Single Genome-wide Association Studies Research Articles

Leaf Na+ effects and multi-trait GWAS point to salt exclusion as the key mechanism for reproductive stage salinity tolerance in rice.

Since salinity stress may occur across stages of rice (Oryza sativa L.) crop growth, understanding the effects of salinity at reproductive stage is important although it has been much less studied than at seedling stage. In this study, lines from the Rice Diversity Panel 1 (RDP1) and the 3000 Rice Genomes (3KRG) were used to screen morphological and physiological traits, map loci controlling salinity tolerance through genome-wide association studies (GWAS), and identify favorable haplotypes associated with reproductive stage salinity tolerance. Salt exclusion was identified as the key tolerance mechanism in this study based on reduced panicle length as flag leaf Na+ increased, and a lack of effect of trimming the leaves in the salinity treatment on genotypic rankings. Since larger biomass showed a negative effect on the number of filled grains in multiple experiments, future studies should investigate the effect of whole-plant transpiration levels on salt uptake. In addition to genome-wide significant peaks identified in the single trait GWAS analyses, six loci showed colocations for multiple traits across experiments. Among these co-locating loci, three candidate loci that exhibited favorable haplotypes were also characterized to be involved in co-expression networks among which apoplast and cell wall functions had been annotated - further highlighting the role of salt exclusion. The loci identified here could be considered as potential sources for improving reproductive stage salinity tolerance in rice.

Sc2GWAS: a comprehensive platform linking single cell and GWAS traits of human.

Identifying cell populations associated with risk variants is essential for uncovering cell-specific mechanisms that drive disease development and progression. Integrating genome-wide association studies (GWAS) with single-cell RNA sequencing (scRNA-seq) has become an effective strategy for detecting trait-cell relationships. The accumulation of trait-related single cell data has led to an urgent need for its comprehensively processing. To address this, we developed sc2GWAS (https://bio.liclab.net/sc2GWAS/), which aims to document large-scale GWAS trait-cell regulatory pairs at single-cell resolution and provide comprehensive annotations and enrichment analyses for these related pairs. The current version of sc2GWAS curates a total of 15078310 candidate trait-cell pairs from>6300000 individual cells, offering a valuable resource for exploring complex regulatory relationships between traits and cells. We applied strict quality control measures on both scRNA-seq data and GWAS data, ensuring the reliability and accuracy of the datasets for the identification of trait-relevant cells and genes. In addition, sc2GWAS provides ranked lists of trait-relevant genes and extensive (epi) genetic annotations, making it a valuable resource for downstream analyses. We demonstrate the utility of the platform by investigating Alzheimer's disease, where we identified significant associations between the disease and microglial cells, with the APOE gene emerging as particularly significant. This platform facilitates detailed research into complex trait-celland trait-gene interactions, we anticipate that sc2GWAS will become a comprehensive and valuable platform for exploring GWAS trait-cell regulatory mechanisms.

Evaluating methods for integrating single-cell data and genetics to understand inflammatory disease complexity.

