Pleiotropy informed adaptive association test of multiple traits using genome‐wide association study summary data

Genetic variants associated with disease outcomes can be used to develop personalized treatment. To reach this precision medicine goal, hundreds of large‐scale genome‐wide association studies (GWAS) have been conducted in the past decade to search for promising genetic variants associated with various traits. They have successfully identified tens of thousands of disease‐related variants. However, in total these identified variants explain only part of the variation for most complex traits. There remain many genetic variants with small effect sizes to be discovered, which calls for the development of (a) GWAS with more samples and more comprehensively genotyped variants, for example, the NHLBI Trans‐Omics for Precision Medicine (TOPMed) Program is planning to conduct whole genome sequencing on over 100 000 individuals; and (b) novel and more powerful statistical analysis methods. The current dominating GWAS analysis approach is the “single trait” association test, despite the fact that many GWAS are conducted in deeply phenotyped cohorts including many correlated and well‐characterized outcomes, which can help improve the power to detect novel variants if properly analyzed, as suggested by increasing evidence that pleiotropy, where a genetic variant affects multiple traits, is the norm in genome‐phenome associations. We aim to develop pleiotropy informed powerful association test methods across multiple traits for GWAS. Since it is generally very hard to access individual‐level GWAS phenotype and genotype data for those existing GWAS, due to privacy concerns and various logistical considerations, we develop rigorous statistical methods for pleiotropy informed adaptive multitrait association test methods that need only summary association statistics publicly available from most GWAS. We first develop a pleiotropy test, which has powerful performance for truly pleiotropic variants but is sensitive to the pleiotropy assumption. We then develop a pleiotropy informed adaptive test that has robust and powerful performance under various genetic models. We develop accurate and efficient numerical algorithms to compute the analytical P‐value for the proposed adaptive test without the need of resampling or permutation. We illustrate the performance of proposed methods through application to joint association test of GWAS meta‐analysis summary data for several glycemic traits. Our proposed adaptive test identified several novel loci missed by individual trait based GWAS meta‐analysis. All the proposed methods are implemented in a publicly available R package.     

Genome‐wide association analysis reveals novel loci for hypoxia adaptability in Tibetan chicken

The Tibetan chicken (TBC), an indigenous chicken breed of the Tibetan Plateau, has adapted to its hypoxic, high‐altitude environment over hundreds of years. The objective of this study was to identify the polymorphisms and genes associated with adaptation to hypoxia in this chicken breed. In the present study, samples were collected during days 18–21 of the incubation period from both surviving chicks and dead embryos, all of which were hatched under hypoxic conditions. A genome‐wide association study was conducted using the Illumina iSelect 60K SNP array with a case–control design, in which the case group consisted of the dead chicken embryos (n = 54) and controls were the surviving chicks (n = 82). Four significant SNPs were detected at the genome‐wide level (P < 0.05), and the results indicated that fork head box G1 (FOXG1) was the main candidate gene. The lead SNP NC_006092.4:g.33368893T>C was confirmed with a polymerase chain reaction‐restriction fragment length polymorphism analysis of 122 cases and 212 controls. A chi‐square test showed a significant association between NC_006092.4:g.33368893T>C and hatchability under hypoxic conditions (P < 0.01). Our results revealed novel polymorphisms and a candidate gene associated with hypoxic adaptation, facilitating further study in this field.     

Meta‐analysis of genome‐wide association from genomic prediction models

Genome‐wide association (GWA) studies based on GBLUP models are a common practice in animal breeding. However, effect sizes of GWA tests are small, requiring larger sample sizes to enhance power of detection of rare variants. Because of difficulties in increasing sample size in animal populations, one alternative is to implement a meta‐analysis (MA), combining information and results from independent GWA studies. Although this methodology has been used widely in human genetics, implementation in animal breeding has been limited. Thus, we present methods to implement a MA of GWA, describing the proper approach to compute weights derived from multiple genomic evaluations based on animal‐centric GBLUP models. Application to real datasets shows that MA increases power of detection of associations in comparison with population‐level GWA, allowing for population structure and heterogeneity of variance components across populations to be accounted for. Another advantage of MA is that it does not require access to genotype data that is required for a joint analysis. Scripts related to the implementation of this approach, which consider the strength of association as well as the sign, are distributed and thus account for heterogeneity in association phase between QTL and SNPs. Thus, MA of GWA is an attractive alternative to summarizing results from multiple genomic studies, avoiding restrictions with genotype data sharing, definition of fixed effects and different scales of measurement of evaluated traits.     

