Comparing allele specific expression and local expression quantitative trait loci and the influence of gene expression on complex trait variation in cattle

Genomic dissection of the correlation between milk yield and various health traits using functional and evolutionary information about imputed sequence variants of 34,497 German Holstein cows

BACKGROUND

Over the last decades, it was subject of many studies to investigate the genomic connection of milk production and health traits in dairy cattle. Thereby, incorporating functional information in genomic analyses has been shown to improve the understanding of biological and molecular mechanisms shaping complex traits and the accuracies of genomic prediction, especially in small populations and across-breed settings. Still, little is known about the contribution of different functional and evolutionary genome partitioning subsets to milk production and dairy health. Thus, we performed a uni- and a bivariate analysis of milk yield (MY) and eight health traits using a set of ~34,497 German Holstein cows with 50K chip genotypes and ~17 million imputed sequence variants divided into 27 subsets depending on their functional and evolutionary annotation. In the bivariate analysis, eight trait-combinations were observed that contrasted MY with each health trait. Two genomic relationship matrices (GRM) were included, one consisting of the 50K chip variants and one consisting of each set of subset variants, to obtain subset heritabilities and genetic correlations. In addition, 50K chip heritabilities and genetic correlations were estimated applying merely the 50K GRM.

RESULTS

In general, 50K chip heritabilities were larger than the subset heritabilities. The largest heritabilities were found for MY, which was 0.4358 for the 50K and 0.2757 for the subset heritabilities. Whereas all 50K genetic correlations were negative, subset genetic correlations were both, positive and negative (ranging from -0.9324 between MY and mastitis to 0.6662 between MY and digital dermatitis). The subsets containing variants which were annotated as noncoding related, splice sites, untranslated regions, metabolic quantitative trait loci, and young variants ranked highest in terms of their contribution to the traits` genetic variance. We were able to show that linkage disequilibrium between subset variants and adjacent variants did not cause these subsets` high effect.

CONCLUSION

Our results confirm the connection of milk production and health traits in dairy cattle via the animals` metabolic state. In addition, they highlight the potential of including functional information in genomic analyses, which helps to dissect the extent and direction of the observed traits` connection in more detail.

Whole genome sequencing identified genomic diversity and candidated genes associated with economic traits in Northeasern Merino in China

Introduction: Northeast Merino (NMS) is a breed developed in Northeast China during the 1960s for wool and meat production. It exhibits excellent traits such as high wool yield, superior meat quality, rapid growth rate, robust disease resistance, and adaptability to cold climates. However, no studies have used whole-genome sequencing data to investigate the superior traits of NMS. Methods: In this study, we investigated the population structure, genetic diversity, and selection signals of NMS using whole-genome sequencing data from 20 individuals. Two methods (integrated haplotype score and composite likelihood ratio) were used for selection signal analysis, and the Fixation Index was used to explore the selection signals of NMS and the other two breeds, Mongolian sheep and South African meat Merino. Results: The results showed that NMS had low inbreeding levels, high genomic diversity, and a pedigree of both Merino breeds and Chinese local breeds. A total length of 14.09 Mb genomic region containing 287 genes was detected using the two methods. Further exploration of the functions of these genes revealed that they are mainly concentrated in wool production performance (IRF2BP2, MAP3K7, and WNT3), meat production performance (NDUFA9, SETBP1, ZBTB38, and FTO), cold resistance (DNAJC13, LPGAT1, and PRDM16), and immune response (PRDM2, GALNT8, and HCAR2). The selection signals of NMS and the other two breeds annotated 87 and 23 genes, respectively. These genes were also mainly focused on wool and meat production performance. Conclusion: These results provide a basis for further breeding improvement, comprehensive use of this breed, and a reference for research on other breeds.

An organism-wide ATAC-seq peak catalog for the bovine and its use to identify regulatory variants

We report the generation of an organism-wide catalog of 976,813 cis-acting regulatory elements for the bovine detected by the assay for transposase accessible chromatin using sequencing (ATAC-seq). We regroup these regulatory elements in 16 components by nonnegative matrix factorization. Correlation between the genome-wide density of peaks and transcription start sites, correlation between peak accessibility and expression of neighboring genes, and enrichment in transcription factor binding motifs support their regulatory potential. Using a previously established catalog of 12,736,643 variants, we show that the proportion of single-nucleotide polymorphisms mapping to ATAC-seq peaks is higher than expected and that this is owing to an approximately 1.3-fold higher mutation rate within peaks. Their site frequency spectrum indicates that variants in ATAC-seq peaks are subject to purifying selection. We generate eQTL data sets for liver and blood and show that variants that drive eQTL fall into liver- and blood-specific ATAC-seq peaks more often than expected by chance. We combine ATAC-seq and eQTL data to estimate that the proportion of regulatory variants mapping to ATAC-seq peaks is approximately one in three and that the proportion of variants mapping to ATAC-seq peaks that are regulatory is approximately one in 25. We discuss the implication of these findings on the utility of ATAC-seq information to improve the accuracy of genomic selection.

