Genomewide rapid association using mixed model and regression: a fast and simple method for genomewide pedigree-based quantitative trait loci association analysis

The impact of disregarding family structure on genome-wide association analysis of complex diseases in cohorts with simple pedigrees

The generalized linear mixed models (GLMMs) methodology is the standard framework for genome-wide association studies (GWAS) of complex diseases in family-based cohorts. Fitting GLMMs in very large cohorts, however, can be computationally demanding. Also, the modified versions of GLMM using faster algorithms may underperform, for instance when a single nucleotide polymorphism (SNP) is correlated with fixed-effects covariates. We investigated the extent to which disregarding family structure may compromise GWAS in cohorts with simple pedigrees by contrasting logistic regression models (i.e., with no family structure) to three LMMs-based ones. Our analyses showed that the logistic regression models in general resulted in smaller P values compared with the LMMs-based models; however, the differences in P values were mostly minor. Disregarding family structure had little impact on determining disease-associated SNPs at genome-wide level of significance (i.e., P < 5E-08) as the four P values resulted from the tested methods for any SNP were all below or all above 5E-08. Nevertheless, larger discrepancies were detected between logistic regression and LMMs-based models at suggestive level of significance (i.e., of 5E-08 ≤ P < 5E-06). The SNP effects estimated by the logistic regression models were not statistically different from those estimated by GLMMs that implemented Wald's test. However, several SNP effects were significantly different from their counterparts in LMMs analyses. We suggest that fitting GLMMs with Wald's test on a pre-selected subset of SNPs obtained from logistic regression models can ensure the balance between the speed of analyses and the accuracy of parameters.

A functional regulatory variant of MYH3 influences muscle fiber-type composition and intramuscular fat content in pigs

Muscle development and lipid accumulation in muscle critically affect meat quality of livestock. However, the genetic factors underlying myofiber-type specification and intramuscular fat (IMF) accumulation remain to be elucidated. Using two independent intercrosses between Western commercial breeds and Korean native pigs (KNPs) and a joint linkage-linkage disequilibrium analysis, we identified a 488.1-kb region on porcine chromosome 12 that affects both reddish meat color (a*) and IMF. In this critical region, only the MYH3 gene, encoding myosin heavy chain 3, was found to be preferentially overexpressed in the skeletal muscle of KNPs. Subsequently, MYH3-transgenic mice demonstrated that this gene controls both myofiber-type specification and adipogenesis in skeletal muscle. We discovered a structural variant in the promotor/regulatory region of MYH3 for which Q allele carriers exhibited significantly higher values of a* and IMF than q allele carriers. Furthermore, chromatin immunoprecipitation and cotransfection assays showed that the structural variant in the 5'-flanking region of MYH3 abrogated the binding of the myogenic regulatory factors (MYF5, MYOD, MYOG, and MRF4). The allele distribution of MYH3 among pig populations worldwide indicated that the MYH3 Q allele is of Asian origin and likely predates domestication. In conclusion, we identified a functional regulatory sequence variant in porcine MYH3 that provides novel insights into the genetic basis of the regulation of myofiber type ratios and associated changes in IMF in pigs. The MYH3 variant can play an important role in improving pork quality in current breeding programs.

Fine Mapping Using Whole-Genome Sequencing Confirms Anti-Müllerian Hormone as a Major Gene for Sex Determination in Farmed Nile Tilapia (Oreochromis niloticus L.)

Nile tilapia (Oreochromis niloticus) is one of the most cultivated and economically important species in world aquaculture. Intensive production promotes the use of monosex animals, due to an important dimorphism that favors male growth. Currently, the main mechanism to obtain all-male populations is the use of hormones in feeding during larval and fry phases. Identifying genomic regions associated with sex determination in Nile tilapia is a research topic of great interest. The objective of this study was to identify genomic variants associated with sex determination in three commercial populations of Nile tilapia. Whole-genome sequencing of 326 individuals was performed, and a total of 2.4 million high-quality bi-allelic single nucleotide polymorphisms (SNPs) were identified after quality control. A genome-wide association study (GWAS) was conducted to identify markers associated with the binary sex trait (males = 1; females = 0). A mixed logistic regression GWAS model was fitted and a genome-wide significant signal comprising 36 SNPs, spanning a genomic region of 536 kb in chromosome 23 was identified. Ten out of these 36 genetic variants intercept the anti-Müllerian (Amh) hormone gene. Other significant SNPs were located in the neighboring Amh gene region. This gene has been strongly associated with sex determination in several vertebrate species, playing an essential role in the differentiation of male and female reproductive tissue in early stages of development. This finding provides useful information to better understand the genetic mechanisms underlying sex determination in Nile tilapia.

Genome-wide association analyses reveal polygenic genomic architecture underlying divergent shell morphology in Spanish Littorina saxatilis ecotypes

Gene flow between diverging populations experiencing dissimilar ecological conditions can theoretically constrain adaptive evolution. To minimize the effect of gene flow, alleles underlying traits essential for local adaptation are predicted to be located in linked genome regions with reduced recombination. Local reduction in gene flow caused by selection is expected to produce elevated divergence in these regions. The highly divergent crab-adapted and wave-adapted ecotypes of the marine snail Littorina saxatilis present a model system to test these predictions. We used genome-wide association (GWA) analysis of geometric morphometric shell traits associated with microgeographic divergence between the two L. saxatilis ecotypes within three separate sampling sites. A total of 477 snails that had individual geometric morphometric data and individual genotypes at 4,066 single nucleotide polymorphisms (SNPs) were analyzed using GWA methods that corrected for population structure among the three sites. This approach allowed dissection of the genomic architecture of shell shape divergence between ecotypes across a wide geographic range, spanning two glacial lineages. GWA revealed 216 quantitative trait loci (QTL) with shell size or shape differences between ecotypes, with most loci explaining a small proportion of phenotypic variation. We found that QTL were evenly distributed across 17 linkage groups, and exhibited elevated interchromosomal linkage, suggesting a genome-wide response to divergent selection on shell shape between the two ecotypes. Shell shape trait-associated loci showed partial overlap with previously identified outlier loci under divergent selection between the two ecotypes, supporting the hypothesis of diversifying selection on these genomic regions. These results suggest that divergence in shell shape between the crab-adapted and wave-adapted ecotypes is produced predominantly by a polygenic genomic architecture with positive linkage disequilibrium among loci of small effect.

A genomic prediction model for racecourse starts in the Thoroughbred horse

Durability traits in Thoroughbred horses are heritable, economically valuable and may affect horse welfare. The aims of this study were to test the hypotheses that (i) durability traits are heritable and (ii) genetic data may be used to predict a horse's potential to have a racecourse start. Heritability for the phenotype 'number of 2- and 3-year-old starts' was estimated to be h m 2  = 0.11 ± 0.02 (n = 4499). A genome-wide association study identified SNP contributions to the trait. The neurotrimin (NTM), opioid-binding protein/cell adhesion molecule like (OPCML) and prolylcarboxypeptidase (PRCP) genes were identified as candidate genes associated with the trait. NTM functions in brain development and has been shown to have been selected during the domestication of the horse. PRCP is an established expression quantitative trait locus involved in the interaction between voluntary exercise and body composition in mice. We hypothesise that variation at these loci contributes to the motivation of the horse to exercise, which may influence its response to the demands of the training and racing environment. A random forest with mixed effects (RFME) model identified a set of SNPs that contributed to 24.7% of the heritable variation in the trait. In an independent validation set (n = 528 horses), the cohort with high genetic potential for a racecourse start had significantly fewer unraced horses (16% unraced) than did low (27% unraced) potential horses and had more favourable race outcomes among those that raced. Therefore, the information from SNPs included in the model may be used to predict horses with a greater chance of a racecourse start.

