disequilibrium iteratively nested keyway (BLINK) identified 11 significant marker-trait associations (MTAs) associated with L*, a*, and b*, and chromosomal distribution patterns revealed predominant locations on chromosomes 2A, 2B, and 4B. A comprehensive annotation uncovered 69 genes within the genomic vicinity of each MTA, providing potential functional insights. Gene expression analysis during seed development identified greater than 2-fold increases or decreases in expression in colored wheat for 16 of 69 genes. Among these, eight genes, including transcription factors and genes related to flavonoid and ubiquitination pathways, exhibited distinct expression patterns during seed development, providing further approaches for exploring seed coloration. This comprehensive exploration expands our understanding of the genetic basis of seed color and paves the way for informed discussions on the molecular intricacies contributing to this phenotypic trait.

Adaptive introgression of a visual preference gene

Visual preferences are important drivers of mate choice and sexual selection, but little is known of how they evolve at the genetic level. In this study, we took advantage of the diversity of bright warning patterns displayed by Heliconius butterflies, which are also used during mate choice. Combining behavioral, population genomic, and expression analyses, we show that two Heliconius species have evolved the same preferences for red patterns by exchanging genetic material through hybridization. Neural expression of regucalcin1 correlates with visual preference across populations, and disruption of regucalcin1 with CRISPR-Cas9 impairs courtship toward conspecific females, providing a direct link between gene and behavior. Our results support a role for hybridization during behavioral evolution and show how visually guided behaviors contributing to adaptation and speciation are encoded within the genome.

SCGB1D2 inhibits growth of Borrelia burgdorferi and affects susceptibility to Lyme disease

Lyme disease is a tick-borne disease caused by bacteria of the genus Borrelia. The host factors that modulate susceptibility for Lyme disease have remained mostly unknown. Using epidemiological and genetic data from FinnGen and Estonian Biobank, we identify two previously known variants and an unknown common missense variant at the gene encoding for Secretoglobin family 1D member 2 (SCGB1D2) protein that increases the susceptibility for Lyme disease. Using live Borrelia burgdorferi (Bb) we find that recombinant reference SCGB1D2 protein inhibits the growth of Bb in vitro more efficiently than the recombinant protein with SCGB1D2 P53L deleterious missense variant. Finally, using an in vivo murine infection model we show that recombinant SCGB1D2 prevents infection by Borrelia in vivo. Together, these data suggest that SCGB1D2 is a host defense factor present in the skin, sweat, and other secretions which protects against Bb infection and opens an exciting therapeutic avenue for Lyme disease.

AI-enabled evaluation of genome-wide association relevance and polygenic risk score prediction in Alzheimer's disease

GWAS focuses on significance loosing false positives; machine learning probes sub-significant features relying on predictivity. Yet, these are far from orthogonal. We sought to explore how these inform each other in sub-genome-wide significant situations to define relevance for predictive features. We introduce the SVM-based RubricOE that selects heavily cross-validated feature sets, and LDpred2 PRS as a strong contrast to SVM, to explore significance and predictivity. Our Alzheimer's test case notoriously lacks strong genetic signals except for few very strong phenotype-SNP associations, which suits the problem we are exploring. We found that the most significant SNPs among ML and PRS-selected SNPs captured most of the predictivity, while weaker associations tend also to contribute weakly to predictivity. SNPs with weak associations tend not to contribute to predictivity, but deletion of these features does not injure it. Significance provides a ranking that helps identify weakly predictive features.

Ancient genomes illuminate Eastern Arabian population history and adaptation against malaria

The harsh climate of Arabia has posed challenges in generating ancient DNA from the region, hindering the direct examination of ancient genomes for understanding the demographic processes that shaped Arabian populations. In this study, we report whole-genome sequence data obtained from four Tylos-period individuals from Bahrain. Their genetic ancestry can be modeled as a mixture of sources from ancient Anatolia, Levant, and Iran/Caucasus, with variation between individuals suggesting population heterogeneity in Bahrain before the onset of Islam. We identify the G6PD Mediterranean mutation associated with malaria resistance in three out of four ancient Bahraini samples and estimate that it rose in frequency in Eastern Arabia from 5 to 6 kya onward, around the time agriculture appeared in the region. Our study characterizes the genetic composition of ancient Arabians, shedding light on the population history of Bahrain and demonstrating the feasibility of studies of ancient DNA in the region.

Mapping crossover events of mouse meiotic recombination by restriction fragment ligation-based Refresh-seq

Single-cell whole-genome sequencing methods have undergone great improvements over the past decade. However, allele dropout, which means the inability to detect both alleles simultaneously in an individual diploid cell, largely restricts the application of these methods particularly for medical applications. Here, we develop a new single-cell whole-genome sequencing method based on third-generation sequencing (TGS) platform named Refresh-seq (restriction fragment ligation-based genome amplification and TGS). It is based on restriction endonuclease cutting and ligation strategy in which two alleles in an individual cell can be cut into equal fragments and tend to be amplified simultaneously. As a new single-cell long-read genome sequencing method, Refresh-seq features much lower allele dropout rate compared with SMOOTH-seq. Furthermore, we apply Refresh-seq to 688 sperm cells and 272 female haploid cells (secondary polar bodies and parthenogenetic oocytes) from F1 hybrid mice. We acquire high-resolution genetic map of mouse meiosis recombination at low sequencing depth and reveal the sexual dimorphism in meiotic crossovers. We also phase the structure variations (deletions and insertions) in sperm cells and female haploid cells with high precision. Refresh-seq shows great performance in screening aneuploid sperm cells and oocytes due to the low allele dropout rate and has great potential for medical applications such as preimplantation genetic diagnosis.

