Proportion Of Genetic Variation Research Articles

Genetic Diversity in Successive Age Cohorts of Ilex paraguariensis in Southern Brazil

Abstract Ilex paraguariensis A. St. Hil. (yerba mate) is a native species widely consumed in southern Brazil, a region that presents a current scenario of forest fragmentation. The objective was to evaluate the genetic diversity, genetic structure, and gene flow among cohorts of I. paraguariensis in two forest remnants. Genetic analyses were conducted using microsatellite markers from leaves from adult (Ad), regenerating (Reg), and seed-derived (Se) populations in Santa Catarina (SC) (Chapecó’s National Forest [CCO]) and Rio Grande do Sul (RS) (Teixeira Soares’ Municipal Natural Park [PTS]). We found unique alleles in both populations, eleven in the CCO and eight in the PTS. There was no significant difference in genetic diversity indexes, whereas high and significant fixation indexes were observed (FCCO = 0.394; FPTS = 0.319). The proportion of genetic variation obtained between populations was lower (8%) than the proportion within populations (92%). The presence of significant genetic structure was verified in the cohorts of I. paraguariensis for two study areas. Moreover, all pairwise genetic divergence between Ad and Reg, and Ad and Se cohorts was significant, except for Ad versus Reg in PTS. Aiming to maintain diversity over generations, we recommend the conservation of remnants and expansion of connectivity between fragments.

The genetic background of the associations between sense of coherence and mental health, self-esteem and personality

PurposeSense of coherence (SOC) represents coping and can be considered an essential component of mental health. SOC correlates with mental health and personality, but the background of these associations is poorly understood. We analyzed the role of genetic factors behind the associations of SOC with mental health, self-esteem and personality using genetic twin modeling and polygenic scores (PGS).MethodsInformation on SOC (13-item Orientation of Life Questionnaire), four mental health indicators, self-esteem and personality (NEO Five Factor Inventory Questionnaire) was collected from 1295 Finnish twins at 20–27 years of age.ResultsIn men and women, SOC correlated negatively with depression, alexithymia, schizotypal personality and overall mental health problems and positively with self-esteem. For personality factors, neuroticism was associated with weaker SOC and extraversion, agreeableness and conscientiousness with stronger SOC. All these psychological traits were influenced by genetic factors with heritability estimates ranging from 19 to 66%. Genetic and environmental factors explained these associations, but the genetic correlations were generally stronger. The PGS of major depressive disorder was associated with weaker, and the PGS of general risk tolerance with stronger SOC in men, whereas in women the PGS of subjective well-being was associated with stronger SOC and the PGSs of depression and neuroticism with weaker SOC.ConclusionOur results indicate that a substantial proportion of genetic variation in SOC is shared with mental health, self-esteem and personality indicators. This suggests that the correlations between these traits reflect a common neurobiological background rather than merely the influence of external stressors.

Ethnic Disparities in Methotrexate Neurotoxicity among Children and Adolescents with Acute Lymphoblastic Leukemia

Abstract Purpose of study: To identify factors related to the increased risk of neurotoxicity in children with acute lymphoblastic leukemia (ALL) after treatment with the antifolate agent methotrexate (MTX), a critical component of curative protocols. Methods: We analyzed the incidence of and factors associated with acute MTX neurotoxicity (neurologic episode within 14d of dose that resulted in treatment modification) in a multi-site study of 280 (48% Latino) newly diagnosed (between 2012–2017) patients treated on recent pediatric ALL protocols. We examined the effects of genetic ancestry and single nucleotide variants in a subset of 190 patients with genotype data. Results: MTX neurotoxicity occurred in 22% of Latino compared to 7% of non- Latino patients; a nearly 2.5-fold increased risk after accounting for other clinical and demographic factors. Patients with neurotoxicity received fewer total MTX doses, and their risk for relapse was 2-fold higher than patients who did not experience neurotoxicity. We also found that 42% of our Latino patients who experienced a first neurotoxic event went on to have additional events, compared to only 21% of non-Latino patients. The proportion of genetic variation that co-segregates with Native American ancestry was overrepresented in individuals with MTX-related neurotoxicity (mean = 35%) vs without neurotoxicity (mean = 23%, p = 0.025). In multivariable models accounting for sex, age at diagnosis, and treatment risk group, every 10% increase in the proportion of Native American genetic ancestry was associated with a 16% increase in neurotoxicity incidence (HR = 1.16; 95% CI: 1.02–1.32). Our data also suggest that Latinos are at higher risk for first (OR = 3.51, p = 0.02) and subsequent (OR = 6.10, p = 0.04) neurotoxic events associated with a missense variant in TCF12, which is more common in admixed Latino (23%) compared to European (3%) or African (<1%) populations. Conclusions: MTX neurotoxicity is more common among Latino children and adolescents with ALL, compromises treatment efficacy, and may contribute to disparities in ALL relapse and survival. Our findings to date highlight that differences in inherited genetic variation, which segregate with ancestry, likely contribute to disparities in the incidence of treatment-related neurotoxicity.

