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 more computing time. Nevertheless, the differences diminished when \u3e5,000 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 \u3e3,000 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

Search for light dark matter from atmosphere in PandaX-4T

We report a search for light dark matter produced through the cascading decay of $\eta$ mesons, which are created as a result of inelastic collisions between cosmic rays and Earth's atmosphere. We introduce a new and general framework, publicly accessible, designed to address boosted dark matter specifically, with which a full and dedicated simulation including both elastic and quasi-elastic processes of Earth attenuation effect on the dark matter particles arriving at the detector is performed. In the PandaX-4T commissioning data of 0.63 tonne$\cdot$year exposure, no significant excess over background is observed. The first constraints on the interaction between light dark matter generated in the atmosphere and nucleus through a light scalar mediator are obtained. The lowest excluded cross-section is set at $5.9 \times 10^{-37}{\rm cm^2}$ for dark matter mass of $0.1$ MeV$/c^2$ and mediator mass of 300 MeV$/c^2$. The lowest upper limit of $\eta$ to dark matter decay branching ratio is $1.6 \times 10^{-7}$

A Search for Light Fermionic Dark Matter Absorption on Electrons in PandaX-4T

We report a search on a sub-MeV fermionic dark matter absorbed by electrons with an outgoing active neutrino using the 0.63 tonne-year exposure collected by PandaX-4T liquid xenon experiment. No significant signals are observed over the expected background. The data are interpreted into limits to the effective couplings between such dark matter and electrons. For axial-vector or vector interactions, our sensitivity is competitive in comparison to existing astrophysical bounds on the decay of such dark matter into photon final states. In particular, we present the first direct detection limits for an axial-vector (vector) interaction which are the strongest in the mass range from 25 to 45 (35 to 50) keV/c$^2$

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 more computing time. Nevertheless, the differences diminished when \u3e5,000 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 \u3e3,000 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

The FLORAL ORGAN NUMBER4 Gene Encoding a Putative Ortholog of Arabidopsis CLAVATA3 Regulates Apical Meristem Size in Rice

To understand the molecular mechanism regulating meristem development in the monocot rice (Oryza sativa), we describe here the isolation and characterization of three floral organ number4 (fon4) alleles and the cloning of the FON4 gene. The fon4 mutants showed abnormal enlargement of the embryonic and vegetative shoot apical meristems (SAMs) and the inflorescence and floral meristems. Likely due to enlarged SAMs, fon4 mutants produced thick culms (stems) and increased numbers of both primary rachis branches and floral organs. We identified FON4 using a map-based cloning approach and found it encodes a small putatively secreted protein, which is the putative ortholog of the Arabidopsis (Arabidopsis thaliana) CLAVATA3 (CLV3) gene. FON4 transcripts mainly accumulated in the small group of cells at the apex of the SAMs, whereas the rice ortholog of CLV1 (FON1) is expressed throughout the SAMs, suggesting that the putative FON4 ligand might be sequestered as a possible mechanism for rice meristem regulation. Exogenous application of the peptides FON4p and CLV3p corresponding to the CLV3/ESR-related (CLE) motifs of FON4 and CLV3, respectively, resulted in termination of SAMs in rice, and treatment with CLV3p caused consumption of both rice and Arabidopsis root meristems, suggesting that the CLV pathway in limiting meristem size is conserved in both rice and Arabidopsis. However, exogenous FON4p did not have an obvious effect on limiting both rice and Arabidopsis root meristems, suggesting that the CLE motifs of Arabidopsis CLV3 and FON4 are potentially functionally divergent

Influence of Microgravity on the Concentration of Circulating Primordial Germ Cells in Silky Chicken Offspring

This study was performed to examine the implications of microgravity on circulating primordial germ cells (PGCs) of the offspring of silky chickens at stages 13 to 17. China's ShenZhou-3 unmanned spaceship was launched on March 25, 2002, at 22: 00 Beijing time (14: 00GMT). The spaceship carried nine fertilized silky chicken eggs (F0) to test the reliability of the life-support system in the space environment. One female and two male chickens were born from these eggs. The three chickens mated naturally and F1 fertilized eggs were collected. Blood was collected from the dorsal aorta or the marginal vein of the embryos at stages 13 to 17, and the number of circulating PGCs of F1 offspring was counted. A similar experimental protocol was performed for the control group (C1 and C2 group). No differences were observed except at stage 15, when the F1 offspring of the flight group (F0 group) showed higher PGC concentrations than the other treatment groups. These results indicated that microgravity may have little effect on the migration and concentration of PGCs in F1 offspring, perhaps because the flight chickens were raised to maturity on Earth under a gravity of 1×g and had sufficient time to recover. Thus, microgravity appeared to have little effect on the PGC concentrations of F1 offspring of silky chickens during circulating stages 13 to 17

Comparison of SNP spacing between two Dairy 5K SNP chips.

<p>The X-axis represents chromosomes, where 30 = chromosome X. SNP spacing is measured as the square root of the average sum of squares of all the distances between two SNPs.</p