Trait Linkage Research Articles

Local haplotyping reveals insights into the genetic control of flowering time variation in wild and domesticated soybean.

The timing of flowering in soybean [Glycine max(L.) Merr.], a key legume crop, is influenced by many factors, including daylight length or photoperiodic sensitivity, that affect crop yield, productivity, and geographical adaptation. Despite its importance, a comprehensive understanding of the local linkage landscape and allelic diversity within regions of the genome influencing flowering and contributing to phenotypic variation in subpopulations has been limited. This study addresses these gaps by conducting an in-depth trait association and linkage analysis coupled with local haplotyping using advanced bioinformatics tools, including crosshap, to characterize genomic variation using a pangenome dataset representing 915 domesticated and wild-type individuals. The association analysis identified eight significant loci on seven chromosomes. Moving beyond traditional association analysis, local haplotyping of targeted regions on chromosomes 6 and 20 identified distinct haplotype structures, variation patterns, and genomic candidates influencing flowering in subpopulations. These results suggest the action of a network of genomic candidates influencing flowering time and an untapped reservoir of genomic variation for this trait in wild germplasm. Notably, GlymaLee.20G147200 on chromosome 20 was identified as a candidate gene that may cause delayed flowering in soybean, potentially through histone modifications of floral repressor loci as seen in Arabidopsis thaliana (L.) Heynh. These findings support future functional validation of haplotype-based alleles for marker-assisted breeding and genomic selection to enhance latitude adaptability of soybean without compromising yield.

Historic breeding practices contribute to germplasm divergence in leaf specialized metabolism and ecophysiology in cultivated sunflower (Helianthus annuus).

The use of hybrid breeding systems to increase crop yields has been the cornerstone of modern agriculture and is exemplified in the breeding and improvement of cultivated sunflower (Helianthus annuus). However, it is poorly understood what effect supporting separate breeding pools in such systems, combined with continued selection for yield, may have on leaf ecophysiology and specialized metabolite variation. We analyzed 288 lines of cultivated H. annuus to examine the genomic basis of several specialized metabolites and agronomically important traits across major heterotic groups. Heterotic group identity supports phenotypic divergences between fertility restoring and cytoplasmic male-sterility maintainer lines in leaf ecophysiology and specialized metabolism. However, the divergence is not associated with physical linkage to nuclear genes that support current hybrid breeding practices in cultivated H. annuus. Additionally, we identified four genomic regions associated with leaf ecophysiology and specialized metabolism that colocalize with previously identified QTLs for quantitative self-compatibility traits and with S-protein homolog (SPH) proteins, a recently discovered family of proteins associated with self-incompatibility and self/nonself recognition in Papaver rhoeas (common poppy) with suggested conserved downstream mechanisms among eudicots. Further work is necessary to confirm the self-incompatibility mechanisms in cultivated H. annuus and their relationship to the integrative and polygenic architecture of leaf ecophysiology and specialized metabolism in cultivated sunflower. However, because self-compatibility is a derived quantitative trait in cultivated H. annuus, trait linkage to divergent phenotypic traits may have partially arisen as a potential unintended consequence of historical breeding practices and selection for yield.

Genetic architecture reconciles linkage and association studies of complex traits.

Linkage studies have successfully mapped loci underlying monogenic disorders, but mostly failed when applied to common diseases. Conversely, genome-wide association studies (GWASs) have identified replicable associations between thousands of SNPs and complex traits, yet capture less than half of the total heritability. In the present study we reconcile these two approaches by showing that linkage signals of height and body mass index (BMI) from 119,000 sibling pairs colocalize with GWAS-identified loci. Concordant with polygenicity, we observed the following: a genome-wide inflation of linkage test statistics; that GWAS results predict linkage signals; and that adjusting phenotypes for polygenic scores reduces linkage signals. Finally, we developed a method using recombination rate-stratified, identity-by-descent sharing between siblings to unbiasedly estimate heritability of height (0.76 ± 0.05) and BMI (0.55 ± 0.07). Our results imply that substantial heritability remains unaccounted for by GWAS-identified loci and this residual genetic variation is polygenic and enriched near these loci.

