An admixture mapping meta-analysis implicates genetic variation at 18q21 with asthma susceptibility in Latinos.

BACKGROUND: Asthma is a common but complex disease with racial/ethnic differences in prevalence, morbidity, and response to therapies. OBJECTIVE: We sought to perform an analysis of genetic ancestry to identify new loci that contribute to asthma susceptibility. METHODS: We leveraged the mixed ancestry of 3902 Latinos and performed an admixture mapping meta-analysis for asthma susceptibility. We replicated associations in an independent study of 3774 Latinos, performed targeted sequencing for fine mapping, and tested for disease correlations with gene expression in the whole blood of more than 500 subjects from 3 racial/ethnic groups. RESULTS: We identified a genome-wide significant admixture mapping peak at 18q21 in Latinos (P = 6.8 × 10-6), where Native American ancestry was associated with increased risk of asthma (odds ratio [OR], 1.20; 95% CI, 1.07-1.34; P = .002) and European ancestry was associated with protection (OR, 0.86; 95% CI, 0.77-0.96; P = .008). Our findings were replicated in an independent childhood asthma study in Latinos (P = 5.3 × 10-3, combined P = 2.6 × 10-7). Fine mapping of 18q21 in 1978 Latinos identified a significant association with multiple variants 5' of SMAD family member 2 (SMAD2) in Mexicans, whereas a single rare variant in the same window was the top association in Puerto Ricans. Low versus high SMAD2 blood expression was correlated with case status (13.4% lower expression; OR, 3.93; 95% CI, 2.12-7.28; P < .001). In addition, lower expression of SMAD2 was associated with more frequent exacerbations among Puerto Ricans with asthma. CONCLUSION: Ancestry at 18q21 was significantly associated with asthma in Latinos and implicated multiple ancestry-informative noncoding variants upstream of SMAD2 with asthma susceptibility. Furthermore, decreased SMAD2 expression in blood was strongly associated with increased asthma risk and increased exacerbations.

Discovery and fine-mapping of adiposity loci using high density imputation of genome-wide association studies in individuals of African ancestry: African Ancestry Anthropometry Genetics Consortium.

Genome-wide association studies (GWAS) have identified >300 loci associated with measures of adiposity including body mass index (BMI) and waist-to-hip ratio (adjusted for BMI, WHRadjBMI), but few have been identified through screening of the African ancestry genomes. We performed large scale meta-analyses and replications in up to 52,895 individuals for BMI and up to 23,095 individuals for WHRadjBMI from the African Ancestry Anthropometry Genetics Consortium (AAAGC) using 1000 Genomes phase 1 imputed GWAS to improve coverage of both common and low frequency variants in the low linkage disequilibrium African ancestry genomes. In the sex-combined analyses, we identified one novel locus (TCF7L2/HABP2) for WHRadjBMI and eight previously established loci at P < 5×10-8: seven for BMI, and one for WHRadjBMI in African ancestry individuals. An additional novel locus (SPRYD7/DLEU2) was identified for WHRadjBMI when combined with European GWAS. In the sex-stratified analyses, we identified three novel loci for BMI (INTS10/LPL and MLC1 in men, IRX4/IRX2 in women) and four for WHRadjBMI (SSX2IP, CASC8, PDE3B and ZDHHC1/HSD11B2 in women) in individuals of African ancestry or both African and European ancestry. For four of the novel variants, the minor allele frequency was low (<5%). In the trans-ethnic fine mapping of 47 BMI loci and 27 WHRadjBMI loci that were locus-wide significant (P < 0.05 adjusted for effective number of variants per locus) from the African ancestry sex-combined and sex-stratified analyses, 26 BMI loci and 17 WHRadjBMI loci contained ≤ 20 variants in the credible sets that jointly account for 99% posterior probability of driving the associations. The lead variants in 13 of these loci had a high probability of being causal. As compared to our previous HapMap imputed GWAS for BMI and WHRadjBMI including up to 71,412 and 27,350 African ancestry individuals, respectively, our results suggest that 1000 Genomes imputation showed modest improvement in identifying GWAS loci including low frequency variants. Trans-ethnic meta-analyses further improved fine mapping of putative causal variants in loci shared between the African and European ancestry populations.