The Promise of Selecting Individuals from the Extremes of Exposure in the Analysis of Gene-Physical Activity Interactions

BACKGROUND

Dichotomization using the lower quartile as cutoff is commonly used for harmonizing heterogeneous physical activity (PA) measures across studies. However, this may create misclassification and hinder discovery of new loci.

OBJECTIVES

This study aimed to evaluate the performance of selecting individuals from the extremes of the exposure (SIEE) as an alternative approach to reduce such misclassification.

METHOD

For systolic and diastolic blood pressure in the Framingham Heart Study, we performed a genome-wide association study with gene-PA interaction analysis using three PA variables derived by SIEE and two other dichotomization approaches. We compared number of loci detected and overlap with loci found using a quantitative PA variable. In addition, we performed simulation studies to assess bias, false discovery rates (FDR), and power under synergistic/antagonistic genetic effects in exposure groups and in the presence/absence of measurement error.

RESULTS

In the empirical analysis, SIEE's performance was neither the best nor the worst. In most simulation scenarios, SIEE was consistently outperformed in terms of FDR and power. Particularly, in a scenario characterized by antagonistic effects and measurement error, SIEE had the least bias and highest power.

CONCLUSION

SIEE's promise appears limited to detecting loci with antagonistic effects. Further studies are needed to evaluate SIEE's full advantage.

Genome Scan for Genomic Regions and Genes Associated with Growth Trait in Pacific White Shrimp Litopeneaus vannamei

The Pacific white shrimp Litopeneaus vannmei (L. vannmei) is a predominant aquaculture shrimp species worldwide, and it is considered as the aquaculture species with the highest single output value. Advances in selective breeding have accelerated the development of L. vannmei aquaculture. Recently, the genome-wide association studies (GWAS) have been applied in aquaculture animals and markers associated with economic traits were identified. In this study, we focused on the growth trait of L. vannamei and performed GWAS to identify SNPs or genes associated with growth. Genomic regions in linkage group 7, 27, 33, and 38 were identified to be associated with body weight and body length of the shrimp. Further, candidate gene association analysis was performed in two independent populations and the result demonstrated that the SNPs in the genes protein kinase C delta type and ras-related protein Rap-2a were significantly associated with the growth trait of L. vannamei. This study showed that GWAS analysis is an efficient approach for screening trait-related markers or genes. The genomic regions and genes identified in this study are essential for further fine mapping of growth-related genes. The identified markers will provide useful information for marker-assisted selection in L. vannamei.

A Fast and Powerful Empirical Bayes Method for Genome-Wide Association Studies

Simple Summary

Improving statistical power and computational efficiency are always the research foci in genome-wide association studies (GWAS). In this study, we proposed a fast empirical Bayes GWAS method, which is based on the linear mixed model framework. The method is called Fast-EB-LMM in short. Results from simulation studies show that the Fast-EB-LMM has the highest power for quantitative trait nucleotides (QTNs) detection, the highest computational efficiency, and the strongest robustness, as compared with the efficient mixed model association (EMMA) and empirical Bayes (EB). Application to beef cattle population also verified the effectiveness of this method. We believe that Fast-EB-LMM is a valuable additional tool for GWAS.

Abstract

Linear mixed model (LMM) is an efficient method for GWAS. There are numerous forms of LMM-based GWAS methods. However, improving statistical power and computing efficiency have always been the research hotspots of the LMM-based GWAS methods. Here, we proposed a fast empirical Bayes method, which is based on linear mixed models. We call it Fast-EB-LMM in short. The novelty of this method is that it uses a modified kinship matrix accounting for individual relatedness to avoid competition between the locus of interest and its counterpart in the polygene. This property has increased statistical power. We adopted two special algorithms to ease the computational burden: Eigenvalue decomposition and Woodbury matrix identity. Simulation studies showed that Fast-EB-LMM has significantly increased statistical power of marker detection and improved computational efficiency compared with two widely used GWAS methods, EMMA and EB. Real data analyses for two carcass traits in a Chinese Simmental beef cattle population showed that the significant single-nucleotide polymorphisms (SNPs) and candidate genes identified by Fast-EB-LMM are highly consistent with results of previous studies. We therefore believe that the Fast-EB-LMM method is a reliable and efficient method for GWAS.

Using Genetic Burden Scores for Gene-by-Methylation Interaction Analysis on Metabolic Syndrome in African Americans

With the rapid advancement of omics-based research, particularly big data such as genome- and epigenome-wide association studies that include extensive environmental and clinical variables, data analytics have become increasingly complex. Researchers face significant challenges regarding how to analyze multifactorial data and make use of the findings for clinical translation. The purpose of this article is to provide a scientific exemplar for use of genetic burden scores as a data analysis method for studies with both genotype and DNA methylation data in which the goal is to evaluate associations with chronic conditions such as metabolic syndrome (MetS). This study included 739 African American men and women from the Genetic Epidemiology Network of Arteriopathy Study who met diagnostic criteria for MetS and had available genetic and epigenetic data. Genetic burden scores for evaluated genes were not significant after multiple testing corrections, but DNA methylation at 2 CpG sites (dihydroorotate dehydrogenase cg22381196 pFDR = .014; CTNNA3 cg00132141 pFDR = .043) was significantly associated with MetS after controlling for multiple comparisons. Interactions between the marginally significant CpG sites and burden scores, however, were not significant. More work is required in this area to identify intermediate biological pathways influenced by environmental, genetic, and epigenetic variation that may explain the high prevalence of MetS among African Americans. This study does serve, however, as an example of the use of the genetic burden score as an alternative data analysis approach for complex studies involving the analysis of genetic and epigenetic data simultaneously.

Mapping of QTL for chicken body weight, carcass composition, and meat quality traits in a slow-growing line

Slow-growing chicken lines are valuable genetic resources for the development of well-perceived alternative free-range production. While there is no constraint on increasing growth rate, breeding programs have to evolve in order to include new traits improving the positioning of such lines in the growing market for parts and processed products. In this study, we used dense genotyping to fine map QTL for chicken growth, body composition, and meat quality traits in view of developing new tools for selection of a slow-growing line. The dataset included a total of 836 birds (10 sires, 87 dams, 739 descendants) and 40,203 SNP. QTL for the 15 traits analyzed were detected by 3 different methods, i.e., linkage and linkage disequilibrium haplotype-based analysis (LDLA), family-based single marker association (FASTA), and Bayesian multi-marker regression (Bayes Cπ). After filtering for QTL redundancy, we found 16, 16, and 9 QTL when using the FASTA, LDLA, and Bayes Cπ methods, respectively, with a threshold of 2.49 × 10-5 for FASTA and LDLA, and a Bayes factor of 150 for the Bayes Cπ analysis. They comprised 17 QTL for body weight, 9 QTL for body composition, and 15 QTL for breast meat quality or behavior at slaughter. The 3 methods agreed in the detection of highly significant QTL such as that detected on GGA24 for body weight at 3, 6, and 9 wk, and the 2 QTL detected on GGA17 and GGA18 for breast meat yield. Several significant QTL were also detected for the different components of breast meat quality. This study provided new locations for investigation in order to improve our understanding of the genetic architecture of growth, carcass composition, and meat quality in the chicken and to develop molecular tools for the selection of these traits in a slow-growing line.