Differential Allele-Specific Expression Revealed Functional Variants and Candidate Genes Related to Meat Quality Traits in B. indicus Muscle

Traditional transcriptomics approaches have been used to identify candidate genes affecting economically important livestock traits. Regulatory variants affecting these traits, however, remain under covered. Genomic regions showing allele-specific expression (ASE) are under the effect of cis-regulatory variants, being useful for improving the accuracy of genomic selection models. Taking advantage of the better of these two methods, we investigated single nucleotide polymorphisms (SNPs) in regions showing differential ASE (DASE SNPs) between contrasting groups for beef quality traits. For these analyses, we used RNA sequencing data, imputed genotypes and genomic estimated breeding values of muscle-related traits from 190 Nelore (Bos indicus) steers. We selected 40 contrasting unrelated samples for the analysis (N = 20 animals per contrasting group) and used a beta-binomial model to identify ASE SNPs in only one group (i.e., DASE SNPs). We found 1479 DASE SNPs (FDR ≤ 0.05) associated with 55 beef-quality traits. Most DASE genes were involved with tenderness and muscle homeostasis, presenting a co-expression module enriched for the protein ubiquitination process. The results overlapped with epigenetics and phenotype-associated data, suggesting that DASE SNPs are potentially linked to cis-regulatory variants affecting simultaneously the transcription and phenotype through chromatin state modulation.

Genetic variation in histone modifications and gene expression identifies regulatory variants in the mammary gland of cattle

BACKGROUND

Causal variants for complex traits, such as eQTL are often found in non-coding regions of the genome, where they are hypothesised to influence phenotypes by regulating gene expression. Many regulatory regions are marked by histone modifications, which can be assayed by chromatin immunoprecipitation followed by sequencing (ChIP-seq). Sequence reads from ChIP-seq form peaks at putative regulatory regions, which may reflect the amount of regulatory activity at this region. Therefore, eQTL which are also associated with differences in histone modifications are excellent candidate causal variants.

RESULTS

We assayed the histone modifications H3K4Me3, H3K4Me1 and H3K27ac and mRNA in the mammary gland of up to 400 animals. We identified QTL for peak height (histone QTL), exon expression (eeQTL), allele specific expression (aseQTL) and allele specific binding (asbQTL). By intersecting these results, we identify variants which may influence gene expression by altering regulatory regions of the genome, and may be causal variants for other traits. Lastly, we find that these variants are found in putative transcription factor binding sites, identifying a mechanism for the effect of many eQTL.

CONCLUSIONS

We find that allele specific and traditional QTL analysis often identify the same genetic variants and provide evidence that many eQTL are regulatory variants which alter activity at regulatory regions of the bovine genome. Our work provides methodological and biological updates on how regulatory mechanisms interplay at multi-omics levels.

Cis-eQTL Analysis and Functional Validation of Candidate Genes for Carcass Yield Traits in Beef Cattle

Carcass yield traits are of considerable economic importance for farm animals, which act as a major contributor to the world’s food supply. Genome-wide association studies (GWASs) have identified many genetic variants associated with carcass yield traits in beef cattle. However, their functions are not effectively illustrated. In this study, we performed an integrative analysis of gene-based GWAS with expression quantitative trait locus (eQTL) analysis to detect candidate genes for carcass yield traits and validate their effects on bovine skeletal muscle satellite cells (BSCs). The gene-based GWAS and cis-eQTL analysis revealed 1780 GWAS and 1538 cis-expression genes. Among them, we identified 153 shared genes that may play important roles in carcass yield traits. Notably, the identified cis-eQTLs of PON3 and PRIM2 were significantly (p < 0.001) enriched in previous GWAS loci for carcass traits. Furthermore, overexpression of PON3 and PRIM2 promoted the BSCs’ proliferation, increased the expression of MYOD and downregulated the expression of MYOG, which indicated that these genes may inhibit myogenic differentiation. In contrast, PON3 and PRIM2 were significantly downregulated during the differentiation of BSCs. These findings suggested that PON3 and PRIM2 may promote the proliferation of BSCs and inhibit them in the pre-differentiation stage. Our results further contribute to the understanding of the molecular mechanisms of carcass yield traits in beef cattle.

