Interchromosomal linkage disequilibrium and linked fitness cost loci associated with selection for herbicide resistance.

The adaptation of weeds to herbicide is both a significant problem in agriculture and a model of rapid adaptation. However, significant gaps remain in our knowledge of resistance controlled by many loci and the evolutionary factors that influence the maintenance of resistance. Here, using herbicide-resistant populations of the common morning glory (Ipomoea purpurea), we perform a multilevel analysis of the genome and transcriptome to uncover putative loci involved in nontarget-site herbicide resistance (NTSR) and to examine evolutionary forces underlying the maintenance of resistance in natural populations. We found loci involved in herbicide detoxification and stress sensing to be under selection and confirmed that detoxification is responsible for glyphosate (RoundUp) resistance using a functional assay. We identified interchromosomal linkage disequilibrium (ILD) among loci under selection reflecting either historical processes or additive effects leading to the resistance phenotype. We further identified potential fitness cost loci that were strongly linked to resistance alleles, indicating the role of genetic hitchhiking in maintaining the cost. Overall, our work suggests that NTSR glyphosate resistance in I. purpurea is conferred by multiple genes which are potentially maintained through generations via ILD, and that the fitness cost associated with resistance in this species is likely a by-product of genetic hitchhiking.

A multilayered post-GWAS assessment on genetic susceptibility to pancreatic cancer.

BACKGROUND: Pancreatic cancer (PC) is a complex disease in which both non-genetic and genetic factors interplay. To date, 40 GWAS hits have been associated with PC risk in individuals of European descent, explaining 4.1% of the phenotypic variance. METHODS: We complemented a new conventional PC GWAS (1D) with genome spatial autocorrelation analysis (2D) permitting to prioritize low frequency variants not detected by GWAS. These were further expanded via Hi-C map (3D) interactions to gain additional insight into the inherited basis of PC. In silico functional analysis of public genomic information allowed prioritization of potentially relevant candidate variants. RESULTS: We identified several new variants located in genes for which there is experimental evidence of their implication in the biology and function of pancreatic acinar cells. Among them is a novel independent variant in NR5A2 (rs3790840) with a meta-analysis p value = 5.91E-06 in 1D approach and a Local Moran's Index (LMI) = 7.76 in 2D approach. We also identified a multi-hit region in CASC8-a lncRNA associated with pancreatic carcinogenesis-with a lowest p value = 6.91E-05. Importantly, two new PC loci were identified both by 2D and 3D approaches: SIAH3 (LMI = 18.24), CTRB2/BCAR1 (LMI = 6.03), in addition to a chromatin interacting region in XBP1-a major regulator of the ER stress and unfolded protein responses in acinar cells-identified by 3D; all of them with a strong in silico functional support. CONCLUSIONS: This multi-step strategy, combined with an in-depth in silico functional analysis, offers a comprehensive approach to advance the study of PC genetic susceptibility and could be applied to other diseases.

Functional models in genome-wide selection.

The development of sequencing technologies has enabled the discovery of markers that are abundantly distributed over the whole genome. Knowledge about the marker locations in reference genomes provides further insights in the search for causal regions and the prediction of genomic values. The present study proposes a Bayesian functional approach for incorporating the marker locations into genomic analysis using stochastic methods to search causal regions and predict genotypic values. For this, three scenarios were analyzed: F2 population with 300 individuals and three different heritability levels (0.2, 0.5, and 0.8), along with 12,150 SNP markers that were distributed through ten linkage groups; F∞ populations with 320 individuals and three different heritability levels (0.2, 0.5, and 0.8), along with 10,020 SNP markers that were distributed through ten linkage groups; and data related to Eucalyptus spp. to measure the model performance in a real LD setting, with 611 individuals whose phenotypes were simulated from QTLs distributed through a panel of 36,812 SNPs with known positions. The performance of the proposed method was compared with those of other genome selection models, namely, RR-BLUP, Bayes B and Bayesian Lasso. The Bayesian functional model presented higher or similar predictive ability when compared with those classical regressions methods in simulated and real scenarios on different LD structures. In general, the Bayesian functional model also achieved higher computational efficiency, using 12 SNPs per MCMC round. The model was efficient in the identification of causal regions and showed high flexibility of analysis, as it is easily adaptable to any genomic selection model.

