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Life history is defined by traits that reflect key components of fitness, especially those relating to reproduction and survival. Research in life history seeks to unravel the relationships among these traits and understand how life history strategies evolve to maximize fitness. As such, life history research integrates the study of the genetic and developmental mechanisms underlying trait determination with the evolutionary and ecological context of Darwinian fitness. As a leading model organism for molecular and developmental genetics, Caenorhabditis elegans is unmatched in the characterization of life history-related processes, including developmental timing and plasticity, reproductive behaviors, sex determination, stress tolerance, and aging. Building on recent studies of natural populations and ecology, the combination of C. elegans ' historical research strengths with new insights into trait variation now positions it as a uniquely valuable model for life history research. In this review, we summarize the contributions of C. elegans and related species to life history and its evolution. We begin by reviewing the key characteristics of C. elegans life history, with an emphasis on its distinctive reproductive strategies and notable life cycle plasticity. Next, we explore intraspecific variation in life history traits and its underlying genetic architecture. Finally, we provide an overview of how C. elegans has guided research on major life history transitions both within the genus Caenorhabditis and across the broader phylum Nematoda. While C. elegans is relatively new to life history research, significant progress has been made by leveraging its distinctive biological traits, establishing it as a highly cross-disciplinary system for life history studies. [ABSTRACT FROM AUTHOR]

To identify modifier loci underlying variation in body mass index (BMI) in persons with cystic fibrosis (pwCF), we performed a genome-wide association study (GWAS). Utilizing longitudinal height and weight data, along with demographic information and covariates from 4,393 pwCF, we calculated AvgBMIz representing the average of per-quarter BMI Z scores. The GWAS incorporated 9.8M single nucleotide polymorphisms (SNPs) with a minor allele frequency (MAF) > 0.005 extracted from whole-genome sequencing (WGS) of each study subject. We observed genome-wide significant association with a variant in FTO (FaT mass and Obesity-associated gene; rs28567725; p value = 1.21e−08; MAF = 0.41, β = 0.106; n = 4,393 individuals) and a variant within ADAMTS5 (A Disintegrin And Metalloproteinase with ThromboSpondin motifs 5; rs162500; p value = 2.11e−10; MAF = 0.005, β = −0.768; n = 4,085 pancreatic-insufficient individuals). Notably, BMI-associated variants in ADAMTS5 occur on a haplotype that is much more common in African (AFR, MAF = 0.183) than European (EUR, MAF = 0.006) populations (1000 Genomes project). A polygenic risk score (PRS) calculated using 924 SNPs (excluding 17 in FTO) showed significant association with AvgBMIz (p value = 2.2e−16; r2 = 0.03). Association between variants in FTO and the PRS correlation reveals similarities in the genetic architecture of BMI in CF and the general population. Inclusion of Black individuals in whom the single-gene disorder CF is much less common but genomic diversity is greater facilitated detection of association with variants that are in LD with functional SNPs in ADAMTS5. Our results illustrate the importance of population diversity, particularly when attempting to identify variants that manifest only under certain physiologic conditions. [Display omitted] A GWAS reveals that BMI variation in persons with CF (pwCF) is determined by loci operating in the general population (e.g., FTO) and a unique locus containing ADAMTS5. Interaction with exocrine pancreatic status (sufficient or insufficient) in pwCF suggests that ADAMTS5 operates in the context of severe nutritional stress. [ABSTRACT FROM AUTHOR]

Assessment of genomic conservation between humans and pigs at the functional level can improve the potential of pigs as a human biomedical model. To address this, we developed a deep learning-based approach to learn the genomic conservation at the functional level (DeepGCF) between species by integrating 386 and 374 functional profiles from humans and pigs, respectively. DeepGCF demonstrated better prediction performance compared with the previous method. In addition, the resulting DeepGCF score captures the functional conservation between humans and pigs by examining chromatin states, sequence ontologies, and regulatory variants. We identified a core set of genomic regions as functionally conserved that plays key roles in gene regulation and is enriched for the heritability of complex traits and diseases in humans. Our results highlight the importance of cross-species functional comparison in illustrating the genetic and evolutionary basis of complex phenotypes.

To identify addiction genes, we evaluate intravenous self-administration of cocaine or saline in 84 inbred and recombinant inbred mouse strains over 10 days. We integrate the behavior data with brain RNA-seq data from 41 strains. The self-administration of cocaine and that of saline are genetically distinct. We maximize power to map loci for cocaine intake by using a linear mixed model to account for this longitudinal phenotype while correcting for population structure. A total of 15 unique significant loci are identified in the genome-wide association study. A transcriptome-wide association study highlights the Trpv2 ion channel as a key locus for cocaine self-administration as well as identifying 17 additional genes, including Arhgef26, Slc18b1, and Slco5a1. We find numerous instances where alternate splice site selection or RNA editing altered transcript abundance. Our work emphasizes the importance of Trpv2, an ionotropic cannabinoid receptor, for the response to cocaine.

Genome-wide association studies have advanced our understanding of complex traits, but studying how a GWAS variant can affect a specific trait in the human population remains challenging due to environmental variability. Drosophila melanogaster is in this regard an excellent model organism for studying the relationship between genetic and phenotypic variation due to its simple handling, standardized growth conditions, low cost, and short lifespan. The Drosophila Genetic Reference Panel (DGRP) in particular has been a valuable tool for studying complex traits, but proper harmonization and indexing of DGRP phenotyping data is necessary to fully capitalize on this resource. To address this, we created a web tool called DGRPool (dgrpool.epfl.ch), which aggregates phenotyping data of 1034 phenotypes across 135 DGRP studies in a common environment. DGRPool enables users to download data and run various tools such as genome-wide (GWAS) and phenome-wide (PheWAS) association studies. As a proof-of-concept, DGRPool was used to study the longevity phenotype and uncovered both established and unexpected correlations with other phenotypes such as locomotor activity, starvation resistance, desiccation survival, and oxidative stress resistance. DGRPool has the potential to facilitate new genetic and molecular insights of complex traits in Drosophila and serve as a valuable, interactive tool for the scientific community.