A continuum of admixture in the Western Hemisphere revealed by the African Diaspora genome.

The African Diaspora in the Western Hemisphere represents one of the largest forced migrations in history and had a profound impact on genetic diversity in modern populations. To date, the fine-scale population structure of descendants of the African Diaspora remains largely uncharacterized. Here we present genetic variation from deeply sequenced genomes of 642 individuals from North and South American, Caribbean and West African populations, substantially increasing the lexicon of human genomic variation and suggesting much variation remains to be discovered in African-admixed populations in the Americas. We summarize genetic variation in these populations, quantifying the postcolonial sex-biased European gene flow across multiple regions. Moreover, we refine estimates on the burden of deleterious variants carried across populations and how this varies with African ancestry. Our data are an important resource for empowering disease mapping studies in African-admixed individuals and will facilitate gene discovery for diseases disproportionately affecting individuals of African ancestry.

Powerful Tests for Multi-Marker Association Analysis Using Ensemble Learning.

Multi-marker approaches have received a lot of attention recently in genome wide association studies and can enhance power to detect new associations under certain conditions. Gene-, gene-set- and pathway-based association tests are increasingly being viewed as useful supplements to the more widely used single marker association analysis which have successfully uncovered numerous disease variants. A major drawback of single-marker based methods is that they do not look at the joint effects of multiple genetic variants which individually may have weak or moderate signals. Here, we describe novel tests for multi-marker association analyses that are based on phenotype predictions obtained from machine learning algorithms. Instead of assuming a linear or logistic regression model, we propose the use of ensembles of diverse machine learning algorithms for prediction. We show that phenotype predictions obtained from ensemble learning algorithms provide a new framework for multi-marker association analysis. They can be used for constructing tests for the joint association of multiple variants, adjusting for covariates and testing for the presence of interactions. To demonstrate the power and utility of this new approach, we first apply our method to simulated SNP datasets. We show that the proposed method has the correct Type-1 error rates and can be considerably more powerful than alternative approaches in some situations. Then, we apply our method to previously studied asthma-related genes in 2 independent asthma cohorts to conduct association tests.

Genetic Factors Influencing B-type Natriuretic Peptide-Mediated Production of Cyclic Guanosine Monophosphate and Blood Pressure Effects in Heart Failure Patients.

Natriuretic peptides (NPs) represent a critical pathway in heart failure (HF). We explored genetic determinants of pharmacodynamic effects of B-type NP (BNP) and changes in plasma cyclic guanosine monophosphate (cGMP) and blood pressure (BP). HF patients (n = 135) received recombinant human BNP (nesiritide) at standard doses, and plasma cGMP levels were measured at baseline and during infusion. We tested the association of 119 single nucleotide polymorphisms (SNPs) in 4 candidate genes (NPR1, NPR2, NPR3, and membrane metallo-endopeptidase (MME)) with the change in cGMP and BP. Gene-based testing for association of genetic variation with endpoints was significant only for MME. Upon individual SNP testing, two loci in MME were associated with ΔcGMP; another (rs16824656) showed association with BP change. In summary, the pharmacodynamic effects of BNP vary substantially in HF patients and are associated with genetic variation in MME. MME genetic variation may be an important determinant of NP-mediated effects in humans.

Joint impact of clinical and behavioral variables on the risk of unplanned readmission and death after a heart failure hospitalization.

