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.     

Large‐scale molecular genetic analysis in plant‐pathogenic fungi: a decade of genome‐wide functional analysis

Plant‐pathogenic fungi cause diseases to all major crop plants world‐wide and threaten global food security. Underpinning fungal diseases are virulence genes facilitating plant host colonization that often marks pathogenesis and crop failures, as well as an increase in staple food prices. Fungal molecular genetics is therefore the cornerstone to the sustainable prevention of disease outbreaks. Pathogenicity studies using mutant collections provide immense function‐based information regarding virulence genes of economically relevant fungi. These collections are rich in potential targets for existing and new biological control agents. They contribute to host resistance breeding against fungal pathogens and are instrumental in searching for novel resistance genes through the identification of fungal effectors. Therefore, functional analyses of mutant collections propel gene discovery and characterization, and may be incorporated into disease management strategies. In the light of these attributes, mutant collections enhance the development of practical solutions to confront modern agricultural constraints. Here, a critical review of mutant collections constructed by various laboratories during the past decade is provided. We used Magnaporthe oryzae and Fusarium graminearum studies to show how mutant screens contribute to bridge existing knowledge gaps in pathogenicity and fungal–host interactions.     

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.     

Genome‐wide analysis of DNA methylation in endometriosis using Illumina Human Methylation 450 K BeadChips

Endometriosis is a common chronic gynecologic disorder characterized by the presence and growth of endometrial‐like tissue outside of the uterine cavity. Although the exact etiology remains unclear, epigenetic modifications, such as DNA methylation, are thought to contribute to the pathogenesis of endometriosis. Here, we used the Illumina Human Methylation 450 K BeadChip Array to analyze the genome‐wide DNA methylation profiles of six endometriotic lesions and six eutopic endometria from patients with ovarian endometriosis and six endometria of women without endometriosis. Compared with the eutopic endometria of women with endometriosis, 12,159 differentially methylated CpG sites and 375 differentially methylated promoter regions were identified in endometriotic lesions. GO analyses showed that these putative differentially methylated genes were primarily associated with immune response, inflammatory response, response to steroid hormone stimulus, cell adhesion, negative regulation of apoptosis, and activation of the MAPK activity. In addition, the expression levels of DNMT1, DNMT3A, DNMT3B, and MBD2 in endometriotic lesions and eutopic endometria were significantly decreased compared with control endometria. Our findings suggest that aberrant DNA methylation status in endometriotic lesions may play a significant role in the pathogenesis and progression of endometriosis.     

A genome‐wide association study using selective DNA pooling identifies candidate markers for fertility in Holstein cattle

The decline in the reproductive efficiency of dairy cows, especially those with high producing potential, has become a challenging problem. In this study, a selective DNA pooling approach was applied to a cow population whose oocytes were fertilized and cultured to obtain phenotypic records of fertilization rate and blastocyst rate. Using a stringent 5% genome‐wide significance level, 22 and five single nucleotide polymorphisms (SNPs) were found to be associated with fertilization rate and blastocyst rate, respectively. SNPs that showed significant association in selective DNA pooling were further evaluated by individual genotyping. Interestingly, the majority of the SNP associations were confirmed by individual genotyping, testifying to the effectiveness of selective DNA pooling using a high‐density SNP genotyping array. This study is the first application of the selective DNA pooling approach using the BovineSNP50 array in cattle.     

A genome‐wide association study of copy number variations with umbilical hernia in swine

