Combined CRISPRi and proteomics screening reveal a cohesin-CTCF-bound allele contributing to increased expression of RUVBL1 and prostate cancer progression.

Genome-wide association studies along with expression quantitative trait locus (eQTL) mapping have identified hundreds of single-nucleotide polymorphisms (SNPs) and their target genes in prostate cancer (PCa), yet functional characterization of these risk loci remains challenging. To screen for potential regulatory SNPs, we designed a CRISPRi library containing 9,133 guide RNAs (gRNAs) to cover 2,166 candidate SNP loci implicated in PCa and identified 117 SNPs that could regulate 90 genes for PCa cell growth advantage. Among these, rs60464856 was covered by multiple gRNAs significantly depleted in screening (FDR < 0.05). Pooled SNP association analysis in the PRACTICAL and FinnGen cohorts showed significantly higher PCa risk for the rs60464856 G allele (p value = 1.2 × 10-16 and 3.2 × 10-7, respectively). Subsequent eQTL analysis revealed that the G allele is associated with increased RUVBL1 expression in multiple datasets. Further CRISPRi and xCas9 base editing confirmed that the rs60464856 G allele leads to elevated RUVBL1 expression. Furthermore, SILAC-based proteomic analysis demonstrated allelic binding of cohesin subunits at the rs60464856 region, where the HiC dataset showed consistent chromatin interactions in prostate cell lines. RUVBL1 depletion inhibited PCa cell proliferation and tumor growth in a xenograft mouse model. Gene-set enrichment analysis suggested an association of RUVBL1 expression with cell-cycle-related pathways. Increased expression of RUVBL1 and activation of cell-cycle pathways were correlated with poor PCa survival in TCGA datasets. Our CRISPRi screening prioritized about one hundred regulatory SNPs essential for prostate cell proliferation. In combination with proteomics and functional studies, we characterized the mechanistic role of rs60464856 and RUVBL1 in PCa progression.

Functional analysis of the 1p34.3 risk locus implicates GNL2 in high-grade serous ovarian cancer.

The high-grade serous ovarian cancer (HGSOC) risk locus at chromosome 1p34.3 resides within a frequently amplified genomic region signifying the presence of an oncogene. Here, we integrate in silico variant-to-function analysis with functional studies to characterize the oncogenic potential of candidate genes in the 1p34.3 locus. Fine mapping of genome-wide association statistics identified candidate causal SNPs local to H3K27ac-demarcated enhancer regions that exhibit allele-specific binding for CTCF in HGSOC and normal fallopian tube secretory epithelium cells (FTSECs). SNP risk associations colocalized with eQTL for six genes (DNALI1, GNL2, RSPO1, SNIP1, MEAF6, and LINC01137) that are more highly expressed in carriers of the risk allele, and three (DNALI1, GNL2, and RSPO1) were upregulated in HGSOC compared to normal ovarian surface epithelium cells and/or FTSECs. Increased expression of GNL2 and MEAF6 was associated with shorter survival in HGSOC with 1p34.3 amplifications. Despite its activation of ß-catenin signaling, RSPO1 overexpression exerted no effects on proliferation or colony formation in our study of ovarian cancer and FTSECs. Instead, GNL2, MEAF6, and SNIP1 silencing impaired in vitro ovarian cancer cell growth. Additionally, GNL2 silencing diminished xenograft tumor formation, whereas overexpression stimulated proliferation and colony formation in FTSECs. GNL2 influences 60S ribosomal subunit maturation and global protein synthesis in ovarian cancer and FTSECs, providing a potential mechanism of how GNL2 upregulation might promote ovarian cancer development and mediate genetic susceptibility of HGSOC.

Combining evidence from Mendelian randomization and colocalization: Review and comparison of approaches.