Identification of epistasis loci underlying rice flowering time by controlling population stratification and polygenic effect

Flowering time is an important agronomic trait, attributed by multiple genes, gene-gene interactions and environmental factors. Population stratification and polygenic effects might confound genetic effects of the causal loci underlying this complex trait. We proposed a two-step approach for detecting epistasis interactions underlying rice flowering time by accounting population structure and polygenic effects. Simulation studies showed that the approach used in this study performs better than classical and PC-linear approaches in terms of powers and false discovery rates in the case of population stratification and polygenic effects. Whole genome epistasis analyses identified 589 putative genetic interactions for flowering time. Eighteen of these interactions are located within 10 kilobases of regions of known protein-protein interactions. Thirty-seven SNPs near to twenty-five genes involve in rice or/and Arabidopsis (orthologue) flowering pathway. Bioinformatics analysis showed that 66.55% pairwise genes of the identified interactions (392 out of the 589 interactions) have similarity in various genomic features. Moreover, significant numbers of detected epistatic genes have high expression in different floral tissues. Our findings highlight the importance of epistasis analysis by controlling population stratification and polygenic effect and provided novel insights into the genetic architecture of rice flowering which could assist breeding programmes.

Linear mixed models for association analysis of quantitative traits with next-generation sequencing data

We develop linear mixed models (LMMs) and functional linear mixed models (FLMMs) for gene-based tests of association between a quantitative trait and genetic variants on pedigrees. The effects of a major gene are modeled as a fixed effect, the contributions of polygenes are modeled as a random effect, and the correlations of pedigree members are modeled via inbreeding/kinship coefficients. F -statistics and χ 2 likelihood ratio test (LRT) statistics based on the LMMs and FLMMs are constructed to test for association. We show empirically that the F -distributed statistics provide a good control of the type I error rate. The F -test statistics of the LMMs have similar or higher power than the FLMMs, kernel-based famSKAT (family-based sequence kernel association test), and burden test famBT (family-based burden test). The F -statistics of the FLMMs perform well when analyzing a combination of rare and common variants. For small samples, the LRT statistics of the FLMMs control the type I error rate well at the nominal levels α = 0.01 and 0.05 . For moderate/large samples, the LRT statistics of the FLMMs control the type I error rates well. The LRT statistics of the LMMs can lead to inflated type I error rates. The proposed models are useful in whole genome and whole exome association studies of complex traits.

Scanning the genomes of parents for imprinted loci acting in their un-genotyped progeny

Depending on their parental origin, alleles at imprinted loci are fully or partially inactivated through epigenetic mechanisms. Their effects contribute to the broader class of parent-of-origin effects. Standard methodology for mapping imprinted quantitative trait loci in association studies requires phenotypes and parental origin of marker alleles (ordered genotypes) to be simultaneously known for each individual. As such, many phenotypes are known from un-genotyped offspring in ongoing breeding programmes (e.g. meat animals), while their parents have known genotypes but no phenotypes. By theoretical considerations and simulations, we showed that the limitations of standard methodology can be overcome in such situations. This is achieved by first estimating parent-of-origin effects, which then serve as dependent variables in association analyses, in which only imprinted loci give a signal. As a theoretical foundation, the regression of parent-of-origin effects on the number of B-alleles at a biallelic locus — representing the un-ordered genotype — equals the imprinting effect. The applicability to real data was demonstrated for about 1800 genotyped Brown Swiss bulls and their un-genotyped fattening progeny. Thus, this approach unlocks vast data resources in various species for imprinting analyses and offers valuable clues as to what extent imprinted loci contribute to genetic variability.

Association score testing for rare variants and binary traits in family data with shared controls

Genome-wide association studies have been an important approach used to localize trait loci, with primary focus on common variants. The multiple rare variant-common disease hypothesis may explain the missing heritability remaining after accounting for identified common variants. Advances of sequencing technologies with their decreasing costs, coupled with methodological advances in the context of association studies in large samples, now make the study of rare variants at a genome-wide scale feasible. The resurgence of family-based association designs because of their advantage in studying rare variants has also stimulated more methods development, mainly based on linear mixed models (LMMs). Other tests such as score tests can have advantages over the LMMs, but to date have mainly been proposed for single-marker association tests. In this article, we extend several score tests (χcorrected2, WQLS, and SKAT) to the multiple variant association framework. We evaluate and compare their statistical performances relative with the LMM. Moreover, we show that three tests can be cast as the difference between marker allele frequencies (AFs) estimated in each of the group of affected and unaffected subjects. We show that these tests are flexible, as they can be based on related, unrelated or both related and unrelated subjects. They also make feasible an increasingly common design that only sequences a subset of affected subjects (related or unrelated) and uses for comparison publicly available AFs estimated in a group of healthy subjects. Finally, we show the great impact of linkage disequilibrium on the performance of all these tests.

A certain invariance property of BLUE in a whole-genome regression context

A curious result from mixed linear models applied to genome-wide association studies was expanded. In particular, a model in which one or more markers are considered as fixed but are allowed to contribute to the covariance structure by treating such markers as random as well was examined. The best linear unbiased estimator of marker effects is invariant with respect to whether those markers are employed in constructing a genomic relationship matrix or are ignored, provided marker effects are uncorrelated with those not being tested. Also, the implications of regarding some marker effects as fixed when, in fact, these possess a non-trivial covariance structure with those declared as random were examined.

Genome-wide meta-analysis identifies novel determinants of circulating serum progranulin

Progranulin is a secreted protein with important functions in processes including immune and inflammatory response, metabolism and embryonic development. The present study aimed at identification of genetic factors determining progranulin concentrations. We conducted a genome-wide association meta-analysis for serum progranulin in three independent cohorts from Europe: Sorbs (N = 848) and KORA (N = 1628) from Germany and PPP-Botnia (N = 335) from Finland (total N = 2811). Single nucleotide polymorphisms (SNPs) associated with progranulin levels were replicated in two additional German cohorts: LIFE-Heart Study (Leipzig; N = 967) and Metabolic Syndrome Berlin Potsdam (Berlin cohort; N = 833). We measured mRNA expression of genes in peripheral blood mononuclear cells (PBMC) by micro-arrays and performed mRNA expression quantitative trait and expression-progranulin association studies to functionally substantiate identified loci. Finally, we conducted siRNA silencing experiments in vitro to validate potential candidate genes within the associated loci. Heritability of circulating progranulin levels was estimated at 31.8% and 26.1% in the Sorbs and LIFE-Heart cohort, respectively. SNPs at three loci reached study-wide significance (rs660240 in CELSR2-PSRC1-MYBPHL-SORT1, rs4747197 in CDH23-PSAP and rs5848 in GRN) explaining 19.4%/15.0% of the variance and 61%/57% of total heritability in the Sorbs/LIFE-Heart Study. The strongest evidence for association was at rs660240 (P = 5.75 × 10-50), which was also associated with mRNA expression of PSRC1 in PBMC (P = 1.51 × 10-21). Psrc1 knockdown in murine preadipocytes led to a consecutive 30% reduction in progranulin secretion. In conclusion, the present meta-GWAS combined with mRNA expression identified three loci associated with progranulin and supports the role of PSRC1 in the regulation of progranulin secretion.

The contribution of myostatin (MSTN) and additional modifying genetic loci to race distance aptitude in Thoroughbred horses racing in different geographic regions

BACKGROUND

Race distance aptitude in Thoroughbred horses is highly heritable and is influenced largely by variation at the myostatin gene (MSTN).

OBJECTIVES

In addition to MSTN, we hypothesised that other modifying loci contribute to best race distance.

STUDY DESIGN

Using 3006 Thoroughbreds, including 835 'elite' horses, which were >3 years old, had race records and were sampled from Europe/Middle-East, Australia/New Zealand, North America and South Africa, we performed genome-wide association (GWA) tests and separately developed a genomic prediction algorithm to comprehensively catalogue additive genetic variation contributing to best race distance.

METHODS

48,896 single-nucleotide polymorphism (SNP) genotypes were generated from high-density SNP genotyping arrays. Heritability estimates, tests of GWA and genomic prediction models were derived for the phenotypes: average race distance, best race distance for elite, nonelite and all winning horses.