Genome-wide association studies (GWASs) have identified thousands of variants for disease risk. These studies have predominantly been conducted in individuals of European ancestries, which raises questions about their transferability to individuals of other ancestries. Of particular interest are admixed populations, usually defined as populations with recent ancestry from two or more continental sources. Admixed genomes contain segments of distinct ancestries that vary in composition across individuals in the population, allowing for the same allele to induce risk for disease on different ancestral backgrounds. This mosaicism raises unique challenges for GWASs in admixed populations, such as the need to correctly adjust for population stratification. In this work we quantify the impact of differences in estimated allelic effect sizes for risk variants between ancestry backgrounds on association statistics. Specifically, while the possibility of estimated allelic effect-size heterogeneity by ancestry (HetLanc) can be modeled when performing a GWAS in admixed populations, the extent of HetLanc needed to overcome the penalty from an additional degree of freedom in the association statistic has not been thoroughly quantified. Using extensive simulations of admixed genotypes and phenotypes, we find that controlling for and conditioning effect sizes on local ancestry can reduce statistical power by up to 72%. This finding is especially pronounced in the presence of allele frequency differentiation. We replicate simulation results using 4,327 African-European admixed genomes from the UK Biobank for 12 traits to find that for most significant SNPs, HetLanc is not large enough for GWASs to benefit from modeling heterogeneity in this way.

STUDY QUESTION Do the genetic determinants of idiopathic severe spermatogenic failure (SPGF) differ between generations? SUMMARY ANSWER Our data support that the genetic component of idiopathic SPGF is impacted by dynamic changes in environmental exposures over decades. WHAT IS KNOWN ALREADY The idiopathic form of SPGF has a multifactorial etiology wherein an interaction between genetic, epigenetic, and environmental factors leads to the disease onset and progression. At the genetic level, genome-wide association studies (GWASs) allow the analysis of millions of genetic variants across the genome in a hypothesis-free manner, as a valuable tool for identifying susceptibility risk loci. However, little is known about the specific role of non-genetic factors and their influence on the genetic determinants in this type of conditions. STUDY DESIGN, SIZE, DURATION Case-control genetic association analyses were performed including a total of 912 SPGF cases and 1360 unaffected controls. PARTICIPANTS/MATERIALS, SETTING, METHODS All participants had European ancestry (Iberian and German). SPGF cases were diagnosed during the last decade either with idiopathic non-obstructive azoospermia (n = 547) or with idiopathic non-obstructive oligozoospermia (n = 365). Case-control genetic association analyses were performed by logistic regression models considering the generation as a covariate and by in silico functional characterization of the susceptibility genomic regions. MAIN RESULTS AND THE ROLE OF CHANCE This analysis revealed 13 novel genetic association signals with SPGF, with eight of them being independent. The observed associations were mostly explained by the interaction between each lead variant and the age-group. Additionally, we established links between these loci and diverse non-genetic factors, such as toxic or dietary habits, respiratory disorders, and autoimmune diseases, which might potentially influence the genetic architecture of idiopathic SPGF. LARGE SCALE DATA GWAS data are available from the authors upon reasonable request. LIMITATIONS, REASONS FOR CAUTION Additional independent studies involving large cohorts in ethnically diverse populations are warranted to confirm our findings. WIDER IMPLICATIONS OF THE FINDINGS Overall, this study proposes an innovative strategy to achieve a more precise understanding of conditions such as SPGF by considering the interactions between a variable exposome through different generations and genetic predisposition to complex diseases. STUDY FUNDING/COMPETING INTEREST(S) This work was supported by the "Plan Andaluz de Investigación, Desarrollo e Innovación (PAIDI 2020)" (ref. PY20_00212, P20_00583), the Spanish Ministry of Economy and Competitiveness through the Spanish National Plan for Scientific and Technical Research and Innovation (ref. PID2020-120157RB-I00 funded by MCIN/ AEI/10.13039/501100011033), and the 'Proyectos I+D+i del Programa Operativo FEDER 2020' (ref. B-CTS-584-UGR20). ToxOmics-Centre for Toxicogenomics and Human Health, Genetics, Oncology and Human Toxicology, is also partially supported by the Portuguese Foundation for Science and Technology (Projects: UIDB/00009/2020; UIDP/00009/2020). The authors declare no competing interests. TRIAL REGISTRATION NUMBER N/A. [ABSTRACT FROM AUTHOR]

Abstract Background Common and rare variants contribute to the etiology of complex traits. However, the extent to which the phenotypic effects of common and rare variants involve shared molecular mediators remains poorly understood. The question is essential to the basic and translational goals of the science of genomics, with critical basic-science, methodological, and clinical consequences. Methods Leveraging the latest release of whole-exome sequencing (WES, for rare variants) and genome-wide association study (GWAS, for common variants) data from the UK Biobank, we developed a metric, the COmmon variant and RAre variant Convergence (CORAC) signature, to quantify the convergence for a broad range of complex traits. We characterized the relationship between CORAC and effective sample size across phenome-wide association studies. Results We found that the signature is positively correlated with effective sample size (Spearman ρ = 0.594, P

Abstract Background Cocaine use (CU) is associated with psychiatric and medical diseases. Little is known about the mechanisms of CU-related comorbidities. Findings from preclinical and clinical studies have suggested that CU is associated with aberrant DNA methylation (DNAm) that may be influenced by genetic variants [i.e., methylation quantitative trait loci (meQTLs)]. In this study, we mapped cis-meQTLs for CU-associated DNAm sites (CpGs) in an HIV-positive cohort (Ntotal = 811) and extended the meQTLs to multiple traits. Results We conducted cis-meQTL analysis for 224 candidate CpGs selected for their association with CU in blood. We identified 7,101 significant meQTLs [false discovery rate (FDR)