Topological linkage disequilibrium calculated from coalescent genealogies.

We revisit the classical, and introduce a novel, concept of two-locus linkage disequilibrium (LD). In contrast to defining haplotypes as allele combinations at two marker loci, we concentrate on the clustering of a sample of chromosomes induced by their coalescent genealogy. The root of a binary coalescent tree defines two clusters of chromosomes, each one of them containing the left and right descendants of the root. At two different loci this assignment may be different as a result of recombination. We show that the proportion of shared chromosomes among clusters at two different loci, measured by the squared correlation, constitutes a natural measure of LD. We call this topological LD (tLD) since it is induced by the topology of the coalescent tree. We find that it is, on average, larger than classical LD for any given distance between loci. Furthermore, tLD has a smaller coefficient of variation, which should provide an advantage, compared to the use of classical LD, for any kind of mapping purposes. We conclude with a practical application to the LCT region in human populations.

Genome-wide assessment of gene-by-smoking interactions in COPD.

Cigarette smoke exposure is a major risk factor in chronic obstructive pulmonary disease (COPD) and its interactions with genetic variants could affect lung function. However, few gene-smoking interactions have been reported. In this report, we evaluated the effects of gene-smoking interactions on lung function using Korea Associated Resource (KARE) data with the spirometric variables-forced expiratory volume in 1 s (FEV1). We found that variations in FEV1 were different among smoking status. Thus, we considered a linear mixed model for association analysis under heteroscedasticity according to smoking status. We found a previously identified locus near SOX9 on chromosome 17 to be the most significant based on a joint test of the main and interaction effects of smoking. Smoking interactions were replicated with Gene-Environment of Interaction and phenotype (GENIE), Multi-Ethnic Study of Atherosclerosis-Lung (MESA-Lung), and COPDGene studies. We found that individuals with minor alleles, rs17765644, rs17178251, rs11870732, and rs4793541, tended to have lower FEV1 values, and lung function decreased much faster with age for smokers. There have been very few reports to replicate a common variant gene-smoking interaction, and our results revealed that statistical models for gene-smoking interaction analyses should be carefully selected.

Single Nucleotide Polymorphisms Associated with Reading Ability Show Connection to Socio-Economic Outcomes.

Impairments in reading and in language have negative consequences on life outcomes, but it is not known to what extent genetic effects influence this association. We constructed polygenic scores for difficulties with language and learning to read from genome-wide data in ~6,600 children, adolescents and young adults, and tested their association with health, socioeconomic outcomes and brain structure measures collected in adults (maximal N = 111,749). Polygenic risk of reading difficulties was associated with reduced income, educational attainment, self-rated health and verbal-numerical reasoning (p < 0.00055). Polygenic risk of language difficulties predicted income (p = 0.0005). The small effect sizes ranged 0.01-0.03 of a standard deviation, but these will increase as genetic studies for reading ability get larger. Polygenic scores for childhood cognitive ability and educational attainment were correlated with polygenic scores of reading and language (up to 0.09 and 0.05, respectively). But when they were included in the prediction models, the observed associations between polygenic reading and adult outcomes mostly remained. This suggests that the pathway from reading ability to social outcomes is not only via associated polygenic loads for general cognitive function and educational attainment. The presence of non-overlapping genetic effect is indicated by the genetic correlations of around 0.40 (childhood intelligence) and 0.70 (educational attainment) with reading ability. Mendelian randomization approaches will be important to dissociate any causal and moderating effects of reading and related traits on social outcomes.

PopNet: A Markov Clustering Approach to Study Population Genetic Structure.

