Increasing calling accuracy, coverage, and read-depth in sequence data by the use of haplotype blocks

High-throughput genotyping of large numbers of lines remains a key challenge in plant genetics, requiring geneticists and breeders to find a balance between data quality and the number of genotyped lines under a variety of different existing genotyping technologies when resources are limited. In this work, we are proposing a new imputation pipeline (“HBimpute”) that can be used to generate high-quality genomic data from low read-depth whole-genome-sequence data. The key idea of the pipeline is the use of haplotype blocks from the software HaploBlocker to identify locally similar lines and subsequently use the reads of all locally similar lines in the variant calling for a specific line. The effectiveness of the pipeline is showcased on a dataset of 321 doubled haploid lines of a European maize landrace, which were sequenced at 0.5X read-depth. The overall imputing error rates are cut in half compared to state-of-the-art software like BEAGLE and STITCH, while the average read-depth is increased to 83X, thus enabling the calling of copy number variation. The usefulness of the obtained imputed data panel is further evaluated by comparing the performance of sequence data in common breeding applications to that of genomic data generated with a genotyping array. For both genome-wide association studies and genomic prediction, results are on par or even slightly better than results obtained with high-density array data (600k). In particular for genomic prediction, we observe slightly higher data quality for the sequence data compared to the 600k array in the form of higher prediction accuracies. This occurred specifically when reducing the data panel to the set of overlapping markers between sequence and array, indicating that sequencing data can benefit from the same marker ascertainment as used in the array process to increase the quality and usability of genomic data.

Genome-wide association of single nucleotide polymorphism loci and candidate genes for frogeye leaf spot (Cercospora sojina) resistance in soybean

Background Frogeye leaf spot (FLS) is a destructive fungal disease that affects soybean production. The most economical and effective strategy to control FLS is the use of resistant cultivars. However, the use of a limited number of resistant loci in FLS management will be countered by the emergence of new high-virulence Cercospora sojina races. Therefore, we identified quantitative trait loci (QTL) that control resistance to FLS and identified novel resistant genes using a genome-wide association study (GWAS) on 234 Chinese soybean cultivars. Results A total of 30,890 single nucleotide polymorphism (SNP) markers were used to estimate linkage disequilibrium (LD) and population structure. The GWAS results showed four loci (p < 0.0001) distributed over chromosomes (Chr.) 5 and 20, that are significantly associated with FLS resistance. No previous studies have reported resistance loci in these regions. Subsequently, 45 genes in the two resistance-related haplotype blocks were annotated. Among them, Glyma20g31630 encoding pyruvate dehydrogenase (PDH), Glyma05g28980, which encodes mitogen-activated protein kinase 7 (MPK7), and Glyma20g31510, Glyma20g31520 encoding calcium-dependent protein kinase 4 (CDPK4) in the haplotype blocks deserves special attention. Conclusions This study showed that GWAS can be employed as an effective strategy for identifying disease resistance traits in soybean and narrowing SNPs and candidate genes. The prediction of candidate genes in the haplotype blocks identified by disease resistance loci can provide a useful reference to study systemic disease resistance.

Causal Haplotype Block Identification in Plant Genome-Wide Association Studies

Genome wide association studies (GWAS) can play an essential role in understanding genetic basis of complex traits in plants and animals. Conventional SNP-based linear mixed models (LMM) used in many GWAS that marginally test single nucleotide polymorphisms (SNPs) have successfully identified many loci with major and minor effects. In plants, the relatively small population size in GWAS and the high genetic diversity found many plant species can impede mapping efforts on complex traits. Here we present a novel haplotype-based trait fine-mapping framework, HapFM, to supplement current GWAS methods. HapFM uses genotype data to partition the genome into haplotype blocks, identifies haplotype clusters within each block, and then performs genome-wide haplotype fine-mapping to infer the causal haplotype blocks of trait. We benchmarked HapFM, GEMMA, BSLMM, and GMMAT in both simulation and real plant GWAS datasets. HapFM consistently resulted in higher mapping power than the other GWAS methods in simulations with high polygenicity. Moreover, it resulted in higher mapping resolution, especially in regions of high LD, by identifying small causal blocks in the larger haplotype block. In the Arabidopsis flowering time (FT10) datasets, HapFM identified four novel loci compared to GEMMA results, and its average mapping interval of HapFM was 9.6 times smaller than that of GEMMA. In conclusion, HapFM is tailored for plant GWAS to result in high mapping power on complex traits and improved mapping resolution to facilitate crop improvement.