Quantification of mutant–allele expression at isoform level in cancer from RNA-seq data

Even though the role of DNA mutations in cancer is well recognized, current quantification of the RNA expression, performed either at gene or isoform level, typically ignores the mutation status. Standard methods for estimating allele-specific expression (ASE) consider gene-level expression, but the functional impact of a mutation is best assessed at isoform level. Hence our goal is to quantify the mutant–allele expression at isoform level. We have developed and implemented a method, named MAX, for quantifying mutant–allele expression given a list of mutations. For a gene of interest, a mutant reference is constructed by incorporating all possible mutant versions of the wild-type isoforms in the transcriptome annotation. The mutant reference is then used for the RNA-seq reads mapping, which in principle works similarly for any quantification tool. We apply an alternating EM algorithm to the read-count data from the mapping step. In a simulation study, MAX performs well against standard isoform-quantification methods. Also, MAX achieves higher accuracy than conventional gene-based ASE methods such as ASEP. An analysis of a real dataset of acute myeloid leukemia reveals a subgroup of NPM1-mutated patients responding well to a kinase inhibitor. Our findings indicate that quantification of mutant–allele expression at isoform level is feasible and has potential added values for assessing the functional impact of DNA mutations in cancers.

Airpart: interpretable statistical models for analyzing allelic imbalance in single-cell datasets

MOTIVATION

Allelic expression analysis aids in detection of cis-regulatory mechanisms of genetic variation, which produce allelic imbalance (AI) in heterozygotes. Measuring AI in bulk data lacking time or spatial resolution has the limitation that cell-type-specific (CTS), spatial- or time-dependent AI signals may be dampened or not detected.

RESULTS

We introduce a statistical method airpart for identifying differential CTS AI from single-cell RNA-sequencing data, or dynamics AI from other spatially or time-resolved datasets. airpart outputs discrete partitions of data, pointing to groups of genes and cells under common mechanisms of cis-genetic regulation. In order to account for low counts in single-cell data, our method uses a Generalized Fused Lasso with Binomial likelihood for partitioning groups of cells by AI signal, and a hierarchical Bayesian model for AI statistical inference. In simulation, airpart accurately detected partitions of cell types by their AI and had lower Root Mean Square Error (RMSE) of allelic ratio estimates than existing methods. In real data, airpart identified differential allelic imbalance patterns across cell states and could be used to define trends of AI signal over spatial or time axes.

AVAILABILITY AND IMPLEMENTATION

The airpart package is available as an R/Bioconductor package at https://bioconductor.org/packages/airpart.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Genomic reaction norms inform predictions of plastic and adaptive responses to climate change

Genomic reaction norms represent the range of gene expression phenotypes (usually mRNA transcript levels) expressed by a genotype along an environmental gradient. Reaction norms derived from common‐garden experiments are powerful approaches for disentangling plastic and adaptive responses to environmental change in natural populations. By treating gene expression as a phenotype in itself, genomic reaction norms represent invaluable tools for exploring causal mechanisms underlying organismal responses to climate change across multiple levels of biodiversity.

Our goal is to provide the context, framework and motivation for applying genomic reaction norms to study the responses of natural populations to climate change.

Here, we describe the utility of integrating genomics with common‐garden‐gradient experiments under a reaction norm analytical framework to answer fundamental questions about phenotypic plasticity, local adaptation, their interaction (i.e. genetic variation in plasticity) and future adaptive potential.

An experimental and analytical framework for constructing and analysing genomic reaction norms is presented within the context of polygenic climate change responses of structured populations with gene flow. Intended for a broad eco‐evo readership, we first briefly review adaptation with gene flow and the importance of understanding the genomic basis and spatial scale of adaptation for conservation and management of structured populations under anthropogenic change. Then, within a high‐dimensional reaction norm framework, we illustrate how to distinguish plastic, differentially expressed (difference in reaction norm intercepts) and differentially plastic (difference in reaction norm slopes) genes, highlighting the areas of opportunity for applying these concepts.

We conclude by discussing how genomic reaction norms can be incorporated into a holistic framework to understand the eco‐evolutionary dynamics of climate change responses from molecules to ecosystems. We aim to inspire researchers to integrate gene expression measurements into common‐garden experimental designs to investigate the genomics of climate change responses as sequencing costs become increasingly accessible.

