The combined effect of SNP-marker and phenotype attributes in genome-wide association studies

Genome-wide association study for growth traits in Blanco Orejinegro and Romosinuano cattle

Growth traits are economically important characteristics for the genetic improvement of local cattle breeds. Genome-wide association studies (GWAS) provide valuable information to enhance the understanding on the genetics of complex traits. The aim of this study was to perform a GWAS to identify genomic regions and genes associated to birth weight, weaning weight adjusted for 240 days, 16 months, and 24 months weight in Romosinuano (ROMO) and Blanco Orejinegro (BON) cattle. A single-step genomic-BLUP was implemented using 596 BON and 569 ROMO individuals that were genotyped with an Illumina BovineSNP50 BeadChip. There were 25 regions of interest identified on different chromosomes, with few of them simultaneously associated with two or more growth traits and some were common to both breeds. The gene mapping allowed to find 173 annotations on these regions, from which 49 represent potential candidate genes with known growth-related functions in cattle and other species. Among the regions that were associated with several growth traits, that at 24 - 27 MB of BTA14, has important candidate genes such as LYPLA1, XKR4, TMEM68 and PLAG1. Another region of interest at 0.40-0.77 Mb of BTA23 was identified in both breeds, containing KHDRBS2 as a potential candidate gene influencing body weight. Future studies targeting these regions could provide more knowledge to uncover the genetic architecture underlying growth traits in BON and ROMO cattle. The genomic regions and genes identified in this study could be used to improve the prediction of genetic merit for growth traits in these creole cattle breeds.

A SNP within the PMCH gene as a molecular marker associated with fertility traits in Angus and Brangus beef heifers raised under a desert environment

Components of the GH/IGF1 endocrine axis regulate growth and reproductive traits in cattle. The pro-melanin-concentrating hormone (PMCH) gene located within chromosome 5 belongs to this axis. Objective herein was to evaluate PMCH single-nucleotide polymorphisms (SNPs) as molecular markers associated with age at first calving, calving interval, and age at second calving in Angus and Brangus beef heifers raised in desert conditions. Five SNPs within the PMCH gene were included in the study. Three of these SNPs had minor allele frequency > 10% and only one SNP did not deviate from Hardy-Weinberg equilibrium. A genotype to phenotype association analyses was performed using a mixed-effects model which included phenotype as the response variable, SNP genotype, breed, year of birth and age of dam as fixed terms, and sire as a random effect. Genotypes from the SNP rs135033882 were found to be associated (P < 0.05) with all evaluated fertility traits, and the term breed resulted as a significant source of variation only for age at second calving. The allele A was the favorable allele because it decreased the age at first calving 98.6 days, the calving interval 85.3 days, and the age at second calving 183.1 days, in Angus and Brangus heifers. In conclusion, we proposed a SNP within the PMCH gene as a potential candidate marker associated with reproductive performance in Angus and Brangus beef heifers raised in a desert climate.

Evaluation of the International Society for Animal Genetics bovine single nucleotide polymorphism parentage panel in South African Bonsmara and Drakensberger cattle

A panel of 200 single nucleotide polymorphisms (SNPs) have been recommended by the International Society for Animal Genetics (ISAG) for use in parentage verification of cattle. While the SNPs included on the ISAG panel are segregating in European Bos taurus and Bos indicus breeds, their applicability in South African (SA) Sanga cattle has never been evaluated. This study, therefore, assessed the usefulness of the ISAG panel in SA Bonsmara (BON) and Drakensberger (DRB) cattle. Genotypes of 185 ISAG SNPs from 64 BON and 97 DRB sire-offspring pairs were available, all of which were validated with 119,375 SNPs. Of the 185 ISAG SNPs, 14 and 18 in the BON and DRB, respectively (9 in common to both breeds), were either monomorphic, exhibited at least one discordance between validated sire-offspring pairs, or had poor call rate or clustering issue. The mean minor allele frequency of the 185 ISAG SNPs was 0.331 in the BON and 0.359 in the DRB. The combined probability of parentage exclusion (P_E) was the same (99.46%) for both breeds, while the probability of identity varied from 1.61 × 10^-48 (BON) to 1.11 × 10^-54 (DRB). Fifteen (23.4%) and 32 (33%) of the already validated sire-offspring pairs for the BON and DRB, respectively, were determined by the ISAG panel to be false-negatives based on a threshold of having at least two discordant SNPs. In comparison to sire discovery using the 119,375 SNPs, sire discovery using only the ISAG panel identified correctly 44 (out of 64 identified using the 119,375 SNPs) unique sire-offspring BON pairs and 62 (out of 97 identified using the 119,375 SNPs) unique sire-offspring DRB when all sires were masked. Five BON and three DRB offspring had > 1 sire nominated. This study demonstrated that the use of the ISAG panel may result in incorrect exclusions and multiple candidate sires for a given animal. Selection of more informative SNPs is, therefore, necessary in the pursuit of a low-cost and effective SNP panel for indigenous cattle breeds in SA.