Understanding genetic underpinnings of immune-mediated inflammatory diseases is crucial to improve treatments. Single-cell RNA sequencing (scRNA-seq) identifies cell states expanded in disease, but often overlooks genetic causality due to cost and small genotyping cohorts. Conversely, large genome-wide association studies (GWAS) are commonly accessible. We present a 3-step robust benchmarking analysis of integrating GWAS and scRNA-seq to identify genetically relevant cell states and genes in inflammatory diseases. First, we applied and compared the results of three recent algorithms, based on pathways (scGWAS), single-cell disease scores (scDRS), or both (scPagwas), according to accuracy/sensitivity and interpretability. While previous studies focused on coarse cell types, we used disease-specific, fine-grained single-cell atlases (183,742 and 228,211 cells) and GWAS data (Ns of 97,173 and 45,975) for rheumatoid arthritis (RA) and ulcerative colitis (UC). Second, given the lack of scRNA-seq for many diseases with GWAS, we further tested the tools' resolution limits by differentiating between similar diseases with only one fine-grained scRNA-seq atlas. Lastly, we provide a novel evaluation of noncoding SNP incorporation methods by testing which enabled the highest sensitivity/accuracy of known cell-state calls. We first found that single-cell based tools scDRS and scPagwas called superior numbers of supported cell states that were overlooked by scGWAS. While scGWAS and scPagwas were advantageous for gene exploration, scDRS effectively accounted for batch effect and captured cellular heterogeneity of disease-relevance without single-cell genotyping. For noncoding SNP integration, we found a key trade-off between statistical power and confidence with positional (e.g. MAGMA) and non-positional approaches (e.g. chromatin-interaction, eQTL). Even when directly incorporating noncoding SNPs through 5' scRNA-seq measures of regulatory elements, non disease-specific atlases gave misleading results by not containing disease-tissue specific transcriptomic patterns. Despite this criticality of tissue-specific scRNA-seq, we showed that scDRS enabled deconvolution of two similar diseases with a single fine-grained scRNA-seq atlas and separate GWAS. Indeed, we identified supported and novel genetic-phenotype linkages separating RA and ankylosing spondylitis, and UC and crohn's disease. Overall, while noting evolving single-cell technologies, our study provides key findings for integrating expanding fine-grained scRNA-seq, GWAS, and noncoding SNP resources to unravel the complexities of inflammatory diseases.

Single and longitudinal genome-wide association studies for dairy traits available in goats with three recorded lactations.

Milk yield and composition phenotypes are systematically recorded across several lactations in goats, but the majority of genome-wide association studies (GWAS) performed so far have rather ignored the longitudinal nature of such data. Here, we have used two different GWAS approaches to analyse data from three lactations recorded in Murciano-Granadina goats. In Analysis 1, independent GWAS have been carried out for each trait and lactation, while a single longitudinal GWAS, jointly considering all data, has been performed in Analysis 2. In both analyses, genome-wide significant QTL for lactose percentage on chromosome 2 (129.77-131.01 Mb) and for milk protein percentage on the chromosome 6 (74.8-94.6 Mb) casein gene cluster region were detected. In Analysis 1, several QTL were not replicated in all three lactations, possibly due to the existence of lactation-specific genetic determinants. In Analysis 2, we identified several genome-wide significant QTL related to milk yield and protein content that were not uncovered in Analysis 1. The increased number of QTL identified in Analysis 2 suggests that the longitudinal GWAS is particularly well suited for the genetic analysis of dairy traits. Moreover, our data confirm that variability within or close to the casein complex is the main genetic determinant of milk protein percentage in Murciano-Granadina goats.

Integrative analysis of genome‐wide association study and transcriptomics to identify potential candidate genes influencing drip loss in Beijing Black pigs

AbstractUnderstanding the genetic factors related to meat drip loss is of great importance for animal breeding and production. In this study, we employed a combination of genome‐wide association study (GWAS) mapping and RNA sequencing (RNA‐seq) data to effectively identify potentially functional single nucleotide polymorphisms (SNPs) as well as candidate genes associated with drip loss (DL) in Beijing Black pigs. Initially, we conducted a single‐ and multi‐trait GWAS on drip loss traits in 441 Beijing Black pigs at 24 (DL24) and 48 (DL48) hours postmortem using the Illumina pig 50K SNP chip. Five SNPs with annotations for four genes (FGGY, LHFPL6, OSBPL1A, and NMNAT3) were consistently identified in single or multiple trait GWAS results, indicating their potential pleiotropic effects on drip loss. Next, a comprehensive comparative transcriptomic analysis was performed on samples of Beijing Black pigs exhibiting extremely high and low drip loss, resulting in the identification of 21 differentially expressed genes (DGEs) as potential candidates. Additionally, protein–protein interaction (PPI) network analysis revealed reciprocal regulatory relationships between FOXO1, OSBPL1A, DOCK1 (identified from GWAS) and the candidate DGEs obtained from RNA‐seq data. Therefore, we propose that these genes may impact drip loss traits through gene interactions. In conclusion, our integrative analysis screened candidate genes that may affect the drip loss traits in Beijing Black pigs, which provides crucial insights into the molecular mechanisms of drip loss and serves as a theoretical reference for improving meat quality in Beijing Black pigs.

