Genotyping by low-coverage whole-genome sequencing in intercross pedigrees from outbred founders: a cost-efficient approach

Complex genetic architecture of the chicken Growth1 QTL region

The genetic complexity of polygenic traits represents a captivating and intricate facet of biological inheritance. Unlike Mendelian traits controlled by a single gene, polygenic traits are influenced by multiple genetic loci, each exerting a modest effect on the trait. This cumulative impact of numerous genes, interactions among them, environmental factors, and epigenetic modifications results in a multifaceted architecture of genetic contributions to complex traits. Given the well-characterized genome, diverse traits, and range of genetic resources, chicken (Gallus gallus) was employed as a model organism to dissect the intricate genetic makeup of a previously identified major Quantitative Trait Loci (QTL) for body weight on chromosome 1. A multigenerational advanced intercross line (AIL) of 3215 chickens whose genomes had been sequenced to an average of 0.4x was analyzed using genome-wide association study (GWAS) and variance-heterogeneity GWAS (vGWAS) to identify markers associated with 8-week body weight. Additionally, epistatic interactions were studied using the natural and orthogonal interaction (NOIA) model. Six genetic modules, two from GWAS and four from vGWAS, were strongly associated with the studied trait. We found evidence of both additive- and non-additive interactions between these modules and constructed a putative local epistasis network for the region. Our screens for functional alleles revealed a missense variant in the gene ribonuclease H2 subunit B (RNASEH2B), which has previously been associated with growth-related traits in chickens and Darwin's finches. In addition, one of the most strongly associated SNPs identified is located in a non-coding region upstream of the long non-coding RNA, ENSGALG00000053256, previously suggested as a candidate gene for regulating chicken body weight. By studying large numbers of individuals from a family material using approaches to capture both additive and non-additive effects, this study advances our understanding of genetic complexities in a highly polygenic trait and has practical implications for poultry breeding and agriculture.

Genome-wide scans for selection signatures in indigenous chickens reveal candidate genes associated with local adaptation

Population growth and climate change pose challenges to the sustainability of poultry farming. The emphasis on high-yield traits in commercialized breeds has led to a decline in their adaptability. Chicken varieties adapted to the local environment, possessing traits that facilitate adaptation to climate change, such as disease resistance and tolerance to extreme weather conditions, can improve hybridization outcomes. In this study, we conducted an analysis of the population structure and genetic diversity of 110 chickens representing indigenous breeds from southern China and two different commercial breeds. Further, we performed comparative population genomics, utilizing nucleotide diversity and fixation statistics, to characterize genomic features of natural selection and to identify unique genetic traits and potential selection markers developed by indigenous breeds after adapting to the local environment. Results based on genetic diversity and population structure analyses showed that indigenous varieties exhibited high levels of genetic diversity. Commercial breeds that have been indigenously bred demonstrated higher levels of genetic diversity than those that have not, and breeds with different selection practices displayed significant differences in genetic structure. Additionally, we further searched for potential genomic regions in native chicken ecotypes, uncovering several candidate genes related to ecological adaptations affecting local breeds, such as IKBKB, S1PR1, TSHR, IL1RAPL1 and AMY2A, which are involved in disease resistance, heat tolerance, immune regulation and behavioral traits. This work provides important insights into the genomic characterization of ecotypes of native chicken in southern China. The identification of candidate genes associated with traits such as disease resistance, heat tolerance, immunomodulation, and behavioral traits is a significant outcome. These candidate genes may contribute to the understanding of the molecular basis of the adaptation of the southern native chicken to the local environment. It is recommended that these genes be integrated into chicken breeding programs to enhance sustainable agriculture and promote effective conservation and utilization strategies.