Chemerin Impact on Alternative mRNA Transcription in the Porcine Luteal Cells

Chemerin participates in the regulation of processes related to physiological and disorder mechanisms in mammals, including metabolism, obesity, inflammation, and reproduction. In this study, we have investigated chemerin influence on alternative mRNA transcription within the porcine luteal cell transcriptome, such as differential expression of long non-coding RNAs (DELs) and their interactions with differentially expressed genes (DEGs), differences in alternative splicing of transcripts (DASs), and allele-specific expression (ASEs) related to the single nucleotide variants (SNVs) frequency. Luteal cells were collected from gilts during the mid-luteal phase of the oestrous cycle. After in vitro culture of cells un-/treated with chemerin, the total RNA was isolated and sequenced using the high-throughput method. The in silico analyses revealed 24 DELs cis interacting with 6 DEGs and trans-correlated with 300 DEGs, 137 DASs events, and 18 ASEs. The results enabled us to analyse metabolic and signalling pathways in detail, providing new insights into the effects of chemerin on the corpus luteum functions related to inflammatory response, leukocyte infiltration, the occurrence of luteotropic and luteolytic signals (leading to apoptosis and/or necroptosis). Validation of the results using qPCR confirmed the predicted expression changes. Chemerin at physiological concentrations significantly modifies the transcription processes in the porcine luteal cells.

Highly-contiguous bovine genomes underpin accurate functional analyses and updated nomenclature of MHC class I

The major histocompatibility complex (MHC) class I region of cattle is both highly polymorphic and, unlike many species, highly variable in gene content between haplotypes. Cattle MHC class I alleles were historically grouped by sequence similarity in the more conserved 3' end of the coding sequence to form phylogenetic allele groups. This has formed the basis of current cattle MHC class I nomenclature. We presently describe and compare five fully assembled MHC class I haplotypes using the latest cattle and yak genome assemblies. Of the five previously described "pseudogenes" in the cattle MHC class I region, Pseudogene 3 is putatively functional in all haplotypes and Pseudogene 6 and Pseudogene 7 are putatively functional in some haplotypes. This was reinforced by evidence of transcription. Based on full gene sequences as well as 3' coding sequence, we identified distinct subgroups of BoLA-3 and BoLA-6 that represent distinct genetic loci. We further examined allele-specific expression using transcriptomic data revealing that certain alleles are consistently weakly expressed compared to others. These observations will help to inform further studies into how MHC class I region variability influences T cell and natural killer cell functions in cattle.

Evidence for and localization of proposed causative variants in cattle and pig genomes

BACKGROUND

This paper reviews the localization of published potential causative variants in contemporary pig and cattle reference genomes, and the evidence for their causality. In spite of the difficulties inherent to the identification of causative variants from genetic mapping and genome-wide association studies, researchers in animal genetics have proposed putative causative variants for several traits relevant to livestock breeding.

RESULTS

For this review, we read the literature that supports potential causative variants in 13 genes (ABCG2, DGAT1, GHR, IGF2, MC4R, MSTN, NR6A1, PHGK1, PRKAG3, PLRL, RYR1, SYNGR2 and VRTN) in cattle and pigs, and localized them in contemporary reference genomes. We review the evidence for their causality, by aiming to separate the evidence for the locus, the proposed causative gene and the proposed causative variant, and report the bioinformatic searches and tactics needed to localize the sequence variants in the cattle or pig genome.

CONCLUSIONS

Taken together, there is usually good evidence for the association at the locus level, some evidence for a specific causative gene at eight of the loci, and some experimental evidence for a specific causative variant at six of the loci. We recommend that researchers who report new potential causative variants use referenced coordinate systems, show local sequence context, and submit variants to repositories.