Mendelian randomization and colocalization are two statistical approaches that can be applied to summarized data from genome-wide association studies (GWASs) to understand relationships between traits and diseases. However, despite similarities in scope, they are different in their objectives, implementation, and interpretation, in part because they were developed to serve different scientific communities. Mendelian randomization assesses whether genetic predictors of an exposure are associated with the outcome and interprets an association as evidence that the exposure has a causal effect on the outcome, whereas colocalization assesses whether two traits are affected by the same or distinct causal variants. When considering genetic variants in a single genetic region, both approaches can be performed. While a positive colocalization finding typically implies a non-zero Mendelian randomization estimate, the reverse is not generally true: there are several scenarios which would lead to a non-zero Mendelian randomization estimate but lack evidence for colocalization. These include the existence of distinct but correlated causal variants for the exposure and outcome, which would violate the Mendelian randomization assumptions, and a lack of strong associations with the outcome. As colocalization was developed in the GWAS tradition, typically evidence for colocalization is concluded only when there is strong evidence for associations with both traits. In contrast, a non-zero estimate from Mendelian randomization can be obtained despite only nominally significant genetic associations with the outcome at the locus. In this review, we discuss how the two approaches can provide complementary information on potential therapeutic targets.

A multi-layer functional genomic analysis to understand noncoding genetic variation in lipids.

A major challenge of genome-wide association studies (GWASs) is to translate phenotypic associations into biological insights. Here, we integrate a large GWAS on blood lipids involving 1.6 million individuals from five ancestries with a wide array of functional genomic datasets to discover regulatory mechanisms underlying lipid associations. We first prioritize lipid-associated genes with expression quantitative trait locus (eQTL) colocalizations and then add chromatin interaction data to narrow the search for functional genes. Polygenic enrichment analysis across 697 annotations from a host of tissues and cell types confirms the central role of the liver in lipid levels and highlights the selective enrichment of adipose-specific chromatin marks in high-density lipoprotein cholesterol and triglycerides. Overlapping transcription factor (TF) binding sites with lipid-associated loci identifies TFs relevant in lipid biology. In addition, we present an integrative framework to prioritize causal variants at GWAS loci, producing a comprehensive list of candidate causal genes and variants with multiple layers of functional evidence. We highlight two of the prioritized genes, CREBRF and RRBP1, which show convergent evidence across functional datasets supporting their roles in lipid biology.

A Genome-wide Association Study Identifies SERPINB10, CRLF3, STX7, LAMP3, IFNG-AS1, and KRT80 As Risk Loci Contributing to Cutaneous Leishmaniasis in Brazil.

BACKGROUND: Our goal was to identify genetic risk factors for cutaneous leishmaniasis (CL) caused by Leishmania braziliensis. METHODS: Genotyping 2066 CL cases and 2046 controls using Illumina HumanCoreExomeBeadChips provided data for 4 498 586 imputed single-nucleotide variants (SNVs). A genome-wide association study (GWAS) using linear mixed models took account of genetic diversity/ethnicity/admixture. Post-GWAS positional, expression quantitative trait locus (eQTL) and chromatin interaction mapping was performed in Functional Mapping and Annotation (FUMA). Transcriptional data were compared between lesions and normal skin, and cytokines measured using flow cytometry and Bioplex assay. RESULTS: Positional mapping identified 32 genomic loci associated with CL, none achieving genome-wide significance (P < 5 × 10-8). Lead SNVs at 23 loci occurred at protein coding or noncoding RNA genes, 15 with eQTLs for functionally relevant cells/tissues and/or showing differential expression in lesions. Of these, the 6 most plausible genetic risk loci were SERPINB10 (Pimputed_1000G = 2.67 × 10-6), CRLF3 (Pimputed_1000G = 5.12 × 10-6), STX7 (Pimputed_1000G = 6.06 × 10-6), KRT80 (Pimputed_1000G = 6.58 × 10-6), LAMP3 (Pimputed_1000G = 6.54 × 10-6), and IFNG-AS1 (Pimputed_1000G = 1.32 × 10-5). LAMP3 (Padjusted = 9.25 × 10-12; +6-fold), STX7 (Padjusted = 7.62 × 10-3; +1.3-fold), and CRLF3 (Padjusted = 9.19 × 10-9; +1.97-fold) were expressed more highly in CL biopsies compared to normal skin; KRT80 (Padjusted = 3.07 × 10-8; -3-fold) was lower. Multiple cis-eQTLs across SERPINB10 mapped to chromatin interaction regions of transcriptional/enhancer activity in neutrophils, monocytes, B cells, and hematopoietic stem cells. Those at IFNG-AS1 mapped to transcriptional/enhancer regions in T, natural killer, and B cells. The percentage of peripheral blood CD3+ T cells making antigen-specific interferon-γ differed significantly by IFNG-AS1 genotype. CONCLUSIONS: This first GWAS for CL identified multiple genetic risk loci including a novel lead to understanding CL pathogenesis through regulation of interferon-γ by IFNG antisense RNA 1.