Genome-Wide Re-Sequencing Data Reveals the Population Structure and Selection Signatures of Tunchang Pigs in China

Tunchang pig is one population of Hainan pig in the Hainan Province of China, with the characteristics of delicious meat, strong adaptability, and high resistance to diseases. To explore the genetic diversity and population structure of Tunchang pigs and uncover their germplasm characteristics, 10 unrelated Tunchang pigs were re-sequenced using the Illumina NovaSeq 150 bp paired-end platform with an average depth of 10×. Sequencing data from 36 individuals of 7 other pig breeds (including 4 local Chinese pig breeds (5 Jinhua, 5 Meishan, 5 Rongchang, and 6 Wuzhishan), and 3 commonly used commercial pig breeds (5 Duorc, 5 Landrace, and 5 Large White)) were downloaded from the NCBI public database. After analysis of genetic diversity and population structure, it has been found that compared to commercial pigs, Tunchang pigs have higher genetic diversity and are genetically close to native Chinese breeds. Three methods, FST, θπ, and XP-EHH, were used to detect selection signals for three breeds of pigs: Tunchang, Duroc, and Landrace. A total of 2117 significantly selected regions and 201 candidate genes were screened. Gene enrichment analysis showed that candidate genes were mainly associated with good adaptability, disease resistance, and lipid metabolism traits. Finally, further screening was conducted to identify potential candidate genes related to phenotypic traits, including meat quality (SELENOV, CBR4, TNNT1, TNNT3, VPS13A, PLD3, SRFBP1, and SSPN), immune regulation (CD48, FBL, PTPRH, GNA14, LOX, SLAMF6, CALCOCO1, IRGC, and ZNF667), growth and development (SYT5, PRX, PPP1R12C, and SMG9), reproduction (LGALS13 and EPG5), vision (SLC9A8 and KCNV2), energy metabolism (ATP5G2), cell migration (EPS8L1), and olfaction (GRK3). In summary, our research results provide a genomic overview of the genetic variation, genetic diversity, and population structure of the Tunchang pig population, which will be valuable for breeding and conservation of Tunchang pigs in the future.

CRISPR-Cas9-based repeat depletion for high-throughput genotyping of complex plant genomes

High-throughput genotyping enables the large-scale analysis of genetic diversity in population genomics and genome-wide association studies that combine the genotypic and phenotypic characterization of large collections of accessions. Sequencing-based approaches for genotyping are progressively replacing traditional genotyping methods because of the lower ascertainment bias. However, genome-wide genotyping based on sequencing becomes expensive in species with large genomes and a high proportion of repetitive DNA. Here we describe the use of CRISPR-Cas9 technology to deplete repetitive elements in the 3.76-Gb genome of lentil (Lens culinaris), 84% consisting of repeats, thus concentrating the sequencing data on coding and regulatory regions (single-copy regions). We designed a custom set of 566,766 gRNAs targeting 2.9 Gbp of repeats and excluding repetitive regions overlapping annotated genes and putative regulatory elements based on ATAC-seq data. The novel depletion method removed ∼40% of reads mapping to repeats, increasing those mapping to single-copy regions by ∼2.6-fold. When analyzing 25 million fragments, this repeat-to-single-copy shift in the sequencing data increased the number of genotyped bases of ∼10-fold compared to nondepleted libraries. In the same condition, we were also able to identify ∼12-fold more genetic variants in the single-copy regions and increased the genotyping accuracy by rescuing thousands of heterozygous variants that otherwise would be missed because of low coverage. The method performed similarly regardless of the multiplexing level, type of library or genotypes, including different cultivars and a closely related species (L. orientalis). Our results showed that CRISPR-Cas9-driven repeat depletion focuses sequencing data on single-copy regions, thus improving high-density and genome-wide genotyping in large and repetitive genomes.

Haplotype-aware pantranscriptome analyses using spliced pangenome graphs

Pangenomics is emerging as a powerful computational paradigm in bioinformatics. This field uses population-level genome reference structures, typically consisting of a sequence graph, to mitigate reference bias and facilitate analyses that were challenging with previous reference-based methods. In this work, we extend these methods into transcriptomics to analyze sequencing data using the pantranscriptome: a population-level transcriptomic reference. Our toolchain, which consists of additions to the VG toolkit and a standalone tool, RPVG, can construct spliced pangenome graphs, map RNA sequencing data to these graphs, and perform haplotype-aware expression quantification of transcripts in a pantranscriptome. We show that this workflow improves accuracy over state-of-the-art RNA sequencing mapping methods, and that it can efficiently quantify haplotype-specific transcript expression without needing to characterize the haplotypes of a sample beforehand.

