Evaluating Sequence-Based Genomic Prediction with an Efficient New Simulator

Abstract

The vast amount of sequence data generated to analyze complex traits is posing new challenges in terms of the analysis and interpretation of the results. Although simulation is a fundamental tool to investigate the reliability of genomic analyses and to optimize experimental design, existing software cannot realistically simulate complete genomes. To remedy this, we have developed a new strategy (Sequence-Based Virtual Breeding, SBVB) that uses real sequence data and simulates new offspring genomes and phenotypes in a very efficient and flexible manner. Using this tool, we studied the efficiency of full sequence in genomic prediction compared to SNP arrays. We used real porcine sequences from three breeds as founder genomes of a 2500-animal pedigree and two genetic architectures: "neutral" and "selective." In the neutral architecture, frequencies and allele effects were sampled independently whereas, in the selective case, SNPs were sites putatively under selection after domestication and a negative correlation between effect and frequency was induced. We compared the effectiveness of different genotyping strategies for genomic selection, including the use of full sequence commercial arrays or randomly chosen SNP sets in both outbred and crossbred experimental designs. We found that accuracy increases using sequence instead of commercial chips but modestly, perhaps by # 4%. This result was robust to extreme genetic architectures. We conclude that full sequence is unlikely to offset commercial arrays for predicting genetic value when the number of loci is relatively large and the prior given to each SNP is uniform. Using sequence to improve selection thus requires optimized prior information and, likely, increased population sizes. The code and manual for SBVB are available at https://github.com/mperezenciso/sbvb0. KEYWORDS complex trait; genomic selection; sequence; forward simulation; pig; GenPred; shared data resource A SCERTAINING the genetic basis of complex traits has been a goal of geneticists for decades; however, this endeavor is proving to be more difficult to attain than anticipated, even with current massive data sets. Nevertheless, molecular information can still be used for genetic prediction. Genomic selection (GS) relies on linkage disequilibrium (LD) between markers and the causal mutations, without the need to identify them (Meuwissen et al. 2001). So far, GS and genome-wide association studies (GWAS) have been mainly performed with manufactured genotyping array SNPs, but the current genomics status quo is being challenged by the dramatic improvement in sequencing technologies. Arraybased experimental designs are now being superseded by analyses of sequence data at population scale. Similarly, for agriculture, the drop in sequencing costs makes it conceivable that GS programs can routinely employ genome sequencing instead of genotyping arrays in the near future.Sequence data contains all the information needed (i.e., the causal variants) to make the most accurate prediction of genetic m...

SeqBreed: a python tool to evaluate genomic prediction in complex scenarios

“…• Population: This class contains the main attributes for running selection experiments and is a container for Individual objects. It includes methods to add new individuals generated by mating two parents or randomly shuffling founder genomes in order to increase the number of base population animals (see [3]). It also prints basic population data and do summary plots.…”

“…• Individual: It allows generation, manipulation and printing of individual genotypes and phenotypes. Internally, an individual's genome is represented by contiguous non recombining blocks rather than by the list of all SNP alleles, which allows dramatic savings in memory and increases in efficiency (see Figure 1 in Pérez-Enciso et al [3]).…”

crossword: A data-driven simulation language for the design of genetic-mapping experiments and breeding strategies

“…Thus, initial coalescent simulations that create founder sequences are often irrelevant and even an obstacle for many breeders, who already have resequencing data for the parent lines in their study and would prefer to use that information. These unrealistic simplifying assumptions about founder structure can also have substantial implications on predicting breeding results [6]. Although, some of these simulators offer a means to supply parental genotypes, this functionally is very difficult to implement in practice.…”

“…Although, some of these simulators offer a means to supply parental genotypes, this functionally is very difficult to implement in practice. Only recently have some researchers begun to build in functionality for direct import of genotypic data [6]. Indeed, to our knowledge, no simulation framework fully supports the integration of empirical data, such as genome sequences and annotations, that are currently available for most crop species.…”

Functional gene categories differentiate maize leaf drought-related microbial epiphytic communities

“…In a maize leaf microbe association genetics experiment, predicted metabolic 321 functions were more heritable than ribotypes, which also suggests that function is 322 key [32]. Selection for specific microbial functional genes or generic markers for 323 pathways could easily be incorporated into newer DNA-based crop genomic selection 324 processes that are sequencing based [50][51][52]. The importance of incorporating microbial 325 sequence predictors lends support to the movement toward sequencing to collect all 326 DNA data, not just filtered SNP sets or SNPs with prior data on causality.…”