Multiple genes in a single GWAS risk locus synergistically mediate aberrant synaptic development and function in human neurons

The mechanistic tie between genome-wide association study (GWAS)-implicated risk variants and disease-relevant cellular phenotypes remains largely unknown. Here, using human induced pluripotent stem cell (hiPSC)-derived neurons as a neurodevelopmental model, we identify multiple schizophrenia (SZ) risk variants that display allele-specific open chromatin (ASoC) and are likely to be functional. Editing the strongest ASoC SNP, rs2027349, near vacuolar protein sorting 45 homolog (VPS45) alters the expression of VPS45, lncRNA AC244033.2, and a distal gene, C1orf54. Notably, the transcriptomic changes in neurons are associated with SZ and other neuropsychiatric disorders. Neurons carrying the risk allele exhibit increased dendritic complexity and hyperactivity. Interestingly, individual/combinatorial gene knockdown shows that these genes alter cellular phenotypes in a non-additive synergistic manner. Our study reveals that multiple genes at a single GWAS risk locus mediate a compound effect on neural function, providing a mechanistic link between a non-coding risk variant and disease-related cellular phenotypes.

Topr: an R package for viewing and annotating genetic association results

BackgroundThe successful identification of genetic loci for complex traits in genome-wide association studies (GWAS) has resulted in thousands of GWAS summary statistics becoming publicly available for hundreds of complex traits from multiple cohorts and studies. Visualisation is an important aid for interpreting, comparing, validating, and obtaining an overview of large amounts of data. However, the current software is limited in its ability and flexibility to annotate and simultaneously display multiple GWAS results which is useful when interpreting and comparing association results. Therefore, I created the topr R package to facilitate visualisation, annotation, and comparisons of single or multiple GWAS results. It contains functions tailored for viewing and analysing GWAS results.Resultstopr provides a fast and elegant visual display of association results, along with the annotation of association peaks with their nearest gene. Association results from multiple analyses can be viewed simultaneously over the entire genome or in a more detailed regional view along with gene information. Users can perform the essential steps of visually exploring and annotating association results and generating elegant publication-ready plots.Conclusionstopr is developed as a package for the R statistical computing environment, released under the GNU General Public License, and is freely available on the Comprehensive R Archive Network (http://cran.r-project.org/package=topr). The source code is available at GitHub (https://github.com/totajuliusd/topr). topr provides several advantages and advances over the current alternatives, particularly in its gene annotation functionality and customisable display of single- or multiple-association results. With topr, I provide a flexible tool with multiple features to aid in the analysis and evaluation of GWAS association results.

Abstract 5218: Genome-wide association study of African Americans reveals new susceptibility loci contributing to lung cancer and mechanisms of population-specific etiology