Polymorphisms of the FCN2 Gene 3’UTR Region and Their Clinical Associations in Preterm Newborns

Ficolin-2 is regarded as an important innate immunity factor endowed with both lectin (carbohydrate recognition) qualities and ability to induce complement activation. The aim of this study was to investigate the association of the FCN2 3’-untranslated region (3’UTR) polymorphisms with ficolin-2 expression and perinatal complications in preterm neonates. The sequencing analysis allowed us to identify six 3’UTR polymorphisms with minor allele frequency (MAF) &gt;1%: rs4521835, rs73664188, rs11103564, rs11103565, rs6537958 and rs6537959. Except for rs4521835, all adhered to Hardy-Weinberg expectations. Moreover, rs6537958 and rs6537959 were shown to be in perfect linkage disequilibrium (LD) with nine other genetic polymorphisms: rs7040372, rs7046516, rs747422, rs7847431, rs6537957, rs6537960, rs6537962, rs11462298 and rs7860507 together stretched on a distance of 1242 bp and very high LD with rs11103565. The 3’UTR region was shown to bind nuclear extract proteins. The polymorphisms at rs4521835 and rs73664188 were found to influence serum ficolin-2 concentration significantly. All polymorphisms identified create (together with exon 8 polymorphism, rs7851696) two haplotype blocks. Among 49 diplotypes (D1-D49) created from rs7851696 (G&gt;T), rs4521835 (T&gt;G), rs73664188 (T&gt;C), rs11103564 (T&gt;C), rs11103565 (G&gt;A) and rs6537959 (T&gt;A), twenty two occurred with frequency &gt;1%. Two diplotypes: D13 (GTTTGT/GGTCGT) and D10 (GTTTGT/GGTCGA), were significantly more frequent among preterm neonates with early onset of infection and pneumonia, compared with newborns with no infectious complications (OR 2.69 and 2.81, respectively; both p&lt;0.05). The minor (C) allele at rs73664188 was associated with an increased risk of very low (≤1500 g) birthweight (OR=1.95, p=0.042) but was associated with the opposite effect at rs11103564 (OR=0.11, p=0.005).

PSXIV-26 Selection footprints in Russian red cattle identified by linkage disequilibrium blocks based on SNP data

Among the variety of cattle breeds in Russia, the Russian red dual-purpose cattle breeds have great importance because of their ability to produce high milk yields as well as to provide excellent milk quality. The low census size of the Russian red cattle breeds requires development of programs for conservation of their biodiversity. Our study aimed to investigate selection footprints in Russian red cattle breeds, using high values of linkage disequilibrium (LD) in SNP haplotype blocks as indicators. For finding such LD blocks, we used the genotypes (≈35K SNPs) of Red Gorbatov (RGB, n = 26), Bestuzhev (BST,n = 27), and Suksun (SKS,n = 17) breeds, as well as Red Holstein (RH,n = 16) as an outgroup. Quality control and LD calculations for different distances were performed in Plink 1.90. Top 0.01% SNP pairs by LD value (0.9≤r2&lt; 1.0) were selected for further analysis. The effective population size derived from LD patterns was estimated using SNeP tool. Comparison of LD values for 70 kb interval between breeds and chromosomes by MANOVA pairwise testing significantly distinguished RH/RGB and BST/SKS breeds (P &lt; 0.05-0.001). LD values among chromosomes were 0.195–0.287 for RH, 0.194–0.272 for RGB, 0.172–0.237 for BST, and 0.157–0.217 for SKS. The SKS and BST breeds had higher Ne values (84 and 113, respectively) compared to RH (63) and RGB (79). Selection footprints by LD blocks in Russian red cattle genome covered several relevant genes on BTA1 (EPHA6,DGKG), BTA2 (LRP1B,THSD7B,STAT1), BTA5 (CPM,BAIAP2L2), BTA9 (TRDN,UTRN), BTA10 (KCNN2,CAPN3), BTA11 (SH3RF3,RABGAP1,RALGPS1), BTA14 (ZNF16,ARHGAP39,TOX,DGAT1), and BTA19 (MYH10), BTA22 (FHIT). Detected genes were found to be responsible for milk fat and protein contents, fatty acid composition, somatic cells score, fertility, feet and legs, and udder conformation traits. Our results can be useful for developing the breeding and conservation programs of the Russian red cattle genetic resources. The study was funded by RFBR within project No. 20-516-00020

Haplotype genomic prediction of phenotypic values based on chromosome distance and gene boundaries using low-coverage sequencing in Duroc pigs

