One-dimensional fluids with second nearest-neighbor interactions

As is well known, one-dimensional systems with interactions restricted to first nearest neighbors admit a full analytically exact statistical-mechanical solution. This is essentially due to the fact that the knowledge of the first nearest-neighbor probability distribution function, $p_1(r)$, is enough to determine the structural and thermodynamic properties of the system. On the other hand, if the interaction between second nearest-neighbor particles is turned on, the analytically exact solution is lost. Not only the knowledge of $p_1(r)$ is not sufficient anymore, but even its determination becomes a complex many-body problem. In this work we systematically explore different approximate solutions for one-dimensional second nearest-neighbor fluid models. We apply those approximations to the square-well and the attractive two-step pair potentials and compare them with Monte Carlo simulations, finding an excellent agreement.Comment: 26 pages, 12 figures; v2: more references adde

StantonGeddes2013_Medicago_nodule_occupancy

Spreadsheet of nodule occupancy data, described in more detail in README file

R script for nodule occupancy accession phenotypes

R script to calculate rhizobia strain nodule occupancy for genome-wide association analysis

StantonGeddes2013_Medicago_plant_data

Spreadsheet of plant phenotype data collected in greenhouse experiment for genome-wide association study. Column headings are: block, pot (individual plant ID), trtmnt (rhizobia treatment applied to the plant, either "rhz_12" for a mixture of two rhizobia strains, or "control" for no rhizobia), HM_accession (Medicago HapMap accession), height_1 (height recorded at about two weeks), leaves_1 (number of leaves at about two weeks), height_2 (intermediate height measurement), height_3 (final height before harvest), branch_3 (number of branches on plant before harvest), flowering date (date first flower observed), nodule_above (number of nodules counted in top 5 cm of roots), nodule_below (number of nodules below 5 cm of root growth). More information is available in the R markdown script "StantonGeddes2013_script.Rmd" that accompanies this file

Additional file 12: of Hybrid assembly with long and short reads improves discovery of gene family expansions

TE genes in Medicago assemblies. (PDF 39 kb

Candidate Genes and Genetic Architecture of Symbiotic and Agronomic Traits Revealed by Whole-Genome, Sequence-Based Association Genetics in <i>Medicago truncatula</i>

<div><p>Genome-wide association study (GWAS) has revolutionized the search for the genetic basis of complex traits. To date, GWAS have generally relied on relatively sparse sampling of nucleotide diversity, which is likely to bias results by preferentially sampling high-frequency SNPs not in complete linkage disequilibrium (LD) with causative SNPs. To avoid these limitations we conducted GWAS with >6 million SNPs identified by sequencing the genomes of 226 accessions of the model legume <i>Medicago truncatula</i>. We used these data to identify candidate genes and the genetic architecture underlying phenotypic variation in plant height, trichome density, flowering time, and nodulation. The characteristics of candidate SNPs differed among traits, with candidates for flowering time and trichome density in distinct clusters of high linkage disequilibrium (LD) and the minor allele frequencies (MAF) of candidates underlying variation in flowering time and height significantly greater than MAF of candidates underlying variation in other traits. Candidate SNPs tagged several characterized genes including nodulation related genes <i>SERK2</i>, <i>MtnodGRP3</i>, <i>MtMMPL1</i>, <i>NFP</i>, <i>CaML3</i>, <i>MtnodGRP3A</i> and flowering time gene <i>MtFD</i> as well as uncharacterized genes that become candidates for further molecular characterization. By comparing sequence-based candidates to candidates identified by <i>in silico</i> 250K SNP arrays, we provide an empirical example of how reliance on even high-density reduced representation genomic makers can bias GWAS results. Depending on the trait, only 30–70% of the top 20 <i>in silico</i> array candidates were within 1 kb of sequence-based candidates. Moreover, the sequence-based candidates tagged by array candidates were heavily biased towards common variants; these comparisons underscore the need for caution when interpreting results from GWAS conducted with sparsely covered genomes.</p></div

MAF distribution of genomic and candidate SNPs (minor allele frequency >0.02) identified using sequence data and 250 K SNP arrays.

<p>Shown are (a) all assayed SNPs, (b) sequence-based candidates for height, (c) top 50 candidate SNPs from 100 <i>in silico</i> platforms, and (d) distributions of sequenced based candidates within 1 kb of any of the top 50 <i>in silico</i> candidates.</p

Proportion of variance attributed to accessions and linkage disequilibrium for top 50 candidates SNPs.

<p>Proportion of variance attributed to accessions and linkage disequilibrium for top 50 candidates SNPs.</p

Additional file 14: of Hybrid assembly with long and short reads improves discovery of gene family expansions

Phylogeny for CRP0355 in Medicago assemblies. (PDF 442 kb

Overlap in candidate SNPs identified using sequence data compared to in silico SNP arrays.

<p>Shown are the average number of top 20 and 50 <i>in silico</i> candidate SNPs within 1 and 20 kb of one of the top 200 sequenced-based candidates. Data are from 100 250 K SNP <i>in silico</i> platforms, the minimum and maximum number of tagged sequence candidates is in parentheses.</p