Optimizing genomic prediction model given causal genes in a dairy cattle population

As genotypic data are moving from SNP chip toward whole-genome sequence, the accuracy of genomic prediction (GP) exhibits a marginal gain, although all genetic variation, including causal genes, are contained in whole-genome sequence data. Meanwhile, genetic analyses on complex traits, such as genome-wide association studies, have identified an increasing number of genomic regions, including potential causal genes, which would be reliable prior knowledge for GP. Many studies have tried to improve the performance of GP by modifying the prediction model to incorporate prior knowledge. Although several plausible results have been obtained from model modification or strategy optimization, most of them were validated in a specific empirical population with a limited variety of genetic architecture for complex traits. An alternative approach is to use simulated genetic architecture with known causal genes (e.g., simulated causative SNP) to evaluate different GP models with given causal genes. Our objectives were to (1) evaluate the performance of GP under a variety of genetic architectures with a subset of known causal genes and (2) compare different GP models modified by highlighting causal genes and different strategies to weight causal genes. In this study, we simulated pseudo-phenotypes under a variety of genetic architectures based on the real genotypes and phenotypes of a dairy cattle population. Besides classical genomic best linear unbiased prediction, we evaluated 3 modified GP models that highlight causal genes as follows: (1) by treating them as fixed effects, (2) by treating them as a separate random component, and (3) by combining them into the genomic relationship matrix as random effects. Our results showed that highlighting the known causal genes, which explained a considerable proportion of genetic variance in the GP models, increased the predictive accuracy. Combining all given causal genes into the genomic relationship matrix was the optimal strategy under all the scenarios validated, and treating causal genes as a separate random component is also recommended, when more than 20% of genetic variance was explained by known causal genes. Moreover, assigning differential weights to each causal gene further improved the predictive accuracy.

Development of six novel microsatellite markers for the Chilean red alga <i>Pyropia orbicularis</i>

Mariculture of edible Porphyra/Pyropia species, which are cosmopolitan red foliose algae, represent an important source of income for the pharmaceutical and food industries. In Chile, the most common alga of this complex is Pyropia orbicularis (Bangiales, Rhodophyta). Here we report 6 microsatellite markers obtained from P. orbicularis through a genomic library from 10 individuals of the gametophytic phase by next generation (Illumina) sequencing. Polymorphism analyses were done from 10 individuals of the conchocelis phase, revealing an allelic diversity ranging from three to six alleles per locus, and observed heterozygosity (HO) and expected heterozygosity (HE) ranging from 0.200 to 0.995, and 0.250 to 0.610, respectively. A large proportion of genetic variance (61%) in P. orbicularis was among individuals within population, and 39% genetic variance was among populations. Nonetheless, these results should be interpreted with caution; further research using larger sample sizes is required. These polymorphic markers could be useful in future studies on population genetic structure, for conservation and applied purposes.