Background Genomic selection using single nucleotide polymorphism (SNP) markers has been widely used for genetic improvement of livestock, but most current methods of genomic selection are based on SNP models. In this study, we investigated the prediction accuracies of haplotype models based on fixed chromosome distances and gene boundaries compared to those of SNP models for genomic prediction of phenotypic values. We also examined the reasons for the successes and failures of haplotype genomic prediction. Methods We analyzed a swine population of 3195 Duroc boars with records on eight traits: body judging score (BJS), teat number (TN), age (AGW), loin muscle area (LMA), loin muscle depth (LMD) and back fat thickness (BF) at 100 kg live weight, and average daily gain (ADG) and feed conversion rate (FCR) from 30 to100 kg live weight. Ten-fold validation was used to evaluate the prediction accuracy of each SNP model and each multi-allelic haplotype model based on 488,124 autosomal SNPs from low-coverage sequencing. Haplotype blocks were defined using fixed chromosome distances or gene boundaries. Results Compared to the best SNP model, the accuracy of predicting phenotypic values using a haplotype model was greater by 7.4% for BJS, 7.1% for AGW, 6.6% for ADG, 4.9% for FCR, 2.7% for LMA, 1.9% for LMD, 1.4% for BF, and 0.3% for TN. The use of gene-based haplotype blocks resulted in the best prediction accuracy for LMA, LMD, and TN. Compared to estimates of SNP additive heritability, estimates of haplotype epistasis heritability were strongly correlated with the increase in prediction accuracy by haplotype models. The increase in prediction accuracy was largest for BJS, AGW, ADG, and FCR, which also had the largest estimates of haplotype epistasis heritability, 24.4% for BJS, 14.3% for AGW, 14.5% for ADG, and 17.7% for FCR. SNP and haplotype heritability profiles across the genome identified several genes with large genetic contributions to phenotypes: NUDT3 for LMA, LMD and BF, VRTN for TN, COL5A2 for BJS, BSND for ADG, and CARTPT for FCR. Conclusions Haplotype prediction models improved the accuracy for genomic prediction of phenotypes in Duroc pigs. For some traits, the best prediction accuracy was obtained with haplotypes defined using gene regions, which provides evidence that functional genomic information can improve the accuracy of haplotype genomic prediction for certain traits.

Complex population structure and haplotype patterns in Western Europe honey bee from sequencing a large panel of haploid drones

Honey bee subspecies originate from specific geographic areas in Africa, Europe and the Middle East. The interest of beekeepers in specific phenotypes has led them to import subspecies to regions outside of their original range. The resulting admixture complicates population genetics analyses and populations stratification can be a major problem for association studies. As a typical example, the case of the French population is studied here. We sequenced 870 haploid drones for SNP detection and identified nine genetic backgrounds in 629 samples. Five correspond to subspecies, two to isolated populations and two to human-mediated population management. We also highlight several large haplotype blocks, some of which coinciding with the position of centromeres. The largest is 3.6 Mb long on chromosome 11, representing 1.6 % of the genome and has two major haplotypes, corresponding to the two dominant genetic background identified.

Darwin's finches - an adaptive radiation constructed from ancestral genetic modules

Recent adaptive radiations are models for investigating mechanisms contributing to the evolution of biodiversity. An unresolved question is the relative importance of new mutations, ancestral variants, and introgressive hybridization for phenotypic evolution and speciation. Here we address this issue using Darwin's finches, which vary in size from an 8g warbler finch with a pointed beak to a 40g large ground finch with a massive blunt beak. We present a highly contiguous genome assembly for one of the species and investigate the genomic architecture underlying phenotypic diversity in the entire radiation. Admixture mapping for beak and body size in the small, medium and large ground finches revealed 28 loci showing strong genetic differentiation. These loci represent ancestral haplotype blocks with origins as old as the Darwin's finch phylogeny (1-2 million years). Genes expressed in the developing beak are overrepresented in these genomic regions. Frequencies of allelic variants at the 28 loci covary with phenotypic similarities in body and beak size across the Darwin's finch phylogeny. These ancestral haplotypes constitute genetic modules for selection, and act as key determinants of the exceptional phenotypic diversity of Darwin's finches. Such ancestral haplotype blocks can be critical for how species adapt to environmental variability and change.

Population validation of reproductive gene mutation loci and association with the litter size in Nubian goat

Litter size is an important component trait of doe reproduction. By improving it, production efficiency and economic benefits can be significantly provided. Genetic marker-assisted selection (MAS) based on proven molecular indicators could enhance the efficacy of goat selection, as well as litter size trait. Many molecular markers have been identified that they can be used to improve litter size in different goat breeds. However, the presence and value of these markers vary among goat breeds. In the present study, we used the reported loci on other breeds of goat as candidate loci to detect whether these loci appear in this Nubian goat population; then we proceed to genotype and detect surrounding loci (50 bp) by multiplex PCR and sequencing technology. As a result, 69 mutations (59 SNPs and 10 indels) were screened out from 23 candidate genes in Nubian goat population, 12 loci were significantly associated with the litter size of first-parity individuals; 5 loci were significantly associated with the litter size of second-parity individuals; 3 loci were significantly associated with the litter size of third-parity individuals. In addition, five loci were significantly associated with the average litter size. The additive effect value of KITLG: g.18047318 G>A in first parity, KITLG: g.18152042G>A in third parity, KISS-1: g.1341674 C>G in first parity, and GHR: g.32134187G>A in second parity exceed more than 0.40, and the preponderant alleles are G, C, A and G, respectively. Further, linkage disequilibrium analysis of 21 mutation loci shows that 3 haplotype blocks are formed, and the litter size of combination type AACC in KISS-1 gene and AAGG in KITLG gene are significantly lower than that of other combinations genotype in first parity (P<0.05). These findings can provide effective candidate DNA markers for selecting superior individuals in Nubian goat breeding.