Detecting selection signatures between Duroc and Duroc synthetic pig populations using high‐density SNP chip

The development of high throughput genotyping techniques has facilitated the identification of selection signatures of pigs. The detection of genomic selection signals in a population subjected to differential selection pressures may provide insights into the genes associated with economically and biologically important traits. To identify genomic regions under selection, we genotyped 488 Duroc (D) pigs and 155 D × Korean native pigs (DKNPs) using the Porcine SNP70K BeadChip. By applying the F_ST and extended haplotype homozygosity (EHH‐Rsb) methods, we detected genes under directional selection associated with growth/stature (DOCK7, PLCB4, HS2ST1, FBP2 and TG), carcass and meat quality (TG, COL14A1, FBXO5, NR3C1, SNX7, ARHGAP26 and DPYD), number of teats (LOC100153159 and LRRC1), pigmentation (MME) and ear morphology (SOX5), which are all mostly near or at fixation. These results could be a basis for investigating the underlying mutations associated with observed phenotypic variation. Validation using genome‐wide association analysis would also facilitate the inclusion of some of these markers in genetic evaluation programs.     

Genome‐wide association study reveals candidate genes associated with body measurement traits in Chinese Wagyu beef cattle

We performed a genome‐wide association study to identify candidate genes for body measurement traits in 463 Wagyu beef cattle typed with the Illumina Bovine HD 770K SNP array. At the genome‐wide level, we detected 18, five and one SNPs associated with hip height, body height and body length respectively. In total, these SNPs are within or near 11 genes, six of which (PENK, XKR4, IMPAD1, PLAG1, CCND2 and SNTG1) have been reported previously and five of which (CSMD3, LAP3, SYN3, FAM19A5 and TIMP3) are novel candidate genes that we found to be associated with body measurement traits. Further exploration of these candidate genes will facilitate genetic improvement in Chinese Wagyu beef cattle.     

Genetic‐based dissection of arsenic accumulation in maize using a genome‐wide association analysis method

Understanding the mechanism of arsenic (As) accumulation in plants is important in reducing As's toxicity to plants and its potential risks to human health. Here, we performed a genome‐wide association study to dissect the genetic basis of the As contents of different maize tissues in Xixian, which was irrigated with As‐rich surface water, and Changge using an association population consisting of 230 representative maize inbred lines. Phenotypic data revealed a wide normal distribution and high repeatability for the As contents in maize tissues. The As concentrations in maize tissues followed the same trend in the two locations: kernels < axes < stems < bracts < leaves. In total, 15, 16 and 15 non‐redundant quantitative trait loci (QTL s) associated with As concentrations were identified (P  ≤ 2.04 × 10^?6) in five tissues from Xixian, Changge, and the combination of the locations, respectively, explaining 9.70%–24.65% of the phenotypic variation for each QTL , on average. Additionally, four QTL s [involving 15 single nucleotide polymorphisms (SNP s)] were detected in the single and the combined locations, indicating that these loci/SNP s might be stable across different environments. The candidate genes associated with these four loci were predicted. In addition, four non‐redundant QTL s (6 SNP s), including a QTL that was detected in multiple locations according to the genome‐wide association study, were found to co‐localize with four previously reported QTL intervals. These results are valuable to understand the genetic architecture of As mechanism in maize and facilitate the genetic improvement of varieties without As toxicity.     

Genetic variation of growth dynamics in maize (Zea mays L.) revealed through automated non‐invasive phenotyping

Hitherto, most quantitative trait loci of maize growth and biomass yield have been identified for a single time point, usually the final harvest stage. Through this approach cumulative effects are detected, without considering genetic factors causing phase‐specific differences in growth rates. To assess the genetics of growth dynamics, we employed automated non‐invasive phenotyping to monitor the plant sizes of 252 diverse maize inbred lines at 11 different developmental time points; 50 k SNP array genotype data were used for genome‐wide association mapping and genomic selection. The heritability of biomass was estimated to be over 71%, and the average prediction accuracy amounted to 0.39. Using the individual time point data, 12 main effect marker‐trait associations (MTAs) and six pairs of epistatic interactions were detected that displayed different patterns of expression at various developmental time points. A subset of them also showed significant effects on relative growth rates in different intervals. The detected MTAs jointly explained up to 12% of the total phenotypic variation, decreasing with developmental progression. Using non‐parametric functional mapping and multivariate mapping approaches, four additional marker loci affecting growth dynamics were detected. Our results demonstrate that plant biomass accumulation is a complex trait governed by many small effect loci, most of which act at certain restricted developmental phases. This highlights the need for investigation of stage‐specific growth affecting genes to elucidate important processes operating at different developmental phases.     