RESULTS

Heritability estimates were high ( h m 2  = 0.51, best race distance - elite; h m 2  = 0.42, best race distance - nonelite; h m 2  = 0.40, best race distance - all) and most of the variation was attributed to the MSTN gene. MSTN locus SNPs were the most strongly associated with the trait and included BIEC2-438999 (ECA18:66913090; P = 4.51 × 10^-110 , average race distance; P = 2.33 × 10^-42 , best race distance - elite). The genomic prediction algorithm enabled the inclusion of variation from all SNPs in a model that partitioned horses into short and long cohorts following assignment of MSTN genotype. Additional genes with minor contributions to best race distance were identified.

MAIN LIMITATIONS

The nongenetic influence of owner/trainer decisions on placement of horses in suitable races could not be controlled.

CONCLUSIONS

MSTN is the single most important genetic contributor to best race distance in the Thoroughbred. Employment of genetic prediction models will lead to more accurate placing of horses in races that are best suited to their inherited genetic potential for distance aptitude.

Co-Evolution of Sink and Source in the Recent Breeding History of Winter Wheat in Germany

Optimizing the interplay between sinks and sources is of crucial importance for breeding progress in winter wheat. However, the physiological limitations of yield from source (e.g. green canopy duration, GCD) and sink (e.g. grain number) are still unclear. Furthermore, there is little information on how the source traits have been modified during the breeding history of winter wheat. This study analyzed the breeding progress of sink and source components and their relationships to yield components. Field trials were conducted over three years with 220 cultivars representing the German breeding history of the past five decades. In addition, genetic associations of QTL for the traits were assessed with genome-wide association studies. Breeding progress mainly resulted from an increase in grain numbers per spike, a sink component, whose variations were largely explained by the photosynthetic activity around anthesis, a source component. Surprisingly, despite significant breeding progress in GCD and other source components, they showed no direct influence on thousand grain weights, indicating that grain filling was not limited by the source strength. Our results suggest that, 1) the potential longevity of the green canopy is predetermined at the time point that the number of grains is fixed; 2) a co-evolution of source and sink strength during the breeding history contribute to the yield formation of the modern cultivars. For future breeding we suggest to choose parental lines with high grain numbers per spike on the sink side, and high photosynthetic activity around anthesis and canopy duration on the source side, and to place emphasis on these traits throughout selection.

Genome-wide association study of Mycobacterium avium subspecies Paratuberculosis infection in Chinese Holstein

Background

Paratuberculosis is a contagious, chronic and enteric disease in ruminants, which is caused by Mycobacterium avium subspecies paratuberculosis (MAP) infection, resulting in enormous economic losses worldwide. There is currently no effective cure for MAP infection or a vaccine, it is thus important to explore the genetic variants that contribute to host susceptibility to infection by MAP, which may provide a better understanding of the mechanisms of paratuberculosis and benefit animal genetic improvement. Herein we performed a genome-wide association study (GWAS) to identify genomic regions and candidate genes associated with susceptibility to MAP infection in dairy cattle.

Results

Using Illumina Bovine 50 K (54,609 SNPs) and GeneSeek HD (138,893 SNPs) chips, two analytical approaches were performed, GRAMMAR-GC and ROADTRIPS in 937 Chinese Holstein cows, among which individuals genotyped by the 50 K chip were imputed to HD SNPs with Beagle software. Consequently, 15 and 11 significant SNPs (P < 5 × 10^− 5) were identified with GRAMMAR-GC and ROADTDRIPS, respectively. A total of 10 functional genes were in proximity to (i.e., within 1 Mb) these SNPs, including IL4, IL5, IL13, IRF1, MyD88, PACSIN1, DEF6, TDP2, ZAP70 and CSF2. Functional enrichment analysis showed that these genes were involved in immune related pathways, such as interleukin, T cell receptor signaling pathways and inflammatory bowel disease (IBD), implying their potential associations with susceptibility to MAP infection. In addition, by examining the publicly available cattle QTLdb, a previous QTL for MAP was found to be overlapped with one of regions detected currently at 32.5 Mb on BTA23, where the TDP2 gene was anchored.

Conclusions

In conclusion, we identified 26 SNPs located on 15 chromosomes in the Chinese Holstein population using two GWAS strategies with high density SNPs. Integrated analysis of GWAS, biological functions and the reported QTL information helps to detect positional candidate genes and the identification of regions associated with susceptibility to MAP traits in dairy cattle.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-5385-3) contains supplementary material, which is available to authorized users.

Copy number variation in the MSRB3 gene enlarges porcine ear size through a mechanism involving miR-584-5p

Background

The size and type of ears are important conformation characteristics that distinguish pig breeds. A significant quantitative trait locus (QTL) for ear size has been identified on SSC5 (SSC for Sus scrofa chromosome) but the underlying causative gene and mutation remain unknown. Thus, our aim was to identify the gene responsible for enlarged ears in pig.

Results

First, we narrowed down the QTL region on SSC5 to a 137.85-kb interval that harbors only the methionine sulfoxide reductase B3 (MSRB3) gene. Then, we identified a 38.7-kb copy number variation (CNV) that affects the last two exons of MSRB3 and could be the candidate causative mutation for this QTL. This CNV showed complete concordance with genotype at the QTL of the founder animals in a white Duroc × Erhualian F₂ intercross and was found only in pigs from six Chinese indigenous breeds with large ears and from the Landrace breed with half-floppy ears. Moreover, it accounted for the significant association with ear size on SSC5 across the five pig populations tested. eQTL mapping revealed that this CNV was significantly associated with the expression of the microRNA (miRNA) miR-584-5p, which interacts with MSRB3, one of its target genes. In vivo and in vitro experiments confirmed that miR-584-5p inhibits the translation of MSRB3 mRNA. Taken together, these results led us to conclude that presence of the 38.7-kb CNV in the genome of some pig breeds affects ear size by altering the expression of miR-584-5p, which consequently hinders the expression of one of its target genes (e.g. MSRB3).

Conclusions

Our findings shed insight into the underlying mechanism of development of external ears in mammals and contribute to a better understanding of how the presence of CNV can regulate gene expression.

Electronic supplementary material

The online version of this article (10.1186/s12711-018-0442-6) contains supplementary material, which is available to authorized users.

Finding invisible quantitative trait loci with missing data

Evolutionary processes during plant polyploidization and speciation have led to extensive presence-absence variation (PAV) in crop genomes, and there is increasing evidence that PAV associates with important traits. Today, high-resolution genetic analysis in major crops frequently implements simple, cost-effective, high-throughput genotyping from single nucleotide polymorphism (SNP) hybridization arrays; however, these are normally not designed to distinguish PAV from failed SNP calls caused by hybridization artefacts. Here, we describe a strategy to recover valuable information from single nucleotide absence polymorphisms (SNaPs) by population-based quality filtering of SNP hybridization data to distinguish patterns associated with genuine deletions from those caused by technical failures. We reveal that including SNaPs in genetic analyses elucidate segregation of small to large-scale structural variants in nested association mapping populations of oilseed rape (Brassica napus), a recent polyploid crop with widespread structural variation. Including SNaP markers in genomewide association studies identified numerous quantitative trait loci, invisible using SNP markers alone, for resistance to two major fungal diseases of oilseed rape, Sclerotinia stem rot and blackleg disease. Our results indicate that PAV has a strong influence on quantitative disease resistance in B. napus and that SNaP analysis using cost-effective SNP array data can provide extensive added value from 'missing data'. This strategy might also be applicable for improving the precision of genetic mapping in many important crop species.