Background: Common and rare variants contribute to the etiology of complex traits. However, the extent to which the phenotypic effects of common and rare variants involve shared molecular mediators remains poorly understood. The question is essential to the basic and translational goals of the science of genomics, with critical basic-science, methodological, and clinical consequences. Methods: Leveraging the latest release of whole-exome sequencing (WES, for rare variants) and genome-wide association study (GWAS, for common variants) data from the UK Biobank, we developed a metric, the COmmon variant and RAre variant Convergence (CORAC) signature, to quantify the convergence for a broad range of complex traits. We characterized the relationship between CORAC and effective sample size across phenome-wide association studies. Results: We found that the signature is positively correlated with effective sample size (Spearman ρ = 0.594, P < 2.2e − 16), indicating increased functional convergence of trait-associated genetic variation, across the allele frequency spectrum, with increased power. Sensitivity analyses, including accounting for heteroskedasticity and varying the number of detected association signals, further strengthened the validity of the finding. In addition, consistent with empirical data, extensive simulations showed that negative selection, in line with enhancing polygenicity, has a dampening effect on the convergence signature. Methodologically, leveraging the convergence leads to enhanced association analysis. Conclusions: The presented framework for the convergence signature has important implications for fine-mapping strategies and drug discovery efforts. In addition, our study provides a blueprint for the expectation from future large-scale whole-genome sequencing (WGS)/WES and sheds methodological light on post-GWAS studies. [ABSTRACT FROM AUTHOR]

Epistasis is central in many domains of biology, but it has not yet been proven useful for understanding the etiology of complex traits. This is partly because complex-trait epistasis involves polygenic interactions that are poorly captured in current models. To address this gap, we developed a model called Epistasis Factor Analysis (EFA). EFA assumes that polygenic epistasis can be factorized into interactions between a few epistasis factors (EFs), which represent latent polygenic components of the observed complex trait. The statistical goals of EFA are to improve polygenic prediction and to increase power to detect epistasis, while the biological goal is to unravel genetic effects into more-homogeneous units. We mathematically characterize EFA and use simulations to show that EFA outperforms current epistasis models when its assumptions approximately hold. Applied to predicting yeast growth rates, EFA outperforms the additive model for several traits with large epistasis heritability and uniformly outperforms the standard epistasis model. We replicate these prediction improvements in a second dataset. We then apply EFA to four previously characterized traits in the UK Biobank and find statistically significant epistasis in all four, including two that are robust to scale transformation. Moreover, we find that the inferred EFs partly recover pre-defined biological pathways for two of the traits. Our results demonstrate that more realistic models can identify biologically and statistically meaningful epistasis in complex traits, indicating that epistasis has potential for precision medicine and characterizing the biology underlying GWAS results. Epistasis has been difficult to study in complex traits due to the statistical challenges of fitting polygenic interactions. Here, we develop a model called Epistasis Factor Analysis and demonstrate its utility for modeling epistasis effects in complex traits. [ABSTRACT FROM AUTHOR]

Summary: Assessment of genomic conservation between humans and pigs at the functional level can improve the potential of pigs as a human biomedical model. To address this, we developed a deep learning-based approach to learn the genomic conservation at the functional level (DeepGCF) between species by integrating 386 and 374 functional profiles from humans and pigs, respectively. DeepGCF demonstrated better prediction performance compared with the previous method. In addition, the resulting DeepGCF score captures the functional conservation between humans and pigs by examining chromatin states, sequence ontologies, and regulatory variants. We identified a core set of genomic regions as functionally conserved that plays key roles in gene regulation and is enriched for the heritability of complex traits and diseases in humans. Our results highlight the importance of cross-species functional comparison in illustrating the genetic and evolutionary basis of complex phenotypes.

Background: Cocaine use (CU) is associated with psychiatric and medical diseases. Little is known about the mechanisms of CU-related comorbidities. Findings from preclinical and clinical studies have suggested that CU is associated with aberrant DNA methylation (DNAm) that may be influenced by genetic variants [i.e., methylation quantitative trait loci (meQTLs)]. In this study, we mapped cis-meQTLs for CU-associated DNAm sites (CpGs) in an HIV-positive cohort (Ntotal = 811) and extended the meQTLs to multiple traits. Results: We conducted cis-meQTL analysis for 224 candidate CpGs selected for their association with CU in blood. We identified 7,101 significant meQTLs [false discovery rate (FDR) < 0.05], which mostly mapped to genes involved in immunological functions and were enriched in immune pathways. We followed up the meQTLs using phenome-wide association study and trait enrichment analyses, which revealed 9 significant traits. We tested for causal effects of CU on these 9 traits using Mendelian Randomization and found evidence that CU plays a causal role in increasing hypertension (p-value = 2.35E-08) and decreasing heel bone mineral density (p-value = 1.92E-19). Conclusions: These findings suggest that genetic variants for CU-associated DNAm have pleiotropic effects on other relevant traits and provide new insights into the causal relationships between cocaine use and these complex traits. [ABSTRACT FROM AUTHOR]

Background: Dilated cardiomyopathy (DCM) is a life-threatening heart disease and a common cause of heart failure due to systolic dysfunction and subsequent left or biventricular dilatation. A significant number of cases have a genetic etiology; however, as a complex disease, the exact genetic risk factors are largely unknown, and many patients remain without a molecular diagnosis. Methods: We performed GWAS followed by whole-genome, transcriptome, and immunohistochemical analyses in a spontaneously occurring canine model of DCM. Canine gene discovery was followed up in three human DCM cohorts. Results: Our results revealed two independent additive loci associated with the typical DCM phenotype comprising left ventricular systolic dysfunction and dilatation. We highlight two novel candidate genes, RNF207 and PRKAA2, known for their involvement in cardiac action potentials, energy homeostasis, and morphology. We further illustrate the distinct genetic etiologies underlying the typical DCM phenotype and ventricular premature contractions. Finally, we followed up on the canine discoveries in human DCM patients and discovered candidate variants in our two novel genes. Conclusions: Collectively, our study yields insight into the molecular pathophysiology of DCM and provides a large animal model for preclinical studies. [ABSTRACT FROM AUTHOR]