Abstract Lung cancer incidence and mortality in the U.S. have substantial ethnic disparities. African Americans of both sexes have the high incidence rate at 56.8 and the highest death rate at 38.1 per 100,000 people from 2015 to 2019. Non-Hispanic black men have the highest incidence at 73.9 and the highest death rate at 56.3 per 100,000 people. While many genome-wide association studies (GWAS) of lung cancer have discovered ~45 putative risk loci, a large proportion of the heritability of lung cancer remains unexplained. To date, most single population-based GWAS have been performed in Europeans while there are a few Asian ancestry-specific genetic studies and two cross-ancestry studies. Non-European GWAS of lung cancer has been underrepresented. Although African Americans have higher lung cancer incidence and poorer lung cancer survival rate, and smoke fewer cigarettes per day compared to Europeans, few comprehensive GWAS of lung cancer in African Americans has been conducted. This study aims to decipher the genetic component of predisposition to lung cancer among African Americans. This study aims to decipher the genetic component of predisposition to lung cancer among African Americans. We conducted an African-ancestry GWAS comprising 2,280 lung cancer cases and 4,301 controls to comprehensively characterize common and low-frequency lung cancer genetic susceptibility loci using HRC imputed lung cancer data. The novel variants in or near VWF on 12p13.31 (OR=1.29, P=6.93 × 10−9) for overall lung cancer and GACAT3 on 2p24.3 (0.65, 1.24 × 10−9), TRIP13 on 5p15.33 (3.49, 1.00 × 10−8), ERC1 on 12p13.33 (4.64, 6.08 × 10−9), LMAN1L (near CYP1A1) on 15q24.1 (4.25, 8.71 × 10−9) for lung squamous cell cancer were identified at a genome-wide significance level. Our GWAS of lung cancer identified five novel genetic susceptibility associations and confirmed several variants on 15q25 and 19q13. Further works are required to elucidate the possible biological mechanisms underlying these associations among lung cancer and histological subtypes in African Americans. Citation Format: Jinyoung Byun, Younghun Han, Xiangjun Xiao, Christine Lusk, Hoda J. Badr, Rayjean Hung, Ann G. Schwartz, Christopher I. Amos, INTEGRAL Consortium. Genome-wide association study of African Americans reveals new susceptibility loci contributing to lung cancer and mechanisms of population-specific etiology. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5218.

Embracing diversity: a genetic marker dataset with increased marker density facilitates association studies in maize

Embracing diversity: a genetic marker dataset with increased marker density facilitates association studies in maize

Leveraging pleiotropy for joint analysis of genome-wide association studies with per trait interpretations.

We introduce pleiotropic association test (PAT) for joint analysis of multiple traits using genome-wide association study (GWAS) summary statistics. The method utilizes the decomposition of phenotypic covariation into genetic and environmental components to create a likelihood ratio test statistic for each genetic variant. Though PAT does not directly interpret which trait(s) drive the association, a per trait interpretation of the omnibus p-value is provided through an extension to the meta-analysis framework, m-values. In simulations, we show PAT controls the false positive rate, increases statistical power, and is robust to model misspecifications of genetic effect. Additionally, simulations comparing PAT to three multi-trait methods, HIPO, MTAG, and ASSET, show PAT identified 15.3% more omnibus associations over the next best method. When these associations were interpreted on a per trait level using m-values, PAT had 37.5% more true per trait interpretations with a 0.92% false positive assignment rate. When analyzing four traits from the UK Biobank, PAT discovered 22,095 novel variants. Through the m-values interpretation framework, the number of per trait associations for two traits were almost tripled and were nearly doubled for another trait relative to the original single trait GWAS.

Cross-GWAS coherence test at the gene and pathway level.

Proximal genetic variants are frequently correlated, implying that the corresponding effect sizes detected by genome-wide association studies (GWAS) are also not independent. Methods already exist to account for this when aggregating effects from a single GWAS across genes or pathways. Here we present a rigorous yet fast method for detecting genes with coherent association signals for two traits, facilitating cross-GWAS analyses. To this end, we devised a new significance test for the covariance of datapoints not drawn independently but with a known inter-sample covariance structure. We show that the distribution of its test statistic is a linear combination of χ2 distributions with positive and negative coefficients. The corresponding cumulative distribution function can be efficiently calculated with Davies' algorithm at high precision. We apply this general framework to test for dependence between SNP-wise effect sizes of two GWAS at the gene level. We extend this test to detect also gene-wise causal links. We demonstrate the utility of our method by uncovering potential shared genetic links between the severity of COVID-19 and (1) being prescribed class M05B medication (drugs affecting bone structure and mineralization), (2) rheumatoid arthritis, (3) vitamin D (25OHD), and (4) serum calcium concentrations. Our method detects a potential role played by chemokine receptor genes linked to TH1 versus TH2 immune response, a gene related to integrin beta-1 cell surface expression, and other genes potentially impacting the severity of COVID-19. Our approach will be useful for similar analyses involving datapoints with known auto-correlation structures.

Recent status of Genotyping by Sequencing (GBS) Technology in cucumber (Cucumis sativus L.): a review.