Genome‐wide association QTL mapping for teat number in a purebred population of Duroc pigs

Because of increasing litter size in Western pig breeds, additional teats are desirable to increase the capacity for nursing offspring. We applied genome‐wide SNP markers to detect QTL regions that affect teat number in a Duroc population. We phenotyped 1024 animals for total teat number. A total of 36 588 SNPs on autosomes were used in the analysis. The estimated heritability for teat number was 0.34 ± 0.05 on the basis of a genomic relationship matrix constructed from all SNP markers. Using a BayesC method, we identified a total of 18 QTL regions that affected teat number in Duroc pigs; 9 of the 18 regions were newly detected.     

Genome‐wide association mapping and pathway analysis of leukosis incidence in a US Holstein cattle population

Bovine leukosis virus is an oncogenic virus that infects B cells, causing bovine leukosis disease. This disease is known to have a negative impact on dairy cattle production and, because no treatment or vaccine is available, finding a possible genetic solution is important. Our objective was to perform a comprehensive genetic analysis of leukosis incidence in dairy cattle. Data on leukosis occurrence, pedigree and molecular information were combined into multitrait GBLUP models with milk yield (MY) and somatic cell score (SCS) to estimate genetic parameters and to perform whole‐genome scans and pathway analysis. Leukosis data were available for 11 554 Holsteins daughters of 3002 sires from 112 herds in 16 US states. Genotypes from a 60K SNP panel were available for 961 of those bulls as well as for 2039 additional bulls. Heritability for leukosis incidence was estimated at about 8%, and the genetic correlations of leukosis disease incidence with MY and SCS were moderate at 0.18 and 0.20 respectively. The genome‐wide scan indicated that leukosis is a complex trait, possibly modulated by many genes. The gene set analysis identified many functional terms that showed significant enrichment of genes associated with leukosis. Many of these terms, such as G‐Protein Coupled Receptor Signaling Pathway, Regulation of Nucleotide Metabolic Process and different calcium‐related processes, are known to be related to retrovirus infection. Overall, our findings contribute to a better understanding of the genetic architecture of this complex disease. The functional categories associated with leukosis may be useful in future studies on fine mapping of genes and development of dairy cattle breeding strategies.     

Population structure and genetic basis of the agronomic traits of upland cotton in China revealed by a genome‐wide association study using high‐density SNPs

Gossypium hirsutum L. represents the largest source of textile fibre, and China is one of the largest cotton‐producing and cotton‐consuming countries in the world. To investigate the genetic architecture of the agronomic traits of upland cotton in China, a diverse and nationwide population containing 503 G. hirsutum accessions was collected for a genome‐wide association study (GWAS) on 16 agronomic traits. The accessions were planted in four places from 2012 to 2013 for phenotyping. The CottonSNP63K array and a published high‐density map based on this array were used for genotyping. The 503 G. hirsutum accessions were divided into three subpopulations based on 11 975 quantified polymorphic single‐nucleotide polymorphisms (SNPs). By comparing the genetic structure and phenotypic variation among three genetic subpopulations, seven geographic distributions and four breeding periods, we found that geographic distribution and breeding period were not the determinants of genetic structure. In addition, no obvious phenotypic differentiations were found among the three subpopulations, even though they had different genetic backgrounds. A total of 324 SNPs and 160 candidate quantitative trait loci (QTL) regions were identified as significantly associated with the 16 agronomic traits. A network was established for multieffects in QTLs and interassociations among traits. Thirty‐eight associated regions had pleiotropic effects controlling more than one trait. One candidate gene, Gh_D08G2376, was speculated to control the lint percentage (LP). This GWAS is the first report using high‐resolution SNPs in upland cotton in China to comprehensively investigate agronomic traits, and it provides a fundamental resource for cotton genetic research and breeding.     

