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Getting your first job
\"This informative volume guides readers through finding work, understanding pay scales and benefits, and learning about hours and schedules. Advice on how to budget and save and what to expect when it comes to taxes round out what to expect from the employment process.\"-- Publisher's description.
Book
LinkImpute: Fast and Accurate Genotype Imputation for Nonmodel Organisms
Obtaining genome-wide genotype data from a set of individuals is the first step in many genomic studies, including genome-wide association and genomic selection. All genotyping methods suffer from some level of missing data, and genotype imputation can be used to fill in the missing data and improve the power of downstream analyses. Model organisms like human and cattle benefit from high-quality reference genomes and panels of reference genotypes that aid in imputation accuracy. In nonmodel organisms, however, genetic and physical maps often are either of poor quality or are completely absent, and there are no panels of reference genotypes available. There is therefore a need for imputation methods designed specifically for nonmodel organisms in which genomic resources are poorly developed and marker order is unreliable or unknown. Here we introduce LinkImpute, a software package based on a k-nearest neighbor genotype imputation method, LD-kNNi, which is designed for unordered markers. No physical or genetic maps are required, and it is designed to work on unphased genotype data from heterozygous species. It exploits the fact that markers useful for imputation often are not physically close to the missing genotype but rather distributed throughout the genome. Using genotyping-by-sequencing data from diverse and heterozygous accessions of apples, grapes, and maize, we compare LD-kNNi with several genotype imputation methods and show that LD-kNNi is fast, comparable in accuracy to the best-existing methods, and exhibits the least bias in allele frequency estimates.
Journal Article
حلول الاستثمار : تعلم إدارة أموالك وحماية مستقبلك المالي
يحتوي هذا الكتاب ثمانية فصول الفصل الأول قرار افعلها بنفسك وأما الفصل الثاني قرار توزيع الأصول والفصل الثالث قرار التنويع والفصل الرابع قرار الاستثمار النشط مقابل الاستثمار الساكن والفصل الخامس قرار إعادة التوازن والفصل السادس مقارنة بماذا والفصل السابع ماذا عن البدائل وأخيرا الفصل الثامن يمكن لأي شخص أن ينجح.
Book
Genome to Phenome Mapping in Apple Using Historical Data
Apple (Malus X. domestica Borkh.) is one of the world's most valuable fruit crops. Its large size and long juvenile phase make it a particularly promising candidate for marker‐assisted selection (MAS). However, advances in MAS in apple have been limited by a lack of phenotype and genotype data from sufficiently large samples. To establish genotype‐phenotype relationships and advance MAS in apple, we extracted over 24,000 phenotype scores from the USDA‐Germplasm Resources Information Network (GRIN) database and linked them with over 8000 single nucleotide polymorphisms (SNPs) from 689 apple accessions from the USDA apple germplasm collection clonally preserved in Geneva, NY. We find significant genetic differentiation between Old World and New World cultivars and demonstrate that the genetic structure of the domesticated apple also reflects the time required for ripening. A genome‐wide association study (GWAS) of 36 phenotypes confirms the association between fruit color and the MYB1 locus, and we also report a novel association between the transcription factor, NAC18.1, and harvest date and fruit firmness. We demonstrate that harvest time and fruit size can be predicted with relatively high accuracies (r > 0.46) using genomic prediction. Rapid decay of linkage disequilibrium (LD) in apples means millions of SNPs may be required for well‐powered GWAS. However, rapid LD decay also promises to enable extremely high resolution mapping of causal variants, which holds great potential for advancing MAS.
Journal Article
LinkImputeR: user-guided genotype calling and imputation for non-model organisms
Background
Genomic studies such as genome-wide association and genomic selection require genome-wide genotype data. All existing technologies used to create these data result in missing genotypes, which are often then inferred using genotype imputation software. However, existing imputation methods most often make use only of genotypes that are successfully inferred after having passed a certain read depth threshold. Because of this, any read information for genotypes that did not pass the threshold, and were thus set to missing, is ignored. Most genomic studies also choose read depth thresholds and quality filters without investigating their effects on the size and quality of the resulting genotype data. Moreover, almost all genotype imputation methods require ordered markers and are therefore of limited utility in non-model organisms.
Results
Here we introduce LinkImputeR, a software program that exploits the read count information that is normally ignored, and makes use of all available DNA sequence information for the purposes of genotype calling and imputation. It is specifically designed for non-model organisms since it requires neither ordered markers nor a reference panel of genotypes. Using next-generation DNA sequence (NGS) data from apple, cannabis and grape, we quantify the effect of varying read count and missingness thresholds on the quantity and quality of genotypes generated from LinkImputeR. We demonstrate that LinkImputeR can increase the number of genotype calls by more than an order of magnitude, can improve genotyping accuracy by several percent and can thus improve the power of downstream analyses. Moreover, we show that the effects of quality and read depth filters can differ substantially between data sets and should therefore be investigated on a per-study basis.