A systematic comparison of normalization methods for eQTL analysis

Expression quantitative trait loci (eQTL) analysis has been widely used in interpreting disease-associated loci through correlating genetic variant loci with the expression of specific genes. RNA-sequencing (RNA-Seq), which can quantify gene expression at the genome-wide level, is often used in eQTL identification. Since different normalization methods of gene expression have substantial impacts on RNA-seq downstream analysis, it is of great necessity to systematically compare the effects of these methods on eQTL identification. Here, by using RNA-seq and genotype data of four different cancers in The Cancer Genome Atlas (TCGA) database, we comprehensively evaluated the effect of eight commonly used normalization methods on eQTL identification. Our results showed that the application of different methods could cause 20-30% differences in the final results of eQTL identification. Among these methods, COUNT, Median of Ratio (MED) and Trimmed Mean of M-values (TMM) generated similar results for identifying eQTLs, while Fragments Per Kilobase Million (FPKM) or RANK produced more differential results compared with other methods. Based on the accuracy and receiver operating characteristic (ROC) curve, the TMM method was found to be the optimal method for normalizing gene expression data in eQTLs analysis. In addition, we also evaluated the performance of different pairwise combinations of these methods. As a result, compared with single normalization methods, the combination of methods can not only identify more cis-eQTLs, but also improve the performance of the ROC curve. Overall, this study provides a comprehensive comparison of normalization methods for identifying eQTLs from RNA-seq data, and proposes some practical recommendations for diverse scenarios.

aScan: A Novel Method for the Study of Allele Specific Expression in Single Individuals

In diploid organisms, two copies of each allele are normally inherited from parents. Paternal and maternal alleles can be regulated and expressed unequally, which is referred to as allele-specific expression (ASE). In this work, we present aScan, a novel method for the identification of ASE from the analysis of matched individual genomic and RNA sequencing data. By performing extensive analyses of both real and simulated data, we demonstrate that aScan can correctly identify ASE with high accuracy and sensitivity in different experimental settings. Additionally, by applying our method to a small cohort of individuals that are not included in publicly available databases of human genetic variation, we outline the value of possible applications of ASE analysis in single individuals for deriving a more accurate annotation of "private" low-frequency genetic variants associated with regulatory effects on transcription. All in all, we believe that aScan will represent a beneficial addition to the set of bioinformatics tools for the analysis of ASE. Finally, while our method was initially conceived for the analysis of RNA-seq data, it can in principle be applied to any quantitative NGS assay for which matched genotypic and expression data are available. AVAILABILITY: aScan is currently available in the form of an open source standalone software package at: https://github.com/Federico77z/aScan/. aScan version 1.0.3, available at https://github.com/Federico77z/aScan/releases/tag/1.0.3, has been used for all the analyses included in this manuscript. A Docker image of the tool has also been made available at https://github.com/pmandreoli/aScanDocker.

Expression quantitative trait loci in sheep liver and muscle contribute to variations in meat traits

Background

Variants that regulate transcription, such as expression quantitative trait loci (eQTL), have shown enrichment in genome-wide association studies (GWAS) for mammalian complex traits. However, no study has reported eQTL in sheep, although it is an important agricultural species for which many GWAS of complex meat traits have been conducted. Using RNA sequence data produced from liver and muscle from 149 sheep and imputed whole-genome single nucleotide polymorphisms (SNPs), our aim was to dissect the genetic architecture of the transcriptome by associating sheep genotypes with three major molecular phenotypes including gene expression (geQTL), exon expression (eeQTL) and RNA splicing (sQTL). We also examined these three types of eQTL for their enrichment in GWAS of multi-meat traits and fatty acid profiles.

Results

Whereas a relatively small number of molecular phenotypes were significantly heritable (h² > 0, P < 0.05), their mean heritability ranged from 0.67 to 0.73 for liver and from 0.71 to 0.77 for muscle. Association analysis between molecular phenotypes and SNPs within ± 1 Mb identified many significant cis-eQTL (false discovery rate, FDR < 0.01). The median distance between the eQTL and transcription start sites (TSS) ranged from 68 to 153 kb across the three eQTL types. The number of common variants between geQTL, eeQTL and sQTL within each tissue, and the number of common variants between liver and muscle within each eQTL type were all significantly (P < 0.05) larger than expected by chance. The identified eQTL were significantly (P < 0.05) enriched in GWAS hits associated with 56 carcass traits and fatty acid profiles. For example, several geQTL in muscle mapped to the FAM184B gene, hundreds of sQTL in liver and muscle mapped to the CAST gene, and hundreds of sQTL in liver mapped to the C6 gene. These three genes are associated with body composition or fatty acid profiles.

Conclusions

We detected a large number of significant eQTL and found that the overlap of variants between eQTL types and tissues was prevalent. Many eQTL were also QTL for meat traits. Our study fills a gap in the knowledge on the regulatory variants and their role in complex traits for the sheep model.