Sex-specific differences in the genetic and environmental effects on cardiac phenotypic variation assessed by echocardiography

The drivers of sexual dimorphism in heart failure phenotypes are currently poorly understood. Divergent phenotypes may result from differences in heritability and genetic versus environmental influences on the interplay of cardiac structure and function. To assess sex-specific heritability and genetic versus environmental contributions to variation and inter-relations between echocardiography traits in a large community-based cohort. We studied Framingham Heart Study participants of Offspring Cohort examination 8 (2005–2008) and Third Generation Cohort examination 1 (2002–2005). Five cardiac traits and six functional traits were measured using standardized echocardiography. Sequential Oligogenic Linkage Analysis Routines (SOLAR) software was used to perform singular and bivariate quantitative trait linkage analysis. In our study of 5674 participants (age 49 ± 15 years; 54% women), heritability for all traits was significant for both men and women. There were no significant differences in traits between men and women. Within inter-trait correlations, there were two genetic, and four environmental trait pairs with sex-based differences. Within both significant genetic trait pairs, men had a positive relation, and women had no significant relation. We observed significant sex-based differences in inter-trait genetic and environmental correlations between cardiac structure and function. These findings highlight potential pathways of sex-based divergent heart failure phenotypes.

Network-based quantitative trait linkage analysis of microbiome composition in inflammatory bowel disease families.

Introduction: Inflammatory bowel disease (IBD) is characterized by a dysbiosis of the gut microbiome that results from the interaction of the constituting taxa with one another, and with the host. At the same time, host genetic variation is associated with both IBD risk and microbiome composition. Methods: In the present study, we defined quantitative traits (QTs) from modules identified in microbial co-occurrence networks to measure the inter-individual consistency of microbial abundance and subjected these QTs to a genome-wide quantitative trait locus (QTL) linkage analysis. Results: Four microbial network modules were consistently identified in two cohorts of healthy individuals, but three of the corresponding QTs differed significantly between IBD patients and unaffected individuals. The QTL linkage analysis was performed in a sub-sample of the Kiel IBD family cohort (IBD-KC), an ongoing study of 256 German families comprising 455 IBD patients and 575 first- and second-degree, non-affected relatives. The analysis revealed five chromosomal regions linked to one of three microbial module QTs, namely on chromosomes 3 (spanning 10.79 cM) and 11 (6.69 cM) for the first module, chr9 (0.13 cM) and chr16 (1.20 cM) for the second module, and chr13 (19.98 cM) for the third module. None of these loci have been implicated in a microbial phenotype before. Discussion: Our study illustrates the benefit of combining network and family-based linkage analysis to identify novel genetic drivers of microbiome composition in a specific disease context.

Genomic prediction in plants: opportunities for ensemble machine learning based approaches.

Background: Many studies have demonstrated the utility of machine learning (ML) methods for genomic prediction (GP) of various plant traits, but a clear rationale for choosing ML over conventionally used, often simpler parametric methods, is still lacking. Predictive performance of GP models might depend on a plethora of factors including sample size, number of markers, population structure and genetic architecture. Methods: Here, we investigate which problem and dataset characteristics are related to good performance of ML methods for genomic prediction. We compare the predictive performance of two frequently used ensemble ML methods (Random Forest and Extreme Gradient Boosting) with parametric methods including genomic best linear unbiased prediction (GBLUP), reproducing kernel Hilbert space regression (RKHS), BayesA and BayesB. To explore problem characteristics, we use simulated and real plant traits under different genetic complexity levels determined by the number of Quantitative Trait Loci (QTLs), heritability ( h 2 and h 2 e ), population structure and linkage disequilibrium between causal nucleotides and other SNPs. Results: Decision tree based ensemble ML methods are a better choice for nonlinear phenotypes and are comparable to Bayesian methods for linear phenotypes in the case of large effect Quantitative Trait Nucleotides (QTNs). Furthermore, we find that ML methods are susceptible to confounding due to population structure but less sensitive to low linkage disequilibrium than linear parametric methods. Conclusions: Overall, this provides insights into the role of ML in GP as well as guidelines for practitioners.

Genomic prediction in plants: opportunities for ensemble machine learning based approaches.