• Heterozygotes for pathogenic APOA5 variants have highly variable TG levels. • Heterozygosity for such variants is distinct from heterozygous familial hypercholesterolemia. • There is little to no evidence for autosomal dominant hypertriglyceridemia. • Hypertriglyceridemia is genetically complex and is almost never mendelian. Biallelic pathogenic variants in APOA5 are an infrequent cause of familial chylomicronemia syndrome characterized by severe, refractory hypertriglyceridemia (HTG), and fasting plasma triglyceride (TG) >10 mmol/L (>875 mg/dL). The TG phenotype of heterozygous individuals with one copy of a pathogenic APOA5 variant is less familiar. We evaluated the longitudinal TG phenotype of individuals with a single pathogenic APOA5 variant allele. Medically stable outpatients from Ontario, Canada were selected for study based on having: 1) a rare pathogenic APOA5 variant in a single allele; and 2) at least three serial fasting TG measurements obtained over >1.5 years of follow-up. Seven patients were followed for a mean of 5.3 ± 3.7 years. Fasting TG levels varied widely both within and between patients. Three patients displayed at least one normal TG measurement (<2.0 mmol/L or <175 mg/dL). All patients displayed mild-to-moderate HTG (2 to 9.9 mmol/L or 175 to 875 mg/dL) at multiple time points. Five patients displayed at least one severe HTG measurement. 10%, 54%, and 36% of all TG measurements were in normal, mild-to-moderate, and severe HTG ranges, respectively. Heterozygosity for pathogenic variants in APOA5 is associated with highly variable TG phenotypes both within and between patients. Heterozygosity confers susceptibility to elevated TG levels, with secondary factors likely modulating the phenotypic severity. [ABSTRACT FROM AUTHOR]

The 22q11.2 deletion syndrome (22q11.2DS) results from non-allelic homologous recombination between low-copy repeats termed LCR22. About 60%-70% of individuals with the typical 3 megabase (Mb) deletion from LCR22A-D have congenital heart disease, mostly of the conotruncal type (CTD), whereas others have normal cardiac anatomy. In this study, we tested whether variants in the hemizygous LCR22A-D region are associated with risk for CTDs on the basis of the sequence of the 22q11.2 region from 1,053 22q11.2DS individuals. We found a significant association (FDR p < 0.05) of the CTD subset with 62 common variants in a single linkage disequilibrium (LD) block in a 350 kb interval harboring CRKL. A total of 45 of the 62 variants were associated with increased risk for CTDs (odds ratio [OR) ranges: 1.64-4.75). Associations of four variants were replicated in a meta-analysis of three genome-wide association studies of CTDs in affected individuals without 22q11.2DS. One of the replicated variants, rs178252, is located in an open chromatin region and resides in the double-elite enhancer, GH22J020947, that is predicted to regulate CRKL (CRK-like proto-oncogene, cytoplasmic adaptor) expression. Approximately 23% of patients with nested LCR22C-D deletions have CTDs, and inactivation of Crkl in mice causes CTDs, thus implicating this gene as a modifier. Rs178252 and rs6004160 are expression quantitative trait loci (eQTLs) of CRKL. Furthermore, set-based tests identified an enhancer that is predicted to target CRKL and is significantly associated with CTD risk (GH22J020946, sequence kernal association test (SKAT) p = 7.21 × 10-5) in the 22q11.2DS cohort. These findings suggest that variance in CTD penetrance in the 22q11.2DS population can be explained in part by variants affecting CRKL expression.

• Heterozygotes for pathogenic LPL variants have highly variable TG levels. • Heterozygous LPL deficiency and familial hypercholesterolemia are not analogous. • There is little to no evidence for autosomal dominant hypertriglyceridemia. • Hypertriglyceridemia is genetically complex and almost never mendelian. Biallelic pathogenic variants in lipoprotein lipase (LPL) cause familial chylomicronemia syndrome with severe hypertriglyceridemia (HTG), defined as plasma triglycerides (TG) > 10 mmol/L (> 885 mg/dL). TG levels in individuals with one copy of a pathogenic LPL gene variant is less familiar; some assume that the phenotype is intermediate between homozygotes and controls. We undertook an evaluation of the longitudinal TG phenotype of individuals heterozygous for pathogenic LPL variants. Medically stable outpatients were evaluated based on having: (1) a single copy of a rare pathogenic LPL variant; and (2) serial fasting TG measurements obtained over > 1.5 years of follow-up. Fifteen patients with a single pathogenic LPL variant were followed for a mean of 10.3 years (range 1.5 to 30.3 years). TG levels varied widely both within and between patients. One patient had normal TG levels < 2.0 mmol/L (< 175 mg/dL) continuously, while four patients had at least one normal TG level. Most patients fluctuated between mild-to-moderate and severe HTG: five patients had only mild-to-moderate HTG, with TG levels ranging from 2.0 to 9.9 mmol/L (175 to 885 mg/dL), while 6 patients had at least one instance of severe HTG. Of the 203 total TG measurements from these patients, 14.8%, 67.0% and 18.2% were in the normal, mild-to-moderate and severe HTG ranges, respectively. The heterozygous LPL deficient phenotype is highly variable both within and between patients. Heterozygosity confers susceptibility to a wide range of TG phenotypes, with severity likely depending on secondary factors. [ABSTRACT FROM AUTHOR]