Current and advanced breeding tools are being used to improve economically important horticultural crops to meet the consumers' needs and preferences. Genotyping-by-sequencing (GBS) is an extremely useful tool in the investigation and analysis of the genetic diversity of different cultivars. Based on a broad range of genetic backgrounds like single nucleotide polymorphism (SNPs), GBS is known as a novel technique to facilitate the detection of quantitative trait loci (QTL) regions robustly linked with interested traits compared to genome-wide association study (GWAS) and QTL. GBS has gained popularity among breeders in recent years and it is also employed in cucumber breeding programs. Cucumbers (C. sativus L.) are monoecious, gynoecious and some of them are parthenocarpic species. Cucumber is one of the most economical and essential crops in the Cucurbitaceae family. For time immemorial, cucumber has been produced and consumed all over the world like other cucurbits. To a large extent, cultivated cucurbits are beneficial to human health for providing necessary minerals and fibers.Therefore, this review portrays the current status of advances made by using GBS and its combination with other tools in various studies of cucumber such as the use of GBS and single nucleotide polymorphism (SNP) markers, GBS and GWAS, also with QTL and marker-assisted selection (MAS) are applied to display and detect explicit genetic architecture complex traits in crops and chromosome rearrangements.Cucumber breeding programs have undoubtedly benefited from genotyping-by-sequencing. Using the GBS method, research discovered lots of new candidate genes that control various traits including spine color, fruit stalk-end color, and disease resistance in cucumber lines.

An Expanded Genome-Wide Association Study of Fructosamine Levels Identifies RCN3 as a Replicating Locus and Implicates FCGRT as the Effector Transcript.

Fructosamine is a measure of short-term glycemic control, which has been suggested as a useful complement to glycated hemoglobin (HbA1c) for the diagnosis and monitoring of diabetes. To date, a single genome-wide association study (GWAS) including 8,951 U.S. White and 2,712 U.S. Black individuals without a diabetes diagnosis has been published. Results in Whites and Blacks yielded different association loci, near RCN3 and CNTN5, respectively. In this study, we performed a GWAS on 20,731 European-ancestry blood donors and meta-analyzed our results with previous data from U.S. White participants from the Atherosclerosis Risk in Communities (ARIC) study (Nmeta = 29,685). We identified a novel association near GCK (rs3757840, βmeta = 0.0062; minor allele frequency [MAF] = 0.49; Pmeta = 3.66 × 10-8) and confirmed the association near RCN3 (rs113886122, βmeta = 0.0134; MAF = 0.17; Pmeta = 5.71 × 10-18). Colocalization analysis with whole-blood expression quantitative trait loci data suggested FCGRT as the effector transcript at the RCN3 locus. We further showed that fructosamine has low heritability (h2 = 7.7%), has no significant genetic correlation with HbA1c and other glycemic traits in individuals without a diabetes diagnosis (P > 0.05), but has evidence of shared genetic etiology with some anthropometric traits (Bonferroni-corrected P < 0.0012). Our results broaden knowledge of the genetic architecture of fructosamine and prioritize FCGRT for downstream functional studies at the established RCN3 locus.

Multi-phenotype genome-wide association studies of the Norfolk Island isolate implicate pleiotropic loci involved in chronic kidney disease

Chronic kidney disease (CKD) is a persistent impairment of kidney function. Genome-wide association studies (GWAS) have revealed multiple genetic loci associated with CKD susceptibility but the complete genetic basis is not yet clear. Since CKD shares risk factors with cardiovascular diseases and diabetes, there may be pleiotropic loci at play but may go undetected when using single phenotype GWAS. Here, we used multi-phenotype GWAS in the Norfolk Island isolate (n = 380) to identify new loci associated with CKD. We performed a principal components analysis on different combinations of 29 quantitative traits to extract principal components (PCs) representative of multiple correlated phenotypes. GWAS of a PC derived from glomerular filtration rate, serum creatinine, and serum urea identified a suggestive peak (pmin = 1.67 × 10–7) that mapped to KCNIP4. Inclusion of other secondary CKD measurements with these three kidney function traits identified the KCNIP4 locus with GWAS significance (pmin = 1.59 × 10–9). Finally, we identified a group of two SNPs with increased minor allele frequencies as potential functional variants. With the use of genetic isolate and the PCA-based multi-phenotype GWAS approach, we have revealed a potential pleotropic effect locus for CKD. Further studies are required to assess functional relevance of this locus.

