SnpRecode: A versatile and fast genotype recoding and correlation function

Genotype imputation is an essential tool used in genomic selection in plants and animals. A popular imputation tool used in animal genomics is FImpute. FImpute, however, accepts a specific genotype format and produces dosages whose conversion to VCF or Plink format requires multiple software packages in a pipeline with a large amount of processing time. We have developed SnpRecode as a helper tool that bridges the gap between regular genotype files and the FImpute imputation software by allowing for fast and seamless conversion of genotypes to-and-from FImpute format. SnpRecode also implements a fast genotype correlation function to estimate and plot the imputation accuracy. We run tests on 6,000 samples with a step of 1,000 to determine the performance of SnpRecode on various sample sizes and runtime and memory usage used as performance measures. The performance of SnpRecode was modest at 10sec/1,000 samples. Written in Python programming language, SnpRecode provides users with great flexibility in implementation with other software packages in a pipeline.

SnpRecode: A Versatile and Fast Genotype Recoding and Correlation Function

Genotype imputation is an essential tool used in genomic selection in plants and animals. A popular imputation tool used in animal genomics is FImpute. FImpute, however, accepts a specific genotype format and produces dosages whose conversion to VCF or Plink format requires multiple software packages in a pipeline with a large amount of processing time. We have developed SnpRecode as a helper tool that bridges the gap between regular genotype files and the FImpute imputation software by allowing for fast and seamless conversion of genotypes to-and-from FImpute format. SnpRecode also implements a fast genotype correlation function to estimate and plot the imputation accuracy. We run tests on 6,000 samples with a step of 1,000 to determine the performance of SnpRecode on various sample sizes and runtime and memory usage used as performance measures. The performance of SnpRecode was modest at 10sec/1,000 samples. Written in Python programming language, SnpRecode provides users with great flexibility in implementation with other software packages in a pipeline.

29 Introduction to functional annotation of animal genomes consortium

The annotation of the functional components of genomes is required for both basic and applied genomic research, yet farmed animal genomics are deficient in such annotation. This talk will introduce the Functional Annotation of ANimal Genomes (FAANG) consortium, which was initiated in 2014 to address this knowledge deficit. An overarching theme of early FAANG efforts is the collaborative approach required for such comprehensive research, thus another emphasis of this talk will be our community efforts to develop the environment for successful FAANG research. FAANG scientists have created a coordinated data collection and analysis enterprise crucial for success of this global effort, and funding of ~$20 million has been secured for current projects in several species, with an anticipated doubling of this support during 2019. Member FAANG consortium labs are producing genome-wide datasets on RNA expression, DNA methylation, chromatin modification, chromatin accessibility, and chromatin interactions for many agriculturally relevant animal species. The goal of a first-generation global chromatin state map for cattle, chicken, pig, and potentially other species is projected for completion in the next 3–5 years. These data will be used both to better understand animal genome function at the epigenetic level, as well as improve the precision and sensitivity of genomic selection for animal improvement. The functional annotation delivered by the FAANG initiative will add value and utility to the greatly improved genome sequences being established for domesticated animal species.

Functional Annotation of Animal Genomes (FAANG): Current Achievements and Roadmap

Functional annotation of genomes is a prerequisite for contemporary basic and applied genomic research, yet farmed animal genomics is deficient in such annotation. To address this, the FAANG (Functional Annotation of Animal Genomes) Consortium is producing genome-wide data sets on RNA expression, DNA methylation, and chromatin modification, as well as chromatin accessibility and interactions. In addition to informing our understanding of genome function, including comparative approaches to elucidate constrained sequence or epigenetic elements, these annotation maps will improve the precision and sensitivity of genomic selection for animal improvement. A scientific community–driven effort has already created a coordinated data collection and analysis enterprise crucial for the success of this global effort. Although it is early in this continuing process, functional data have already been produced and application to genetic improvement reported. The functional annotation delivered by the FAANG initiative will add value and utility to the greatly improved genome sequences being established for domesticated animal species.