Umbilical hernia (UH) is one of the most common congenital defects in pigs, leading to considerable economic loss and serious animal welfare problems. To test whether copy number variations (CNVs) contribute to pig UH, we performed a case–control genome‐wide CNV association study on 905 pigs from the Duroc, Landrace and Yorkshire breeds using the Porcine SNP60 BeadChip and penncnv algorithm. We first constructed a genomic map comprising 6193 CNVs that pertain to 737 CNV regions. Then, we identified eight CNVs significantly associated with the risk for UH in the three pig breeds. Six of seven significantly associated CNVs were validated using quantitative real‐time PCR. Notably, a rare CNV (CNV14:13030843–13059455) encompassing the NUGGC gene was strongly associated with UH (permutation‐corrected P = 0.0015) in Duroc pigs. This CNV occurred exclusively in seven Duroc UH‐affected individuals. SNPs surrounding the CNV did not show association signals, indicating that rare CNVs may play an important role in complex pig diseases such as UH. The NUGGC gene has been implicated in human omphalocele and inguinal hernia. Our finding supports that CNVs, including the NUGGC CNV, contribute to the pathogenesis of pig UH.     

Methyl CpG Binding Domain Ultra‐Sequencing: a novel method for identifying inter‐individual and cell‐type‐specific variation in DNA methylation

Experience‐dependent changes in DNA methylation can exert profound effects on neuronal function and behaviour. A single learning event can induce a variety of DNA modifications within the neuronal genome, some of which may be common to all individuals experiencing the event, whereas others may occur in a subset of individuals. Variations in experience‐induced DNA methylation may subsequently confer increased vulnerability or resilience to the development of neuropsychiatric disorders. However, the detection of experience‐dependent changes in DNA methylation in the brain has been hindered by the interrogation of heterogeneous cell populations, regional differences in epigenetic states and the use of pooled tissue obtained from multiple individuals. Methyl CpG Binding Domain Ultra‐Sequencing (MBD Ultra‐Seq) overcomes current limitations on genome‐wide epigenetic profiling by incorporating fluorescence‐activated cell sorting and sample‐specific barcoding to examine cell‐type‐specific CpG methylation in discrete brain regions of individuals. We demonstrate the value of this method by characterizing differences in 5‐methylcytosine (5mC) in neurons and non‐neurons of the ventromedial prefrontal cortex of individual adult C57BL/6 mice, using as little as 50 ng of genomic DNA per sample. We find that the neuronal methylome is characterized by greater CpG methylation as well as the enrichment of 5mC within intergenic loci. In conclusion, MBD Ultra‐Seq is a robust method for detecting DNA methylation in neurons derived from discrete brain regions of individual animals. This protocol will facilitate the detection of experience‐dependent changes in DNA methylation in a variety of behavioural paradigms and help identify aberrant experience‐induced DNA methylation that may underlie risk and resiliency to neuropsychiatric disease.     

Pathway analysis of genome‐wide association datasets of personality traits

Although several genome‐wide association (GWA) studies of human personality have been recently published, genetic variants that are highly associated with certain personality traits remain unknown, due to difficulty reproducing results. To further investigate these genetic variants, we assessed biological pathways using GWA datasets. Pathway analysis using GWA data was performed on 1089 Korean women whose personality traits were measured with the Revised NEO Personality Inventory for the 5‐factor model of personality. A total of 1042 pathways containing 8297 genes were included in our study. Of these, 14 pathways were highly enriched with association signals that were validated in 1490 independent samples. These pathways include association of: Neuroticism with axon guidance [L1 cell adhesion molecule (L1CAM) interactions]; Extraversion with neuronal system and voltage‐gated potassium channels; Agreeableness with L1CAM interaction, neurotransmitter receptor binding and downstream transmission in postsynaptic cells; and Conscientiousness with the interferon‐gamma and platelet‐derived growth factor receptor beta polypeptide pathways. Several genes that contribute to top‐ranked pathways in this study were previously identified in GWA studies or by pathway analysis in schizophrenia or other neuropsychiatric disorders. Here we report the first pathway analysis of all five personality traits. Importantly, our analysis identified novel pathways that contribute to understanding the etiology of personality traits.     