Most current methods for modeling rehospitalization events in heart failure patients make use of only clinical and medications data that is available in the electronic health records. However, information about patient-reported functional limitations, behavioral variables and socio-economic background of patients may also play an important role in predicting the risk of readmission in heart failure patients. We developed methods for predicting the risk of rehospitalization in heart failure patients using models that integrate clinical characteristics with patient-reported functional limitations, behavioral and socio-economic characteristics. Our goal was to estimate the predictive accuracy of the joint model and compare it with models that make use of clinical data alone or behavioral and socio-economic characteristics alone, using real patient data. We collected data about the occurrence of hospital readmissions from a cohort of 789 heart failure patients for whom a range of clinical and behavioral characteristics data is also available. We applied the Cox model, four different variants of the Cox proportional hazards framework as well as an alternative non-parametric approach and determined the predictive accuracy for different categories of variables. The concordance index obtained from the joint prediction model including all types of variables was significantly higher than the accuracy obtained from using only clinical factors or using only behavioral, socioeconomic background and functional limitations in patients as predictors. Collecting information on behavior, patient-reported estimates of physical limitations and frailty and socio-economic data has significant value in the predicting the risk of readmissions with regards to heart failure events and can lead to substantially more accurate events prediction models.

Genome-wide association study and admixture mapping reveal new loci associated with total IgE levels in Latinos.

BACKGROUND: IgE is a key mediator of allergic inflammation, and its levels are frequently increased in patients with allergic disorders. OBJECTIVE: We sought to identify genetic variants associated with IgE levels in Latinos. METHODS: We performed a genome-wide association study and admixture mapping of total IgE levels in 3334 Latinos from the Genes-environments & Admixture in Latino Americans (GALA II) study. Replication was evaluated in 454 Latinos, 1564 European Americans, and 3187 African Americans from independent studies. RESULTS: We confirmed associations of 6 genes identified by means of previous genome-wide association studies and identified a novel genome-wide significant association of a polymorphism in the zinc finger protein 365 gene (ZNF365) with total IgE levels (rs200076616, P = 2.3 × 10(-8)). We next identified 4 admixture mapping peaks (6p21.32-p22.1, 13p22-31, 14q23.2, and 22q13.1) at which local African, European, and/or Native American ancestry was significantly associated with IgE levels. The most significant peak was 6p21.32-p22.1, where Native American ancestry was associated with lower IgE levels (P = 4.95 × 10(-8)). All but 22q13.1 were replicated in an independent sample of Latinos, and 2 of the peaks were replicated in African Americans (6p21.32-p22.1 and 14q23.2). Fine mapping of 6p21.32-p22.1 identified 6 genome-wide significant single nucleotide polymorphisms in Latinos, 2 of which replicated in European Americans. Another single nucleotide polymorphism was peak-wide significant within 14q23.2 in African Americans (rs1741099, P = 3.7 × 10(-6)) and replicated in non-African American samples (P = .011). CONCLUSION: We confirmed genetic associations at 6 genes and identified novel associations within ZNF365, HLA-DQA1, and 14q23.2. Our results highlight the importance of studying diverse multiethnic populations to uncover novel loci associated with total IgE levels.

Integrated genome-wide association, coexpression network, and expression single nucleotide polymorphism analysis identifies novel pathway in allergic rhinitis.

