Jeremy B. Yoder

Candidate genes and genetic architecture of symbiotic and agronomic traits revealed by whole-genome, sequence-based association genetics in Medicago truncatula.

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 Medicago truncatula. 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 SERK2, MtnodGRP3, MtMMPL1, NFP, CaML3, MtnodGRP3A and flowering time gene MtFD as well as uncharacterized genes that become candidates for further molecular characterization. By comparing sequence-based candidates to candidates identified by in silico 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 in silico 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.

Genomic Signature of Adaptation to Climate in Medicago truncatula

Local adaptation and adaptive clines are pervasive in natural plant populations, yet the effects of these types of adaptation on genomic diversity are not well understood. With a data set of 202 accessions of Medicago truncatula genotyped at almost 2 million single nucleotide polymorphisms, we used mixed linear models to identify candidate loci responsible for adaptation to three climatic gradients—annual mean temperature (AMT), precipitation in the wettest month (PWM), and isothermality (ITH)—representing the major axes of climate variation across the species’ range. Loci with the strongest association to these climate gradients tagged genome regions with high sequence similarity to genes with functional roles in thermal tolerance, drought tolerance, or resistance to herbivores of pathogens. Genotypes at these candidate loci also predicted the performance of an independent sample of plant accessions grown in climate-controlled conditions. Compared to a genome-wide sample of randomly drawn reference SNPs, candidates for two climate gradients, AMT and PWM, were significantly enriched for genic regions, and genome segments flanking genic AMT and PWM candidates harbored less nucleotide diversity, elevated differentiation between haplotypes carrying alternate alleles, and an overrepresentation of the most common haplotypes. These patterns of diversity are consistent with a history of soft selective sweeps acting on loci underlying adaptation to climate, but not with a history of long-term balancing selection.

Phylogenetic Signal Variation in the Genomes of Medicago (Fabaceae)

Genome-scale data offer the opportunity to clarify phylogenetic relationships that are difficult to resolve with few loci, but they can also identify genomic regions with evolutionary history distinct from that of the species history. We collected whole-genome sequence data from 29 taxa in the legume genus Medicago, then aligned these sequences to the Medicago truncatula reference genome to confidently identify 87 596 variable homologous sites. We used this data set to estimate phylogenetic relationships among Medicago species, to investigate the number of sites needed to provide robust phylogenetic estimates and to identify specific genomic regions supporting topologies in conflict with the genome-wide phylogeny. Our full genomic data set resolves relationships within the genus that were previously intractable. Subsampling the data reveals considerable variation in phylogenetic signal and power in smaller subsets of the data. Even when sampling 5000 sites, no random sample of the data supports a topology identical to that of the genome-wide phylogeny. Phylogenetic relationships estimated from 500-site sliding windows revealed genome regions supporting several alternative species relationships among recently diverged taxa, consistent with the expected effects of deep coalescence or introgression in the recent history of Medicago.

Estimating heritability using genomic data

Summary n nHeritability (h2) represents the potential for short-term response of a quantitative trait to selection. Unfortunately, estimating h2 through traditional crossing experiments is not practical for many species, and even for those in which mating can be manipulated, it may not be possible to assay them in ecologically relevant environments. nWe evaluated an approach, GCTA, that uses relatedness estimated from genomic data to estimate the proportion of phenotypic variance due to genotyped SNPs, which can be used to infer h2. Using phenotypic and genotypic data from eight replicates of experimentally grown plants of the annual legume Medicago truncatula, we examined how h2 estimates from GCTA (h2GCTA) related to traditional estimates of heritability (clonal repeatability for these inbred lines). Further, we examined how h2GCTA estimates were affected by SNP number, minor allele frequency, the number of individuals assayed and the exclusion of causative SNPs. nWe found that the average h2GCTA estimates for each trait made with the full data set (>5xa0million SNPs, 200 individuals) were strongly correlated (rxa0=xa00·99) with estimates of clonal repeatability. However, this result masks considerable variation among replicate estimates of h2GCTA, even in relatively uniform greenhouse conditions. h2GCTA estimates with 250xa0000 and 25xa0000 SNPs were very similar to those obtained with >5xa0million SNPs, but with 2500 SNPs, h2GCTA were lower and had higher variance than those with ≥25xa0k SNPs. h2GCTA estimates were slightly lower when only common SNPs were used. Excluding putatively causative SNPs had little effect on the estimates of h2GCTA, suggesting that genotyping putatively causative SNPs is not necessary to obtain accurate estimates of h2. The number of accessions sampled had the greatest effect on h2GCTA estimates, and variance greatly increased as fewer accessions were included. With only 50 accessions sampled, the range of h2GCTA ranged from 0 to 1 for all traits. nThese results indicate that the GCTA method may be useful for estimating h2 using data sets of a size that are available from reduced-representation genotyping but that hundreds of individuals may need to be sampled to obtain robust estimates of h2.