Post genome-wide association analysis: dissecting computational pathway/network-based approaches.

Over thousands of genetic associations to diseases have been identified by genome-wide association studies (GWASs), which conceptually is a single-marker-based approach. There are potentially many uses of these identified variants, including a better understanding of the pathogenesis of diseases, new leads for studying underlying risk prediction and clinical prediction of treatment. However, because of inadequate power, GWAS might miss disease genes and/or pathways with weak genetic or strong epistatic effects. Driven by the need to extract useful information from GWAS summary statistics, post-GWAS approaches (PGAs) were introduced. Here, we dissect and discuss advances made in pathway/network-based PGAs, with a particular focus on protein-protein interaction networks that leverage GWAS summary statistics by combining effects of multiple loci, subnetworks or pathways to detect genetic signals associated with complex diseases. We conclude with a discussion of research areas where further work on summary statistic-based methods is needed.

Two-phase sample selection strategies for design and analysis in post-genome-wide association fine-mapping studies.

Post-GWAS analysis, in many cases, focuses on fine-mapping targeted genetic regions discovered at GWAS-stage; that is, the aim is to pinpoint potential causal variants and susceptibility genes for complex traits and disease outcomes using next-generation sequencing (NGS) technologies. Large-scale GWAS cohorts are necessary to identify target regions given the typically modest genetic effect sizes. In this context, two-phase sampling design and analysis is a cost-reduction technique that utilizes data collected during phase 1 GWAS to select an informative subsample for phase 2 sequencing. The main goal is to make inference for genetic variants measured via NGS by efficiently combining data from phases 1 and 2. We propose two approaches for selecting a phase 2 design under a budget constraint. The first method identifies sampling fractions that select a phase 2 design yielding an asymptotic variance covariance matrix with certain optimal characteristics, for example, smallest trace, via Lagrange multipliers (LM). The second relies on a genetic algorithm (GA) with a defined fitness function to identify exactly a phase 2 subsample. We perform comprehensive simulation studies to evaluate the empirical properties of the proposed designs for a genetic association study of a quantitative trait. We compare our methods against two ranked designs: residual-dependent sampling and a recently identified optimal design. Our findings demonstrate that the proposed designs, GA in particular, can render competitive power in combined phase 1 and 2 analysis compared with alternative designs while preserving type 1 error control. These results are especially evident under the more practical scenario where design values need to be defined a priori and are subject to misspecification. We illustrate the proposed methods in a study of triglyceride levels in the North Finland Birth Cohort of 1966. R code to reproduce our results is available at github.com/egosv/TwoPhase_postGWAS.

Cocaine reward is reduced by decreased expression of receptor-type protein tyrosine phosphatase D (PTPRD) and by a novel PTPRD antagonist.

Receptor-type protein tyrosine phosphatase D (PTPRD) is a neuronal cell-adhesion molecule/synaptic specifier that has been implicated in addiction vulnerability and stimulant reward by human genomewide association and mouse cocaine-conditioned place-preference data. However, there have been no reports of effects of reduced expression on cocaine self-administration. There have been no reports of PTPRD targeting by any small molecule. There are no data about behavioral effects of any PTPRD ligand. We now report (i) robust effects of heterozygous PTPRD KO on cocaine self-administration (These data substantially extend prior conditioned place-preference data and add to the rationale for PTPRD as a target for addiction therapeutics.); (ii) identification of 7-butoxy illudalic acid analog (7-BIA) as a small molecule that targets PTPRD and inhibits its phosphatase with some specificity; (iii) lack of toxicity when 7-BIA is administered to mice acutely or with repeated dosing; (iv) reduced cocaine-conditioned place preference when 7-BIA is administered before conditioning sessions; and (v) reductions in well-established cocaine self-administration when 7-BIA is administered before a session (in WT, not PTPRD heterozygous KOs). These results add to support for PTPRD as a target for medications to combat cocaine use disorders. 7-BIA provides a lead compound for addiction therapeutics.

Progress on functional mechanisms of colorectal cancer causal SNPs in post-GWAS.