A genome‐wide association study suggests new candidate genes for milk production traits in Chinese Holstein cattle

A genome‐wide association study (GWAS) was conducted on 15 milk production traits in Chinese Holstein. The experimental population consisted of 445 cattle, each genotyped by the GGP (GeneSeek genomic profiling)‐BovineLD V3 SNP chip, which had 26 151 public SNPs in its manifest file. After data cleaning, 20 326 SNPs were retained for the GWAS. The phenotypes were estimated breeding values of traits, provided by a public dairy herd improvement program center that had been collected once a month for 3 years. Two statistical models, a fixed‐effect linear regression model and a mixed‐effect linear model, were used to estimate the association effects of SNPs on each of the phenotypes. Genome‐wide significant and suggestive thresholds were set at 2.46E‐06 and 4.95E‐05 respectively. The two statistical models concurrently identified two genome‐wide significant (P < 0.05) SNPs on milk production traits in this Chinese Holstein population. The positional candidate genes, which were the ones closest to these two identified SNPs, were EEF2K (eukaryotic elongation factor 2 kinase) and KLHL1 (kelch like family member 1). These two genes could serve as new candidate genes for milk yield and lactation persistence, yet their roles need to be verified in further function studies.     

Genome‐wide association studies for hematological traits in swine

Improving immune capacity may increase the profitability of animal production if it enables animals to better cope with infections. Hematological traits play pivotal roles in animal immune capacity and disease resistance. Thus far, few studies have been conducted using a high‐density swine SNP chip panel to unravel the genetic mechanism of the immune capability in domestic animals. In this study, using mixed model‐based single‐locus regression analyses, we carried out genome‐wide association studies, using the Porcine SNP60 BeadChip, for immune responses in piglets for 18 hematological traits (seven leukocyte traits, seven erythrocyte traits, and four platelet traits) after being immunized with classical swine fever vaccine. After adjusting for multiple testing based on permutations, 10, 24, and 77 chromosome‐wise significant SNPs were identified for the leukocyte traits, erythrocyte traits, and platelet traits respectively, of which 10 reached genome‐wise significance level. Among the 53 SNPs for mean platelet volume, 29 are located in a linkage disequilibrium block between 32.77 and 40.59 Mb on SSC6. Four genes of interest are located within the block, providing genetic evidence that this genomic segment may be considered a candidate region relevant to the platelet traits. Other candidate genes of interest for red blood cell, hemoglobin, and red blood cell volume distribution width also have been found near the significant SNPs. Our genome‐wide association study provides a list of significant SNPs and candidate genes that offer valuable information for future dissection of molecular mechanisms regulating hematological traits.     

Genome-wide association study reveals five nucleotide sequence variants for carcass traits in beef cattle

Genetic associations of nucleotide sequence variants with carcass traits in beef cattle were investigated using a genome-wide single nucleotide polymorphism (SNP) assay. Three hundred and thirteen Korean cattle were genotyped with the Illumina BovineSNP50 BeadChip, and 39,129 SNPs from 311 animals were analysed for each carcass phenotype after filtering by quality assurance. Five sequence markers were associated with one of the meat quantity or quality traits; rs109593638 on chromosome 3 with marbling score, rs109821175 on chromosome 11 and rs110862496 on chromosome 13 with backfat thickness (BFT), and rs110228023 on chromosome 6 and rs110201414 on chromosome 16 with eye muscle area (EMA) (P < 1.27 × 10(-6) , Bonferonni P < 0.05). The ss96319521 SNP, located within a gene with functions of muscle development, dishevelled homolog 1 (DVL1), would be a desirable candidate marker. Individuals with genotype CC at this gene appeared to have increased both EMA and carcass weight. Fine-mapping would be required to refine each of the five association signals shown in the current study for future application in marker-assisted selection for genetic improvement of beef quality and quantity.     

Genome‐wide association mapping and examination of possible maternal effect for the pace trait of horses

Previously, a single nucleotide polymorphism (SNP) related to gait type was identified at position 22 999 655 of chromosome 23 in the coding region of DMRT3 (DMRT3:Ser301Ter) by showing that a cytosine (C) to adenine (A) mutation of this SNP induced pace in the Icelandic horse. We investigated the effect of DMRT3:Ser301Ter on the gait of Hokkaido Native Horses, a Japanese native breed, and examined genetic factors other than DMRT3 by exploring genome‐wide SNPs related to gait determination. All animals exhibiting pace were AA for DMRT3:Ser301Ter, confirming the association of DMRT3:Ser301Ter with gait determination; however, 14.3% of the animals exhibiting trot also had AA for DMRT3:Ser301Ter, suggesting the presence of another factor(s) cooperatively working with DMRT3:Ser301Ter for gait determination. SNPs on chromosomes 13 and 23 were detected by genome‐wide association analysis (false discovery rate <0.05), although SNPs on chromosome 23 were all located in the vicinity of DMRT3:Ser301Ter, confirming the association with DMRT3. A genome‐wide association study targeting only animals with AA for DMRT3:Ser301Ter to examine genetic factors cooperatively working with DMRT3:Ser301Ter for gait determination suggested associations of 23 SNPs on six chromosomes. In a series of analyses of the effect of a maternal factor (dam's gait) on gait determination, the effect was suggested in comparison of the frequencies of exhibiting pace in gait checks in only two animal groups having dams with different DMRT3:Ser301Ter genotypes (P < 0.05), suggesting that the gait of the dam does not have a major effect on whether progeny homozygous for the DMRT3:Ser301Ter mutation will preferentially pace or trot.     