Genome-wide analysis of expression QTL (eQTL) and allele-specific expression (ASE) in pig muscle identifies candidate genes for meat quality traits

BACKGROUND

Genetic analysis of gene expression level is a promising approach for characterizing candidate genes that are involved in complex economic traits such as meat quality. In the present study, we conducted expression quantitative trait loci (eQTL) and allele-specific expression (ASE) analyses based on RNA-sequencing (RNAseq) data from the longissimus muscle of 189 Duroc × Luchuan crossed pigs in order to identify some candidate genes for meat quality traits.

RESULTS

Using a genome-wide association study based on a mixed linear model, we identified 7192 cis-eQTL corresponding to 2098 cis-genes (p ≤ 1.33e-3, FDR ≤ 0.05) and 6400 trans-eQTL corresponding to 863 trans-genes (p ≤ 1.13e-6, FDR ≤ 0.05). ASE analysis using RNAseq SNPs identified 9815 significant ASE-SNPs in 2253 unique genes. Integrative analysis between the cis-eQTL and ASE target genes identified 540 common genes, including 33 genes with expression levels that were correlated with at least one meat quality trait. Among these 540 common genes, 63 have been reported previously as candidate genes for meat quality traits, such as PHKG1 (q-value = 1.67e-6 for the leading SNP in the cis-eQTL analysis), NUDT7 (q-value = 5.67e-13), FADS2 (q-value = 8.44e-5), and DGAT2 (q-value = 1.24e-3).

CONCLUSIONS

The present study confirmed several previously published candidate genes and identified some novel candidate genes for meat quality traits via eQTL and ASE analyses, which will be useful to prioritize candidate genes in further studies.

Signatures of selection reveal candidate genes involved in economic traits and cold acclimation in five Swedish cattle breeds

Background

Thousands of years of natural and artificial selection have resulted in indigenous cattle breeds that are well-adapted to the environmental challenges of their local habitat and thereby are considered as valuable genetic resources. Understanding the genetic background of such adaptation processes can help us design effective breeding objectives to preserve local breeds and improve commercial cattle. To identify regions under putative selection, GGP HD 150 K single nucleotide polymorphism (SNP) arrays were used to genotype 106 individuals representing five Swedish breeds i.e. native to different regions and covering areas with a subarctic cold climate in the north and mountainous west, to those with a continental climate in the more densely populated south regions.

Results

Five statistics were incorporated within a framework, known as de-correlated composite of multiple signals (DCMS) to detect signatures of selection. The obtained p-values were adjusted for multiple testing (FDR < 5%), and significant genomic regions were identified. Annotation of genes in these regions revealed various verified and novel candidate genes that are associated with a diverse range of traits, including e.g. high altitude adaptation and response to hypoxia (DCAF8, PPP1R12A, SLC16A3, UCP2, UCP3, TIGAR), cold acclimation (AQP3, AQP7, HSPB8), body size and stature (PLAG1, KCNA6, NDUFA9, AKAP3, C5H12orf4, RAD51AP1, FGF6, TIGAR, CCND2, CSMD3), resistance to disease and bacterial infection (CHI3L2, GBP6, PPFIBP1, REP15, CYP4F2, TIGD2, PYURF, SLC10A2, FCHSD2, ARHGEF17, RELT, PRDM2, KDM5B), reproduction (PPP1R12A, ZFP36L2, CSPP1), milk yield and components (NPC1L1, NUDCD3, ACSS1, FCHSD2), growth and feed efficiency (TMEM68, TGS1, LYN, XKR4, FOXA2, GBP2, GBP5, FGD6), and polled phenotype (URB1, EVA1C).

Conclusions

We identified genomic regions that may provide background knowledge to understand the mechanisms that are involved in economic traits and adaptation to cold climate in cattle. Incorporating p-values of different statistics in a single DCMS framework may help select and prioritize candidate genes for further analyses.

Regulatory mechanisms of major depressive disorder risk variants

Major depressive disorder (MDD) is one of the most prevalent psychiatric disorders and a leading cause of disability worldwide. Though recent genome-wide association studies (GWAS) have identified multiple risk variants for MDD, how these variants confer MDD risk remains largely unknown. Here we systematically characterize the regulatory mechanism of MDD risk variants using a functional genomics approach. By integrating chromatin immunoprecipitation sequencing (ChIP-Seq) (from human brain tissues or neuronal cells) and position weight matrix (PWM) data, we identified 34 MDD risk SNPs that disrupt the binding of 15 transcription factors (TFs). We verified the regulatory effect of the TF binding-disrupting SNPs with reporter gene assays, allelic-specific expression analysis, and CRISPR-Cas9-mediated genome editing. Expression quantitative trait loci (eQTL) analysis identified the target genes that might be regulated by these regulatory risk SNPs. Finally, we found that NEGR1 (regulated by the TF binding-disrupting MDD risk SNP rs3101339) was dysregulated in the brains of MDD cases compared with controls, implying that rs3101339 may confer MDD risk by affecting NEGR1 expression. Our findings reveal how genetic variants contribute to MDD risk by affecting TF binding and gene regulation. More importantly, our study identifies the potential MDD causal variants and their target genes, thus providing pivotal candidates for future mechanistic study and drug development.