The genetic variants introduced into the ancestors of modern humans from interbreeding with Neanderthals have been suggested to contribute an unexpected extent to complex human traits. However, testing this hypothesis has been challenging due to the idiosyncratic population genetic properties of introgressed variants. We developed rigorous methods to assess the contribution of introgressed Neanderthal variants to heritable trait variation and applied these methods to analyze 235,592 introgressed Neanderthal variants and 96 distinct phenotypes measured in about 300,000 unrelated white British individuals in the UK Biobank. Introgressed Neanderthal variants make a significant contribution to trait variation (explaining 0.12% of trait variation on average). However, the contribution of introgressed variants tends to be significantly depleted relative to modern human variants matched for allele frequency and linkage disequilibrium (about 59% depletion on average), consistent with purifying selection on introgressed variants. Different from previous studies (McArthur et al., 2021), we find no evidence for elevated heritability across the phenotypes examined. We identified 348 independent significant associations of introgressed Neanderthal variants with 64 phenotypes. Previous work (Skov et al., 2020) has suggested that a majority of such associations are likely driven by statistical association with nearby modern human variants that are the true causal variants. Applying a customized fine-mapping led us to identify 112 regions across 47 phenotypes containing 4303 unique genetic variants where introgressed variants are highly likely to have a phenotypic effect. Examination of these variants reveals their substantial impact on genes that are important for the immune system, development, and metabolism.

Purpose of Review: Genetic studies in humans and animal models have improved our understanding of the role of numerous genes in the etiology of nonsyndromic tooth agenesis (TA). The purpose of this review is to discuss recently identified genes potentially contributing to TA. Recent Findings: Despite research progress, understanding the genetic factors underlying nonsyndromic TA has been challenging given the genetic heterogeneity, variable expressivity, and incomplete penetrance of putatively pathogenic variants often observed associated with the condition. Next-generation sequencing technologies have provided a platform for novel gene and variant discoveries and informed paradigm-shifting concepts in the etiology of TA. Summary: This review summarizes the current knowledge on genes and pathways related to nonsyndromic TA with a focus on recently identified genes/variants. Evidence suggesting possible multi-locus variation in TA is also presented. [ABSTRACT FROM AUTHOR]

Dollo's law of irreversibility argues that evolution cannot revert to earlier states. It has remained controversial ever since its inception in the 19th century. Enabled by advances in phylogenomics and functional genomics, recent studies show that there are very likely some cases of 'breaking Dollo's law'. As post-genomic research grows from showing patterns to revealing processes, new emphasis is needed on the molecular mechanisms by which Dollo's law might be broken. Shifting the argument from 'if it happened' to 'how it happened' will provide richer understanding of organismal and evolutionary biology. Motivated by case studies and novel avenues to test trait loss and regain, we outline a set of alternative hypotheses to be evaluated and what the outcomes tell us about evolution., Competing Interests: Declaration of interests No interests are declared., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Genome-wide association studies have advanced our understanding of complex traits, but studying how a GWAS variant can affect a specific trait in the human population remains challenging due to environmental variability. Drosophila melanogaster is in this regard an excellent model organism for studying the relationship between genetic and phenotypic variation due to its simple handling, standardized growth conditions, low cost, and short lifespan. The Drosophila Genetic Reference Panel (DGRP) in particular has been a valuable tool for studying complex traits, but proper harmonization and indexing of DGRP phenotyping data is necessary to fully capitalize on this resource. To address this, we created a web tool called DGRPool (dgrpool.epfl.ch), which aggregates phenotyping data of 1034 phenotypes across 135 DGRP studies in a common environment. DGRPool enables users to download data and run various tools such as genome-wide (GWAS) and phenome-wide (PheWAS) association studies. As a proof-of-concept, DGRPool was used to study the longevity phenotype and uncovered both established and unexpected correlations with other phenotypes such as locomotor activity, starvation resistance, desiccation survival, and oxidative stress resistance. DGRPool has the potential to facilitate new genetic and molecular insights of complex traits in Drosophila and serve as a valuable, interactive tool for the scientific community., Competing Interests: VG, RB, FD, ER, RR, BD No competing interests declared, (© 2023, Gardeux et al.)

Objective: To investigate the relationship between plasma lipoprotein (a) (Lp[a]) and lipid profiles in patients with severe hypertriglyceridaemia (HTG)., Methods: This case-control study undertook a retrospective chart review of patients from the Lipid Genetics Clinic at London Health Sciences Centre in Ontario, Canada. Plasma Lp(a) was compared between patients with severe HTG and healthy normolipidaemic control subjects. Severe HTG was defined by plasma triglycerides (TG) ≥ 10 mmol/l. Pairwise correlations between Lp(a), TG, apolipoprotein B (apo B) and non-high-density lipoprotein cholesterol (non-HDL-C) were evaluated., Results: This study reviewed 4400 patients and identified 154 patients with severe HTG, which were compared with 272 control subjects. The median Lp(a) was significantly lower in patients with severe HTG compared with control subjects (5.0 versus 10.2 mg/dl, respectively). No correlation was observed between Lp(a) and TG or non-HDL-C. Lp(a) and apo B were modestly correlated in patients with severe HTG ( r  = 0.235) and control subjects ( r  = 0.175). There were no significant differences between the genetic subgroups of patients with severe HTG., Conclusions: Patients with severe HTG have lower plasma Lp(a) than normolipidaemic control subjects. The basis for this relationship is not immediately apparent but is hypothesis-generating and warrants further investigation., Competing Interests: Declaration of conflicting interestR.A.H. reports consulting fees from Acasti, Aegerion, Akcea/Ionis, Amgen, Arrowhead, Boston Heart, HLS Therapeutics, Pfizer, Novartis, Regeneron, Sanofi and Ultragenyx. The other authors have no conflicts to disclose.

External tagging methods can aid in the research of leopard coral grouper (Plectropomus leopardus) in terms of its spatio-temporal behavior at population and individual scales. However, due to the strong exclusion ability and the damage to the body wall of P. leopardus, the retention rate of traditional invasive tagging methods is low. To develop a non-invasive identification method for P. leopardus, we adopted a multiscale image processing method based on matched filters with Gaussian kernels and partial differential equation (PDE) multiscale hierarchical decomposition with the deep convolutional neural network (CNN) models VGG19 and ResNet50 to extract shape and texture image features of individuals. Then based on image features, we used three classifiers Random forest (RF), support vector machine (SVM), and multilayer perceptron (MLP)) for individual recognition on sequential images of P. leopardus captured for 50 days. The PDE, ResNet50 and MLP combination obtained a maximum accuracy of 0.985 ± 0.045 on the test set. For individual temporal tracking recognition, feature extraction and model training were performed using images taken in 1-20 days. The classifier could achieve an accuracy of 0.960 ± 0.049 on the test set consisting of images collected in the periods of 20-50 days. The results show that CNNs with the PDE decomposition can effectively and accurately identify P. leopardus.