Genomic signatures of local adaptation to the degree of environmental predictability in rotifers

Environmental fluctuations are ubiquitous and thus essential for the study of adaptation. Despite this, genome evolution in response to environmental fluctuations —and more specifically to the degree of environmental predictability– is still unknown. Saline lakes in the Mediterranean region are remarkably diverse in their ecological conditions, which can lead to divergent local adaptation patterns in the inhabiting aquatic organisms. The facultatively sexual rotifer Brachionus plicatilis shows diverging local adaptation in its life-history traits in relation to estimated environmental predictability in its habitats. Here, we used an integrative approach —combining environmental, phenotypic and genomic data for the same populations– to understand the genomic basis of this diverging adaptation. Firstly, a novel draft genome for B. plicatilis was assembled. Then, genome-wide polymorphisms were studied using genotyping by sequencing on 270 clones from nine populations in eastern Spain. As a result, 4,543 high-quality SNPs were identified and genotyped. More than 90 SNPs were found to be putatively under selection with signatures of diversifying and balancing selection. Over 140 SNPs were correlated with environmental or phenotypic variables revealing signatures of local adaptation, including environmental predictability. Putative functions were associated to most of these SNPs, since they were located within annotated genes. Our results reveal associations between genomic variation and the degree of environmental predictability, providing genomic evidence of adaptation to local conditions in natural rotifer populations.

An atlas of genetic associations in UK Biobank

Genome-wide association studies (GWAS) have identified many loci contributing to variation in complex traits, yet the majority of loci that contribute to the heritability of complex traits remain elusive. Large study populations with sufficient statistical power are required to detect the small effect sizes of the yet unidentified genetic variants. However, the analysis of huge cohorts, like UK Biobank, is challenging. Here, we present an atlas of genetic associations for 118 non-binary and 660 binary traits of 452,264 UK Biobank participants of European ancestry. Results are compiled in a publicly accessible database that allows querying genome-wide association results for 9,113,133 genetic variants, as well as downloading GWAS summary statistics for over 30 million imputed genetic variants (>23 billion phenotype-genotype pairs). Our atlas of associations (GeneATLAS, http://geneatlas.roslin.ed.ac.uk ) will help researchers to query UK Biobank results in an easy and uniform way without the need to incur high computational costs.

Coping with family structure in genome-wide association studies: a comparative evaluation

In this paper, a fully statistical investigation of the control of family structure as random effects is analyzed and discussed, using both the genome-wide association studies (GWAS) data and simulated data. Three modeling strategies are proposed and the analysis results suggest the hybrid use of results from all possible models should be combined in practice.

Genetic dissection of bull fertility in US Jersey dairy cattle

The service sire has been recognized as an important factor affecting herd fertility in dairy cattle. Recent studies suggest that genetic factors explain part of the difference in fertility among Holstein sires. The main objective of this study was to dissect the genetic architecture of sire fertility in US Jersey cattle. The dataset included 1.5 K Jersey bulls with sire conception rate (SCR) records and 96 K single nucleotide polymorphism (SNP) markers spanning the whole genome. The analysis included whole‐genome scans for both additive and non‐additive effects and subsequent functional enrichment analyses using KEGG Pathway, Gene Ontology (GO) and Medical Subject Headings (MeSH) databases. Ten genomic regions located on eight different chromosomes explained more than 0.5% of the additive genetic variance for SCR. These regions harbor genes, such as PKDREJ,EPB41L2,PDGFD,STX2,SLC25A20 and IP6K1, that are directly implicated in testis development and spermatogenesis, sperm motility and the acrosome reaction. In addition, the genomic scan for non‐additive effects identified two regions on BTA11 and BTA25 with marked recessive effects. These regions harbor three genes—FER1L5,CNNM4 and DNAH3—with known roles in sperm biology. Moreover, the gene‐set analysis revealed terms associated with calcium regulation and signaling, membrane fusion, sperm cell energy metabolism, GTPase activity and MAPK signaling. These gene sets are directly implicated in sperm physiology and male fertility. Overall, this integrative genomic study unravels genetic variants and pathways affecting Jersey bull fertility. These findings may contribute to the development of novel genomic strategies for improving sire fertility in Jersey cattle.

Genome-wide association study using haplotype alleles for the evaluation of reproductive traits in Nelore cattle

Zebu cattle (Bos taurus indicus) are highly adapted to tropical regions. However, females reach puberty after taurine heifers, which affects the economic efficiency of beef cattle breeding in the tropical regions. The aims of this study were to establish associations between the haplotype alleles of the bovine genome and age at first calving (AFC) in the Nelore cattle, and to identify the genes and quantitative trait loci (QTL) related to this phenotype. A total of 2,273 Nelore cattle (995 males and 1,278 females) genotyped using the Illumina BovineHD BeadChip were used in the current study. The association analysis included females with valid first calving records as well as open heifers. Linkage disequilibrium (LD) analysis among the markers was performed using blocks of 5, 10, and 15 markers, which were determined by sliding windows shifting one marker at a time. Then, the haplotype block size to be used in the association study was chosen based on the highest r² average among the SNPs in the block. The five HapAlleles most strongly associated with the trait (top five) were considered as significant associations. The results of the analysis revealed four genomic regions related to AFC, which overlapped with 20 QTL of the reproductive traits reported previously. Furthermore, there were 19 genes related to reproduction in those regions. In conclusion, the use of haplotypes allowed the detection of chromosomal regions associated with AFC in Nelore cattle, and provided the basis for elucidating the mechanisms underlying this trait.

Tantalizing dilemma in risk prediction from disease scoring statistics

Over the past decade, human host genome-wide association studies (GWASs) have contributed greatly to our understanding of the impact of host genetics on phenotypes. Recently, the microbiome has been recognized as a complex trait in host genetic variation, leading to microbiome GWAS (mGWASs). For these, many different statistical methods and software tools have been developed for association mapping. Applications of these methods and tools have revealed several important findings; however, the establishment of causal factors and the direction of causality in the interactive role between human genetic polymorphisms, the microbiome and the host phenotypes are still a huge challenge. Here, we review disease scoring approaches in host and mGWAS and their underlying statistical methods and tools. We highlight the challenges in pinpointing the genetic-associated causal factors in host and mGWAS and discuss the role of multi-omic approach in disease scoring statistics that may provide a better understanding of human phenotypic variation by enabling further system biological experiment to establish causality.

Polygenic Basis and Variable Genetic Architectures Contribute to the Complex Nature of Body Weight -A Genome-Wide Study in Four Chinese Indigenous Chicken Breeds

Body weight (BW) is one of the most important economic traits for animal production and breeding, and it has been studied extensively for its phenotype–genotype associations. While mapping studies have mostly aimed at finding as many loci as possible that contributed to the variation in BW, the role of other factors in its genetic architecture, including their frequencies in the population and their interactions, have been largely overlooked. To comprehensively characterized the genetic architecture of BW, we performed a genome-wide association study (GWAS) both at the single-marker and haplotype level on birds from four indigenous Chinese chicken breeds (Chahua, Silkie, Langshan, and Beard), rather than studying crosses between two founder lines. Additionally, samples from two more breeds (Red Junglefowl and Recessive White) were included to better reflect variable genetic characteristics across populations. Six loci were mapped in this study, revealing the polygenic basis underlying BW. Moreover, by further examining the frequencies of the significantly associated haplotypes in each subpopulation and their effect sizes, most of the loci were found to affect BW in the Beard chicken breed alone. Two loci in GGA9 and GGA27, however, had a common effect on BW across subpopulations, showing that different underlying genetic mechanisms contribute to the phenotypic variability. These findings, particularly the variable genetic architectures found in different loci, improve our understanding of the overall genetic contributions to the large variability in BW among Chinese indigenous chicken breeds. These findings thus will have important implications for future chicken breeding.

Genome-wide association study reveals a QTL and strong candidate genes for umbilical hernia in pigs on SSC14

Background

Umbilical hernia is one of the most prevalent congenital defect in pigs, causing economic losses and substantial animal welfare problems. Identification and implementation of genomic regions controlling umbilical hernia in breeding is of great interest to reduce incidences of hernia in commercial pig production. The aim of this study was to identify such regions and possibly identify causative variation affecting umbilical hernia in pigs. A case/control material consisting of 739 Norwegian Landrace pigs was collected and applied in a GWAS study with a genome-wide distributed panel of 60 K SNPs. Additionally candidate genes were sequenced to detect additional polymorphisms that were used for single SNP and haplotype association analyses in 453 of the pigs.