Differential chromatin accessibility landscape reveals structural and functional features of the allopolyploid wheat chromosomes

BackgroundOur understanding of how the complexity of the wheat genome influences the distribution of chromatin states along the homoeologous chromosomes is limited. Using a differential nuclease sensitivity assay, we investigate the chromatin states of the coding and repetitive regions of the allopolyploid wheat genome.ResultsAlthough open chromatin is found to be significantly enriched around genes, the majority of MNase-sensitive regions are located within transposable elements (TEs). Chromatin of the smaller D genome is more accessible than that of the larger A and B genomes. Chromatin states of different TEs vary among families and are influenced by the TEs’ chromosomal position and proximity to genes. While the chromatin accessibility of genes is influenced by proximity to TEs, and not by their position on the chromosomes, we observe a negative chromatin accessibility gradient along the telomere-centromere axis in the intergenic regions, positively correlated with the distance between genes. Both gene expression levels and homoeologous gene expression bias are correlated with chromatin accessibility in promoter regions. The differential nuclease sensitivity assay accurately predicts previously detected centromere locations. SNPs located within more accessible chromatin explain a higher proportion of genetic variance for a number of agronomic traits than SNPs located within more closed chromatin.ConclusionsChromatin states in the wheat genome are shaped by the interplay of repetitive and gene-encoding regions that are predictive of the functional and structural organization of chromosomes, providing a powerful framework for detecting genomic features involved in gene regulation and prioritizing genomic variation to explain phenotypes.

Identification of potential genomic regions for egg weight by a haplotype-based genome-wide association study using Bayesian methods

ABSTRACT 1. Theoretically, haplotype blocks might be a more suitable alternative to SNP genotypes as they are usually better at capturing multi-allelic QTL effects, compared to individual SNP genotypes in genome-wide association studies. The objectives of this study were to identify genomic regions related to egg weight traits by Bayesian methods (BayesA, BayesB, and BayesN) that fit fixed-length haplotypes using GenSel software. 2. Genotypes at 294,705 SNPs, that were common on a 600K Affymetrix chip, were phased for an egg-laying hen population of 1,063 birds. Recorded traits included first egg weight (FEW) and average egg weight at 28, 36, 56, 66, 72 and 80 weeks of age. 2. Fitting 1Mb haplotypes from BayesB resulted in the highest proportion of genetic variance explained for the egg weight traits. Based on the trait, the genetic variance explained by each marker ranged from 27% to 76%. 3. Different haplotype windows associated with egg weight traits only explained a small percentage of the genetic variance. 4. The top one 1-Mb window on GGA1 explained approximately 4.05% of total genetic variance for the FEW. Candidate genes, including PRKAR2B, HMGA2, LEMD3, GRIP1, EHBP1, MAP3K7, and MYH were identified for egg weight traits. 5. Several genomic regions, potentially associated with egg weight traits, were identified, some of which overlapped with known genes and previously reported QTL regions for egg production traits.

The Impacts of Flowering Time and Tillering on Grain Yield of Sorghum Hybrids across Diverse Environments

Sorghum in Australia is grown in water-limited environments of varying extent, generating substantial genotype × environment interactions (GEIs) for grain yield. Much of the yield variation and GEI results from variations in flowering time and tillering through their effects on canopy development. The confounding effects of flowering and tillering complicate the interpretation of breeding trials. In this study, we evaluated the impacts of both flowering time (DTF) and tillering capacity (FTN) on the yield of 1741 unique test hybrids derived from three common female testers in 21 yield testing trials (48 tester/trial combinations) across the major sorghum production regions in Australia in three seasons. Contributions of DTF and FTN to genetic variation in grain yield were significant in 14 and 12 tester/trial combinations, respectively. The proportion of genetic variance in grain yield explained by DTF and FTN ranged from 0.2% to 61.0% and from 1.4% to 56.9%, respectively, depending on trials and genetic background of female testers. The relationship of DTF or FTN with grain yield of hybrids was frequently positive but varied across the genetic background of testers. Accounting for the effects of DTF and FTN using linear models did not substantially increase the between-trial genetic correlations for grain yield. The results suggested that other factors affecting canopy development dynamics and grain yield might contribute GEI and/or the linear approach to account for DTF and FTN on grain yield did not capture the complex non-linear interactions.