Precise control of protein degradation is critical for life, yet how natural genetic variation affects this essential process is largely unknown. Here, we developed a statistically powerful mapping approach to characterize how genetic variation affects protein degradation by the ubiquitin-proteasome system (UPS). Using the yeast Saccharomyces cerevisiae, we systematically mapped genetic influences on the N-end rule, a UPS pathway in which protein N-terminal amino acids function as degradation-promoting signals. Across all 20 possible N-terminal amino acids, we identified 149 genomic loci that influence UPS activity, many of which had pathway- or substrate-specific effects. Fine-mapping of four loci identified multiple causal variants in each of four ubiquitin system genes whose products process (NTA1), recognize (UBR1 and DOA10), and ubiquitinate (UBC6) cellular proteins. A cis-acting promoter variant that modulates UPS activity by altering UBR1 expression alters the abundance of 36 proteins without affecting levels of the corresponding mRNA transcripts. Our results reveal a complex genetic basis of variation in UPS activity.

The cultivation of hybrids with favorable complex traits is one of the important goals for animal, plant, and microbial breeding practices. A method that can closely predict the production performance of hybrids is of great significance for research and practice. In our study, polygenic risk scores (PRSs) were introduced to estimate the production performance of Saccharomyces cerevisiae. The genetic variation of 971 published isolates and their growth ratios under 35 medium conditions were analyzed by genome-wide association analysis, and the precise p-value threshold for each phenotype was calculated. Risk markers for the above 35 phenotypes were obtained. By estimating the genotype of F1 hybrids according to that of the parents, the PRS of 613 F1 hybrids was predicted. There was a significant linear correlation between the maximum growth rate at 40 °C and PRS in F1 hybrids and their parents (R2 = 0.2582, R2 = 0.2414, respectively), which indicates that PRS can be used to estimate the production performance of individuals and their hybrids. Our method can provide a reference for strain selection and F1 prediction in cross-breeding yeasts, reduce workload, and improve work efficiency. [ABSTRACT FROM AUTHOR]

Heritability enrichment analysis is an important means of exploring the genetic architecture of complex traits in human genetics. Heritability enrichment is typically defined as the proportion of an SNP subset explained heritability, divided by the proportion of SNPs. Heritability enrichment enables better study of underlying complex traits, such as functional variant/gene subsets, biological networks and metabolic pathways detected through integrating explosively increased omics data. This would be beneficial for genomic prediction of disease risk in humans and genetic values estimation of important economical traits in livestock and plant species. However, in livestock, factors affecting the heritability enrichment estimation of complex traits have not been examined. Previous studies on humans reported that the frequencies, effect sizes, and levels of linkage disequilibrium (LD) of underlying causal variants (CVs) would affect the heritability enrichment estimation. Therefore, the distribution of heritability across the genome should be fully considered to obtain the unbiased estimation of heritability enrichment. To explore the performance of different heritability enrichment models in livestock populations, we used the VanRaden, GCTA and α models, assuming different α values, and the LDAK model, considering LD weight. We simulated three types of phenotypes, with CVs from various minor allele frequency (MAF) ranges: genome-wide (0.005 ≤ MAF ≤ 0.5), common (0.05 ≤ MAF ≤ 0.5), and uncommon (0.01 ≤ MAF < 0.05). The performances of the models with two different subsets (one of which contained known CVs and the other consisting of randomly selected markers) were compared to verify the accuracy of heritability enrichment estimation of functional variant sets. Our results showed that models with known CV subsets provided more robust enrichment estimation. Models with different α values tended to provide stable and accurate estimates for common and genome-wide CVs (relative deviation 0.5–2.2%), while tending to underestimate the enrichment of uncommon CVs. As the α value increased, enrichments from 15.73% higher than true value (i.e., 3.00) to 48.93% lower than true value for uncommon CVs were observed. In addition, the long-range LD windows (e.g., 5000 kb) led to large bias of the enrichment estimations for both common and uncommon CVs. Overall, heritability enrichment estimations were sensitive for the α value assumption and LD weight consideration of different models. Accuracy would be greatly improved by using a suitable model. This study would be helpful in understanding the genetic architecture of complex traits and provides a reference for genetic analysis in the livestock population. [ABSTRACT FROM AUTHOR]

Statistical models play a significant role in designing competent breeding programs related to complex traits. Recently; the holo-omics framework has been productively utilized in trait prediction; but it contains many complexities. Therefore; it is desirable to establish prediction accuracy while combining the host's genome and microbiome data. Several methods can be used to combine the two data in the model and study their effectiveness by estimating the prediction accuracy. We validate our holo-omics interaction models with analysis from two publicly available datasets and compare them with genomic and microbiome prediction models. We illustrate that the holo-omics interactive models achieved the highest prediction accuracy in ten out of eleven traits. In particular; the holo-omics interaction matrix estimated using the Hadamard product displayed the highest accuracy in nine out of eleven traits, with the direct holo-omics model and microbiome model showing the highest prediction accuracy in the remaining two traits. We conclude that comparing prediction accuracy in different traits using real data showed important intuitions into the holo-omics architecture of complex traits. [ABSTRACT FROM AUTHOR]