Colorectal cancer (CRC) is caused by genetic and environmental factors, and the genetic component plays a significant role in CRC development. Currently, genome-wide association studies (GWAS) have identified a large number of genetic loci associated with CRC risk. In the post-GWAS era, more and more efforts focus on deciphering the biological mechanisms behind these potential causal variants by using multi-omics data and functional experiments. Many analyses have revealed that most risk single nucleotide polymorphisms (SNPs) are located in non-coding regions and these variants may regulate the expression of target genes by altering the transcription factor-binding motif, epigenetic modification, chromatin accessibility or 3D genome conformation. Results obtained from post-GWAS era have highlighted the possibility of moving from association to function. In this review, we summarize the current status of CRC post-GWAS studies and discusses the clinical application as well as future directions of CRC GWAS, in order to better gain insight into the molecular basis of CRC and provide evidence for prevention.

Genetics of Parkinson's disease: An introspection of its journey towards precision medicine.

A substantial proportion of risk for Parkinson's disease (PD) is driven by genetics. Progress in understanding the genetic basis of PD has been significant. So far, highly-penetrant rare genetic alterations in SNCA, LRRK2, VPS35, PRKN, PINK1, DJ-1 and GBA have been linked with typical familial PD and common genetic variability at 90 loci have been linked to risk for PD. In this review, we outline the journey thus far of PD genetics, highlighting how significant advances have improved our knowledge of the genetic basis of PD risk, onset and progression. Despite remarkable progress, our field has yet to unravel how genetic risk variants disrupt biological pathways and molecular networks underlying the pathobiology of the disease. We highlight that currently identified genetic risk factors only represent a fraction of the likely genetic risk for PD. Identifying the remaining genetic risk will require us to diversify our efforts, performing genetic studies across different ancestral groups. This work will inform us on the varied genetic basis of disease across populations and also aid in fine mapping discovered loci. If we are able to take this course, we foresee that genetic discoveries in PD will directly influence our ability to predict disease and aid in defining etiological subtypes, critical steps for the implementation of precision medicine for PD.

[Noncoding Polymorphism rs6832151 Is an Attractive Candidate for Genome Editing Aimed at Finding New Molecular Mechanisms of Autoimmune Diseases].

Currently only a small fraction of the proteins encoded in the human genome serve as pharmaceutical targets. Genome-wide association studies are a powerful tool to uncover new genetic loci responsible for predisposition to complex diseases, such as autoimmune disorders. However, further work is still required to identify causative single-nucleotide polymorphisms (SNPs) which directly mediate the disease risk at these loci, and to determine their target genes. These genes can be located millions base pairs away from the regulatory SNPs. Here, by using bioinformatic tools and databases, we identified five intergenic autoimmunity-associated polymorphisms with high probability of being causative, for which the target genes are still unknown. We tested their ability to influence gene expression using luciferase reporter system. The polymorphism rs6832151 affected the reporter expression in the CEM human T-cell line upon the highest enhancer activity. Target genes of this SNP could be further identified by introducing point mutations to the genome and comparison of transcriptomes of the derivative cell sublines carrying alternative alleles of rs6832151.

Genome-wide association studies pathway-based meta-analysis for residual feed intake in beef cattle.

Genome-wide association studies (GWASes) have been performed to search for genomic regions associated with residual feed intake (RFI); however inconsistent results have been obtained. A meta-analysis may improve these results by decreasing the false-positive rate. Additionally, pathway analysis is a powerful tool that complements GWASes, as it enables identification of gene sets involved in the same pathway that explain the studied phenotype. Because there are no reports on GWAS pathways-based meta-analyses for RFI in beef cattle, we used several GWAS results to search for significant pathways that may explain the genetic mechanism underlying this trait. We used an efficient permutation hypothesis test that takes into account the linkage disequilibrium patterns between SNPs and the functional feasibility of the identified genes over the whole genome. One significant pathway (valine, leucine and isoleucine degradation) related to RFI was found. The three genes in this pathway-methylcrotonoyl-CoA carboxylase 1 (MCCC1), aldehyde oxidase 1 (AOX1) and propionyl-CoA carboxylase alpha subunit (PCCA)-were found in three different studies. This same pathway was also reported in a transcriptome analysis from two cattle populations divergently selected for high and low RFI. We conclude that a GWAS pathway-based meta-analysis can be an appropriate method to uncover biological insights into RFI by combining useful information from different studies.