Allele-specific expression is widespread in Bos indicus muscle and affects meat quality candidate genes

Differences between the expression of the two alleles of a gene are known as allele-specific expression (ASE), a common event in the transcriptome of mammals. Despite ASE being a source of phenotypic variation, its occurrence and effects on genetic prediction of economically relevant traits are still unexplored in bovines. Furthermore, as ASE events are likely driven by cis-regulatory mutations, scanning them throughout the bovine genome represents a significant step to elucidate the mechanisms underlying gene expression regulation. To address this question in a Bos indicus population, we built the ASE profile of the skeletal muscle tissue of 190 Nelore steers, using RNA sequencing data and SNPs genotypes from the Illumina BovineHD BeadChip (770 K bp). After quality control, 820 SNPs showed at least one sample with ASE. These SNPs were widespread among all autosomal chromosomes, being 32.01% found in 3'UTR and 31.41% in coding regions. We observed a considerable variation of ASE profile among individuals, which highlighted the need for biological replicates in ASE studies. Functional analysis revealed that ASE genes play critical biological functions in the development and maintenance of muscle tissue. Additionally, some of these genes were previously reported as associated with beef production and quality traits in livestock, thus indicating a possible source of bias on genomic predictions for these traits.

Unravelling the complex genetics of common kidney diseases: from variants to mechanisms

Genome-wide association studies (GWAS) have identified hundreds of loci associated with kidney-related traits such as glomerular filtration rate, albuminuria, hypertension, electrolyte and metabolite levels. However, these impressive, large-scale mapping approaches have not always translated into an improved understanding of disease or development of novel therapeutics. GWAS have several important limitations. Nearly all disease-associated risk loci are located in the non-coding region of the genome and therefore, their target genes, affected cell types and regulatory mechanisms remain unknown. Genome-scale approaches can be used to identify associations between DNA sequence variants and changes in gene expression (quantified through bulk and single-cell methods), gene regulation and other molecular quantitative trait studies, such as chromatin accessibility, DNA methylation, protein expression and metabolite levels. Data obtained through these approaches, used in combination with robust computational methods, can deliver robust mechanistic inferences for translational exploitation. Understanding the genetic basis of common kidney diseases means having a comprehensive picture of the genes that have a causal role in disease development and progression, of the cells, tissues and organs in which these genes act to affect the disease, of the cellular pathways and mechanisms that drive disease, and of potential targets for disease prevention, detection and therapy.

cis-regulatory variation modulates susceptibility to enteric infection in the Drosophila genetic reference panel

BACKGROUND

Resistance to enteric pathogens is a complex trait at the crossroads of multiple biological processes. We have previously shown in the Drosophila Genetic Reference Panel (DGRP) that resistance to infection is highly heritable, but our understanding of how the effects of genetic variants affect different molecular mechanisms to determine gut immunocompetence is still limited.

RESULTS

To address this, we perform a systems genetics analysis of the gut transcriptomes from 38 DGRP lines that were orally infected with Pseudomonas entomophila. We identify a large number of condition-specific, expression quantitative trait loci (local-eQTLs) with infection-specific ones located in regions enriched for FOX transcription factor motifs. By assessing the allelic imbalance in the transcriptomes of 19 F1 hybrid lines from a large round robin design, we independently attribute a robust cis-regulatory effect to only 10% of these detected local-eQTLs. However, additional analyses indicate that many local-eQTLs may act in trans instead. Comparison of the transcriptomes of DGRP lines that were either susceptible or resistant to Pseudomonas entomophila infection reveals nutcracker as the only differentially expressed gene. Interestingly, we find that nutcracker is linked to infection-specific eQTLs that correlate with its expression level and to enteric infection susceptibility. Further regulatory analysis reveals one particular eQTL that significantly decreases the binding affinity for the repressor Broad, driving differential allele-specific nutcracker expression.