BACKGROUND: Allergic rhinitis is a common disease whose genetic basis is incompletely explained. We report an integrated genomic analysis of allergic rhinitis. METHODS: We performed genome wide association studies (GWAS) of allergic rhinitis in 5633 ethnically diverse North American subjects. Next, we profiled gene expression in disease-relevant tissue (peripheral blood CD4+ lymphocytes) collected from subjects who had been genotyped. We then integrated the GWAS and gene expression data using expression single nucleotide (eSNP), coexpression network, and pathway approaches to identify the biologic relevance of our GWAS. RESULTS: GWAS revealed ethnicity-specific findings, with 4 genome-wide significant loci among Latinos and 1 genome-wide significant locus in the GWAS meta-analysis across ethnic groups. To identify biologic context for these results, we constructed a coexpression network to define modules of genes with similar patterns of CD4+ gene expression (coexpression modules) that could serve as constructs of broader gene expression. 6 of the 22 GWAS loci with P-value ≤ 1x10-6 tagged one particular coexpression module (4.0-fold enrichment, P-value 0.0029), and this module also had the greatest enrichment (3.4-fold enrichment, P-value 2.6 × 10-24) for allergic rhinitis-associated eSNPs (genetic variants associated with both gene expression and allergic rhinitis). The integrated GWAS, coexpression network, and eSNP results therefore supported this coexpression module as an allergic rhinitis module. Pathway analysis revealed that the module was enriched for mitochondrial pathways (8.6-fold enrichment, P-value 4.5 × 10-72). CONCLUSIONS: Our results highlight mitochondrial pathways as a target for further investigation of allergic rhinitis mechanism and treatment. Our integrated approach can be applied to provide biologic context for GWAS of other diseases.

Genetic and nongenetic factors influencing pharmacokinetics of B-type natriuretic peptide.

BACKGROUND: Natriuretic peptides (NPs) represent a critical pathway in heart failure (HF). However, there is wide individual variability in NP system activity, which could be partly genetic in origin. We explored genetic and nongenetic contributions to B-type natriuretic peptide (BNP) inactivation. METHODS: Chronic HF patients (n = 95) received recombinant human BNP (nesiritide) at standard doses, and BNP levels were measured at baseline, after 2 hours of infusion, and 30 minutes after discontinuation. Genomic DNA was genotyped for 91 single-nucleotide polymorphisms (SNP) in 2 candidate genes. We tested the association of patient characteristics and genotype with 5 pharmacokinetics (PK) parameters: elimination rate constant, ΔBNP, BNP clearance, adjusted BNP clearance, and half-life. Linear regression with pleiotropic analysis was used to test genotype associations with PK. RESULTS: Participants' mean age was 63 years, 44% were female, and 46% were African American. PK parameters varied widely, some >10-fold. HF type (preserved vs reduced) was associated with PK (P < .01), whereas renal function, demographics, and body mass index and were not. Two SNPs in MME (rs989692, rs6798179) and 2 in NPR3 (rs6880564, rs2062708) also had associations with PK (P < .05). CONCLUSIONS: The pharmacokinetics of BNP varies greatly in HF patients, differs by HF type, and possibly by MME or NPR3 genotype. Additional study is warranted.

Inferring ancestry from population genomic data and its applications.

Ancestry inference is a frequently encountered problem and has many applications such as forensic analyses, genetic association studies, and personal genomics. The main goal of ancestry inference is to identify an individual's population of origin based on our knowledge of natural populations. Because both self-reported ancestry in humans or the sampling location of an organism can be inaccurate for this purpose, the use of genetic markers can facilitate accurate and reliable inference of an individual's ancestral origins. At a higher level, there are two different paradigms in ancestry inference: global ancestry inference which tries to compute the genome-wide average of the population contributions and local ancestry inference which tries to identify the regional ancestry of a genomic segment. In this mini review, I describe the numerous approaches that are currently available for both kinds of ancestry inference from population genomic datasets. I first describe the general ideas underlying such inference methods and their relationship to one another. Then, I describe practical applications in which inference of ancestry has proven useful. Lastly, I discuss challenges and directions for future research work in this area.

Nocturnal asthma and the importance of race/ethnicity and genetic ancestry.

