Optimal Design of Low-Density SNP Arrays for Genomic Prediction: Algorithm and Applications.

Low-density (LD) single nucleotide polymorphism (SNP) arrays provide a cost-effective solution for genomic prediction and selection, but algorithms and computational tools are needed for the optimal design of LD SNP chips. A multiple-objective, local optimization (MOLO) algorithm was developed for design of optimal LD SNP chips that can be imputed accurately to medium-density (MD) or high-density (HD) SNP genotypes for genomic prediction. The objective function facilitates maximization of non-gap map length and system information for the SNP chip, and the latter is computed either as locus-averaged (LASE) or haplotype-averaged Shannon entropy (HASE) and adjusted for uniformity of the SNP distribution. HASE performed better than LASE with ≤1,000 SNPs, but required considerably more computing time. Nevertheless, the differences diminished when >5,000 SNPs were selected. Optimization was accomplished conditionally on the presence of SNPs that were obligated to each chromosome. The frame location of SNPs on a chip can be either uniform (evenly spaced) or non-uniform. For the latter design, a tunable empirical Beta distribution was used to guide location distribution of frame SNPs such that both ends of each chromosome were enriched with SNPs. The SNP distribution on each chromosome was finalized through the objective function that was locally and empirically maximized. This MOLO algorithm was capable of selecting a set of approximately evenly-spaced and highly-informative SNPs, which in turn led to increased imputation accuracy compared with selection solely of evenly-spaced SNPs. Imputation accuracy increased with LD chip size, and imputation error rate was extremely low for chips with ≥3,000 SNPs. Assuming that genotyping or imputation error occurs at random, imputation error rate can be viewed as the upper limit for genomic prediction error. Our results show that about 25% of imputation error rate was propagated to genomic prediction in an Angus population. The utility of this MOLO algorithm was also demonstrated in a real application, in which a 6K SNP panel was optimized conditional on 5,260 obligatory SNP selected based on SNP-trait association in U.S. Holstein animals. With this MOLO algorithm, both imputation error rate and genomic prediction error rate were minimal.

An efficient isolation method for domestic hen (Gallus domesticus) ovarian primary follicles.

An enzymatic method of isolating primary follicles in the turkey has been described previously, but no similar work has been done in the hen. In this study, primary follicles from domestic hens (Gallus domesticus) were isolated using an enzymatic method, and the isolated follicular quantity, quality, and development in vitro were assessed. About 400 primary follicles ranging from 60 to 1125 microm in diameter were recovered with trypsin and collagenase from hen ovaries (per ovary). Of these, 76.5% were intact follicles with a complete single layer of granulose cells surrounded by the basement membrane, and their ultrastructures appeared this way in situ. Follicles (351 to 500 microm in diameter) were cultured in vitro, and 46.67% of them survived after 5 days. Ultrastructural examination showed that elongated mitochondria forming a ring were distributed to the periphery of the oocyte, the Golgi was oriented with the maturing face toward the granulosa cell layer, and the oocyte plasma membrane presented a few short microvilli lying on the oocyte surface, which confirmed that the surviving follicles were developmental. These results suggest that a simple, rapid, effective enzymatic method can be used to isolate a great number of intact primary follicles from the hen ovary.