Results

The GWAS in this report detected a highly significant region affecting umbilical hernia around 50 Mb on SSC14 (P < 0.0001) explaining up to 8.6% of the phenotypic variance of the trait. The region is rather broad and includes 62 significant SNPs in high linkage disequilibrium with each other. Targeted sequencing of candidate genes within the region revealed polymorphisms within the Leukemia inhibitory factor (LIF) and Oncostatin M (OSM) that were significantly associated with umbilical hernia (P < 0.001).

Conclusions

A highly significant QTL for umbilical hernia in Norwegian Landrace pigs was detected around 50 Mb on SSC14. Resequencing of candidate genes within the region revealed SNPs within LIF and OSM highly associated with the trait. However, because of extended LD within the region, studies in other populations and functional studies are needed to determine whether these variants are causal or not. Still without this knowledge, SNPs within the region can be used as genetic markers to reduce incidences of umbilical hernia in Norwegian Landrace pigs.

Genome-wide association studies to identify rice salt-tolerance markers

Salinity is an ever increasing menace that affects agriculture worldwide. Crops such as rice are salt sensitive, but its degree of susceptibility varies widely between cultivars pointing to extensive genetic diversity that can be exploited to identify genes and proteins that are relevant in the response of rice to salt stress. We used a diversity panel of 306 rice accessions and collected phenotypic data after short (6 h), medium (7 d) and long (30 d) salinity treatment (50 mm NaCl). A genome-wide association study (GWAS) was subsequently performed, which identified around 1200 candidate genes from many functional categories, but this was treatment period dependent. Further analysis showed the presence of cation transporters and transcription factors with a known role in salinity tolerance and those that hitherto were not known to be involved in salt stress. Localization analysis of single nucleotide polymorphisms (SNPs) showed the presence of several hundred non-synonymous SNPs (nsSNPs) in coding regions and earmarked specific genomic regions with increased numbers of nsSNPs. It points to components of the ubiquitination pathway as important sources of genetic diversity that could underpin phenotypic variation in stress tolerance.

Genome-Wide Association Study for Susceptibility to and Recoverability From Mastitis in Danish Holstein Cows

Because mastitis is very frequent and unavoidable, adding recovery information into the analysis for genetic evaluation of mastitis is of great interest from economical and animal welfare point of view. Here we have performed genome-wide association studies (GWAS) to identify associated single nucleotide polymorphisms (SNPs) and investigate the genetic background not only for susceptibility to – but also for recoverability from mastitis. Somatic cell count records from 993 Danish Holstein cows genotyped for a total of 39378 autosomal SNP markers were used for the association analysis. Single SNP regression analysis was performed using the statistical software package DMU. Substitution effect of each SNP was tested with a t-test and a genome-wide significance level of P-value < 10^-4 was used to declare significant SNP-trait association. A number of significant SNP variants were identified for both traits. Many of the SNP variants associated either with susceptibility to – or recoverability from mastitis were located in or very near to genes that have been reported for their role in the immune system. Genes involved in lymphocyte developments (e.g., MAST3 and STAB2) and genes involved in macrophage recruitment and regulation of inflammations (PDGFD and PTX3) were suggested as possible causal genes for susceptibility to – and recoverability from mastitis, respectively. However, this is the first GWAS study for recoverability from mastitis and our results need to be validated. The findings in the current study are, therefore, a starting point for further investigations in identifying causal genetic variants or chromosomal regions for both susceptibility to – and recoverability from mastitis.

A high-density SNP chip for genotyping great tit (Parus major) populations and its application to studying the genetic architecture of exploration behaviour

High-density SNP microarrays ("SNP chips") are a rapid, accurate and efficient method for genotyping several hundred thousand polymorphisms in large numbers of individuals. While SNP chips are routinely used in human genetics and in animal and plant breeding, they are less widely used in evolutionary and ecological research. In this article, we describe the development and application of a high-density Affymetrix Axiom chip with around 500,000 SNPs, designed to perform genomics studies of great tit (Parus major) populations. We demonstrate that the per-SNP genotype error rate is well below 1% and that the chip can also be used to identify structural or copy number variation. The chip is used to explore the genetic architecture of exploration behaviour (EB), a personality trait that has been widely studied in great tits and other species. No SNPs reached genomewide significance, including at DRD4, a candidate gene. However, EB is heritable and appears to have a polygenic architecture. Researchers developing similar SNP chips may note: (i) SNPs previously typed on alternative platforms are more likely to be converted to working assays; (ii) detecting SNPs by more than one pipeline, and in independent data sets, ensures a high proportion of working assays; (iii) allele frequency ascertainment bias is minimized by performing SNP discovery in individuals from multiple populations; and (iv) samples with the lowest call rates tend to also have the greatest genotyping error rates.

An analysis of effects of heterozygosity in dairy cattle for bovine tuberculosis resistance

Genetic selection of cattle more resistant to bovine tuberculosis (bTB) may offer a complementary control strategy. Hypothesising underlying non-additive genetic variation, we present an approach using genome-wide high density markers to identify genomic loci with dominance effects on bTB resistance and to test previously published regions with heterozygote advantage in bTB. Our data comprised 1151 Holstein-Friesian cows from Northern Ireland, confirmed bTB cases and controls, genotyped with the 700K Illumina BeadChip. Genome-wide markers were tested for associations between heterozygosity and bTB status using marker-based relationships. Results were tested for robustness against genetic structure, and the genotypic frequencies of a significant locus were tested for departures from Hardy-Weinberg equilibrium. Genomic regions identified in our study and in previous publications were tested for dominance effects. Genotypic effects were estimated through ASReml mixed models. A SNP (rs43032684) on chromosome 6 was significant at the chromosome-wide level, explaining 1.7% of the phenotypic variance. In the controls, there were fewer heterozygotes for rs43032684 (P < 0.01) with the genotypic values suggesting that heterozygosity confers a heterozygote disadvantage. The region surrounding rs43032684 had a significant dominance effect (P < 0.01). SNP rs43032684 resides within a pseudogene with a parental gene involved in macrophage response to infection and within a copy-number-variation region previously associated with nematode resistance. No dominance effect was found for the region on chromosome 11, as indicated by a previous candidate region bTB study. These findings require further validation with large-scale data.

Association study highlights the influence of ELOVL fatty acid elongase 6 gene region on backfat fatty acid composition in Large White pig breed

Dietary fatty acid (FA) composition has an impact on human health. There is an increasing request from consumers for healthier food and pork industry must respond to it without worsening performance and the technological properties of pork products. The inclusion of genetic markers for carcass FA composition in pig selection schemes could be a useful tool to reach the right balance between unsaturated and saturated FAs to satisfy market demands. With the aim of finding genomic regions associated with porcine backfat FA composition, a genome-wide association study was performed on 798 Italian Large White pigs genotyped using Illumina PorcineSNP60 k. The strongest associations with backfat contents of palmitic, palmitoleic, oleic, medium-chain and long-chain FAs were found for the Sus scrofa chromosome (SSC) 8 region located at 119 to 122 Mb, where the gene ELOVL FA elongase 6 is mapped. Palmitic, palmitoleic, stearic and oleic acid contents were also found associated with SSC14, in particular with the genomic region at 121 to 124 Mb, where stearoyl-CoA desaturase Δ9 gene lies. On the other hand, the genomic regions associated with backfat contents of arachidic, arachidonic, n-6 and n-3 FAs showed to harbour mainly genes involved in dietary lipids and carbohydrates digestion, absorption and utilisation. To our knowledge, this is the first study performed in Large White pigs identifying markers and genomic regions associated with backfat FA composition. The results validate in Large White some associations previously detected in other pig breeds and indicate the involvement of distinct metabolic pathways in the deposition pattern of essential and non-essential FAs.