Background: Many studies have demonstrated the utility of machine learning (ML) methods for genomic prediction (GP) of various plant traits, but a clear rationale for choosing ML over conventionally used, often simpler parametric methods, is still lacking. Predictive performance of GP models might depend on a plethora of factors including sample size, number of markers, population structure and genetic architecture. Methods: Here, we investigate which problem and dataset characteristics are related to good performance of ML methods for genomic prediction. We compare the predictive performance of two frequently used ensemble ML methods (Random Forest and Extreme Gradient Boosting) with parametric methods including genomic best linear unbiased prediction (GBLUP), reproducing kernel Hilbert space regression (RKHS), BayesA and BayesB. To explore problem characteristics, we use simulated and real plant traits under different genetic complexity levels determined by the number of Quantitative Trait Loci (QTLs), heritability ( h 2 and h 2 e ), population structure and linkage disequilibrium between causal nucleotides and other SNPs. Results: Decision tree based ensemble ML methods are a better choice for nonlinear phenotypes and are comparable to Bayesian methods for linear phenotypes in the case of large effect Quantitative Trait Nucleotides (QTNs). Furthermore, we find that ML methods are susceptible to confounding due to population structure but less sensitive to low linkage disequilibrium than linear parametric methods. Conclusions: Overall, this provides insights into the role of ML in GP as well as guidelines for practitioners.

New Evidence on the Linkage of Population Trends and Species Traits to Long-Term Niche Changes

Despite the assessment of long-term niche dynamics could provide crucial information for investigating species responses to environmental changes, it is a poorly investigated topic in ecology. Here, we present a case study of multi-species niche analysis for 71 common breeding birds in Northern Italy, exploring long-term niche changes from 1992 to 2017 and their relationship with both population trends and species traits. We (i) quantified the realized Grinnellian niche in the environmental space, (ii) compared variations in niche breadth and centroid, (iii) tested niche divergence and conservatism through equivalency and similarity tests, (iv) calculated niche temporal overlap, expansion and unfilling indices, and (v) investigated their association with both population changes and species traits. Results supported niche divergence (equivalency test) for 32% of species, although two-thirds were not supported by the similarity test. We detected a general tendency to adjust the niche centroids towards warmer thermal conditions. Increasing populations were positively correlated with niche expansion, while negatively correlated with niche overlap, albeit at the limit of the significance threshold. We found moderate evidence for a non-random association between niche changes and species traits, especially for body size, clutch size, number of broods per year, inhabited landscape type, and migration strategy. We encourage studies correlating long-term population trends and niche changes with species traits’ information and a specific focus on cause-effect relationship at both the single and multiple-species level.

Non-genetic linkage of personality traits and the divergence of Eastern and Western cultures: association with Hofstede’s cultural dimensions

Non-genetic linkage of personality traits and the divergence of Eastern and Western cultures: association with Hofstede’s cultural dimensions

Analysis of a Multi-Environment Trial for Black Raspberry (Rubus occidentalis L.) Quality Traits.

U.S. black raspberry (BR) production is currently limited by narrowly adapted, elite germplasm. An improved understanding of genetic control and the stability of pomological traits will inform the development of improved BR germplasm and cultivars. To this end, the analysis of a multiple-environment trial of a BR mapping population derived from a cross that combines wild ancestors introgressed with commercial cultivars on both sides of its pedigree has provided insights into genetic variation, genotype-by-environment interactions, quantitative trait loci (QTL), and QTL-by-environment interactions (QEI) of fruit quality traits among diverse field environments. The genetic components and stability of four fruit size traits and six fruit biochemistry traits were characterized in this mapping population following their evaluation over three years at four distinct locations representative of current U.S. BR production. This revealed relatively stable genetic control of the four fruit size traits across the tested production environments and less stable genetic control of the fruit biochemistry traits. Of the fifteen total QTL, eleven exhibited significant QEI. Closely overlapping QTL revealed the linkage of several fruit size traits: fruit mass, drupelet count, and seed fraction. These and related findings are expected to guide further genetic characterization of BR fruit quality, management of breeding germplasm, and development of improved BR cultivars for U.S. production.