Genome-wide association studies for heat stress response in Bos taurus × Bos indicus crossbred cattle.

Heat stress is an important issue in the global dairy industry. In tropical areas, an alternative to overcome heat stress is the use of crossbred animals or synthetic breeds, such as the Girolando. In this study, we performed a genome-wide association study (GWAS) and post-GWAS analyses for heat stress in an experimental Gir × Holstein F2 population. Rectal temperature (RT) was measured in heat-stressed F2 animals, and the variation between 2 consecutive RT measurements (ΔRT) was used as the dependent variable. Illumina BovineSNP50v1 BeadChip (Illumina Inc., San Diego, CA) and single-SNP approach were used for GWAS. Post-GWAS analyses were performed by gene ontology terms enrichment and gene-transcription factor (TF) networks, generated from enriched TF. The breed origin of marker alleles in the F2 population was assigned using the breed of origin of alleles (BOA) approach. Heritability and repeatability estimates (± standard error) for ΔRT were 0.13 ± 0.08 and 0.29 ± 0.06, respectively. Association analysis revealed 6 SNP significantly associated with ΔRT. Genes involved with biological processes in response to heat stress effects (LIF, OSM, TXNRD2, and DGCR8) were identified as putative candidate genes. After performing the BOA approach, the 10% of F2 animals with the lowest breeding values for ΔRT were classified as low-ΔRT, and the 10% with the highest breeding values for ΔRT were classified as high-ΔRT. On average, 49.4% of low-ΔRT animals had 2 alleles from the Holstein breed (HH), and 39% had both alleles from the Gir breed (GG). In high-ΔRT animals, the average proportion of animals for HH and GG were 1.4 and 50.2%, respectively. This study allowed the identification of candidate genes for ΔRT in Gir × Holstein crossbred animals. According to the BOA approach, Holstein breed alleles could be associated with better response to heat stress effects, which could be explained by the fact that Holstein animals are more affected by heat stress than Gir animals and thus require a genetic architecture to defend the body from the deleterious effects of heat stress. Future studies can provide further knowledge to uncover the genetic architecture underlying heat stress in crossbred cattle.

Prioritizing candidate genes post-GWAS using multiple sources of data for mastitis resistance in dairy cattle.

BACKGROUND: Improving resistance to mastitis, one of the costliest diseases in dairy production, has become an important objective in dairy cattle breeding. However, mastitis resistance is influenced by many genes involved in multiple processes, including the response to infection, inflammation, and post-infection healing. Low genetic heritability, environmental variations, and farm management differences further complicate the identification of links between genetic variants and mastitis resistance. Consequently, studies of the genetics of variation in mastitis resistance in dairy cattle lack agreement about the responsible genes. RESULTS: We associated 15,552,968 imputed whole-genome sequencing markers for 5147 Nordic Holstein cattle with mastitis resistance in a genome-wide association study (GWAS). Next, we augmented P-values for markers in genes in the associated regions using Gene Ontology terms, Kyoto Encyclopedia of Genes and Genomes pathway analysis, and mammalian phenotype database. To confirm results of gene-based analyses, we used gene expression data from E. coli-challenged cow udders. We identified 22 independent quantitative trait loci (QTL) that collectively explained 14% of the variance in breeding values for resistance to clinical mastitis (CM). Using association test statistics with multiple pieces of independent information on gene function and differential expression during bacterial infection, we suggested putative causal genes with biological relevance for 12 QTL affecting resistance to CM in dairy cattle. CONCLUSION: Combining information on the nearest positional genes, gene-based analyses, and differential gene expression data from RNA-seq, we identified putative causal genes (candidate genes with biological evidence) in QTL for mastitis resistance in Nordic Holstein cattle. The same strategy can be applied for other traits.

Use of biological priors enhances understanding of genetic architecture and genomic prediction of complex traits within and between dairy cattle breeds.