Eigen decomposition expedites longitudinal genome-wide association studies for milk production traits in Chinese Holstein

Background

Pseudo-phenotypes, such as 305-day yields, estimated breeding values or deregressed proofs, are usually used as response variables for genome-wide association studies (GWAS) of milk production traits in dairy cattle. Computational inefficiency challenges the direct use of test-day records for longitudinal GWAS with large datasets.

Results

We propose a rapid longitudinal GWAS method that is based on a random regression model. Our method uses Eigen decomposition of the phenotypic covariance matrix to rotate the data, thereby transforming the complex mixed linear model into weighted least squares analysis. We performed a simulation study that showed that our method can control type I errors well and has higher power than a longitudinal GWAS method that does not include time-varied additive genetic effects. We also applied our method to the analysis of milk production traits in the first three parities of 6711 Chinese Holstein cows. The analysis for each trait was completed within 1 day with known variances. In total, we located 84 significant single nucleotide polymorphisms (SNPs) of which 65 were within previously reported quantitative trait loci (QTL) regions.

Conclusions

Our rapid method can control type I errors in the analysis of longitudinal data and can be applied to other longitudinal traits. We detected QTL that were for the most part similar to those reported in a previous study in Chinese Holstein. Moreover, six additional SNPs for fat percentage and 13 SNPs for protein percentage were identified by our method. These additional 19 SNPs could be new candidate quantitative trait nucleotides for milk production traits in Chinese Holstein.

Electronic supplementary material

The online version of this article (10.1186/s12711-018-0383-0) contains supplementary material, which is available to authorized users.

An independent validation study of loci associated with nematode resistance in sheep

Gastrointestinal nematode infection is a constraint on sheep production worldwide. Selective breeding programmes to enhance resistance to nematode infection are currently being implemented in a number of countries. Identification of loci associated with resistance to infection or causative mutations for resistance would enable more effective selection. Loci associated with indicator traits for nematode resistance has been identified in previous studies. In this study, Scottish Blackface, Texel and Suffolk lambs were used to validate the effects at eight genomic regions previously associated with nematode resistance (OAR3, 4, 5, 7, 12, 13, 14, 21). No SNP was significantly associated with nematode resistance at the region-wide level but seven SNPs in three of the regions (OAR4, 12, 14) were nominally associated with trichostrongyle egg count in this study and six of these were also significant when fitted as single SNP effects. Nematodirus egg count was nominally associated with SNPs on OAR3, 4, 7 and 12.

Genome wide association study of fatty acid composition in Duroc swine

Objective

Genome wide association study was conducted to identify and validate candidate genes associated with fatty acid composition of pork.

Methods

A total of 480 purebreed Duroc pigs were genotyped using IlluminaPorcine60k bead chips while the association test was implemented following genome-wide rapid association using Mixed Model and Regression-Genomic Control (GRAMMAR-GC) approach.

Results

A total of 25, 29, and 16 single nucleotide polymorphisms (SNPs) were significantly associated with stearic (18:0), oleic (18:1) and saturated fatty acids (SFA), respectively. Genome wide significant variants were located on the same region of swine chromosome 14 (SSC14) that spanned from 120 to 124 Mb. Top SNP ALGA008191 was located at 5 kb near the stearoyl-CoA desaturase (SCD) gene. This gene is directly involved in desaturation of stearic acid into oleic acid. General relationship of significant SNPs showed high linkage disequilibrium thus genome-wide signals was attributed to SCD gene. However, understanding the role of other genes like elongation of very long chain fatty acids-3 (ELOVL3) located on this chromosomal segment might help in further understanding of metabolism and biosynthesis of fatty acids.

Conclusion

Overall, this study provides evidence that validates SCD gene as strong candidate gene associated with fatty acid composition in Duroc pigs. Moreover, this study confirms significant SNPs near ELOVL3 gene.

Whole-genome scan reveals significant non-additive effects for sire conception rate in Holstein cattle

Background

Service sire has a considerable impact on reproductive success in dairy cattle. Most gene mapping studies for bull fertility have focused on additive effects, while non-additive effects have been largely ignored. The main goal of this study was to assess the relevance of non-additive effects on Sire Conception Rate (SCR) in Holstein dairy cattle. The analysis included 7.5 k Holstein bulls with both SCR records and 57.8 k single nucleotide polymorphism (SNP) markers spanning the entire genome.

Results

The importance of non-additive effects was evaluated using an efficient two-step mixed model-based approach. Four genomic regions located on chromosomes BTA8, BTA9, BTA13 and BTA17 showed marked dominance and/or recessive effects. Most of these regions harbor genes, such as ADAM28, DNAJA1, TBC1D20, SPO11, PIWIL3 and TMEM119, that are directly implicated in testis development, male germ line maintenance, and sperm maturation.

Conclusions

This study provides further evidence for the relevance of non-additive effects in fitness-related traits, such as male fertility. In addition, these findings may point out new strategies for improving service sire fertility in dairy cattle via marker-assisted selection.

Cadmium-Associated Differential Methylation throughout the Placental Genome: Epigenome-Wide Association Study of Two U.S. Birth Cohorts

BACKGROUND

Cadmium (Cd) is a ubiquitous environmental toxicant that can accumulate in the placenta during pregnancy, where it may impair placental function and affect fetal development.

OBJECTIVES

We aimed to investigate Cd-associated variations in placental DNA methylation (DNAM) and associations with gene expression; we also aimed to identify novel pathways involved in Cd-associated reproductive toxicity.

METHODS

Using placental DNAM and Cd concentrations in the New Hampshire Birth Cohort Study (NHBCS, n=343) and the Rhode Island Child Health Study (RICHS, n=141), we performed an epigenome-wide association study (EWAS) between Cd and DNAM, adjusting for tissue heterogeneity using a reference-free method. Cohort-specific results were aggregated via inverse variance weighted fixed effects meta-analysis, and variably methylated CpGs were associated with gene expression. We then performed functional enrichment analysis and tests for associations between gene expression and birth size metrics.

RESULTS

We identified 17 Cd-associated differentially methylated CpG sites with meta-analysis p-values<1×10⁻⁵, two of which were within a 5% false discovery rate (FDR). DNAM levels at 9 of the 17 loci were associated with increased expression of 6 genes (5% FDR): TNFAIP2, EXOC3L4, GAS7, SREBF1, ACOT7, and RORA. Higher placental expression of TNFAIP2 and ACOT7 and lower expression of RORA were associated with lower birth weight z-scores (p-values<0.05).

CONCLUSION

Cd-associated differential DNAM and corresponding DNAM-expression associations were observed at loci involved in inflammatory signaling and cell growth. The expression levels of genes involved in inflammatory signaling (TNFAIP2, ACOT7, and RORA) were also associated with birth weight, suggesting a role for inflammatory processes in Cd-associated reproductive toxicity. https://doi.org/10.1289/EHP2192.

Pathway analysis of expression-related SNPs on genome-wide association study of basal cell carcinoma

Genome-wide association studies (GWASs) have primarily focused on the association between individual genetic markers and risk of disease. We applied a novel approach that integrates skin expression-related single-nucleotide polymorphisms (eSNPs) and pathway analysis for GWAS of basal cell carcinoma (BCC) to identify potential novel biological pathways. We evaluated the associations between 70,932 skin eSNPs and risk of BCC among 2,323 cases and 7,275 controls of European ancestry, and then assigned them to the pathways defined by KEGG, GO, and BioCarta databases. Three KEGG pathways (colorectal cancer, actin cytoskeleton, and BCC), two GO pathways (cellular component disassembly in apoptosis, and nucleus organization), and four BioCarta pathways (Ras signaling, T cell receptor signaling, natural killer cell-mediated cytotoxicity, and links between Pyk2 and Map Kinases) showed significant association with BCC risk with p-value<0.05 and FDR<0.2. These pathways also ranked at top in sensitivity analyses. Two positive controls in KEGG, the hedgehog pathway and the BCC pathway, showed significant association with BCC risk in both main and sensitivity analyses. Our results indicate that SNPs that are undetectable by conventional GWASs are significantly associated with BCC when tested as pathways. Biological studies of these gene groups suggest their potential roles in the etiology of BCC.