Openness Is Related to Proenvironmental Behavior Both Within and Across Families

Environment-friendly behaviors may be desirable in helping to solve worldwide ecological issues. This has sparked interest in the associations of such behaviors with established psychological constructs such as the Five-Factor Model personality traits. Of these, Openness has been most consistently linked with proenvironmental behavior; yet, the extent of causality in this association is unclear. Using a sample of 168 individuals, including 84 sibling pairs, the present study replicated the association while controlling for factors in which families differ (environmental factors that siblings share and a proportion of genetic variance). Proenvironmental behavior was correlated with Openness ( r = .51) and the association could be observed both between ( r = .57) and within families ( r = .29), with adjustments for various demographic variables. These findings indicate that more open individuals tend to engage in proenvironmental behaviors, even when controlling for possibly confounding factors shared and not shared between siblings.

Identification and validation of QTL and their associated genes for pre-emergent metribuzin tolerance in hexaploid wheat (Triticum aestivum L.)

BackgroundHerbicide tolerance is an important trait that allows effective weed management in wheat crops. Genetic knowledge of metribuzin tolerance in wheat is needed to develop new cultivars for the industry. Here, we evaluated metribuzin tolerance in a recombinant inbred line (RIL) mapping population derived from Synthetic W7984 and Opata 85 over two consecutive years to identify quantitative trait loci (QTL) contributing to the trait. Herbicide tolerance was measured by two chlorophyll traits, SPAD chlorophyll content index (CCI) and visual senescence score (SNS). The markers associated with major QTL from Synthetic W7984, positively contributing to reduced phytotoxic effects under herbicide treatment were validated in two F3/4 recombinant inbred populations developed from crosses of Synthetic W7984 × Westonia and Synthetic W7984 × Lang.ResultsComposite interval mapping (CIM) identified four QTL, two on chromosome 4A and one each on chromosomes 2D and 1A. The chromosomal position of the two QTL mapped on 4A within 10 cM intervals was refined and validated by multiple interval mapping (MIM). The major QTL affecting both measures of tolerance jointly explained 42 and 45% of the phenotypic variation by percentage CCI reduction and SNS, respectively. The identified QTL have a pure additive effect. The metribuzin tolerant allele of markers, Xgwm33 and Xbarc343, conferred lower phytotoxicity and explained the maximum phenotypic variation of 28.8 and 24.5%, respectively. The approximate physical localization of the QTL revealed the presence of five candidate genes (ribulose-bisphosphate carboxylase, oxidoreductase (rbcS), glycosyltransferase, serine/threonine-specific protein kinase and phosphotransferase) with a direct role in photosynthesis and/or metabolic detoxification pathways.ConclusionMetribuzin causes photo-inhibition by interrupting electron flow in PSII. Consequently, chlorophyll traits enabled the measure of high proportion of genetic variability in the mapping population. The validated molecular markers associated with metribuzin tolerance mediating QTL may be used in marker-assisted breeding to select metribuzin tolerant lines. Alternatively, validated favourable alleles could be introgressed into elite wheat cultivars to enhance metribuzin tolerance and improve grain yield in dryland farming for sustainable wheat production.

Effects of alleles in crossbred pigs estimated for genomic prediction depend on their breed-of-origin