With the advent of low cost, high-throughput genome sequencing technology, population genomic data sets are being generated for hundreds of species of pathogenic, industrial, and agricultural importance. The challenge is how best to analyze and visually display these complex data sets to yield intuitive representations capable of capturing complex evolutionary relationships. Here we present PopNet, a novel computational method that identifies regions of shared ancestry in the chromosomes of related strains through clustering patterns of genetic variation. These relationships are subsequently visualized within a network by a novel implementation of chromosome painting. We apply PopNet to three diverse populations that feature differential rates of recombination and demonstrate its ability to capture evolutionary relationships as well as associate traits to specific loci. Compared with existing tools, PopNet provides substantial advances by both removing the need to predefine a single reference genome that can bias interpretation of population structure, as well as its ability to visualize multiple evolutionary relationships, such as recombination events and shared ancestry, across hundreds of strains.

Multiple SNP Set Analysis for Genome-Wide Association Studies Through Bayesian Latent Variable Selection.

The power of genome-wide association studies (GWAS) for mapping complex traits with single-SNP analysis (where SNP is single-nucleotide polymorphism) may be undermined by modest SNP effect sizes, unobserved causal SNPs, correlation among adjacent SNPs, and SNP-SNP interactions. Alternative approaches for testing the association between a single SNP set and individual phenotypes have been shown to be promising for improving the power of GWAS. We propose a Bayesian latent variable selection (BLVS) method to simultaneously model the joint association mapping between a large number of SNP sets and complex traits. Compared with single SNP set analysis, such joint association mapping not only accounts for the correlation among SNP sets but also is capable of detecting causal SNP sets that are marginally uncorrelated with traits. The spike-and-slab prior assigned to the effects of SNP sets can greatly reduce the dimension of effective SNP sets, while speeding up computation. An efficient Markov chain Monte Carlo algorithm is developed. Simulations demonstrate that BLVS outperforms several competing variable selection methods in some important scenarios.

Improving the computational efficiency of fully Bayes inference and assessing the effect of misspecification of hyperparameters in whole-genome prediction models.

BACKGROUND: The reliability of whole-genome prediction models (WGP) based on using high-density single nucleotide polymorphism (SNP) panels critically depends on proper specification of key hyperparameters. A currently popular WGP model labeled BayesB specifies a hyperparameter π, that is `loosely used to describe the proportion of SNPs that are in linkage disequilibrium (LD) with causal variants. The remaining markers are specified to be random draws from a Student t distribution with key hyperparameters being degrees of freedom v and scale s(2). METHODS: We consider three alternative Markov chain Monte Carlo (MCMC) approaches based on the use of Metropolis-Hastings (MH) to estimate these key hyperparameters. The first approach, termed DFMH, is based on a previously published strategy for which s(2) is drawn by a Gibbs step and v is drawn by a MH step. The second strategy, termed UNIMH, substitutes MH for Gibbs when drawing s(2) and further collapses or marginalizes the full conditional density of v. The third strategy, termed BIVMH, is based on jointly drawing the two hyperparameters in a bivariate MH step. We also tested the effect of misspecification of s(2) for its effect on accuracy of genomic estimated breeding values (GEBV), yet allowing for inference on the other hyperparameters. RESULTS: The UNIMH and BIVMH strategies had significantly greater (P < 0.05) computational efficiencies for estimating v and s(2) than DFMH in BayesA (π = 1) and BayesB implementations. We drew similar conclusions based on an analysis of the public domain heterogeneous stock mice data. We also determined significant drops (P < 0.01) in accuracies of GEBV under BayesA by overspecifying s(2), whereas BayesB was more robust to such misspecifications. However, understating s(2) was compensated by counterbalancing inferences on v in BayesA and BayesB, and on π in BayesB. CONCLUSIONS: Sampling strategies based solely on MH updates of v and s(2), and collapsed representations of full conditional densities can improve the computational efficiency of MCMC relative to the use of Gibbs updates. We believe that proper inferences on s(2), v and π are vital to ensure that the accuracy of GEBV is maximized when using parametric WGP models.

The genome-wide structure of two economically important indigenous Sicilian cattle breeds.