A unified local objective function for optimally selecting SNPs on arrays for agricultural genomics applications

Over the years, ad-hoc procedures were used for designing SNP arrays, but the procedures and strategies varied considerably case by case. Recently, a multiple-objective, local optimization (MOLO) algorithm was proposed to select SNPs for SNP arrays, which maximizes the adjusted SNP information (E score) under multiple constraints, e.g. on MAF, uniformness of SNP locations (U score), the inclusion of obligatory SNPs and the number and size of gaps. In the MOLO, each chromosome is split into equally spaced segments and local optima are selected as the SNPs having the highest adjusted E score within each segment, conditional on the presence of obligatory SNPs. The computation of the adjusted E score, however, is empirical, and it does not scale well between the uniformness of SNP locations and SNP informativeness. In addition, the MOLO objective function does not accommodate the selection of uniformly distributed SNPs. In the present study, we proposed a unified local function for optimally selecting SNPs, as an amendment to the MOLO algorithm. This new local function takes scalable weights between the uniformness and informativeness of SNPs, which allows the selection of SNPs under varied scenarios. The results showed that the weighting between the U and the E scores led to a higher imputation concordance rate than the U score or E score alone. The results from the evaluation of six commercial bovine SNP chips further confirmed this conclusion.

Tag SNP selection for prediction of tick resistance in Brazilian Braford and Hereford cattle breeds using Bayesian methods

BACKGROUND

Cattle resistance to ticks is known to be under genetic control with a complex biological mechanism within and among breeds. Our aim was to identify genomic segments and tag single nucleotide polymorphisms (SNPs) associated with tick-resistance in Hereford and Braford cattle. The predictive performance of a very low-density tag SNP panel was estimated and compared with results obtained with a 50 K SNP dataset.

RESULTS

BayesB (π = 0.99) was initially applied in a genome-wide association study (GWAS) for this complex trait by using deregressed estimated breeding values for tick counts and 41,045 SNP genotypes from 3455 animals raised in southern Brazil. To estimate the combined effect of a genomic region that is potentially associated with quantitative trait loci (QTL), 2519 non-overlapping 1-Mb windows that varied in SNP number were defined, with the top 48 windows including 914 SNPs and explaining more than 20% of the estimated genetic variance for tick resistance. Subsequently, the most informative SNPs were selected based on Bayesian parameters (model frequency and t-like statistics), linkage disequilibrium and minor allele frequency to propose a very low-density 58-SNP panel. Some of these tag SNPs mapped close to or within genes and pseudogenes that are functionally related to tick resistance. Prediction ability of this SNP panel was investigated by cross-validation using K-means and random clustering and a BayesA model to predict direct genomic values. Accuracies from these cross-validations were 0.27 ± 0.09 and 0.30 ± 0.09 for the K-means and random clustering groups, respectively, compared to respective values of 0.37 ± 0.08 and 0.43 ± 0.08 when using all 41,045 SNPs and BayesB with π = 0.99, or of 0.28 ± 0.07 and 0.40 ± 0.08 with π = 0.999.

CONCLUSIONS

Bayesian GWAS model parameters can be used to select tag SNPs for a very low-density panel, which will include SNPs that are potentially linked to functional genes. It can be useful for cost-effective genomic selection tools, when one or a few key complex traits are of interest.