Low-coverage sequencing in a deep intercross of the Virginia body weight lines provides insight to the polygenic genetic architecture of growth: novel loci revealed by increased power and improved genome-coverage

Genetic dissection of highly polygenic traits is a challenge, in part due to the power necessary to confidently identify loci with minor effects. Experimental crosses are valuable resources for mapping such traits. Traditionally, genome-wide analyses of experimental crosses have targeted major loci using data from a single generation (often the F₂) with individuals from later generations being generated for replication and fine-mapping. Here, we aim to confidently identify minor-effect loci contributing to the highly polygenic basis of the long-term, bi-directional selection responses for 56-d body weight in the Virginia body weight chicken lines. To achieve this, a strategy was developed to make use of data from all generations (F₂-F₁₈) of the advanced intercross line, developed by crossing the low and high selected lines after 40 generations of selection. A cost-efficient low-coverage sequencing based approach was used to obtain high-confidence genotypes in 1Mb bins across 99.3% of the chicken genome for >3,300 intercross individuals. In total, 12 genome-wide significant, and 30 additional suggestive QTL reaching a 10% FDR threshold, were mapped for 56-d body weight. Only 2 of these QTL reached genome-wide significance in earlier analyses of the F₂ generation. The minor-effect QTL mapped here were generally due to an overall increase in power by integrating data across generations, with contributions from increased genome-coverage and improved marker information content. The 12 significant QTL explain >37% of the difference between the parental lines, three times more than 2 previously reported significant QTL. The 42 significant and suggestive QTL together explain >80%. Making integrated use of all available samples from multiple generations in experimental crosses are economically feasible using the low-cost, sequencing-based genotyping strategies outlined here. Our empirical results illustrate the value of this strategy for mapping novel minor-effect loci contributing to complex traits to provide a more confident, comprehensive view of the individual loci that form the genetic basis of the highly polygenic, long-term selection responses for 56-d body weight in the Virginia body weight chicken lines.

Aquaculture Molecular Breeding Platform (AMBP): a comprehensive web server for genotype imputation and genetic analysis in aquaculture

It is of vital importance to understand the population structure, dissect the genetic bases of performance traits, and make proper strategies for selection in breeding programs. However, there is no single webserver covering the specific needs in aquaculture. We present Aquaculture Molecular Breeding Platform (AMBP), the first web server for genetic data analysis in aquatic species of farming interest. AMBP integrates the haplotype reference panels of 18 aquaculture species, which greatly improves the accuracy of genotype imputation. It also supports multiple tools to infer genetic structures, dissect the genetic architecture of performance traits, estimate breeding values, and predict optimum contribution. All the tools are coherently linked in a web-interface for users to generate interpretable results and evaluate statistical appropriateness. The webserver supports standard VCF and PLINK (PED, MAP) files, and implements automated pipelines for format transformation and visualization to simplify the process of analysis. As a demonstration, we applied the webserver to Pacific white shrimp and Atlantic salmon datasets. In summary, AMBP constitutes comprehensive resources and analytical tools for exploring genetic data and guiding practical breeding programs. AMBP is available at http://mgb.qnlm.ac.

The construction of a haplotype reference panel using extremely low coverage whole genome sequences and its application in genome-wide association studies and genomic prediction in Duroc pigs

Extremely low coverage whole genome sequencing (lcWGS) is an economical technique to obtain high-density single nucleotide polymorphisms (SNPs). Here, we explored the feasibility of constructing a haplotype reference panel (lcHRP) using lcWGS and evaluated the effects of lcHRP through a genome-wide association study (GWAS) and genomic prediction in pigs. A total of 297 and 974 Duroc pigs were genotyped using lcWGS and a 50 K SNP array, respectively. We obtained 19,306,498 SNPs using lcWGS with an accuracy of 0.984. With the help of lcHRP, the accuracy of imputation from the SNP array to lcWGS was 0.922. Compared to the SNP array findings, those from the imputation-based GWAS identified more signals across four traits. With the integration of the top 1% imputation-based GWAS findings as genomic features, the accuracies of genomic prediction was improved by 6.0% to 13.2%. This study showed the great potential of lcWGS in pigs' molecular breeding.