Recent natural selection causes adaptive evolution of an avian polygenic trait

We used extensive data from a long-term study of great tits (Parus major) in the United Kingdom and Netherlands to better understand how genetic signatures of selection translate into variation in fitness and phenotypes. We found that genomic regions under differential selection contained candidate genes for bill morphology and used genetic architecture analyses to confirm that these genes, especially the collagen gene COL4A5, explained variation in bill length. COL4A5 variation was associated with reproductive success, which, combined with spatiotemporal patterns of bill length, suggested ongoing selection for longer bills in the United Kingdom. Last, bill length and COL4A5 variation were associated with usage of feeders, suggesting that longer bills may have evolved in the United Kingdom as a response to supplementary feeding.

On the impact of relatedness on SNP association analysis

BACKGROUND

When testing for SNP (single nucleotide polymorphism) associations in related individuals, observations are not independent. Simple linear regression assuming independent normally distributed residuals results in an increased type I error and the power of the test is also affected in a more complicate manner. Inflation of type I error is often successfully corrected by genomic control. However, this reduces the power of the test when relatedness is of concern. In the present paper, we derive explicit formulae to investigate how heritability and strength of relatedness contribute to variance inflation of the effect estimate of the linear model. Further, we study the consequences of variance inflation on hypothesis testing and compare the results with those of genomic control correction. We apply the developed theory to the publicly available HapMap trio data (N=129), the Sorbs (a self-contained population with N=977 characterised by a cryptic relatedness structure) and synthetic family studies with different sample sizes (ranging from N=129 to N=999) and different degrees of relatedness.

RESULTS

We derive explicit and easily to apply approximation formulae to estimate the impact of relatedness on the variance of the effect estimate of the linear regression model. Variance inflation increases with increasing heritability. Relatedness structure also impacts the degree of variance inflation as shown for example family structures. Variance inflation is smallest for HapMap trios, followed by a synthetic family study corresponding to the trio data but with larger sample size than HapMap. Next strongest inflation is observed for the Sorbs, and finally, for a synthetic family study with a more extreme relatedness structure but with similar sample size as the Sorbs. Type I error increases rapidly with increasing inflation. However, for smaller significance levels, power increases with increasing inflation while the opposite holds for larger significance levels. When genomic control is applied, type I error is preserved while power decreases rapidly with increasing variance inflation.

CONCLUSIONS

Stronger relatedness as well as higher heritability result in increased variance of the effect estimate of simple linear regression analysis. While type I error rates are generally inflated, the behaviour of power is more complex since power can be increased or reduced in dependence on relatedness and the heritability of the phenotype. Genomic control cannot be recommended to deal with inflation due to relatedness. Although it preserves type I error, the loss in power can be considerable. We provide a simple formula for estimating variance inflation given the relatedness structure and the heritability of a trait of interest. As a rule of thumb, variance inflation below 1.05 does not require correction and simple linear regression analysis is still appropriate.

Design and validation of a 90K SNP genotyping assay for the water buffalo (Bubalus bubalis)

Background

The availability of the bovine genome sequence and SNP panels has improved various genomic analyses, from exploring genetic diversity to aiding genetic selection. However, few of the SNP on the bovine chips are polymorphic in buffalo, therefore a panel of single nucleotide DNA markers exclusive for buffalo was necessary for molecular genetic analyses and to develop genomic selection approaches for water buffalo. The creation of a 90K SNP panel for river buffalo and testing in a genome wide association study for milk production is described here.

Methods

The genomes of 73 buffaloes of 4 different breeds were sequenced and aligned against the bovine genome, which facilitated the identification of 22 million of sequence variants among the buffalo genomes. Based on frequencies of variants within and among buffalo breeds, and their distribution across the genome, inferred from the bovine genome sequence, 90,000 putative single nucleotide polymorphisms were selected to create an Axiom® Buffalo Genotyping Array 90K.

Results

This 90K “SNP-Chip” was tested in several river buffalo populations and found to have ∼70% high quality and polymorphic SNPs. Of the 90K SNPs about 24K were also found to be polymorphic in swamp buffalo. The SNP chip was used to investigate the structure of buffalo populations, and could distinguish buffalo from different farms. A Genome Wide Association Study identified genomic regions on 5 chromosomes putatively involved in milk production.

Conclusion

The 90K buffalo SNP chip described here is suitable for the analysis of the genomes of river buffalo breeds, and could be used for genetic diversity studies and potentially as a starting point for genome-assisted selection programmes. This SNP Chip could also be used to analyse swamp buffalo, but many loci are not informative and creation of a revised SNP set specific for swamp buffalo would be advised.

Multivariate discovery and replication of five novel loci associated with Immunoglobulin G N-glycosylation

Joint modeling of a number of phenotypes using multivariate methods has often been neglected in genome-wide association studies and if used, replication has not been sought. Modern omics technologies allow characterization of functional phenomena using a large number of related phenotype measures, which can benefit from such joint analysis. Here, we report a multivariate genome-wide association studies of 23 immunoglobulin G (IgG) N-glycosylation phenotypes. In the discovery cohort, our multi-phenotype method uncovers ten genome-wide significant loci, of which five are novel (IGH, ELL2, HLA-B-C, AZI1, FUT6-FUT3). We convincingly replicate all novel loci via multivariate tests. We show that IgG N-glycosylation loci are strongly enriched for genes expressed in the immune system, in particular antibody-producing cells and B lymphocytes. We empirically demonstrate the efficacy of multivariate methods to discover novel, reproducible pleiotropic effects.Multivariate analysis methods can uncover the relationship between phenotypic measures characterised by modern omic techniques. Here the authors conduct a multivariate GWAS on IgG N-glycosylation phenotypes and identify 5 novel loci enriched in immune system genes.

Identification of additional loci associated with antibody response to Mycobacterium avium ssp. Paratuberculosis in cattle by GSEA-SNP analysis

Mycobacterium avium subsp. paratuberculosis: (MAP) causes a contagious chronic infection results in Johne's disease in a wide range of animal species, including cattle. Several genome-wide association studies (GWAS) have been carried out to identify loci putatively associated with MAP susceptibility by testing each marker separately and identifying SNPs that show a significant association with the phenotype, while SNP with modest effects are usually ignored. The objective of this study was to identify modest-effect genes associated with MAP susceptibility using a pathway-based approach. The Illumina BovineSNP50 BeadChip was used to genotype 966 Holstein cows, 483 positive and 483 negative for antibody response to MAP, data were then analyzed using novel SNP-based Gene Set Enrichment Analysis (GSEA-SNP) and validated with Adaptive Rank Truncated Product methodology. An allele-based test was carried out to estimate the statistical association for each marker with the phenotype, subsequently SNPs were mapped to the closest genes, considering for each gene the single variant with the highest value within a window of 50 kb, then pathway-statistics were tested using the GSEA-SNP method. The GO biological process "embryogenesis and morphogenesis" was most highly associated with antibody response to MAP. Within this pathway, five genes code for proteins which play a role in the immune defense relevant to response to bacterial infection. The immune response genes identified would not have been considered using a standard GWAS, thus demonstrating that the pathway approach can extend the interpretation of genome-wide association analyses and identify additional candidate genes for target traits.