Genome-wide association analysis reveals quantitative trait loci and candidate genes involved in yield components under multiple field environments in cotton (Gossypium hirsutum)

BackgroundNumerous quantitative trait loci (QTLs) and candidate genes associated with yield-related traits have been identified in cotton by genome-wide association study (GWAS) analysis. However, most of the phenotypic data were from a single or few environments, and the stable loci remained to be validated under multiple field environments.ResultsHere, 242 upland cotton accessions collected from different origins were continuously investigated for phenotypic data of four main yield components, including boll weight (BW) and lint percentage (LP) under 13 field environments, and boll number per plant (BN) and seed index (SI) under 11 environments. Correlation analysis revealed a positive correlation between BN and LP, BW and SI, while SI had a negative correlation with LP and BN. Genetic analysis indicated that LP had the highest heritability estimates of 94.97%, followed by 92.08% for SI, 86.09% for BW, and 72.92% for BN, indicating LP and SI were more suitable traits for genetic improvement. Based on 56,010 high-quality single nucleotide polymorphisms (SNPs) and GWAS analysis, a total of 95 non-redundant QTLs were identified, including 12 of BN, 23 of BW, 45 of LP, and 33 of SI, respectively. Of them, 10 pairs of homologous QTLs were detected between A and D sub-genomes. We also found that 15 co-located QTLs with more than two traits and 12 high-confidence QTLs were detected under more than six environments, respectively. Further, two NET genes (GH_A08G0716 and GH_A08G0783), located in a novel QTL hotspot (qtl24, qtl25 and qlt26) were predominately expressed in early fiber development stages, exhibited significant correlation with LP and SI. The GH_A07G1389 in the stable qtl19 region encoded a tetratricopeptide repeat (TPR)-like superfamily protein and was a homologous gene involved in short fiber mutant ligon lintless-y (Liy), implying important roles in cotton yield.ConclusionsThe present study provides a foundation for understanding the regulatory mechanisms of yield components and may enhance yield improvement through molecular breeding in cotton.

GWA-based pleiotropic analysis identified potential SNPs and genes related to type 2 diabetes and obesity.

Metabolic syndrome is a cluster of symptoms including excessive body fat and insulin resistance which may lead to obesity and type 2 diabetes (T2D). The physiological and pathological cross-talk between T2D and obesity is crucial and complex, meanwhile, the genetic connection between T2D and obesity is largely unknown. The purpose of this study is to identify pleiotropic SNPs and genes between these two associated conditions by applying genetic analysis incorporating pleiotropy and annotation (GPA) on two large genome-wide association studies (GWAS) data sets: a body mass index (BMI) data set containing 339,224 subjects and a T2D data set containing 110,452 subjects. In all, 5182 SNPs showed pleiotropy in both T2D and obesity. After further prioritization based on suggested local false discovery rates (FDR) by the GPA model, 2146 SNPs corresponding to 217 unique genes are significantly associated with both traits (FDR < 0.2), among which 187 are newly identified pleiotropic genes compare with original GWAS in individual traits. Subsequently, gene enrichment and pathway analyses highlighted several pleiotropic SNPs including rs849135 (FDR = 0.0002), rs2119812 (FDR = 0.0018), rs4506565 (FDR = 1.23E-08), rs1558902 (7.23E-10) and corresponding genes JAZF1, SYN2, TCF7L2, FTO which may play crucial rol5es in the etiology of both T2D and obesity. Additional evidences from expression data analysis of pleiotropic genes strongly supports that the pleiotropic genes including JAZF1 (p = 1.39E-05 and p = 2.13E-05), SYN2 (p = 5.49E-03 and p = 5.27E-04), CDKN2C (p = 1.99E-12 and p = 6.27E-11), RABGAP1 (p = 3.08E-03 and p = 7.46E-03), and UBE2E2 (p = 1.83E-04 and p = 8.22E-03) play crucial roles in both obesity and T2D pathogenesis. Pleiotropic analysis integrated with functional network identified several novel and causal SNPs and genes involved in both BMI and T2D which may be ignored in single GWAS.