Genomic technologies, such as high-throughput genotyping based on SNP arrays, provided background information concerning genome structure in domestic animals. The aim of this work was to investigate the genetic structure, the genome-wide estimates of inbreeding, coancestry, effective population size (Ne), and the patterns of linkage disequilibrium (LD) in 2 economically important Sicilian local cattle breeds, Cinisara (CIN) and Modicana (MOD), using the Illumina Bovine SNP50K v2 BeadChip. To understand the genetic relationship and to place both Sicilian breeds in a global context, genotypes from 134 other domesticated bovid breeds were used. Principal component analysis showed that the Sicilian cattle breeds were closer to individuals of Bos taurus taurus from Eurasia and formed nonoverlapping clusters with other breeds. Between the Sicilian cattle breeds, MOD was the most differentiated, whereas the animals belonging to the CIN breed showed a lower value of assignment, the presence of substructure, and genetic links with the MOD breed. The average molecular inbreeding and coancestry coefficients were moderately high, and the current estimates of Ne were low in both breeds. These values indicated a low genetic variability. Considering levels of LD between adjacent markers, the average r(2) in the MOD breed was comparable to those reported for others cattle breeds, whereas CIN showed a lower value. Therefore, these results support the need of more dense SNP arrays for a high-power association mapping and genomic selection efficiency, particularly for the CIN cattle breed. Controlling molecular inbreeding and coancestry would restrict inbreeding depression, the probability of losing beneficial rare alleles, and therefore the risk of extinction. The results generated from this study have important implications for the development of conservation and/or selection breeding programs in these 2 local cattle breeds.

Clusthaplo: a plug-in for MCQTL to enhance QTL detection using ancestral alleles in multi-cross design.

KEY MESSAGE: We enhance power and accuracy of QTL mapping in multiple related families, by clustering the founders of the families on their local genomic similarity. MCQTL is a linkage mapping software application that allows the joint QTL mapping of multiple related families. In its current implementation, QTLs are modeled with one or two parameters for each parent that is a founder of the multi-cross design. The higher the number of parents, the higher the number of model parameters which can impact the power and the accuracy of the mapping. We propose to make use of the availability of denser and denser genotyping information on the founders to lessen the number of MCQTL parameters and thus boost the QTL discovery. We developed clusthaplo, an R package ( http://cran.r-project.org/web/packages/clusthaplo/index.html ), which aims to cluster haplotypes using a genomic similarity that reflects the probability of sharing the same ancestral allele. Computed in a sliding window along the genome and followed by a clustering method, the genomic similarity allows the local clustering of the parent haplotypes. Our assumption is that the haplotypes belonging to the same class transmit the same ancestral allele. So their putative QTL allelic effects can be modeled with the same parameter, leading to a parsimonious model, that is plugged in MCQTL. Intensive simulations using three maize data sets showed the significant gain in power and in accuracy of the QTL mapping with the ancestral allele model compared to the classical MCQTL model. MCQTL_LD (clusthaplo outputs plug in MCQTL) is a versatile and powerful tool for QTL mapping in multiple related families that makes use of linkage and linkage disequilibrium (web site http://carlit.toulouse.inra.fr/MCQTL/ ).

Genetic diversity and linkage disequilibrium in the chemokine receptor CCR2-CCR5 region among individuals and populations.

BACKGROUND: Chemokine receptors CCR2 and CCR5 play a key role in immune and inflammatory responses and have been associated with several diseases, including AIDS. In order to comprehend health disparities it is important to understand the nature of genetic variation in specific genes of interest in different populations. Current studies of the CCR2 and CCR5 receptor genes are primarily focused on the CCR5-Δ32, and CCR2-V64I SNPs. METHODS: Sanger sequencing was used to sequence the regions containing 16 SNPs in the adjacent CCR2 and CCR5 genes (including CCR5-Δ32, and CCR2-V64I) in 249 subjects of African, European and Hispanic ancestry. Linkage disequilibrium (LD) and haplotypes were determined using Haploview. RESULTS: The data revealed large differences in allele frequencies of several SNPs and LD patterns among the ethnic groups, including SNPs that were restricted to Africans or Europeans. Seven known CCR5 haplotypes and six novel CCR2 haplotypes were identified. A rare case of an HIV+ subject with the CCR5-Δ32/Δ32 was identified. CONCLUSIONS: These data demonstrate a LD between CCR2 and CCR5 at several loci and provide new information about CCR2 that contributes to our understanding of its population-specific genetic variability. The data indicate that in addition to CCR5-Δ32 and CCR2-V64I, other SNPs and haplotypes may be important genetic determinants of disease and should be investigated.