A bioinformatic platform to integrate target capture and whole genome sequences of various read depths for phylogenomics

The increasing availability of short‐read whole genome sequencing (WGS) provides unprecedented opportunities to study ecological and evolutionary processes. Although loci of interest can be extracted from WGS data and combined with target sequence data, this requires suitable bioinformatic workflows. Here, we test different assembly and locus extraction strategies and implement them into secapr, a pipeline that processes short‐read data into multilocus alignments for phylogenetics and molecular ecology analyses. We integrate the processing of data from low‐coverage WGS (<30×) and target sequence capture into a flexible framework, while optimizing de novo contig assembly and loci extraction. Specifically, we test different assembly strategies by contrasting their ability to recover loci from targeted butterfly protein‐coding genes, using four data sets: a WGS data set across different average coverages (10×, 5× and 2×) and a data set for which these loci were enriched prior to sequencing via target sequence capture. Using the resulting de novo contigs, we account for potential errors within contigs and infer phylogenetic trees to evaluate the ability of each assembly strategy to recover species relationships. We demonstrate that choosing multiple sizes of kmer simultaneously for assembly results in the highest yield of extracted loci from de novo assembled contigs, while data sets derived from sequencing read depths as low as 5× recovers the expected species relationships in phylogenetic trees. By making the tested assembly approaches available in the secapr pipeline, we hope to inspire future studies to incorporate complementary data and make an informed choice on the optimal assembly strategy.

Accelerated deciphering of the genetic architecture of agricultural economic traits in pigs using a low-coverage whole-genome sequencing strategy

BACKGROUND

Uncovering the genetic architecture of economic traits in pigs is important for agricultural breeding. However, high-density haplotype reference panels are unavailable in most agricultural species, limiting accurate genotype imputation in large populations. Moreover, the infinitesimal model of quantitative traits implies that weak association signals tend to be spread across most of the genome, further complicating the genetic analysis. Hence, there is a need to develop new methods for sequencing large cohorts without large reference panels.

RESULTS

We describe a Tn5-based highly accurate, cost- and time-efficient, low-coverage sequencing method to obtain 11.3 million whole-genome single-nucleotide polymorphisms in 2,869 Duroc boars at a mean depth of 0.73×. On the basis of these single-nucleotide polymorphisms, a genome-wide association study was performed, resulting in 14 quantitative trait loci (QTLs) for 7 of 21 important agricultural traits in pigs. These QTLs harbour genes, such as ABCD4 for total teat number and HMGA1 for back fat thickness, and provided a starting point for further investigation. The inheritance models of the different traits varied greatly. Most follow the minor-polygene model, but this can be attributed to different reasons, such as the shaping of genetic architecture by artificial selection for this population and sufficiently interconnected minor gene regulatory networks.

CONCLUSIONS

Genome-wide association study results for 21 important agricultural traits identified 14 QTLs/genes and showed their genetic architectures, providing guidance for genetic improvement harnessing genomic features. The Tn5-based low-coverage sequencing method can be applied to large-scale genome studies for any species without a good reference panel and can be used for agricultural breeding.

Characterization of a haplotype-reference panel for genotyping by low-pass sequencing in Swiss Large White pigs

BACKGROUND

The key-ancestor approach has been frequently applied to prioritize individuals for whole-genome sequencing based on their marginal genetic contribution to current populations. Using this approach, we selected 70 key ancestors from two lines of the Swiss Large White breed that have been selected divergently for fertility and fattening traits and sequenced their genomes with short paired-end reads.

RESULTS

Using pedigree records, we estimated the effective population size of the dam and sire line to 72 and 44, respectively. In order to assess sequence variation in both lines, we sequenced the genomes of 70 boars at an average coverage of 16.69-fold. The boars explained 87.95 and 95.35% of the genetic diversity of the breeding populations of the dam and sire line, respectively. Reference-guided variant discovery using the GATK revealed 26,862,369 polymorphic sites. Principal component, admixture and fixation index (F_ST) analyses indicated considerable genetic differentiation between the lines. Genomic inbreeding quantified using runs of homozygosity was higher in the sire than dam line (0.28 vs 0.26). Using two complementary approaches, we detected 51 signatures of selection. However, only six signatures of selection overlapped between both lines. We used the sequenced haplotypes of the 70 key ancestors as a reference panel to call 22,618,811 genotypes in 175 pigs that had been sequenced at very low coverage (1.11-fold) using the GLIMPSE software. The genotype concordance, non-reference sensitivity and non-reference discrepancy between thus inferred and Illumina PorcineSNP60 BeadChip-called genotypes was 97.60, 98.73 and 3.24%, respectively. The low-pass sequencing-derived genomic relationship coefficients were highly correlated (r > 0.99) with those obtained from microarray genotyping.