CONCLUSIONS

Our collective findings point to a large number of infection-specific cis- and trans-acting eQTLs in the DGRP, including one common non-coding variant that lowers enteric infection susceptibility.

Quantifying the contribution of sequence variants with regulatory and evolutionary significance to 34 bovine complex traits

Many genome variants shaping mammalian phenotype are hypothesized to regulate gene transcription and/or to be under selection. However, most of the evidence to support this hypothesis comes from human studies. Systematic evidence for regulatory and evolutionary signals contributing to complex traits in a different mammalian model is needed. Sequence variants associated with gene expression (expression quantitative trait loci [eQTLs]) and concentration of metabolites (metabolic quantitative trait loci [mQTLs]) and under histone-modification marks in several tissues were discovered from multiomics data of over 400 cattle. Variants under selection and evolutionary constraint were identified using genome databases of multiple species. These analyses defined 30 sets of variants, and for each set, we estimated the genetic variance the set explained across 34 complex traits in 11,923 bulls and 32,347 cows with 17,669,372 imputed variants. The per-variant trait heritability of these sets across traits was highly consistent (r > 0.94) between bulls and cows. Based on the per-variant heritability, conserved sites across 100 vertebrate species and mQTLs ranked the highest, followed by eQTLs, young variants, those under histone-modification marks, and selection signatures. From these results, we defined a Functional-And-Evolutionary Trait Heritability (FAETH) score indicating the functionality and predicted heritability of each variant. In additional 7,551 cattle, the high FAETH-ranking variants had significantly increased genetic variances and genomic prediction accuracies in 3 production traits compared to the low FAETH-ranking variants. The FAETH framework combines the information of gene regulation, evolution, and trait heritability to rank variants, and the publicly available FAETH data provide a set of biological priors for cattle genomic selection worldwide.

Comparative Analysis of the Liver Transcriptome among Cattle Breeds Using RNA-seq

Global gene expression in liver transcriptome varies among cattle breeds. The present investigation was aimed to identify the differentially expressed genes (DEGs), metabolic gene networks and metabolic pathways in bovine liver transcriptome of young bulls. In this study, we comparatively analyzed the bovine liver transcriptome of dairy (Polish Holstein Friesian (HF); n = 6), beef (Hereford; n = 6), and dual purpose (Polish-Red; n = 6) cattle breeds. This study identified 895, 338, and 571 significant (p < 0.01) differentially expressed (DE) gene-transcripts represented as 745, 265, and 498 hepatic DE genes through the Polish-Red versus Hereford, Polish-HF versus Hereford, and Polish-HF versus Polish-Red breeds comparisons, respectively. By combining all breeds comparisons, 75 hepatic DE genes (p < 0.01) were identified as commonly shared among all the three breed comparisons; 70, 160, and 38 hepatic DE genes were commonly shared between the following comparisons: (i) Polish-Red versus Hereford and Polish-HF versus Hereford; (ii) Polish-Red versus Hereford and Polish-HF versus Polish-Red; and (iii) Polish-HF versus Hereford and Polish-HF versus Polish-Red, respectively. A total of 440, 82, and 225 hepatic DE genes were uniquely observed for the Polish-Red versus Hereford, Polish-HF versus Hereford, and Polish-Red versus Polish-HF comparisons, respectively. Gene ontology (GO) analysis identified top-ranked enriched GO terms (p < 0.01) including 17, 16, and 31 functional groups and 151, 61, and 140 gene functions that were DE in all three breed liver transcriptome comparisons. Gene network analysis identified several potential metabolic pathways involved in glutamine family amino-acid, triglyceride synthesis, gluconeogenesis, p38MAPK cascade regulation, cholesterol biosynthesis (Polish-Red versus Hereford); IGF-receptor signaling, catecholamine transport, lipoprotein lipase, tyrosine kinase binding receptor (Polish-HF versus Hereford), and PGF-receptor binding, (Polish-HF versus Polish-Red). Validation results showed that the relative expression values were consistent to those obtained by RNA-seq, and significantly correlated between the quantitative reverse transcription PCR (RT-qPCR) and RNA-seq (Pearson’s r  > 0.90). Our results provide new insights on bovine liver gene expressions among dairy versus dual versus beef breeds by identifying the large numbers of DEGs markers submitted to NCBI gene expression omnibus (GEO) accession number GSE114233, which can serve as useful genetic tools to develop the gene assays for trait-associated studies as well as, to effectively implement in genomics selection (GS) cattle breeding programs in Poland.