Experimental assessment of the accuracy of genomic selection in sugarcane.

Sugarcane cultivars are interspecific hybrids with an aneuploid, highly heterozygous polyploid genome. The complexity of the sugarcane genome is the main obstacle to the use of marker-assisted selection in sugarcane breeding. Given the promising results of recent studies of plant genomic selection, we explored the feasibility of genomic selection in this complex polyploid crop. Genetic values were predicted in two independent panels, each composed of 167 accessions representing sugarcane genetic diversity worldwide. Accessions were genotyped with 1,499 DArT markers. One panel was phenotyped in Reunion Island and the other in Guadeloupe. Ten traits concerning sugar and bagasse contents, digestibility and composition of the bagasse, plant morphology, and disease resistance were used. We used four statistical predictive models: bayesian LASSO, ridge regression, reproducing kernel Hilbert space, and partial least square regression. The accuracy of the predictions was assessed through the correlation between observed and predicted genetic values by cross validation within each panel and between the two panels. We observed equivalent accuracy among the four predictive models for a given trait, and marked differences were observed among traits. Depending on the trait concerned, within-panel cross validation yielded median correlations ranging from 0.29 to 0.62 in the Reunion Island panel and from 0.11 to 0.5 in the Guadeloupe panel. Cross validation between panels yielded correlations ranging from 0.13 for smut resistance to 0.55 for brix. This level of correlations is promising for future implementations. Our results provide the first validation of genomic selection in sugarcane.

Evaluation of linkage disequilibrium in wheat with an L1-regularized sparse Markov network.

Linkage disequilibrium (LD) is defined as a stochastic dependence between alleles at two or more loci. Although understanding LD is important in the study of the genetics of many species, little attention has been paid on how a covariance structure between many loci distributed across the genome should be represented. Given that biological systems at the cellular level often involve gene networks, it is appealing to evaluate LD from a network perspective, i.e., as a set of associated loci involved in a complex system. We applied a Markov network (MN) to study LD using data on 1,279 markers derived from 599 wheat inbred lines. The MN attempts to account for association between two markers, conditionally on the remaining markers in the network model. In this study, the recovery of the structure of a LD network was done through two variants of pseudo-likelihoods subject to an L1 penalty on the MN parameters. It is shown that, while the L1-regularized Markov network preserves features of a Bayesian network (BN), the nodes in the resulting networks have fewer links. The resulting sparse network, encoding conditional independencies, provides a clearer picture of association than marginal LD metrics, and a sparse graph eases interpretation markedly, since it includes a smaller number of edges than a BN. Thus, an L1-regularized sparse Markov network seems appealing for representing conditional LD with high-dimensional genomic data, where variables, e.g., single nucleotide polymorphism markers, are expected to be sparsely connected.

Ancestry inference in complex admixtures via variable-length Markov chain linkage models.

Inferring the ancestral origin of chromosomal segments in admixed individuals is key for genetic applications, ranging from analyzing population demographics and history, to mapping disease genes. Previous methods addressed ancestry inference by using either weak models of linkage disequilibrium, or large models that make explicit use of ancestral haplotypes. In this paper we introduce ALLOY, an efficient method that incorporates generalized, but highly expressive, linkage disequilibrium models. ALLOY applies a factorial hidden Markov model to capture the parallel process producing the maternal and paternal admixed haplotypes, and models the background linkage disequilibrium in the ancestral populations via an inhomogeneous variable-length Markov chain. We test ALLOY in a broad range of scenarios ranging from recent to ancient admixtures with up to four ancestral populations. We show that ALLOY outperforms the previous state of the art, and is robust to uncertainties in model parameters.