Breeding for Economically and Environmentally Sustainable Wheat Varieties: An Integrated Approach from Genomics to Selection.

Simple SummaryThe BREEDWHEAT project has laid the foundation for future commercial varieties by providing new and original modern molecular tools to breeders who have applied them to: (1) efficiently analyze and structure the genetic diversity; (2) decipher traits of agronomical interest including biotic and abiotic resistance and tolerance; (3) develop methodologies to implement genomic and phenomic selection; (4) store and bring tools to the world wheat community to access all of the results and data. This will provide a helping hand to developing new original and adapted wheat varieties that will be more productive in terms of the quantity and quality in the context of sustainable agriculture using less fertilizers, pesticides, fungicides, and water. This would help in tackling the challenges that we have to face, especially with regard to global change.There is currently a strong societal demand for sustainability, quality, and safety in bread wheat production. To address these challenges, new and innovative knowledge, resources, tools, and methods to facilitate breeding are needed. This starts with the development of high throughput genomic tools including single nucleotide polymorphism (SNP) arrays, high density molecular marker maps, and full genome sequences. Such powerful tools are essential to perform genome-wide association studies (GWAS), to implement genomic and phenomic selection, and to characterize the worldwide diversity. This is also useful to breeders to broaden the genetic basis of elite varieties through the introduction of novel sources of genetic diversity. Improvement in varieties particularly relies on the detection of genomic regions involved in agronomical traits including tolerance to biotic (diseases and pests) and abiotic (drought, nutrient deficiency, high temperature) stresses. When enough resolution is achieved, this can result in the identification of candidate genes that could further be characterized to identify relevant alleles. Breeding must also now be approached through in silico modeling to simulate plant development, investigate genotype × environment interactions, and introduce marker–trait linkage information in the models to better implement genomic selection. Breeders must be aware of new developments and the information must be made available to the world wheat community to develop new high-yielding varieties that can meet the challenge of higher wheat production in a sustainable and fluctuating agricultural context. In this review, we compiled all knowledge and tools produced during the BREEDWHEAT project to show how they may contribute to face this challenge in the coming years.

Description of breed ancestry and genetic health traits in arctic sled dog breeds

BackgroundThis study describes the presence and frequency of health traits among three populations of dogs traditionally used for sledding and explores their ancestry and breed composition as provided by the commercially available Embark dog DNA test. The three populations include the purebred Siberian Husky and the admixed populations of Alaskan sled dogs and Polar Huskies. While the Siberian Husky represents a well-established breed with extensive historical and health data, the Alaskan sled dog is less studied but has been the subject of nutritional, physiological, and genetic studies related to ancestry and performance. In contrast, the Polar Husky is a relatively obscure and rare group of dogs used for arctic exploration with very little-known information. The three populations were compared using Embark results, providing new insight into the health traits circulating within the populations and the potential ancestral linkage of the health traits between the sledding populations. Embark results are based upon 228,588 single-nucleotide polymorphisms (SNPs) spanning the canine genome, characterized using a custom-designed Illumina beadchip array.ResultsSpecifically, breed composition was summarized for the two admixed populations with most of the dogs being predominantly categorized as Alaskan husky- type dog or “Supermutt”. Mitochondrial and Y chromosome haplogroups and haplotypes were found with Alaskan sled dogs carrying most of the haplogroups and types found in Siberian and Polar Huskies. Genomic principal component analysis reflected population structure corresponding to breed and substructure within the Alaskan sled dogs related to sprint or distance competition. Genetic markers associated with Alanine Aminotransferase activity, Alaskan Husky Encephalopathy, dilated cardiomyopathy, Collie eye anomaly, degenerative myelopathy, ichthyosis, and factor VII deficiency were identified in the populations of sledding breeds.ConclusionThese results provide a preliminary description of genetic characteristics found in sledding breeds, improving the understanding and care of working sled dogs.