Nucleotide polymorphisms in a pine ortholog of the Arabidopsis degrading enzyme cellulase KORRIGAN are associated with early growth performance in Pinus pinaster

We have carried out a candidate-gene-based association genetic study in Pinus pinaster Aiton and evaluated the predictive performance for genetic merit gain of the most significantly associated genes and single nucleotide polymorphisms (SNPs). We used a second generation 384-SNP array enriched with candidate genes for growth and wood properties to genotype mother trees collected in 20 natural populations covering most of the European distribution of the species. Phenotypic data for total height, polycyclism, root-collar diameter and biomass were obtained from a replicated provenance-progeny trial located in two sites with contrasting environments (Atlantic vs Mediterranean climate). General linear models identified strong associations between growth traits (total height and polycyclism) and four SNPs from the korrigan candidate gene, after multiple testing corrections using false discovery rate. The combined genomic breeding value predictions assessed for the four associated korrigan SNPs by ridge regression-best linear unbiased prediction (RR-BLUP) and cross-validation accounted for up to 8 and 15% of the phenotypic variance for height and polycyclic growth, respectively, and did not improve adding SNPs from other growth-related candidate genes. For root-collar diameter and total biomass, they accounted for 1.6 and 1.1% of the phenotypic variance, respectively, but increased to 15 and 4.1% when other SNPs from lp3.1, lp3.3 and cad were included in RR-BLUP models. These results point towards a desirable integration of candidate-gene studies as a means to pre-select relevant markers, and aid genomic selection in maritime pine breeding programs.

Genetic differentiation of brackish water populations of cod Gadus morhua in the southern Baltic, inferred from genotyping using SNP-arrays

The Baltic is a semi-enclosed sea characterised by decreasing salinity in the eastern and northern direction with only the deeper parts of the southern Baltic suitable as spawning grounds for marine species like cod. Baltic cod exhibits various adaptations to brackish water conditions, yet the inflow of salty North Sea water near the bottom remains an influence on the spawning success of the Baltic cod. The eastern Baltic population has been very weakly studied in comparison with the western population. The aim of this study is to demonstrate for the first time genetic differentiation by the use of a large number of SNPs between eastern and western Baltic populations existing in differentiated salinity conditions. Two cod samples were collected from the Bay of Gdańsk, Poland and one from the Kiel Bight, Germany. Samples were genotyped using a cod derived SNP-array (Illumina) with 10 913 SNPs. A selection of diagnostic SNPs was performed. A set of 7944 validated SNPs were analysed to assess the differentiation of three samples of cod. Results indicated a clear distinctness of the Kiel Bight from the populations of the eastern Baltic. FST comparison between both eastern samples was non-significant. Clustering analysis, principal coordinates analysis and assignment test clearly indicated that the eastern samples should be considered as one subpopulation, well differentiated from the western subpopulation. With the SNP approach, no differentiation between groups containing 'healthy' and 'non-healthy' cod individuals was observed.

The RIPK2 gene: a positional candidate for tick burden supported by genetic associations in cattle and immunological response of knockout mouse

Ticks and tick-borne diseases have a detrimental impact on livestock production causing estimated losses of around $200 million per year in Australia alone. Host resistance to ticks is heritable, within-breed heritability estimates being around 0.35, and with large differences between breeds. Previously a QTL for tick burden was detected on BTA14 at ~72 Mb distal to the centromere, near the gene receptor-interacting serine-threonine kinase 2 (RIPK2). To identify polymorphisms in this region, we sequenced all exons of the RIPK2 gene, identifying 46 single nucleotide polymorphism (SNP). Using SNP from RIPK2 as well as SNP from the bovine genome sequence, we genotyped two samples, one of 1,122 taurine dairy cattle and one of 761 zebu and zebu composite beef cattle. We confirmed that SNP and haplotypes from this region, including from RIPK2, were associated with tick burden in both dairy and beef cattle. To determine whether RIPK2 influences response to tick salivary gland extract (SGE), an immunisation experiment with tick SGE in a RIPK2 knockout (RIPK2 −/−) mouse strain was conducted. There was a significant (P < 0.05) reduction in IgG production in the RIPK2 −/− mouse in response to the SGE compared to its background strain C57BL/ 6 as well as the outbred CD1 mouse strain. In addition, antibodies generated by RIPK2 −/− mice recognised a different set of antigens within SGE when compared to parental-derived antibodies. In summary, the SNP association with tick burden at BTA14 was confirmed and quantitative and qualitative differences in antibody production were observed between RIPK2 −/− and wild-type mice.