RATIONALE: Nocturnal asthma is a common presentation and is associated with a more severe form of the disease. However, there are few epidemiologic studies of nocturnal asthma, particularly in minority populations. OBJECTIVES: To identify factors associated with nocturnal asthma, including the contribution of self-identified race/ethnicity and genetic ancestry. METHODS: The analysis included individuals from the Study for Asthma Phenotypes and Pharmacogenomic Interactions by Race-ethnicity (SAPPHIRE) cohort. Nocturnal asthma symptoms were assessed by questionnaire. Genome-wide genotype data were used to estimate genetic ancestry in a subset of African American participants. Logistic regression was used evaluate the association of various factors with nocturnal asthma, such as self-identified race/ethnicity and genetic ancestry. MEASUREMENT AND MAIN RESULTS: The study comprised 3,380 African American and 1,818 European Americans individuals with asthma. After adjusting for other potential explanatory variables, including controller medication use, African Americans were more than twice as likely (odds ratio, 2.56; 95% confidence interval, 2.24-2.93) to report nocturnal asthma when compared with European American individuals. Among the subset of African American participants with genome-wide genotype data (n = 1,040), estimated proportion of African ancestry was also associated with an increased risk of nocturnal asthma (P = 0.007). Differences in lung function explained a small, but statistically significant (P = 0.02), proportion of the relationship between genetic ancestry and nocturnal asthma symptoms. CONCLUSIONS: Both self-identified race/ethnicity and African ancestry appear to be independent predictors of nocturnal asthma. The mechanism by which genetic ancestry contributes to population-level differences in nocturnal asthma appears to be largely independent of lung function.

Differing effects of metformin on glycemic control by race-ethnicity.

CONTEXT: Metformin is considered first-line treatment for type 2 diabetes mellitus. However, little is known about its effects in African American individuals. OBJECTIVE: The objective of the study was to assess whether metformin's effect on glycemic control differs by race-ethnicity Design: Electronic health records were used to identify adults who had a diagnosis of diabetes, two or more fills of metformin, and two or more glycated hemoglobin (HbA1c) measurements. Pharmacy claims were used to estimate metformin exposure based on fill frequency and dose dispensed. Regression analyses modeled the relationship between metformin exposure and HbA1c levels. Analyses were stratified by race-ethnicity and baseline HbA1c values. SETTING: The study was conducted at a large health system in southeast Michigan. MAIN OUTCOME MEASURE: Differences in HbA1c levels while on metformin were measured. RESULTS: We identified 19 672 patients with diabetes taking metformin; 7429 were African American and 8783 were European American. Baseline HbA1c values in these two groups were 7.81% (61.8 mmol/mol) and 7.38% (57.1 mmol/mol), respectively. Compared with no use, metformin was associated with a 0.62% (6.8 mmol/mol) reduction in HbA1c; however, there was a significant difference by race-ethnicity (P < .001). Among African American individuals, metformin use was associated with a 0.90% (9.8 mmol/mol) reduction in HbA1c levels, whereas among European Americans, metformin was associated with a 0.42% (4.6 mmol/mol) reduction. Irrespective of baseline HbA1c, metformin use was associated with lower HbA1c levels in African American individuals. CONCLUSIONS: African American individuals appear to have a better glycemic response to metformin when compared with European Americans. Further studies are needed to determine whether this translates to commensurate reductions in diabetes complications.

Genome-wide interaction studies reveal sex-specific asthma risk alleles.

Asthma is a complex disease with sex-specific differences in prevalence. Candidate gene studies have suggested that genotype-by-sex interaction effects on asthma risk exist, but this has not yet been explored at a genome-wide level. We aimed to identify sex-specific asthma risk alleles by performing a genome-wide scan for genotype-by-sex interactions in the ethnically diverse participants in the EVE Asthma Genetics Consortium. We performed male- and female-specific genome-wide association studies in 2653 male asthma cases, 2566 female asthma cases and 3830 non-asthma controls from European American, African American, African Caribbean and Latino populations. Association tests were conducted in each study sample, and the results were combined in ancestry-specific and cross-ancestry meta-analyses. Six sex-specific asthma risk loci had P-values < 1 × 10(-6), of which two were male specific and four were female specific; all were ancestry specific. The most significant sex-specific association in European Americans was at the interferon regulatory factor 1 (IRF1) locus on 5q31.1. We also identify a Latino female-specific association in RAP1GAP2. Both of these loci included single-nucleotide polymorphisms that are known expression quantitative trait loci and have been associated with asthma in independent studies. The IRF1 locus is a strong candidate region for male-specific asthma susceptibility due to the association and validation we demonstrate here, the known role of IRF1 in asthma-relevant immune pathways and prior reports of sex-specific differences in interferon responses.