A genome‐wide association analysis for carcass traits in a commercial Duroc pig population

We performed a genome‐wide association study to map the genetic determinants of carcass traits in 350 Duroc pigs typed with the Porcine SNP60 BeadChip. Association analyses were carried out using the gemma software. The proportion of phenotypic variance explained by the SNPs ranged between negligible to moderate (= 0.01–0.30) depending on the trait under consideration. At the genome‐wide level, we detected one significant association between backfat thickness between the 3^rd and 4^th ribs and six SNPs mapping to SSC12 (37–40 Mb). We also identified several chromosome‐wide significant associations for ham weight (SSC11: 51–53 Mb, three SNPs; 67–68 Mb, two SNPs), carcass weight (SSC11: 66–68 Mb, two SNPs), backfat thickness between the 3^rd and 4^th ribs (SSC12: 21 Mb, one SNP; 33–40 Mb, 17 SNPs; 51–58 Mb, two SNPs), backfat thickness in the last rib (SSC12: 37 Mb, one SNP; 40–41 Mb, nine SNPs) and lean meat content (SSC13: 34 Mb, three SNPs and SSC16: 45.1 Mb, one SNP; 62–63 Mb, 10 SNPs; 71–75 Mb, nine SNPs). The ham weight trait‐associated region on SSC11 contains two genes (UCHL3 and LMO7) related to muscle development. In addition, the ACACA gene, which encodes an enzyme for the catalysis of fatty acid synthesis, maps to the SSC12 (37–41 Mb) region harbouring trait‐associated regions for backfat thickness traits. Sequencing of these candidate genes may help to uncover the causal mutations responsible for the associations found in the present study.     

Genome‐wide variation in DNA methylation is associated with stress resilience and plumage brightness in a wild bird

Individuals often differ in their ability to cope with challenging environmental and social conditions. Evidence from model systems suggests that patterns of DNA methylation are associated with variation in coping ability. These associations could arise directly if methylation plays a role in controlling the physiological response to stressors by, among other things, regulating the release of glucocorticoids in response to challenges. Alternatively, the association could arise indirectly if methylation and resilience have a common cause, such as early‐life conditions. In either case, methylation might act as a biomarker for coping ability. At present, however, relatively little is known about whether variation in methylation is associated with organismal performance and resilience under natural conditions. We studied genome‐wide patterns of DNA methylation in free‐living female tree swallows (Tachycineta bicolor) using methylated DNA immunoprecipitation (MeDIP) and a tree swallow genome that was assembled for this study. We identified areas of the genome that were differentially methylated with respect to social signal expression (breast brightness) and physiological traits (ability to terminate the glucocorticoid stress response through negative feedback). We also asked whether methylation predicted resilience to a subsequent experimentally imposed challenge. Individuals with brighter breast plumage and higher stress resilience had lower methylation at differentially methylated regions across the genome. Thus, widespread differences in methylation predicted both social signal expression and the response to future challenges under natural conditions. These results have implications for predicting individual differences in resilience, and for understanding the mechanistic basis of resilience and its environmental and social mediators.     

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.     

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 DNA methylation changes with age in disease‐free human skeletal muscle

A decline in skeletal muscle mass and function with aging is well recognized, but remains poorly characterized at the molecular level. Here, we report for the first time a genome‐wide study of DNA methylation dynamics in skeletal muscle of healthy male individuals during normal human aging. We predominantly observed hypermethylation throughout the genome within the aged group as compared to the young subjects. Differentially methylated CpG (dmCpG) nucleotides tend to arise intragenically and are underrepresented in promoters and are overrepresented in the middle and 3′ end of genes. The intragenic methylation changes are overrepresented in genes that guide the formation of the junction of the motor neuron and myofibers. We report a low level of correlation of gene expression from previous studies of aged muscle with our current analysis of DNA methylation status. For those genes that had both changes in methylation and gene expression with age, we observed a reverse correlation, with the exception of intragenic hypermethylated genes that were correlated with an increased gene expression. We suggest that a minimal number of dmCpG sites or select sites are required to be altered in order to correlate with gene expression changes. Finally, we identified 500 dmCpG sites that perform well in discriminating young from old samples. Our findings highlight epigenetic links between aging postmitotic skeletal muscle and DNA methylation.