Conclusions
By exploiting DNA sequence data that is normally ignored during genotype calling and imputation, LinkImputeR can significantly improve both the quantity and quality of genotype data generated from NGS technologies. It enables the user to quickly and easily examine the effects of varying thresholds and filters on the number and quality of the resulting genotype calls. In this manner, users can decide on thresholds that are most suitable for their purposes. We show that LinkImputeR can significantly augment the value and utility of NGS data sets, especially in non-model organisms with poor genomic resources.
Journal Article
Extending long-range phasing and haplotype library imputation algorithms to large and heterogeneous datasets
Background
We describe the latest improvements to the long-range phasing (LRP) and haplotype library imputation (HLI) algorithms for successful phasing of both datasets with one million individuals and datasets genotyped using different sets of single nucleotide polymorphisms (SNPs). Previous publicly available implementations of the LRP algorithm implemented in AlphaPhase could not phase large datasets due to the computational cost of defining surrogate parents by exhaustive all-against-all searches. Furthermore, the AlphaPhase implementations of LRP and HLI were not designed to deal with large amounts of missing data that are inherent when using multiple SNP arrays.
Methods
We developed methods that avoid the need for all-against-all searches by performing LRP on subsets of individuals and then concatenating the results. We also extended LRP and HLI algorithms to enable the use of different sets of markers, including missing values, when determining surrogate parents and identifying haplotypes. We implemented and tested these extensions in an updated version of AlphaPhase, and compared its performance to the software package Eagle2.
Results
A simulated dataset with one million individuals genotyped with the same 6711 SNPs for a single chromosome took less than a day to phase, compared to more than seven days for Eagle2. The percentage of correctly phased alleles at heterozygous loci was 90.2 and 99.9% for AlphaPhase and Eagle2, respectively. A larger dataset with one million individuals genotyped with 49,579 SNPs for a single chromosome took AlphaPhase 23 days to phase, with 89.9% of alleles at heterozygous loci phased correctly. The phasing accuracy was generally lower for datasets with different sets of markers than with one set of markers. For a simulated dataset with three sets of markers, 1.5% of alleles at heterozygous positions were phased incorrectly, compared to 0.4% with one set of markers.
Conclusions
The improved LRP and HLI algorithms enable AlphaPhase to quickly and accurately phase very large and heterogeneous datasets. AlphaPhase is an order of magnitude faster than the other tested packages, although Eagle2 showed a higher level of phasing accuracy. The speed gain will make phasing achievable for very large genomic datasets in livestock, enabling more powerful breeding and genetics research and application.
Journal Article
The Impact of Spatial Delineation on the Assessment of Species Recovery Outcomes
In 2021, the International Union for Conservation of Nature (IUCN) introduced a novel method for assessing species recovery and conservation impact: the IUCN Green Status of Species. The Green Status standardizes recovery using a metric called the Green Score, which ranges from 0% to 100%. This study focuses on one crucial step in the Green Status method—the division of a species’ range into so-called “spatial units”—and evaluates whether different approaches for delineating spatial units affect the outcome of the assessment (i.e., the Green Score). We compared Green Scores generated using biologically based spatial units (the recommended method) to Green Scores generated using ecologically based or country-based spatial units for 29 species of birds and mammals in Europe. We found that while spatial units delineated using ecoregions and countries (fine-scale) produced greater average numbers of spatial units and significantly lower average Green Scores than biologically based spatial units, coarse-scale spatial units delineated using biomes and countries above a range proportion threshold did not differ significantly from biologically based results for average spatial unit number or average Green Score. However, case studies focusing on results for individual species (rather than a group average) showed that, depending on characteristics of the species’ distribution, even these coarse-scale delineations of ecological or country spatial units often over- or under-predict the Green Score compared to biologically based spatial units. We discuss cases in which the use of ecologically based or country-based spatial units is recommended or discouraged, in hopes that our results will strengthen the new Green Status framework and ensure consistency in application.