With the advent of high throughput sequencing technologies, genome-wide association studies (GWAS) have become a powerful paradigm for dissecting the genetic origins of the observed phenotypic variation. We recently completely sequenced the genome of 1011 Saccharomyces cerevisiae isolates, laying a strong foundation for GWAS. To assess the feasibility and the limits of this approach, we performed extensive simulations using five selected subpopulations as well as the total set of 1011 genomes. We measured the ability to detect the causal genetic variants involved in Mendelian and more complex traits using a linear mixed model approach. The results showed that population structure is well accounted for and is not the main problem when the sample size is high enough. While the genetic determinant of a Mendelian trait is easily mapped in all studied subpopulations, discrepancies are seen between datasets when performing GWAS on a complex trait in terms of detection, false positive and false negative rate. Finally, we performed GWAS on the different defined subpopulations using a real quantitative trait (resistance to copper sulfate) and showed the feasibility of this approach. The performance of each dataset depends simultaneously on several factors such as sample size, relatedness and population evolutionary history. This article is part of the theme issue 'Genetic basis of adaptation and speciation: from loci to causative mutations'. [ABSTRACT FROM AUTHOR]

Complex behaviors are often observed at a spectrum in the population, and psychiatric disorders represent extremes of such behavioral spectra. While grasping the underlying cellular and molecular basis of these disorders represents a major challenge, it is believed that studies of complex behaviors in model organisms, where genotyping and phenotyping can be more conveniently carried out and cause-effect relationships can be further discerned, will help address this challenge. Here we report the characterization of a natural dark aversion behavior in larval zebrafish, which is previously shown to be fear or anxiety-associated. Phenotyping ∼200 individuals using a light/dark choice assay uncovered that, while a majority of individuals displayed medium level of dark aversion (mda), a small number of individuals exhibited strong dark aversion (sda), and a third small cohort showed variable dark aversion (vda). Through selective breeding and phenotyping of the next generation, we demonstrated that both the sda and vda traits are heritable, with sda being invariable while vda being highly variable across multiple trials. Additionally, sda appears to be recessive and vda appears to be dominant over the common allele(s) in the population. Moreover, compared to vda, sda showed increased thigmotaxis (preference for the walls in an open field), another measure of anxiety. Together, these findings reveal a naturally heritable variation of anxiety-like behavior in a tractable model organism, thereby laying foundation for future dissection of the underlying molecular and cellular mechanisms.

Although genome-wide association studies (GWASs) have identified thousands of risk loci for many complex traits and diseases, the causal variants and genes at these loci remain largely unknown. Here, we introduce a method for estimating the local genetic correlation between gene expression and a complex trait and utilize it to estimate the genetic correlation due to predicted expression between pairs of traits. We integrated gene expression measurements from 45 expression panels with summary GWAS data to perform 30 multi-tissue transcriptome-wide association studies (TWASs). We identified 1,196 genes whose expression is associated with these traits; of these, 168 reside more than 0.5 Mb away from any previously reported GWAS significant variant. We then used our approach to find 43 pairs of traits with significant genetic correlation at the level of predicted expression; of these, eight were not found through genetic correlation at the SNP level. Finally, we used bi-directional regression to find evidence that BMI causally influences triglyceride levels and that triglyceride levels causally influence low-density lipoprotein. Together, our results provide insight into the role of gene expression in the susceptibility of complex traits and diseases.

Abstract Venomous animals are a striking example of the convergent evolution of a complex trait. These animals have independently evolved an apparatus that synthesizes, stores, and secretes a mixture of toxic compounds to the target animal through the infliction of a wound. Among these distantly related animals, some can modulate and compartmentalize functionally distinct venoms related to predation and defense. A process to separate distinct venoms can occur within and across complex life cycles as well as more streamlined ontogenies, depending on their life-history requirements. Moreover, the morphological and cellular complexity of the venom apparatus likely facilitates the functional diversity of venom deployed within a given life stage. Intersexual variation of venoms has also evolved further contributing to the massive diversity of toxic compounds characterized in these animals. These changes in the biochemical phenotype of venom can directly affect the fitness of these animals, having important implications in their diet, behavior, and mating biology. In this review, we explore the current literature that is unraveling the temporal dynamics of the venom system that are required by these animals to meet their ecological functions. These recent findings have important consequences in understanding the evolution and development of a convergent complex trait and its organismal and ecological implications.

Polygenic scores (PGS) are important tools for carrying out genetic prediction of common diseases and disease related complex traits, facilitating the development of precision medicine. Unfortunately, despite the critical importance of PGS and the vast number of PGS methods recently developed, few comprehensive comparison studies have been performed to evaluate the effectiveness of PGS methods. To fill this critical knowledge gap, we performed a comprehensive comparison study on 12 different PGS methods through internal evaluations on 25 quantitative and 25 binary traits within the UK Biobank with sample sizes ranging from 147 408 to 336 573, and through external evaluations via 25 cross-study and 112 cross-ancestry analyses on summary statistics from multiple genome-wide association studies with sample sizes ranging from 1415 to 329 345. We evaluate the prediction accuracy, computational scalability, as well as robustness and transferability of different PGS methods across datasets and/or genetic ancestries, providing important guidelines for practitioners in choosing PGS methods. Besides method comparison, we present a simple aggregation strategy that combines multiple PGS from different methods to take advantage of their distinct benefits to achieve stable and superior prediction performance. To facilitate future applications of PGS, we also develop a PGS webserver (http://www.pgs-server.com/) that allows users to upload summary statistics and choose different PGS methods to fit the data directly. We hope that our results, method and webserver will facilitate the routine application of PGS across different research areas. [ABSTRACT FROM AUTHOR]

The genetic contribution of additive vs. non-additive (epistatic) effects in the regulation of complex traits is unclear. While genome-wide association studies typically ignore gene-gene interactions, in part because of the lack of statistical power for detecting them, mouse chromosome substitution strains (CSSs) represent an alternate approach for detecting epistasis given their limited allelic variation. Therefore, we utilized CSSs to identify and map both additive and epistatic loci that regulate a range of hematologic- and metabolism-related traits, as well as hepatic gene expression. Quantitative trait loci (QTL) were identified using a CSS-based backcross strategy involving the segregation of variants on the A/J-derived substituted chromosomes 4 and 6 on an otherwise C57BL/6J genetic background. In the liver transcriptomes of offspring from this cross, we identified and mapped additive QTL regulating the hepatic expression of 768 genes, and epistatic QTL pairs for 519 genes. Similarly, we identified additive QTL for fat pad weight, platelets, and the percentage of granulocytes in blood, as well as epistatic QTL pairs controlling the percentage of lymphocytes in blood and red cell distribution width. The variance attributed to the epistatic QTL pairs was approximately equal to that of the additive QTL; however, the SNPs in the epistatic QTL pairs that accounted for the largest variances were undetected in our single locus association analyses. These findings highlight the need to account for epistasis in association studies, and more broadly demonstrate the importance of identifying genetic interactions to understand the complete genetic architecture of complex traits.