Gene-Environment Joint Linkage and Association Analysis of Arsenic Exposure and Diabetes-Related Traits in the Strong Heart Family Study

BACKGROUND AND AIM: Inorganic arsenic exposure is of increasing interest as a possible risk factor for diabetes, and although there is growing research on potential toxicological mechanisms, the biology remains incompletely understood. METHODS: This is a cross-sectional, gene-environment interaction study using data from n=1,915 participants from 40 pedigrees in the Strong Heart Family Study with measured arsenic exposure (sum of inorganic and methylated urine arsenic species, dichotomized for analysis), age, sex, educational attainment, body mass index, genotypes, family relationships, fasting glucose, HOMA-IR, and HOMA-B. We conducted a multipoint variance components linkage analysis testing for arsenic-locus interactions (GxE) predicting glucose, HOMA-IR, or HOMA-B in separate models, adjusted for potential confounders of the arsenic-outcome relationships. We then tested for SNP-trait associations and SNP GxE in linkage regions. Analyses were implemented using the software Sequential Oligogenic Linkage Analysis Routines (SOLAR). RESULTS:We localized three quantitative trait loci that showed suggestive evidence for differential genetic contributions to the outcome in the presence of higher vs. lower inorganic arsenic exposure. The first was an interaction locus for fasting glucose at chromosome 17q22 (LOD 2.6 in GxE model, LOD 1.5 in standard linkage model without interaction). The second was an interaction locus for HOMA-IR at chromosome 7p13 (LOD 2.0 in GxE model, 0.4 in the standard linkage model). The third was an interaction locus for HOMA-B at chromosome 18q22.2 (LOD 2.0 in GxE model, LOD 1.7 in standard linkage model). Several SNPs showed Bonferroni-significant associations with diabetes-related traits, with the strongest evidence for rs4793861 in the MSI2 gene for glucose; however, no tested SNPs showed Bonferroni-significant interactions with arsenic. CONCLUSIONS:We found suggestive evidence for different locus-specific contributions to diabetes trait variability in the presence of higher inorganic arsenic exposure, and SNPs in these regions that were associated with diabetes traits. Further research (e.g., linkage fine-mapping) might further elucidate this biology. KEYWORDS: Environmental Epidemiology, Toxicology, Heavy Metals

Identification of a Novel Locus for Gait Speed Decline With Aging: The Long Life Family Study.

Gait speed is a powerful indicator of health with aging. Potential genetic contributions to gait speed and its decline with aging are not well defined. We determined the heritability of and potential genetic regions underlying change in gait speed using longitudinal data from 2379 individuals belonging to 509 families in the Long Life Family Study (mean age 64 ± 12, range 30-110 years; 45% men). Gait speed was measured over 4 m at baseline and follow-up (7 ± 1 years). Quantitative trait linkage analyses were completed using pedigree-based maximum likelihood methods with logarithm of the odds (LOD) scores greater than 3.0, indicating genome-wide significance. We also performed linkage analysis in the top 10% of families contributing to LOD scores to allow for heterogeneity among families (HLOD). Data were adjusted for age, sex, height, and field center. At baseline, 26.9% of individuals had "slow" gait speed less than 1.0 m/s (mean: 1.1 ± 0.2 m/s) and gait speed declined at a rate of -0.02 ± 0.03 m/s per year (p < .0001). Baseline and change in gait speed were significantly heritable (h2 = 0.24-0.32, p < .05). We did not find significant evidence for linkage for baseline gait speed; however, we identified a significant locus for change in gait speed on chromosome 16p (LOD = 4.2). A subset of 21 families contributed to this linkage peak (HLOD = 6.83). Association analyses on chromosome 16 showed that the strongest variant resides within the ADCY9 gene. Further analysis of the chromosome 16 region, and ADCY9 gene, may yield new insight on the biology of mobility decline with aging.

Functional Traits in Lichen Ecology: A Review of Challenge and Opportunity.

Community ecology has experienced a major transition, from a focus on patterns in taxonomic composition, to revealing the processes underlying community assembly through the analysis of species functional traits. The power of the functional trait approach is its generality, predictive capacity such as with respect to environmental change, and, through linkage of response and effect traits, the synthesis of community assembly with ecosystem function and services. Lichens are a potentially rich source of information about how traits govern community structure and function, thereby creating opportunity to better integrate lichens into ‘mainstream’ ecological studies, while lichen ecology and conservation can also benefit from using the trait approach as an investigative tool. This paper brings together a range of author perspectives to review the use of traits in lichenology, particularly with respect to European ecosystems from the Mediterranean to the Arctic-Alpine. It emphasizes the types of traits that lichenologists have used in their studies, both response and effect, the bundling of traits towards the evolution of life-history strategies, and the critical importance of scale (both spatial and temporal) in functional trait ecology.