Genome-wide association analyses identify known and novel loci for teat number in Duroc pigs using single-locus and multi-locus models

BackgroundMore teats are necessary for sows to nurse larger litters to provide immunity and nutrient for piglets prior to weaning. Previous studies have reported the strong effect of an insertion mutation in the Vertebrae Development Associated (VRTN) gene on Sus scrofa chromosome 7 (SSC7) that increased the number of thoracic vertebrae and teat number in pigs. We used genome-wide association studies (GWAS) to map genetic markers and genes associated with teat number in two Duroc pig populations with different genetic backgrounds. A single marker method and several multi-locus methods were utilized. A meta-analysis that combined the effects and P-values of 34,681 single nucleotide polymorphisms (SNPs) that were common in the results of single marker GWAS of American and Canadian Duroc pigs was conducted. We also performed association tests between the VRTN insertion and teat number in the same populations.ResultsA total of 97 SNPs were found to be associated with teat number. Among these, six, eight and seven SNPs were consistently detected with two, three and four multi-locus methods, respectively. Seven SNPs were concordantly identified between single marker and multi-locus methods. Moreover, 26 SNPs were newly found by multi-locus methods to be associated with teat number. Notably, we detected one consistent quantitative trait locus (QTL) on SSC7 for teat number using single-locus and meta-analysis of GWAS and the top SNP (rs692640845) explained 8.68% phenotypic variance of teat number in the Canadian Duroc pigs. The associations between the VRTN insertion and teat number in two Duroc pig populations were substantially weaker. Further analysis revealed that the effect of VRTN on teat number may be mediated by its LD with the true causal mutation.ConclusionsOur study suggested that VRTN insertion may not be a strong or the only candidate causal mutation for the QTL on SSC7 for teat number in the analyzed Duroc pig populations. The combination of single-locus and multi-locus GWAS detected additional SNPs that were absent using only one model. The identified SNPs will be useful for the genetic improvement of teat number in pigs by assigning higher weights to associated SNPs in genomic selection.

Twelve New Genomic Loci Associated With Bone Mineral Density.

Aiming to identify more genomic loci associated with bone mineral density (BMD), we conducted a joint association analysis of 2 genome-wide association study (GWAS) by the integrative association method multi-trait analysis of GWAS (MTAG). The first one is the single GWAS of estimated heel BMD (eBMD) in the UK biobank (UKB) cohort (N = 426,824), and the second one is the GWAS meta-analysis of total body BMD (TB-BMD) in 66,628 participants from 30 studies. Approximate conditional association analysis was performed in the identified novel loci to identify secondary association signal. Statistical fine-mapping was conducted to prioritize plausible credible risk variants (CRVs). Candidate genes were prioritized based on the analyses of cis- expression quantitative trait locus (cis-eQTL) and cis-protein QTL (cis-pQTL) information as well as the functional category of the SNP. By integrating the information carried in over 490,000 participants, this largest joint analysis of BMD GWAS identified 12 novel genomic loci at the genome-wide significance level (GWS, p = 5.0 × 10−8), nine of which were for eBMD and four were for TB-BMD, explaining an additional 0.11 and 0.23% heritability for the two traits, respectively. These loci include 1p33 (lead SNP rs10493130, peBMD = 3.19 × 10−8), 5q13.2 (rs4703589, peBMD = 4.78 × 10−8), 5q31.3 (rs9324887, pTB−BMD = 1.36 × 10−9), 6p21.32 (rs6905837, peBMD = 3.32 × 10−8), 6q14.1 (rs10806234, peBMD = 2.63 × 10−8), 7q21.11 (rs10806234, pTB−BMD = 3.37 × 10−8), 8q24.12 (rs11995866, peBMD = 6.72 × 10−9), 12p13.31 (rs1639122, peBMD = 4.43 × 10−8), 12p12.1 (rs58489179, peBMD = 4.74 × 10−8), 12q24.23 (rs75499226, peBMD = 1.44 × 10−8), 19q13.31 (rs7255083, pTB−BMD = 2.18 × 10−8) and 22q11.23 (rs13056137, pTB−BMD = 2.54 × 10−8). All lead SNPs in these 12 loci are nominally significant in both original studies as well as consistent in effect direction between them, providing solid evidence of replication. Approximate conditional analysis identified one secondary signal in 5q13.2 (rs11738874, pconditional = 5.06 × 10−9). Statistical fine-mapping analysis prioritized 269 CRVs. A total of 65 candidate genes were prioritized, including those with known biological function to bone development (such as FGF1, COL11A2 and DEPTOR). Our findings provide novel insights into a better understanding of the genetic mechanism underlying bone development as well as candidate genes for future functional investigation.