Journal Article
Quantifying apple diversity: A phenomic characterization of Canada’s Apple Biodiversity Collection
Societal Impact Statement A future with a secure and safe food supply requires humanity to preserve and exploit the vast variation available across agricultural plant species. Apples are one of the most widely consumed fruits and provide significant nutritional value worldwide. Here, we characterize key agricultural traits in a diverse collection of apples to provide a foundation for future apple improvement. We show that commercially successful apple varieties capture only a small fraction of apple diversity, and demonstrate that significant improvement is possible by tapping into existing genetic diversity. Summary ●Here we present a comprehensive evaluation of apple diversity through phenotyping of Canada's Apple Biodiversity Collection (ABC) which contains over 1000 apple accessions. ●We assessed, over a 4‐year period, more than 20,000 individual apples and quantified variation across 39 phenotypes, including phenology and fruit quality both at harvest and after 3 months of cold storage. ●We observe that apples in the ABC display a wide range of phenotypic variation that may prove useful for future apple improvement. For example, apples can differ by nearly 61‐fold in weight, 18‐fold in acidity, and 100‐fold in phenolic content. We quantified the dramatic changes to apple physiology that occur during 3 months of cold storage: on average, apples lost 39% of their firmness, 31% of their acidity, and 9% of their weight, but gained 7% in soluble solids. Harvest date, flowering date, and time to ripen were all positively correlated with firmness, which suggests that the developmental pathways that drive phenological events throughout the growing season may play a role in determining an apple's texture. Finally, we show that apple breeding has selected for a significant decline in phenolic content over the past 200 years: apple cultivars released after 1940 had a 30% lower median phenolic content than cultivars released before 1940. ●The data and analyses presented here not only provide a comprehensive quantification of the range across, and relationships among diverse apple phenotypes, but they also enable genetic mapping studies that will provide the foundation for future apple improvement via genomics‐assisted breeding. A future with a secure and safe food supply requires humanity to preserve and exploit the vast variation available across agricultural plant species. Apples are one of the most widely consumed fruits and provide significant nutritional value worldwide. Here, we characterize key agricultural traits in a diverse collection of apples to provide a foundation for future apple improvement. We show that commercially successful apple varieties capture only a small fraction of apple diversity, and demonstrate that significant improvement is possible by tapping into existing genetic diversity.
Journal Article
Inferring Gene Family Histories in Yeast Identifies Lineage Specific Expansions
The complement of genes found in the genome is a balance between gene gain and gene loss. Knowledge of the specific genes that are gained and lost over evolutionary time allows an understanding of the evolution of biological functions. Here we use new evolutionary models to infer gene family histories across complete yeast genomes; these models allow us to estimate the relative genome-wide rates of gene birth, death, innovation and extinction (loss of an entire family) for the first time. We show that the rates of gene family evolution vary both between gene families and between species. We are also able to identify those families that have experienced rapid lineage specific expansion/contraction and show that these families are enriched for specific functions. Moreover, we find that families with specific functions are repeatedly expanded in multiple species, suggesting the presence of common adaptations and that these family expansions/contractions are not random. Additionally, we identify potential specialisations, unique to specific species, in the functions of lineage specific expanded families. These results suggest that an important mechanism in the evolution of genome content is the presence of lineage-specific gene family changes.
Journal Article
Characterizing the Phylogenetic Tree-Search Problem
Phylogenetic trees are important in many areas of biological research, ranging from systematic studies to the methods used for genome annotation. Finding the best scoring tree under any optimality criterion is an NP-hard problem, which necessitates the use of heuristics for tree-search. Although tree-search plays a major role in obtaining a tree estimate, there remains a limited understanding of its characteristics and how the elements of the statistical inferential procedure interact with the algorithms used. This study begins to answer some of these questions through a detailed examination of maximum likelihood tree-search on a wide range of real genome-scale data sets. We examine all 10,395 trees for each of the 106 genes of an eight-taxa yeast phylogenomic data set, then apply different tree-search algorithms to investigate their performance. We extend our findings by examining two larger genome-scale data sets and a large disparate data set that has been previously used to benchmark the performance of tree-search programs. We identify several broad trends occurring during tree-search that provide an insight into the performance of heuristics and may, in the future, aid their development. These trends include a tendency for the true maximum likelihood (best) tree to also be the shortest tree in terms of branch lengths, a weak tendency for tree-search to recover the best tree, and a tendency for tree-search to encounter fewer local optima in genes that have a high information content. When examining current heuristics for tree-search, we find that nearest-neighbor-interchange performs poorly, and frequently finds trees that are significantly different from the best tree. In contrast, subtree-pruning-and-regrafting tends to perform well, nearly always finding trees that are not significantly different to the best tree. Finally, we demonstrate that the precise implementation of a tree-search strategy, including when and where parameters are optimized, can change the character of tree-search, and that good strategies for tree-search may combine existing tree-search programs.
Journal Article