The impact of endogenous Avian Leukosis Viruses (ALVE) on production traits in elite layer lines

Avian Leukosis Virus subgroup E (ALVE) integrations are endogenous retroviral elements found in the chicken genome. The presence of ALVE has been reported to have negative impacts on multiple traits, including egg production and body weight. The recent development of rapid, inexpensive and specific ALVE detection methods has facilitated their characterization in elite commercial egg production lines across multiple generations. The presence of 20 ALVE was examined in 8 elite lines, from 3 different breeds. Seventeen of these ALVE (85%) were informative and found to be segregating in at least one of the lines. To test for an association between specific ALVE inserts and traits, a large genotype by phenotype study was undertaken. Genotypes were obtained for 500 to 1500 males per line, and the phenotypes used were sire-daughter averages. Phenotype data were analyzed by line with a linear model that included the effects of generation, ALVE genotype and their interaction. If genotype effect was significant, the number of ALVE copies was fitted as a regression to estimate additive ALVE gene substitution effect. Significant associations between the presence of specific ALVE inserts and 18 commercially relevant performance and egg quality traits, including egg production, egg weight and albumen height, were observed. When an ALVE was segregating in more than one line, these associations did not always have the same impact (negative, positive or none) in each line. It is hypothesized that the presence of ALVE in the chicken genome may influence production traits by 3 mechanisms: viral protein production may modulate the immune system and impact overall production performance (virus effect); insertional mutagenesis caused by viral integration may cause direct gene alterations or affect gene regulation (gene effect); or the integration site may be within or adjacent to a quantitative trait region which impacts a performance trait (linkage disequilibrium, marker effect).

ABO B gene is associated with introversion personality tendancies through linkage with dopamine beta hydroxylase gene

Although personality is thought to be 50% heritable, consensus has not been reached about the specific genes involved, interest in genes affecting personality and behavior continues because of the linkage of personality traits with both physical and mental illness. One hundred and twenty years of study of the ABO blood types and the genes causing them has led to more precise assignment of genotype-phenotype linkage. Countries like Japan and Korea with 100years history of study of ABO blood groups and personality as well as other countries have published research with no consensus. Introversion is a well-studied personality trait with 50% genetic causation. Dopamine system has been linked to the introversion-extroversion spectrum with high dopamine linked to introversion. Dopamine beta hydroxylase determines the ratio of dopamine to norepinephrine. Dopamine beta hydroxylase gene, DBH, is in linkage disequilibrium with ABO gene thus offering insight into the ABO findings related to personality traits. ABO blood groups have been studied with some but not all studies finding type AB associated with introversion. One explanation for the inconsistent replication of findings could be that genotypes would show more differences than phenotypes. ABO A allele, ABO B allele or the synergism of both alleles could be the driver of introversion trait in ABO AB. Patients in an obstetric gynecology practice appeared to demonstrate a tendancy to introversion in phenotype ABO B. Since we had access to ABO blood phenotypes instead of genotypes of our obstetric gynecology office patients, we hypothesized that phenotype ABO AB when compared with ABO O would be associated with higher introversion since these phenotypes were also genotypes and since ABO O unlike ABO AB is not found associated with introversion in extant research. Though ABO B allele was observed to be the likely cause of introversion in patients and since we had available only phenotypes of ABO to use in search of genetic allele that causes introversion trait, ABO AB needs to be dissected to see whether ABO A allele or ABO B allele is the driver of the introspective trait. Based on the literature and on observation, the hypothesis was that ABO B is the driver of introversion trait. A pilot student of 225 obstetric gynecology patients using the online NPA personality test showed higher scores for introversion tendancies in ABO AB compared to ABO O and when ABO AB was compared to ABO A. This supports ABO B as the driver of introversion tendancies in the ABO gene. Studies using ABO genotypes instead of ABO phenotypes should provide further support for this hypothesis. Given the support for introversion tendancies in personality caused by higher dopamine genetically with dopamine beta hydroxylase low activity and this dopamine state being common mechanism for such conditions as schizophrenia and autism, continued discoveries of genes that impact this state will lead to many health implications.