Association of genetic variation with gene expression and protein abundance within the natriuretic peptide pathway.

The natriuretic peptide (NP) system is a critical physiologic pathway in heart failure with wide individual variability in functioning. We investigated the genetic component by testing the association of single nucleotide polymorphisms (SNP) with RNA and protein expression. Samples of DNA, RNA, and tissue from human kidney (n = 103) underwent genotyping, RT-PCR, and protein quantitation (in lysates), for four candidate genes [NP receptor 1 (NPR1), NPR2, and NPR3 and membrane metalloendopeptidase]. The association of genetic variation with expression was tested using linear regression for individual SNPs, and a principal components (PC) method for overall gene variation. Eleven SNPs in NPR2 were significantly associated with protein expression (false discovery rate ≤0.05), but not RNA quantity. RNA and protein quantity correlated poorly with each other. The PC analysis showed only NPR2 as significant. Assessment of the clinical impact of NPR2 genetic variation is needed.

Meiotic gene-conversion rate and tract length variation in the human genome.

Meiotic recombination occurs in the form of two different mechanisms called crossing-over and gene-conversion and both processes have an important role in shaping genetic variation in populations. Although variation in crossing-over rates has been studied extensively using sperm-typing experiments, pedigree studies and population genetic approaches, our knowledge of variation in gene-conversion parameters (ie, rates and mean tract lengths) remains far from complete. To explore variability in population gene-conversion rates and its relationship to crossing-over rate variation patterns, we have developed and validated using coalescent simulations a comprehensive Bayesian full-likelihood method that can jointly infer crossing-over and gene-conversion rates as well as tract lengths from population genomic data under general variable rate models with recombination hotspots. Here, we apply this new method to SNP data from multiple human populations and attempt to characterize for the first time the fine-scale variation in gene-conversion parameters along the human genome. We find that the estimated ratio of gene-conversion to crossing-over rates varies considerably across genomic regions as well as between populations. However, there is a great degree of uncertainty associated with such estimates. We also find substantial evidence for variation in the mean conversion tract length. The estimated tract lengths did not show any negative relationship with the local heterozygosity levels in our analysis.European Journal of Human Genetics advance online publication, 27 February 2013; doi:10.1038/ejhg.2013.30.

Bayesian population genomic inference of crossing over and gene conversion.

Meiotic recombination is a fundamental cellular mechanism in sexually reproducing organisms and its different forms, crossing over and gene conversion both play an important role in shaping genetic variation in populations. Here, we describe a coalescent-based full-likelihood Markov chain Monte Carlo (MCMC) method for jointly estimating the crossing-over, gene-conversion, and mean tract length parameters from population genomic data under a Bayesian framework. Although computationally more expensive than methods that use approximate likelihoods, the relative efficiency of our method is expected to be optimal in theory. Furthermore, it is also possible to obtain a posterior sample of genealogies for the data using this method. We first check the performance of the new method on simulated data and verify its correctness. We also extend the method for inference under models with variable gene-conversion and crossing-over rates and demonstrate its ability to identify recombination hotspots. Then, we apply the method to two empirical data sets that were sequenced in the telomeric regions of the X chromosome of Drosophila melanogaster. Our results indicate that gene conversion occurs more frequently than crossing over in the su-w and su-s gene sequences while the local rates of crossing over as inferred by our program are not low. The mean tract lengths for gene-conversion events are estimated to be ∼70 bp and 430 bp, respectively, for these data sets. Finally, we discuss ideas and optimizations for reducing the execution time of our algorithm.