Haplotype-based genome-wide association studies for carcass and growth traits in chicken

There have been several genome-wide association study (GWAS) reported for carcass, growth, and meat traits in chickens. Most of these studies have been based on single SNPs GWAS. In contrast, haplotype-based GWAS reports have been limited. In the present study, 2 Northeast Agricultural University broiler lines divergently selected for abdominal fat content (NEAUHLF) and genotyped with the chicken 60K SNP chip were used to perform a haplotype-based GWAS. The lean and fat chicken lines were selected for abdominal fat content for 11 yr. Abdominal fat weight was significantly different between the 2 lines; however, there was no difference for body weight between the lean and fat lines. A total of 132 haplotype windows were significantly associated with abdominal fat weight. These significantly associated haplotype windows were primarily located on chromosomes 2, 4, 8, 10, and 26. Seven candidate genes, including SHH, LMBR1, FGF7, IL16, PLIN1, IGF1R, and SLC16A1, were located within these associated regions. These genes may play important roles in the control of abdominal fat content. Two regions on chromosomes 3 and 10 were significantly associated with testis weight. These 2 regions were previously detected by the single SNP GWAS using this same resource population. TCF21 on chromosome 3 was identified as a potentially important candidate gene for testis growth and development based on gene expression analysis and the reported function of this gene. TCF12, which was previously detected in our SNP by SNP interaction analysis, was located in a region on chromosome 10 that was significantly associated with testis weight. Six candidate genes, including TNFRSF1B, PLOD1, NPPC, MTHFR, EPHB2, and SLC35A3, on chromosome 21 may play important roles in bone development based on the known function of these genes. In addition, several regions were significantly associated with other carcass and growth traits, but no candidate genes were identified. The results of the present study may be helpful in understanding the genetic mechanisms of carcass and growth traits in chickens.

Genomic prediction and GWAS of yield, quality and disease-related traits in spring barley and winter wheat

Genome-wide association study (GWAS) and genomic prediction (GP) are extensively employed to accelerate genetic gain and identify QTL in plant breeding. In this study, 1,317 spring barley and 1,325 winter wheat breeding lines from a commercial breeding program were genotyped with the Illumina 9 K barley or 15 K wheat SNP-chip, and phenotyped in multiple years and locations. For GWAS, in spring barley, a QTL on chr. 4H associated with powdery mildew and ramularia resistance were found. There were several SNPs on chr. 4H showing genome-wide significance with yield traits. In winter wheat, GWAS identified two SNPs on chr. 6A, and one SNP on chr. 1B, significantly associated with quality trait moisture, as well as one SNP located on chr. 5B associated with starch content in the seeds. The significant SNPs identified by multiple trait GWAS were generally the same as those found in single trait GWAS. GWAS including genotype-location information in the model identified significant SNPs in each tested location, which were not found previously when including all locations in the GWAS. For GP, in spring barley, GP using the Bayesian Power Lasso model had higher accuracy than ridge regression BLUP in powdery mildew and yield traits, whereas the prediction accuracies were similar using Bayesian Power Lasso model and rrBLUP for yield traits in winter wheat.