Functional Paleoecology and the Pollen-Plant Functional Trait Linkage

Paleoecological investigations using the pollen-plant functional trait linkage are increasing in value as new insights to past ecological function and dynamics are revealed. These retrospective approaches link pollen sequences to plant functional trait measurements to reveal long-term changes in ecosystem properties that are difficult to resolve using traditional paleoecological methods. Despite these methodological improvements and the newfound perspectives, there has yet to be thorough testing of whether transforming pollen to ecological function tracks functional trait distributions in geographic space. We assess this in North America by linking surface pollen samples to measurements of three functional traits that represent major axes of plant ecological strategy. Pollen-derived estimates of function were first used to investigate occupied trait space at different scales. These estimates were used to reconstruct the latitudinal functional diversity gradient of North America, and results were compared to the continent’s functional diversity gradient estimated from tree assemblages and gradients based on pollen richness and evenness. Results indicated that the patterns in pollen-based function sufficiently track ecological function in trait and geographic space and the macroecological biodiversity pattern was reconstructed, although there were minor differences between the slopes of the functional diversity and each of the pollen index gradients. Taken together, the outcomes of our investigation indicate reliability in extending the pollen-plant functional trait linkage into deeper time, at least for examining North American functional dynamics.

Genome-Wide Linkage Analysis Identifies a Novel Locus for Grip Strength: The Long Life Family Study

Grip strength declines with aging, is an indicator of overall health, and predicts mortality among older adults. Herein, we quantified the genetic contributions to grip strength among 4534 individuals, belonging to 574 families in the Long Life Family Study (age 70.3 ± 15.7, range 24–110 years; 56% women). Grip strength was measured using a handheld dynamometer, and the maximum value of two trials in the stronger hand was used. Quantitative trait linkage analysis was completed using pedigree-based maximum-likelihood methods with logarithm of the odds (LOD) scores >3.0 indicating genome-wide significance. Linkage analysis in the top 10% of families contributing to LOD scores was also performed to allow for heterogeneity among families (HLOD). All analyses were adjusted for age, sex, height and field center. Grip strength was lower per one year of older age (β: -0.34 ± 0.01kg, p <0.01), and overall: 24.3% of men and 19.3% of women had “low” grip strength according to European Working Group on Sarcopenia definitions. Grip strength was highly heritable (h2 = 0.37, p<0.05). We identified a potentially novel locus for grip strength on chromosome 18p (LOD 3.18) with 26 families contributing to this linkage peak (HLOD = 10.94). Deep sequencing of the chromosome 18 region may yield fundamental insight on the biology of muscle weakness with aging, and may help identify novel therapeutic targets for treatment and prevention of this common condition.

Linkage of fine and coarse litter traits to soil microbial characteristics and nitrogen mineralization across topographic positions in a temperate natural forest

Topographic positions within a natural forest can considerably influence litter traits, soil microbial characteristics, and nitrogen (N) mineralization, causing plant–soil feedbacks. Despite the high abundance of coarse litter (woody debris and coarse roots) in forest ecosystems, most studies have focused on linkages between fine litter (leaves and fine roots) and N dynamics and/or the soil microbial community. We investigated the association of fine and coarse litter with soil microbial biomass, community structure, and N mineralization at upper and lower slope positions on sedimentary rocks in a temperate forest dominated by Fagus crenata. Greater coarse litter abundance and litter C-to-N ratio, and lower soil microbial biomass, bacterial abundance, and N mineralization potential were found in upper positions than in lower positions. Among litter traits, coarse litter abundance and litter C-to-N ratio were the best predictors of the microbial biomass and fungal-to-bacterial dominance, possibly due to differences in climatic stress among plant communities. Microbial traits were the best predictors of N mineralization potential. Fine litter traits and coarse litter abundance are likely linked to soil microbial characteristics and N mineralization in natural forests with variable topography.