BackgroundThis study investigated if the allele effect of a given single nucleotide polymorphism (SNP) for crossbred performance in pigs estimated in a genomic prediction model differs depending on its breed-of-origin, and how these are related to estimated effects for purebred performance.ResultsSNP-allele substitution effects were estimated for a commonly used SNP panel using a genomic best linear unbiased prediction model with breed-specific partial relationship matrices. Estimated breeding values for purebred and crossbred performance were converted to SNP-allele effects by breed-of-origin. Differences between purebred and crossbred, and between breeds-of-origin were evaluated by comparing percentage of variance explained by genomic regions for back fat thickness (BF), average daily gain (ADG), and residual feed intake (RFI). From ten regions explaining most additive genetic variance for crossbred performance, 1 to 5 regions also appeared in the top ten for purebred performance. The proportion of genetic variance explained by a genomic region and the estimated effect of a haplotype in such a region were different depending upon the breed-of-origin. To illustrate underlying mechanisms, we evaluated the estimated effects across breeds-of-origin for haplotypes associated to the melanocortin 4 receptor (MC4R) gene, and for the MC4Rsnp itself which is a missense mutation with a known effect on BF and ADG. Although estimated allele substitution effects of the MC4Rsnp mutation were very similar across breeds, explained genetic variance of haplotypes associated to the MC4R gene using a SNP panel that does not include the mutation, was considerably lower in one of the breeds where the allele frequency of the mutation was the lowest.ConclusionsSimilar regions explaining similar additive genetic variance were observed across purebred and crossbred performance. Moreover, there was some overlap across breeds-of-origin between regions that explained relatively large proportions of genetic variance for crossbred performance; albeit that the actual proportion of variance deviated across breeds-of-origin. Results based on a missense mutation in MC4R confirmed that even if a causal locus has similar effects across breeds-of-origin, estimated effects and explained variance in its region using a commonly used SNP panel can strongly depend on the allele frequency of the underlying causal mutation.

Including gene networks to predict calving difficulty in Holstein, Brown Swiss and Jersey cattle

BackgroundCalving difficulty or dystocia has a great economic impact in the US dairy industry. Reported risk factors associated with calving difficulty are feto-pelvic disproportion, gestation length and conformation. Different dairy cattle breeds have different incidence of calving difficulty, with Holstein having the highest dystocia rates and Jersey the lowest. Genomic selection becomes important especially for complex traits with low heritability, where the accuracy of conventional selection is lower. However, for complex traits where a large number of genes influence the phenotype, genome-wide association studies showed limitations. Biological networks could overcome some of these limitations and better capture the genetic architecture of complex traits. In this paper, we characterize Holstein, Brown Swiss and Jersey breed-specific dystocia networks and employ them in genomic predictions.ResultsMarker association analysis identified single nucleotide polymorphisms explaining the largest average proportion of genetic variance on BTA18 in Holstein, BTA25 in Brown Swiss, and BTA15 in Jersey. Gene networks derived from the genome-wide association included 1272 genes in Holstein, 1454 genes in Brown Swiss, and 1455 genes in Jersey. Furthermore, 256 genes in Holstein network, 275 genes in the Brown Swiss network, and 253 genes in the Jersey network were within previously reported dystocia quantitative trait loci. The across-breed network included 80 genes, with 9 genes being within previously reported dystocia quantitative trait loci. The gene-gene interactions in this network differed in the different breeds. Gene ontology enrichment analysis of genes in the networks showed Regulation of ARF GTPase was very significant (FDR ≤ 0.0098) on Holstein. Neuron morphogenesis and differentiation was the term most enriched (FDR ≤ 0.0539) on the across-breed network. Genomic prediction models enriched with network-derived relationship matrices did not outperform regular GBLUP models.ConclusionsRegions identified in the genome were in the proximity of previously described quantitative trait loci that would most likely affect calving difficulty by altering the feto-pelvic proportion. Inclusion of identified networks did not increase prediction accuracy. The approach used in this paper could be extended to any instance with asymmetric distribution of phenotypes, for example, resistance to disease data.

A haplotype regression approach for genetic evaluation using sequences from the 1000 bull genomes Project

Haplotypes from sequencing data may improve the prediction accuracy in genomic evaluations as haplotypes are in stronger linkage disequilibrium with quantitative trait loci than markers from SNP chips. This study focuses first, on the creation of haplotypes in a population sample of 450 Holstein animals, with full-sequence data from the 1000 bull genomes project; and second, on incorporating them into the whole genome prediction model. In total, 38,319,258 SNPs (and indels) from Next Generation Sequencing were included in the analysis. After filtering variants with minor allele frequency (MAF< 0.025) 13,912,326 SNPs were available for the haplotypes extraction with findhap.f90. The number of SNPs in the haploblocks was on average 924 SNP (166,552 bp). Unique haplotypes were around 97% in all chromosomes and were ignored leaving 153,428 haplotypes. Estimated haplotypes had a large contribution to the total variance of genomic estimated breeding values for kilogram of protein, Global Type Index, Somatic Cell Score and Days Open (between 32 and 99.9%). Haploblocks containing haplotypes with large effects were selected by filtering for each trait, haplotypes whose effect was larger/lower than the mean plus/minus 3 times the standard deviation (SD) and 1 SD above the mean of the haplotypes effect distribution. Results showed that filtering by 3 SD would not be enough to capture a large proportion of genetic variance, whereas filtering by 1 SD could be useful but model convergence should be considered. Additionally, sequence haplotypes were able to capture additional genetic variance to the polygenic effect for traits undergoing lower selection intensity like fertility and health traits.