Population genomic footprints of selection and associations with climate in natural populations of Arabidopsis halleri from the Alps

Natural genetic variation is essential for the adaptation of organisms to their local environment and to changing environmental conditions. Here, we examine genomewide patterns of nucleotide variation in natural populations of the outcrossing herb Arabidopsis halleri and associations with climatic variation among populations in the Alps. Using a pooled population sequencing (Pool‐Seq) approach, we discovered more than two million SNPs in five natural populations and identified highly differentiated genomic regions and SNPs using F_ST‐based analyses. We tested only the most strongly differentiated SNPs for associations with a nonredundant set of environmental factors using partial Mantel tests to identify topo‐climatic factors that may underlie the observed footprints of selection. Possible functions of genes showing signatures of selection were identified by Gene Ontology analysis. We found 175 genes to be highly associated with one or more of the five tested topo‐climatic factors. Of these, 23.4% had unknown functions. Genetic variation in four candidate genes was strongly associated with site water balance and solar radiation, and functional annotations were congruent with these environmental factors. Our results provide a genomewide perspective on the distribution of adaptive genetic variation in natural plant populations from a highly diverse and heterogeneous alpine environment.     

A genome‐wide SNP scan reveals two loci associated with the chicken resistance to Marek's disease

Marek's disease (MD) is a neoplastic disease in chickens, caused by the Marek's disease virus (MDV). To investigate host genetic resistance to MD, we conducted a genome‐wide association study (GWAS) on 67 MDV‐infected chickens based on a case and control design, including 57 susceptible chickens in the case group and 10 resistant chickens as controls. After searching 38 655 valid genomic markers, two SNPs were found to be associated with host resistance to MD. One SNP, rs14527240, reaching chromosome‐wide significance level (P < 0.01) was located in the SPARC‐related modular calcium‐binding 1 (SMOC1) gene on GGA5. The other one, GGaluGA156129, reaching genome‐wide significance (P < 0.05), was located in the protein tyrosine phosphatase, non‐receptor type 3 (PTPN3) gene on GGA2. In addition, expression patterns of these two genes in spleens were detected by qPCR. The expression of SMOC1 was significantly up‐regulated (P < 0.05), whereas the expression of PTNP3 did not show significance when the case group was compared with the control group. Up‐regulation of SMOC1 in susceptible spleens suggests its important roles in MD tumorigenesis. This is the first study to investigate MD‐resistant loci, and it demonstrates the power of GWASs for mapping genes associated with MD resistance.     

A genome‐wide association study reveals candidate genes for the supernumerary nipple phenotype in sheep (Ovis aries)

Genome‐wide association studies (GWASs) have been widely applied in livestock to identify genes associated with traits of economic interest. Here, we conducted the first GWAS of the supernumerary nipple phenotype in Wadi sheep, a native Chinese sheep breed, based on Ovine Infinium HD SNP BeadChip genotypes in a total of 144 ewes (75 cases with four teats, including two normal and two supernumerary teats, and 69 control cases with two teats). We detected 63 significant SNPs at the chromosome‐wise threshold. Additionally, one candidate region (chr1: 170.723–170.734 Mb) was identified by haplotype‐based association tests, with one SNP (rs413490006) surrounding functional genes BBX and CD47 on chromosome 1 being commonly identified as significant by the two mentioned analyses. Moreover, Gene Ontology enrichment for the significant SNPs identified by the GWAS analysis was functionally clustered into the categories of receptor activity and synaptic membrane. In addition, pathway mapping revealed four promising pathways (Wnt, oxytocin, MAPK and axon guidance) involved in the development of the supernumerary nipple phenotype. Our results provide novel and important insights into the genetic mechanisms underlying the phenotype of supernumerary nipples in mammals, including humans. These findings may be useful for future breeding and genetics in sheep and other livestock.