BACKGROUND: A better understanding of the genetic architecture underlying complex traits (e.g., the distribution of causal variants and their effects) may aid in the genomic prediction. Here, we hypothesized that the genomic variants of complex traits might be enriched in a subset of genomic regions defined by genes grouped on the basis of "Gene Ontology" (GO), and that incorporating this independent biological information into genomic prediction models might improve their predictive ability. RESULTS: Four complex traits (i.e., milk, fat and protein yields, and mastitis) together with imputed sequence variants in Holstein (HOL) and Jersey (JER) cattle were analysed. We first carried out a post-GWAS analysis in a HOL training population to assess the degree of enrichment of the association signals in the gene regions defined by each GO term. We then extended the genomic best linear unbiased prediction model (GBLUP) to a genomic feature BLUP (GFBLUP) model, including an additional genomic effect quantifying the joint effect of a group of variants located in a genomic feature. The GBLUP model using a single random effect assumes that all genomic variants contribute to the genomic relationship equally, whereas GFBLUP attributes different weights to the individual genomic relationships in the prediction equation based on the estimated genomic parameters. Our results demonstrate that the immune-relevant GO terms were more associated with mastitis than milk production, and several biologically meaningful GO terms improved the prediction accuracy with GFBLUP for the four traits, as compared with GBLUP. The improvement of the genomic prediction between breeds (the average increase across the four traits was 0.161) was more apparent than that it was within the HOL (the average increase across the four traits was 0.020). CONCLUSIONS: Our genomic feature modelling approaches provide a framework to simultaneously explore the genetic architecture and genomic prediction of complex traits by taking advantage of independent biological knowledge.

Whole-genome association analysis of pork meat pH revealed three significant regions and several potential genes in Finnish Yorkshire pigs.

BACKGROUND: One of the most commonly used quality measurements of pork is pH measured 24 h after slaughter. The most probable mode of inheritance for this trait is oligogenic with several known major genes, such as PRKAG3. In this study, we used whole-genome SNP genotypes of over 700 AI boars; after a quality check, 42,385 SNPs remained for association analysis. All the boars were purebred Finnish Yorkshire. To account for relatedness of the animals, a pedigree-based relationship matrix was used in a mixed linear model to test the effect of SNPs on pH measured from loin. A bioinformatics analysis was performed to identify the most promising genes in the significant regions related to meat quality. RESULTS: Genome-wide association study (GWAS) revealed three significant chromosomal regions: one on chromosome 3 (39.9 Mb-40.1 Mb) and two on chromosome 15 (58.5 Mb-60.5 Mb and 132 Mb-135 Mb including PRKAG3). A conditional analysis with a significant SNP in the PRKAG3 region, MARC0083357, as a covariate in the model retained the significant SNPs on chromosome 3. Even though linkage disequilibrium was relatively high over a long distance between MARC0083357 and other significant SNPs on chromosome 15, some SNPs retained their significance in the conditional analysis, even in the vicinity of PRKAG3. The significant regions harbored several genes, including two genes involved in cyclic AMP (cAMP) signaling: ADCY9 and CREBBP. Based on functional and transcription factor-gene networks, the most promising candidate genes for meat pH are ADCY9, CREBBP, TRAP1, NRG1, PRKAG3, VIL1, TNS1, and IGFBP5, and the key transcription factors related to these genes are HNF4A, PPARG, and Nkx2-5. CONCLUSIONS: Based on SNP association, pathway, and transcription factor analysis, we were able to identify several genes with potential to control muscle cell homeostasis and meat quality. The associated SNPs can be used in selection for better pork. We also showed that post-GWAS analysis reveals important information about the genes' potential role on meat quality. The gained information can be used in later functional studies.

Effect of polymorphisms in the CAMKMT gene on growth traits in Ujumqin sheep.

Our previous genome-wide association study in sheep revealed that OAR3-84073899.1 (SNP31) in intron 8 of the CAMKMT gene was significantly associated with post-weaning gain at the genomic level. Herein, we performed a replication study to investigate single nucleotide polymorphisms (SNPs) within the CAMKMT gene exons, and 1000 bp of the 5'- and 3'-intranslated regions (UTRs) and their associations with growth traits in Ujumqin sheep. Five SNPs were identified through DNA pool sequencing technology: SNP26 in the 5'-UTR, SNP06 in exon 5, SNP07 in exon 8 and SNP27 and SNP28 in the 3'-UTR. Six SNPs, including SNP31 in intron 8, were genotyped in the validation group of 343 Ujumqin sheep, and each SNP was classified into three genotypes. The chi-square test suggested that all the variations were in Hardy-Weinberg equilibrium (P > 0.05) except for SNP28 and SNP31. Linkage disequilibrium analysis showed that SNP07 and SNP31 were strongly linked. An association analysis suggested that SNP06 was significantly associated with chest girth at 6 months of age (P < 0.05). SNP07 exhibited significant correlation with body weight and chest girth at 4 months of age and with body weight, chest girth and chest width at 6 months of age (P < 0.05). SNP27 was highly associated with body weight and chest girth at 4 months of age (P < 0.05), and SNP28 was extremely significantly associated with body weight and chest girth at 4 months of age and with chest girth at 6 months of age (P < 0.01). SNP31 was significantly associated with body weight and shin circumference at 4 months of age and with post-weaning gain (P < 0.05). Association analysis of the combined effect of SNP07 and SNP31 showed significant correlation with body weight and chest girth at four of months of age (P < 0.05) and with body weight and chest girth at 6 months of age (P < 0.05). These results indicate that the SNPs could be used as meritorious and available genetic markers in growth traits breeding and that the CAMKMT gene may be one of the key candidate genes that affect Ujumqin economic traits.