An across-breed genome wide association analysis of susceptibility to paratuberculosis in dairy cattle.

Paratuberculosis is a chronic disease of ruminants caused by Mycobacterium avium subspecies paratuberculosis (MAP). It occurs worldwide and causes a significant loss in the animal production industry. There is no cure for MAP infection and vaccination is problematic. Identification of genetics of susceptibility could be a useful adjunct for programs that focus on management, testing and culling of diseased animals. A case-control, genome-wide association study (GWAS) was conducted using Holstein and Jersey cattle in a combined analysis in order to identify markers and chromosomal regions associated with susceptibility to MAP infection across-breed. A mixed-model method (GRAMMAR-GC) implemented in the GenABEL R package and a Bayes C analysis implemented in GenSel software were used as alternative approaches to conduct GWAS analysis focused on single SNPs and chromosomal segments, respectively. After conducting quality control, 22 406 SNPs from 2157 individuals were available for the GRAMMAR-GC (Bayes C) analysis and 45 640 SNPs from 2199 individuals were available for the Bayes C analysis. One SNP located on BTA27 (8·6 Mb) was identified as moderately associated (P < 5 × 10-5, FDR = 0·44) in the GRAMMAR-GC analysis of the combined breed data. Nine 1 Mb windows located on BTA 2, 3 (3 windows), 6, 8, 25, 27 and 29 each explained ≥1% of the total proportion of genetic variance in the Bayes C analysis. In an analysis ignoring differences in linkage phase, two moderately significantly associated SNPs were identified; ARS-BFGL-NGS-19381 on BTA23 (32 Mb) and Hapmap40994-BTA-46361 on BTA19 (61 Mb). New common genomic regions and candidate genes have been identified from the across-breed analysis that might be involved in the immune response and susceptibility to MAP infection.

Assessing genetic diversity for the USA endemic carnivorous plant Pinguicula ionantha R.K. Godfrey (Lentibulariaceae)

Understanding patterns of genetic diversity and population structure for rare, narrowly endemic plant species, such as Pinguicula ionantha (Godfrey’s butterwort; Lentibulariaceae), informs conservation goals and can directly affect management decisions. Pinguicula ionantha is a federally listed species endemic to the Florida Panhandle in the southeastern USA. The main goal of our study was to assess patterns of genetic diversity and structure in 17 P. ionantha populations, and to determine if diversity is associated with geographic location or population characteristics. We scored 240 individuals at a total of 899 AFLP markers (893 polymorphic markers). We found no relationship between the estimated population size with either of two measures of diversity (proportion of loci polymorphic, P = 0.37; Nei’s gene diversity, P = 0.50). We also found low levels of population genetic structure; there was no clear relationship of genetic isolation by distance (P = 0.23) and only a small (but significant) proportion of genetic variation was partitioned amongst regions (2.4 %, P = 0.02) or populations (20.8 %, P < 0.001). STRUCTURE analysis found that the model with two inferred clusters (K = 2) best described the AFLP data; the dominant cluster at each site corresponded to the results from PCoA and Nei’s genetic distance analyses. The observed patterns of genetic diversity suggest that although P. ionantha populations are isolated spatially by distance and both natural and anthropogenic barriers, some gene flow occurs among them or isolation has been too recent to leave a genetic signature. The relatively low level of genetic diversity associated with this species is a concern as it may impair fitness and evolutionary capability in a changing environment. The results of this study provide the foundation for the development of management practices that will assist in the protection of this rare carnivorous plant.