An initial assessment of linkage disequilibrium (LD) in coffee trees: LD patterns in groups of Coffea canephora Pierre using microsatellite analysis.

BACKGROUND: A reciprocal recurrent selection program has been under way for the Coffea canephora coffee tree for approximately thirty years in the Ivory Coast. Association genetics would help to speed up this program by more rapidly selecting zones of interest in the genome. However, prior to any such studies, the linkage disequilibrium (LD) needs to be assessed between the markers on the genome. These data are essential for guiding association studies. RESULTS: This article describes the first results of an LD assessment in a coffee tree species. Guinean and Congolese breeding populations of C. canephora have been used for this work, with the goal of identifying ways of using these populations in association genetics. We identified changes in the LD along the genome within the different C. canephora diversity groups. In the different diversity groups studied, the LD was variable. Some diversity groups displayed disequilibria over long distances (up to 25 cM), whereas others had disequilibria not exceeding 1 cM. We also discovered a fine structure within the Guinean group. CONCLUSIONS: Given these results, association studies can be used within the species C. canephora. The coffee recurrent selection scheme being implemented in the Ivory Coast can thus be optimized. Lastly, our results could be used to improve C. arabica because one of its parents is closely related to C. canephora.

Distribution and linkage disequilibrium analysis of polymorphisms of MC4R, LEP, H-FABP genes in the different populations of pigs, associated with economic traits in DIV2 line.

PCR-RFLP was used to analyze the polymorphisms of MC4R, LEP, H-FABP genes in a swine breed composite (DIV2) and 4 swine breeds (Yorkshire, Landrace, Meishan, Bamei). The association study of these polymorphisms with several economic traits was carried out on a DIV2 population. The results obtained showed that MC4R/TaqI genotype had an effect for average backfat thickness (P < 0.05) and lean meat percentage (P < 0.05). At locus LEP/HinfI animals of AA genotype had lower test daily gain than that of BB (P < 0.01) or AB genotype (P < 0.05). At the H-FABP/HaeIII locus lean meat percentage of the individuals with genotype DD were higher than that with genotype dd (P < 0.05). Linkage disequilibrium analysis among MC4R, LEP and H-FABP revealed that these genes were independent. This represented two or more genes that could be combined together within one genotype in order to facilitate breeding for objective traits. In addition, a method allowing simultaneous detection of fragments of MC4R and LEP gene was developed.

Reconsidering association testing methods using single-variant test statistics as alternatives to pooling tests for sequence data with rare variants.

Association tests that pool minor alleles into a measure of burden at a locus have been proposed for case-control studies using sequence data containing rare variants. However, such pooling tests are not robust to the inclusion of neutral and protective variants, which can mask the association signal from risk variants. Early studies proposing pooling tests dismissed methods for locus-wide inference using nonnegative single-variant test statistics based on unrealistic comparisons. However, such methods are robust to the inclusion of neutral and protective variants and therefore may be more useful than previously appreciated. In fact, some recently proposed methods derived within different frameworks are equivalent to performing inference on weighted sums of squared single-variant score statistics. In this study, we compared two existing methods for locus-wide inference using nonnegative single-variant test statistics to two widely cited pooling tests under more realistic conditions. We established analytic results for a simple model with one rare risk and one rare neutral variant, which demonstrated that pooling tests were less powerful than even Bonferroni-corrected single-variant tests in most realistic situations. We also performed simulations using variants with realistic minor allele frequency and linkage disequilibrium spectra, disease models with multiple rare risk variants and extensive neutral variation, and varying rates of missing genotypes. In all scenarios considered, existing methods using nonnegative single-variant test statistics had power comparable to or greater than two widely cited pooling tests. Moreover, in disease models with only rare risk variants, an existing method based on the maximum single-variant Cochran-Armitage trend chi-square statistic in the locus had power comparable to or greater than another existing method closely related to some recently proposed methods. We conclude that efficient locus-wide inference using single-variant test statistics should be reconsidered as a useful framework for devising powerful association tests in sequence data with rare variants.