CONCLUSIONS

We assessed genetic diversity within and between two lines of the Swiss Large White pig breed. Our analyses revealed considerable differentiation, even though the split into two populations occurred only few generations ago. The sequenced haplotypes of the key ancestor animals enabled us to implement genotyping by low-pass sequencing which offers an intriguing cost-effective approach to increase the variant density over current array-based genotyping by more than 350-fold.

Multiparental Population in Crops: Methods of Development and Dissection of Genetic Traits

Multiparental populations are located midway between association mapping that relies on germplasm collections and classic linkage analysis, based upon biparental populations. They provide several key advantages such as the possibility to include a higher number of alleles and increased level of recombination with respect to biparental populations, and more equilibrated allelic frequencies than association mapping panels. Moreover, in these populations new allele's combinations arise from recombination that may reveal transgressive phenotypes and make them a useful pre-breeding material. Here we describe the strategies for working with multiparental populations, focusing on nested association mapping populations (NAM) and multiparent advanced generation intercross populations (MAGIC). We provide details from the selection of founders, population development, and characterization to the statistical methods for genetic mapping and quantitative trait detection.

Genetic Dissection of Growth Traits in a Unique Chicken Advanced Intercross Line

The advanced intercross line (AIL) that is created by successive generations of pseudo-random mating after the F₂ generation is a valuable resource, especially in agricultural livestock and poultry species, because it improves the precision of quantitative trait loci (QTL) mapping compared with traditional association populations by introducing more recombination events. The growth traits of broilers have significant economic value in the chicken industry, and many QTLs affecting growth traits have been identified, especially on chromosomes 1, 4, and 27, albeit with large confidence intervals that potentially contain dozens of genes. To promote a better understanding of the underlying genetic architecture of growth trait differences, specifically body weight and bone development, in this study, we report a nine-generation AIL derived from two divergent outbred lines: High Quality chicken Line A (HQLA) and Huiyang Bearded (HB) chicken. We evaluate the genetic architecture of the F₀, F₂, F₈, and F₉ generations of AIL and demonstrate that the population of the F₉ generation sufficiently randomized the founder genomes and has the characteristics of rapid linkage disequilibrium decay, limited allele frequency decline, and abundant nucleotide diversity. This AIL yielded a much narrower QTL than the F₂ generations, especially the QTL on chromosome 27, which was reduced to 120 Kb. An ancestral haplotype association analysis showed that most of the dominant haplotypes are inherited from HQLA but with fluctuation of the effects between them. We highlight the important role of four candidate genes (PHOSPHO1, IGF2BP1, ZNF652, and GIP) in bone growth. We also retrieved a missing QTL from AIL on chromosome 4 by identifying the founder selection signatures, which are explained by the loss of association power that results from rare alleles. Our study provides a reasonable resource for detecting quantitative trait genes and tracking ancestor history and will facilitate our understanding of the genetic mechanisms underlying chicken bone growth.

Haplotype Purging After Relaxation of Selection in Lines of Chickens that Had Undergone Long-Term Selection for High and Low Body Weight

Bi-directional selection for increased and decreased 56-day body weights (BW56) has been applied to two lines of White Plymouth Rock chickens—the Virginia high (HWS) and low (LWS) body weight lines. Correlated responses have been observed, including negative effects on traits related to fitness. Here, we use high and low body weight as proxies for fitness. On a genome-wide level, relaxed lines (HWR, LWR) bred from HWS and LWS purged some genetic variants in the selected lines. Whole-genome re-sequencing was here used to identify individual loci where alleles that accumulated during directional selection were purged when selection was relaxed. In total, 11 loci with significant purging signals were identified, five in the low (LW) and six in the high (HW) body weight lineages. Associations between purged haplotypes in these loci and BW56 were tested in an advanced intercross line (AIL). Two loci with purging signals and haplotype associations to BW56 are particularly interesting for further functional characterization, one locus on chromosome 6 in the LW covering the sour-taste receptor gene PKD2L1, a functional candidate gene for the decreased appetite observed in the LWS and a locus on chromosome 20 in the HW containing a skeletal muscle hypertrophy gene, DNTTIP1.