Deriving Gene Networks from SNP Associated with Triacylglycerol and Phospholipid Fatty Acid Fractions from Ribeyes of Angus Cattle.

The fatty acid profile of beef is a complex trait that can benefit from gene-interaction network analysis to understand relationships among loci that contribute to phenotypic variation. Phenotypic measures of fatty acid profile from triacylglycerol and phospholipid fractions of longissimus muscle, pedigree information, and Illumina 54 k bovine SNP genotypes were utilized to derive an annotated gene network associated with fatty acid composition in 1,833 Angus beef cattle. The Bayes-B statistical model was utilized to perform a genome wide association study to estimate associations between 54 k SNP genotypes and 39 individual fatty acid phenotypes within each fraction. Posterior means of the effects were estimated for each of the 54 k SNP and for the collective effects of all the SNP in every 1-Mb genomic window in terms of the proportion of genetic variance explained by the window. Windows that explained the largest proportions of genetic variance for individual lipids were found in the triacylglycerol fraction. There was almost no overlap in the genomic regions explaining variance between the triacylglycerol and phospholipid fractions. Partial correlations were used to identify correlated regions of the genome for the set of largest 1 Mb windows that explained up to 35% genetic variation in either fatty acid fraction. SNP were allocated to windows based on the bovine UMD3.1 assembly. Gene network clusters were generated utilizing a partial correlation and information theory algorithm. Results were used in conjunction with network scoring and visualization software to analyze correlated SNP across 39 fatty acid phenotypes to identify SNP of significance. Significant pathways implicated in fatty acid metabolism through GO term enrichment analysis included homeostasis of number of cells, homeostatic process, coenzyme/cofactor activity, and immunoglobulin. These results suggest different metabolic pathways regulate the development of different types of lipids found in bovine muscle tissues. Network analysis using partial correlations and annotation of significant SNPs can yield information about the genetic architecture of complex traits.

Genetic diversity of the cytochrome b gene fragment haplotypes in red-backed vole Myodes (Clethrionomys) rutilus Pallas, 1779

For the first time, genetic analysis of the cytochrome b gene fragment haplotypes encoding the identical and the most common cytochrome b polypeptide (F1) in M. rutilus from Eastern and Beringian mateml lineages was carried out. The F1 frequencies for the vole populations from Northern Priokhotye and the Kolyma basin were calculated. Considerable polymorphism of the cytochrome b F1 haplotypes within two major phylo- groups of red-backed vole was supported by high molecular diversity indices for these clades. The proportion of genetic variation between the maternal lineages of F1 red-backed vole individuals (60.71%) was considerably higher than inter-(24.44%) and intrapopulation (14.85%) components. The data obtained make it possible to advance a hypothesis on the convergence of the cytochrome b polypeptide structure upon sequence divergence of the corresponding gene.

Integrating Paleodistribution Models and Phylogeography in the Grass-Cutting Ant Acromyrmex striatus (Hymenoptera: Formicidae) in Southern Lowlands of South America.

Past climate changes often have influenced the present distribution and intraspecific genetic diversity of organisms. The objective of this study was to investigate the phylogeography and historical demography of populations of Acromyrmex striatus (Roger, 1863), a leaf-cutting ant species restricted to the open plains of South America. Additionally, we modeled the distribution of this species to predict its contemporary and historic habitat. From the partial sequences of the mitochondrial gene cytochrome oxidase I of 128 A. striatus workers from 38 locations we estimated genetic diversity and inferred historical demography, divergence time, and population structure. The potential distribution areas of A. striatus for current and quaternary weather conditions were modeled using the maximum entropy algorithm. We identified a total of 58 haplotypes, divided into five main haplogroups. The analysis of molecular variance (AMOVA) revealed that the largest proportion of genetic variation is found among the groups of populations. Paleodistribution models suggest that the potential habitat of A. striatus may have decreased during the Last Interglacial Period (LIG) and expanded during the Last Maximum Glacial (LGM). Overall, the past potential distribution recovered by the model comprises the current potential distribution of the species. The general structuring pattern observed was consistent with isolation by distance, suggesting a balance between gene flow and drift. Analysis of historical demography showed that populations of A. striatus had remained constant throughout its evolutionary history. Although fluctuations in the area of their potential historic habitat occurred during quaternary climate changes, populations of A. striatus are strongly structured geographically. However, explicit barriers to gene flow have not been identified. These findings closely match those in Mycetophylax simplex, another ant species that in some areas occurs in sympatry with A. striatus. Ecophysiological traits of this species and isolation by distance may together have shaped the phylogeographic pattern.