Presymptomatic risk assessment for chronic non-communicable diseases.

The prevalence of common chronic non-communicable diseases (CNCDs) far overshadows the prevalence of both monogenic and infectious diseases combined. All CNCDs, also called complex genetic diseases, have a heritable genetic component that can be used for pre-symptomatic risk assessment. Common single nucleotide polymorphisms (SNPs) that tag risk haplotypes across the genome currently account for a non-trivial portion of the germ-line genetic risk and we will likely continue to identify the remaining missing heritability in the form of rare variants, copy number variants and epigenetic modifications. Here, we describe a novel measure for calculating the lifetime risk of a disease, called the genetic composite index (GCI), and demonstrate its predictive value as a clinical classifier. The GCI only considers summary statistics of the effects of genetic variation and hence does not require the results of large-scale studies simultaneously assessing multiple risk factors. Combining GCI scores with environmental risk information provides an additional tool for clinical decision-making. The GCI can be populated with heritable risk information of any type, and thus represents a framework for CNCD pre-symptomatic risk assessment that can be populated as additional risk information is identified through next-generation technologies.

Genomewide SNP variation reveals relationships among landraces and modern varieties of rice.

Rice, the primary source of dietary calories for half of humanity, is the first crop plant for which a high-quality reference genome sequence from a single variety was produced. We used resequencing microarrays to interrogate 100 Mb of the unique fraction of the reference genome for 20 diverse varieties and landraces that capture the impressive genotypic and phenotypic diversity of domesticated rice. Here, we report the distribution of 160,000 nonredundant SNPs. Introgression patterns of shared SNPs revealed the breeding history and relationships among the 20 varieties; some introgressed regions are associated with agronomic traits that mark major milestones in rice improvement. These comprehensive SNP data provide a foundation for deep exploration of rice diversity and gene-trait relationships and their use for future rice improvement.

Evolutionary history of GS3, a gene conferring grain length in rice.

Unlike maize and wheat, where artificial selection is associated with an almost uniform increase in seed or grain size, domesticated rice exhibits dramatic phenotypic diversity for grain size and shape. Here we clone and characterize GS3, an evolutionarily important gene controlling grain size in rice. We show that GS3 is highly expressed in young panicles in both short- and long-grained varieties but is not expressed in leaves or panicles after flowering, and we use genetic transformation to demonstrate that the dominant allele for short grain complements the long-grain phenotype. An association study revealed that a C to A mutation in the second exon of GS3 (A allele) was associated with enhanced grain length in Oryza sativa but was absent from other Oryza species. Linkage disequilibrium (LD) was elevated and there was a 95.7% reduction in nucleotide diversity (theta(pi)) across the gene in accessions carrying the A allele, suggesting positive selection for long grain. Haplotype analysis traced the origin of the long-grain allele to a Japonica-like ancestor and demonstrated introgression into the Indica gene pool. This study indicates a critical role for GS3 in defining the seed morphologies of modern subpopulations of O. sativa and enhances the potential for genetic manipulation of grain size in rice.

Molecular and evolutionary history of melanism in North American gray wolves.

Morphological diversity within closely related species is an essential aspect of evolution and adaptation. Mutations in the Melanocortin 1 receptor (Mc1r) gene contribute to pigmentary diversity in natural populations of fish, birds, and many mammals. However, melanism in the gray wolf, Canis lupus, is caused by a different melanocortin pathway component, the K locus, that encodes a beta-defensin protein that acts as an alternative ligand for Mc1r. We show that the melanistic K locus mutation in North American wolves derives from past hybridization with domestic dogs, has risen to high frequency in forested habitats, and exhibits a molecular signature of positive selection. The same mutation also causes melanism in the coyote, Canis latrans, and in Italian gray wolves, and hence our results demonstrate how traits selected in domesticated species can influence the morphological diversity of their wild relatives.