Systems genetics: From GWAS to disease pathways.

Most common diseases are complex, involving multiple genetic and environmental factors and their interactions. In the past decade, genome-wide association studies (GWAS) have successfully identified thousands of genetic variants underlying susceptibility to complex diseases. However, the results from these studies often do not provide evidence on how the variants affect downstream pathways and lead to the disease. Therefore, in the post-GWAS era the greatest challenge lies in combining GWAS findings with additional molecular data to functionally characterize the associations. The advances in various ~omics techniques have made it possible to investigate the effect of risk variants on intermediate molecular levels, such as gene expression, methylation, protein abundance or metabolite levels. As disease aetiology is complex, no single molecular analysis is expected to fully unravel the disease mechanism. Multiple molecular levels can interact and also show plasticity in different physiological conditions, cell types and disease stages. There is therefore a great need for new integrative approaches that can combine data from different molecular levels and can help construct the causal inference from genotype to phenotype. Systems genetics is such an approach; it is used to study genetic effects within the larger scope of systems biology by integrating genotype information with various ~omics datasets as well as with environmental and physiological variables. In this review, we describe this approach and discuss how it can help us unravel the molecular mechanisms through which genetic variation causes disease. This article is part of a Special Issue entitled: From Genome to Function.

A systems biology-based identification and in vivo functional screening of Alzheimer's disease risk genes reveal modulators of memory function.

Genome-wide association studies (GWASs) have uncovered over 75 genomic loci associated with risk for late-onset Alzheimer's disease (LOAD), but identification of the underlying causal genes remains challenging. Studies of induced pluripotent stem cell (iPSC)-derived neurons from LOAD patients have demonstrated the existence of neuronal cell-intrinsic functional defects. Here, we searched for genetic contributions to neuronal dysfunction in LOAD using an integrative systems approach that incorporated multi-evidence-based gene mapping and network-analysis-based prioritization. A systematic perturbation screening of candidate risk genes in Caenorhabditis elegans (C. elegans) revealed that neuronal knockdown of the LOAD risk gene orthologs vha-10 (ATP6V1G2), cmd-1 (CALM3), amph-1 (BIN1), ephx-1 (NGEF), and pho-5 (ACP2) alters short-/intermediate-term memory function, the cognitive domain affected earliest during LOAD progression. These results highlight the impact of LOAD risk genes on evolutionarily conserved memory function, as mediated through neuronal endosomal dysfunction, and identify new targets for further mechanistic interrogation.

GWAS breakthroughs: mapping the journey from one locus to 393 significant CAD associations.

Coronary artery disease (CAD) poses a substantial threat to global health, leading to significant morbidity and mortality worldwide. It has a significant genetic component that has been studied through genome-wide association studies (GWAS) over the past 17 years. These studies have made progress with larger sample sizes, diverse ancestral backgrounds, and the discovery of multiple genomic regions related to CAD risk. In this review, we provide a comprehensive overview of CAD GWAS, including information about the genetic makeup of the disease and the importance of ethnic diversity in these studies. We also discuss challenges of identifying causal genes and variants within GWAS loci with a focus on non-coding regions. Additionally, we highlight tissues and cell types relevant to CAD, and discuss clinical implications of GWAS findings including polygenic risk scores, sex-specific differences in CAD genetics, ethnical aspects of personalized interventions, and GWAS guided drug development.