Rare Variants in Transcript and Potential Regulatory Regions Explain a Small Percentage of the Missing Heritability of Complex Traits in Cattle.

The proportion of genetic variation in complex traits explained by rare variants is a key question for genomic prediction, and for identifying the basis of “missing heritability”–the proportion of additive genetic variation not captured by common variants on SNP arrays. Sequence variants in transcript and regulatory regions from 429 sequenced animals were used to impute high density SNP genotypes of 3311 Holstein sires to sequence. There were 675,062 common variants (MAF>0.05), 102,549 uncommon variants (0.01<MAF<0.05), and 83,856 rare variants (MAF<0.01). We describe a novel method for estimating the proportion of the rare variants that are sequencing errors using parent-progeny duos. We then used mixed model methodology to estimate the proportion of variance captured by these different classes of variants for fat, milk and protein yields, as well as for fertility. Common sequence variants captured 83%, 77%, 76% and 84% of the total genetic variance for fat, milk, and protein yields and fertility, respectively. This was between 2 and 5% more variance than that captured from 600k SNPs on a high density chip, although the difference was not significant. Rare variants captured 3%, 0%, 1% and 14% of the genetic variance for fat, milk and protein yields, and fertility respectively, whereas pedigree explained the remaining amount of genetic variance (none for fertility). The proportion of variation explained by rare variants is likely to be under-estimated due to reduced accuracies of imputation for this class of variants. Using common sequence variants slightly improved accuracy of genomic predictions for fat and milk yield, compared to high density SNP array genotypes. However, including rare variants from transcript regions did not increase the accuracy of genomic predictions. These results suggest that rare variants recover a small percentage of the missing heritability for complex traits, however very large reference sets will be required to exploit this to improve the accuracy of genomic predictions. Our results do suggest the contribution of rare variants to genetic variation may be greater for fitness traits.

Characterization of Five Microsatellite Markers For Genetic Diversity Structure Analysis of Walnut (Juglans Regia L) in Five Village in Duhok Province

Five microsatellites markers (WGA202, WGA009, WGA332, WGA225 and WGA069) were used to characterize Persian walnuts (Juglans regia L.) populations in five villages (Sharanesh, Bedohe, Kanizarke (Akre), Kashane and Kuzo) of Duhok province. The microsatellites amplified (PCR products) a total of 186 alleles across all populations. The number of alleles per locus ranged from 4 alleles in Sharanesh and Kashane populations to 10 alleles also in Sharanesh and Kashane populations, with an average of 7.4. The molecular sizes of the amplified bands ranged from 158 bp to 289 bp in all populations. The observed heterozygosity (Ho) within populations ranged from 0.59 at WGA225 to 0.65 in the locus WGA202 with an average of 0.61. The PIC value (0.88) indicated that all markers were highly informative and useful for genetic diversity studies in these populations. The proportion of genetic variation presented among populations accounted for 8.4% of the total genetic diversity so it indicated a moderate level of genetic diversity between populations. The Fis average 0.24 indicated that, there was a regular tendency toward heterozygote deficiency and indicated the presence of inbreeding within the populations. The phylogenetic analysis or unrooted neighbor-joining tree highlighted the genetic distance among those five populations and separated them into two main groups placing each population according to its genetic background. The first group consists of populations Bedohe and Sharanesh in one subgroup and Kashane in second subgroup. The second group consists of populations Akre and Kuzo.