Genetic diversity and population structure of Indonesian native chickens based on single nucleotide polymorphism markers

Indonesian native chickens are considered an important genetic resource, particularly with respect to their excellent traits for meat and egg production. However, few molecular genetic studies of these native chickens have been conducted. We analyzed the genetic diversity and differentiation of 4 populations of Indonesian native chickens: Black Kedu (BK), Kedu (KD), Kampung (LOC), and Arab (AR). Blood samples from 188 individuals were collected in central and western Java. Genomic DNA was genotyped using 98 autosomal SNP markers, of which 87 were found to be polymorphic. The proportion of polymorphic loci and the average heterozygosity of each population were in the range of 0.765 to 0.878 and 0.224 to 0.263, respectively. The 4 populations of Indonesian chickens appeared to be derived from 3 genetic populations (K = 3): maximum likelihood clustering showed that the BK variety and AR breed were each assigned to a distinct cluster, whereas the LOC ecotype and KD variety were admixed populations with similar proportions of membership. Principal components analysis revealed that eigenvector 1 separated BK and AR from the other 2 populations. Neighbor-joining trees constructed from pairwise distance matrix (F(ST)) estimates, for individuals and between populations, corroborated that the LOC ecotype and KD variety were related closely, whereas the BK variety and AR breed diverged at greater distances. These results also confirmed the usefulness of SNP markers for the study of genetic diversity.

Whole-genome QTL scan for ultrasound and carcass merit traits in beef cattle using Bayesian shrinkage method

Fine mapping of quantitative trait loci (QTL) for 16 ultrasound measurements and carcass merit traits that were collected from 418 hybrid steers was conducted using 1207 SNP markers covering the entire genome. These SNP markers were evaluated using a Bayesian shrinkage estimation method and the empirical critical significant thresholds (α = 0.05 and α = 0.01) were determined by permutation based on 3500 permuted datasets for each trait to control the genome-wide type I error rates. The analyses identified a total of 105 QTLs (p < 0.05) for seven ultrasound measure traits including ultrasound backfat, ultrasound marbling and ultrasound ribeye area and 113 QTLs for seven carcass merit traits of carcass weight, grade fat, average backfat, ribeye area, lean meat yield, marbling and yield grade. Proportion of phenotypic variance accounted for by a single QTL ranged from 0.06% for mean ultrasound backfat to 4.83% for carcass marbling (CMAR) score, while proportion of the phenotypic variance accounted for by all significant (p < 0.05) QTL identified for a single trait ranged from 4.54% for carcass weight to 23.87% for CMAR.

Statistical Optimization of Pharmacogenomics Association Studies: Key Considerations from Study Design to Analysis

Research in human genetics and genetic epidemiology has grown significantly over the previous decade, particularly in the field of pharmacogenomics. Pharmacogenomics presents an opportunity for rapid translation of associated genetic polymorphisms into diagnostic measures or tests to guide therapy as part of a move towards personalized medicine. Expansion in genotyping technology has cleared the way for widespread use of whole-genome genotyping in the effort to identify novel biology and new genetic markers associated with pharmacokinetic and pharmacodynamic endpoints. With new technology and methodology regularly becoming available for use in genetic studies, a discussion on the application of such tools becomes necessary. In particular, quality control criteria have evolved with the use of GWAS as we have come to understand potential systematic errors which can be introduced into the data during genotyping. There have been several replicated pharmacogenomic associations, some of which have moved to the clinic to enact change in treatment decisions. These examples of translation illustrate the strength of evidence necessary to successfully and effectively translate a genetic discovery. In this review, the design of pharmacogenomic association studies is examined with the goal of optimizing the impact and utility of this research. Issues of ascertainment, genotyping, quality control, analysis and interpretation are considered.

Data-driven assessment of eQTL mapping methods

Background

The analysis of expression quantitative trait loci (eQTL) is a potentially powerful way to detect transcriptional regulatory relationships at the genomic scale. However, eQTL data sets often go underexploited because legacy QTL methods are used to map the relationship between the expression trait and genotype. Often these methods are inappropriate for complex traits such as gene expression, particularly in the case of epistasis.

Results

Here we compare legacy QTL mapping methods with several modern multi-locus methods and evaluate their ability to produce eQTL that agree with independent external data in a systematic way. We found that the modern multi-locus methods (Random Forests, sparse partial least squares, lasso, and elastic net) clearly outperformed the legacy QTL methods (Haley-Knott regression and composite interval mapping) in terms of biological relevance of the mapped eQTL. In particular, we found that our new approach, based on Random Forests, showed superior performance among the multi-locus methods.

Conclusions

Benchmarks based on the recapitulation of experimental findings provide valuable insight when selecting the appropriate eQTL mapping method. Our battery of tests suggests that Random Forests map eQTL that are more likely to be validated by independent data, when compared to competing multi-locus and legacy eQTL mapping methods.