Host genetic variants associated with COVID-19 reconsidered in a Slovak cohort

We present the results of an association study involving hospitalized coronavirus disease 2019 (COVID-19) patients with a clinical background during the 3rd pandemic wave of COVID-19 in Slovakia. Seventeen single nucleotide variants (SNVs) in the eleven most relevant genes, according to the COVID-19 Host Genetics Initiative, were investigated. Our study confirms the validity of the influence of LZTFL1 and 2'-5'-oligoadenylate synthetase (OAS)1/OAS3 genetic variants on the severity of COVID-19. For two LZTFL1 SNVs in complete linkage disequilibrium, rs17713054 and rs73064425, the odds ratios of baseline allelic associations and logistic regressions (LR) adjusted for age and sex ranged in the four tested designs from 2.04 to 2.41 and from 2.05 to 3.98, respectively. The OAS1/OAS3 haplotype 'gttg' carrying a functional allele G of splice-acceptor variant rs10774671 manifested its protective function in the Delta pandemic wave. Significant baseline allelic associations of two DPP9 variants in all tested designs and two IFNAR2 variants in the Omicron pandemic wave were not confirmed by adjusted LR. Nevertheless, adjusted LR showed significant associations of NOTCH4 rs3131294 and TYK2 rs2304256 variants with severity of COVID-19. Hospitalized patients' reported comorbidities were not correlated with genetic variants, except for obesity, smoking (IFNAR2), and hypertension (NOTCH4). The results of our study suggest that host genetic variations have an impact on the severity and duration of acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Considering the differences in allelic associations between pandemic waves, they support the hypothesis that every new SARS-CoV-2 variant may modify the host immune response by reconfiguring involved pathways.

The Resilient Dairy Genome Project-A general overview of methods and objectives related to feed efficiency and methane emissions

The Resilient Dairy Genome Project (RDGP) is an international large-scale applied research project that aims to generate genomic tools to breed more resilient dairy cows. In this context, improving feed efficiency and reducing greenhouse gases from dairy is a high priority. The inclusion of traits related to feed efficiency (e.g., dry matter intake [DMI]) or greenhouse gases (e.g., methane emissions [CH4]) relies on available genotypes as well as high quality phenotypes. Currently, 7 countries (i.e., Australia, Canada, Denmark, Germany, Spain, Switzerland, and United States) contribute with genotypes and phenotypes including DMI and CH4. However, combining data are challenging due to differences in recording protocols, measurement technology, genotyping, and animal management across sources. In this study, we provide an overview of how the RDGP partners address these issues to advance international collaboration to generate genomic tools for resilient dairy. Specifically, we describe the current state of the RDGP database, data collection protocols in each country, and the strategies used for managing the shared data. As of February 2022, the database contains 1,289,593 DMI records from 12,687 cows and 17,403 CH4 records from 3,093 cows and continues to grow as countries upload new data over the coming years. No strong genomic differentiation between the populations was identified in this study, which may be beneficial for eventual across-country genomic predictions. Moreover, our results reinforce the need to account for the heterogeneity in the DMI and CH4 phenotypes in genomic analysis.

kalis: a modern implementation of the Li & Stephens model for local ancestry inference in R

BACKGROUND

Approximating the recent phylogeny of N phased haplotypes at a set of variants along the genome is a core problem in modern population genomics and central to performing genome-wide screens for association, selection, introgression, and other signals. The Li & Stephens (LS) model provides a simple yet powerful hidden Markov model for inferring the recent ancestry at a given variant, represented as an N × N distance matrix based on posterior decodings.

RESULTS

We provide a high-performance engine to make these posterior decodings readily accessible with minimal pre-processing via an easy to use package kalis, in the statistical programming language R. kalis enables investigators to rapidly resolve the ancestry at loci of interest and developers to build a range of variant-specific ancestral inference pipelines on top. kalis exploits both multi-core parallelism and modern CPU vector instruction sets to enable scaling to hundreds of thousands of genomes.

CONCLUSIONS

The resulting distance matrices accessible via kalis enable local ancestry, selection, and association studies in modern large scale genomic datasets.

Transcription factor encoding gene OsC1 regulates leaf sheath color through anthocyanidin metabolism in Oryza rufipogon and Oryza sativa

Carbohydrates, proteins, lipids, minerals and vitamins are nutrient substances commonly seen in rice grains, but anthocyanidin, with benefit for plant growth and animal health, exists mainly in the common wild rice but hardly in the cultivated rice. To screen the rice germplasm with high intensity of anthocyanidins and identify the variations, we used metabolomics technique and detected significant different accumulation of anthocyanidins in common wild rice (Oryza rufipogon, with purple leaf sheath) and cultivated rice (Oryza sativa, with green leaf sheath). In this study, we identified and characterized a well-known MYB transcription factor, OsC1, through phenotypic (leaf sheath color) and metabolic (metabolite profiling) genome-wide association studies (pGWAS and mGWAS) in 160 common wild rice (O. rufipogon) and 151 cultivated (O. sativa) rice varieties. Transgenic experiments demonstrated that biosynthesis and accumulation of cyanidin-3-Galc, cyanidin 3-O-rutinoside and cyanidin O-syringic acid, as well as purple pigmentation in leaf sheath were regulated by OsC1. A total of 25 sequence variations of OsC1 constructed 16 functional haplotypes (higher accumulation of the three anthocyanidin types within purple leaf sheath) and 9 non-functional haplotypes (less accumulation of anthocyanidins within green leaf sheath). Three haplotypes of OsC1 were newly identified in our germplasm, which have potential values in functional genomics and molecular breeding of rice. Gene-to-metabolite analysis by mGWAS and pGWAS provides a useful and efficient tool for functional gene identification and omics-based crop genetic improvement.