Queer in STEM: Workplace Experiences Reported in a National Survey of LGBTQA Individuals in Science, Technology, Engineering, and Mathematics Careers.

ABSTRACT A survey of individuals working in science, technology, engineering, and mathematics (STEM) fields who identify as lesbian, gay, bisexual, trans*, queer, or asexual (LGTBQA) was administered online in 2013. Participants completed a 58-item questionnaire to report their professional areas of expertise, levels of education, geographic location, and gender and sexual identities and rated their work and social communities as welcoming or hostile to queer identities. An analysis of 1,427 responses to this survey provided the first broad portrait of this population, and it revealed trends related to workplace practices that can inform efforts to improve queer inclusivity in STEM workplaces.

Sanctions, partner recognition, and variation in mutualism

Mutualistic interactions can be stabilized against invasion by noncooperative individuals by putting such “cheaters” at a selective disadvantage. Selection against cheaters should eliminate genetic variation in partner quality—yet such variation is often found in natural populations. One explanation for this paradox is that mutualism outcomes are determined not only by responses to partner performance but also by partner signals. Here, we build a model of coevolution in a symbiotic mutualism, in which hosts’ ability to sanction noncooperative symbionts and recognition of symbiont signals are determined by separate loci, as are symbionts’ cooperation and expression of signals. In the model, variation persists without destabilizing the interaction, in part because coevolution of symbiont signals and host recognition is altered by the coevolution of sanctions and cooperation, and vice versa. Individual-based simulations incorporating population structure strongly corroborate these results. The dual systems of sanctions and partner recognition converge toward conditions similar to some economic models of mutualistic symbiosis, in which hosts offering the right incentives to potential symbionts can initiate symbiosis without screening for partner quality. These results predict that mutualists can maintain variation in recognition of partner signals or in the ability to sanction noncooperators without destabilizing mutualism, and they reinforce the notion that studies of mutualism should consider communication between partners as well as the exchange of benefits.

Effects of Gene Action, Marker Density, and Timing of Selection on the Performance of Landscape Genomic Scans of Local Adaptation

Abstract Genomic “scans” to identify loci that contribute to local adaptation are becoming increasingly common. Many methods used for such studies have assumed that local adaptation is created by loci experiencing antagonistic pleiotropy (AP) and that the selected locus itself is assayed, and few consider how signals of selection change through time. However, most empirical data sets have marker density too low to assume that a selected locus itself is assayed, researchers seldom know when selection was first imposed, and many locally adapted loci likely experience not AP but conditional neutrality (CN). We simulated data to evaluate how these factors affect the performance of tests for genotype‐environment association (GEA). We found that 3 types of regression‐based analyses (linear models, mixed linear models, and latent factor mixed models) and an implementation of BayEnv all performed well, with high rates of true positives and low rates of false positives, when the selected locus experienced AP, and when the selected locus was assayed directly. However, all tests had reduced power to detect loci experiencing CN, and the probability of detecting associations was sharply reduced when physically linked rather than causative loci were sampled. AP also maintained detectable GEAs much longer than CN. Our analyses suggest that if local adaptation is often driven by loci experiencing CN, genome‐scan methods will have limited capacity to find loci responsible for local adaptation.

Coming out: the experience of LGBT+ people in STEM

Continuing with our Q&A series discussing issues of diversity in STEM fields, Genome Biology spoke with three openly LGBT+ researchers on their experiences in biology.

Sanctions, partner recognition, and variation in mutualistic symbiosis

Mutualistic interactions can be stabilized against invasion by non-cooperative individuals by putting such “cheaters” at a selective disadvantage. Selection against cheaters should eliminate genetic variation in partner quality — yet such variation is often found in natural populations. One explanation for this paradox is that mutualism outcomes are determined not only by responses to partner performance, but also by partner signals. Here, we build a model of coevolution in a symbiotic mutualism, in which hosts’ ability to sanction non-cooperative symbionts and ability to recognize symbiont signals are determined by separate loci, as are symbionts’ cooperation and expression of signals. In the model, variation persists without destabilizing the interaction, in part because coevolution of symbiont signals and host recognition is altered by the coevolution of sanctions and cooperation, and vice-versa. Individual-based simulations incorporating population structure strongly corroborate these results. The dual systems of sanctions and partner recognition converge toward conditions similar to some economic models of mutualistic symbiosis in which hosts offering the right incentives to potential symbionts can initiate symbiosis without screening for partner quality. These results predict that mutualists can maintain variation in recognition of partner signals, or in the ability to sanction non-cooperators, without destabilizing mutualism, and reinforce the notion that studies of mutualism should consider communication between partners as well as the exchange of benefits.