Genetic risk assessment of type 2 diabetes-associated polymorphisms in African Americans.

OBJECTIVE: Multiple single nucleotide polymorphisms (SNPs) associated with type 2 diabetes (T2D) susceptibility have been identified in predominantly European-derived populations. These SNPs have not been extensively investigated for individual and cumulative effects on T2D risk in African Americans. RESEARCH DESIGN AND METHODS: Seventeen index T2D risk variants were genotyped in 2,652 African American case subjects with T2D and 1,393 nondiabetic control subjects. Individual SNPs and cumulative risk allele loads were assessed for association with risk for T2D. Cumulative risk was assessed by counting risk alleles and evaluating the difference in cumulative risk scores between case subjects and control subjects. A second analysis weighted risk scores (ln [OR]) based on previously reported European-derived effect sizes. RESULTS: Frequencies of risk alleles ranged from 8.6 to 99.9%. Eleven SNPs had ORs >1, and 5 from ADAMTS9, WFS1, CDKAL1, JAZF1, and TCF7L2 trended or had nominally significant evidence of T2D association (P < 0.05). Individuals carried between 13 and 29 risk alleles. Association was observed between T2D and increase in risk allele load (unweighted OR 1.04 [95% CI 1.01-1.08], P = 0.010; weighted 1.06 [1.03-1.10], P = 8.10 × 10(-5)). When TCF7L2 SNP rs7903146 was included as a covariate, the risk score was no longer associated with T2D in either model (unweighted 1.02 [0.98-1.05], P = 0.33; weighted 1.02 [0.98-1.06], P = 0.40). CONCLUSIONS: The trend of increase in risk for T2D with increasing risk allele load is similar to observations in European-derived populations; however, these analyses indicate that T2D genetic risk is primarily mediated through the effect of TCF7L2 in African Americans.

Physiology and Endocrinology Symposium: How single nucleotide polymorphism chips will advance our knowledge of factors controlling puberty and aid in selecting replacement beef females.

The promise of genomic selection is accurate prediction of the genetic potential of animals from their genotypes. Simple DNA tests might replace low-accuracy predictions for expensive or lowly heritable measures of puberty and fertility based on performance and pedigree. Knowing with some certainty which DNA variants (e.g., SNP) affect puberty and fertility is the best way to fulfill the promise. Several SNP from the BovineSNP50 assay have tentatively been associated with reproductive traits including age at puberty, antral follicle count, and pregnancy observed on different sets of heifers. However, sample sizes are too small and SNP density is too sparse to definitively determine genomic regions harboring causal variants affecting reproductive success. Additionally, associations between individual SNP and similar phenotypes are inconsistent across data sets, and genomic predictions do not appear to be globally applicable to cattle of different breeds. Discrepancies may be a result of different QTL segregating in the sampled populations, differences in linkage disequilibrium (LD) patterns such that the same SNP are not correlated with the same QTL, and spurious correlations with phenotype. Several approaches can be used independently or in combination to improve detection of genomic factors affecting heifer puberty and fertility. Larger samples and denser SNP will increase power to detect real associations with SNP having more consistent LD with underlying QTL. Meta-analysis combining results from different studies can also be used to effectively increase sample size. High-density genotyping with heifers pooled by pregnancy status or early and late puberty can be a cost-effective means to sample large numbers. Networks of genes, implicated by associations with multiple traits correlated with puberty and fertility, could provide insight into the complex nature of these traits, especially if corroborated by functional annotation, established gene interaction pathways, and transcript expression. Example analyses are provided to demonstrate how integrating information about gene function and regulation with statistical associations from whole-genome SNP genotyping assays might enhance knowledge of genomic mechanisms affecting puberty and fertility, enabling reliable DNA tests to guide heifer selection decisions.