Abstract Leaf shape is a defining feature of how we recognize and classify plant species. Although there is extensive variation in leaf shape within many species, few studies have disentangled the underlying genetic architecture. We characterized the genetic architecture of leaf shape variation in Eurasian aspen (Populus tremula L.) by performing genome‐wide association study (GWAS) for physiognomy traits. To ascertain the roles of identified GWAS candidate genes within the leaf development transcriptional program, we generated RNA‐Seq data that we used to perform gene co‐expression network analyses from a developmental series, which is publicly available within the PlantGenIE resource. We additionally used existing gene expression measurements across the population to analyze GWAS candidate genes in the context of a population‐wide co‐expression network and to identify genes that were differentially expressed between groups of individuals with contrasting leaf shapes. These data were integrated with expression GWAS (eQTL) results to define a set of candidate genes associated with leaf shape variation. Our results identified no clear adaptive link to leaf shape variation and indicate that leaf shape traits are genetically complex, likely determined by numerous small‐effect variations in gene expression. Genes associated with shape variation were peripheral within the population‐wide co‐expression network, were not highly connected within the leaf development co‐expression network, and exhibited signatures of relaxed selection. As such, our results are consistent with the omnigenic model.

Abstract Background Genetic association studies that seek to explain the inheritance of complex traits typically fail to explain a majority of the heritability of the trait under study. Thus, we are left with a gap in the map from genotype to phenotype. Several approaches have been used to fill this gap, including those that attempt to map endophenotype such as the transcriptome, proteome or metabolome, that underlie complex traits. Here we used metabolomics to explore the nature of genetic variation for hydrogen peroxide (H2O2) resistance in the sequenced inbred Drosophila Genetic Reference Panel (DGRP). Results We first studied genetic variation for H2O2 resistance in 179 DGRP lines and along with identifying the insulin signaling modulator u-shaped and several regulators of feeding behavior, we estimate that a substantial amount of phenotypic variation can be explained by a polygenic model of genetic variation. We then profiled a portion of the aqueous metabolome in subsets of eight ‘high resistance’ lines and eight ‘low resistance’ lines. We used these lines to represent collections of genotypes that were either resistant or sensitive to the stressor, effectively modeling a discrete trait. Across the range of genotypes in both populations, flies exhibited surprising consistency in their metabolomic signature of resistance. Importantly, the resistance phenotype of these flies was more easily distinguished by their metabolome profiles than by their genotypes. Furthermore, we found a metabolic response to H2O2 in sensitive, but not in resistant genotypes. Metabolomic data further implicated at least two pathways, glycogen and folate metabolism, as determinants of sensitivity to H2O2. We also discovered a confounding effect of feeding behavior on assays involving supplemented food. Conclusions This work suggests that the metabolome can be a point of convergence for genetic variation influencing complex traits, and can efficiently elucidate mechanisms underlying trait variation.

Molecular breeding, a powerful technique to increase crop yield, tries to predict yield by crop growth models with genotype specific, environment-independent yield components and environmental indices as inputs. A fluorescence-trait-based approach is presented to approximate some costly and time-consuming measurements of yield components. Temporal monitoring of chlorophyll a fluorescence resulted in fluorescence traits with high heritability (0.60-0.82) that could act as proxies for model inputs. Medium-sized Pearson's correlations were calculated between fluorescence traits, light-use efficiency (LUE), and fruit related parameters up to 0.53. Multi-trait quantitative trait locus (QTL) analyses identified genomic regions of pepper (Capsicum annuum L.) involved in the phenotypic variation of the fluorescence traits. Fluorescence QTLs found on linkage groups P6, P7, and P11 corresponded to QTLs for number of fruits, partitioning into fruits, and LUE. Fluorescence parameters within 1 min of the fluorescence response curve can thus be useful to approximate yield component traits.

Abstract Background Traditional selection in livestock and crops focuses on additive genetic values or breeding values of the individuals. While traditional selection utilizes variation between individuals, differences between gametes within individuals have been less frequently exploited in selection programs. With the successful implementation of genomic selection in livestock and crops, estimation and selection for gametic variation is becoming possible. Results The gamevar.f90 software is designed to estimate individual-level variance of genetic values of gametes for complex traits in large populations. The software estimates the (co)variances of gametic diversity as well as other diversity parameters that are useful for selection programs and mating designs. The calculation is carried out chromosome by chromosome and can be easily parallelized. The gamevar.f90 program is written in Fortran with efficient computing algorithms in a user-friendly software package with easily-handled input and output files. Finally, we applied the program to estimate gametic variance for hundreds of bulls for lifetime net merit, productive life, and livability. The RPTA (relative predicted transmitting ability), assuming a future selection intensity (i f ) of 1.5, showed larger variance than GEBV/2, indicating that greater future genetic gains can be obtained using an index that includes gametic variances. We also used the relative coefficient of variation to estimate with 95% confidence the sample sizes required to observe 90% variability of the progeny for lifetime net merit (or to allow at maximum 10% of change in the EBV predicted from progeny data). Conclusions Collectively, we develop an efficient computer program package, gamevar.f90, for estimating gametic variance for large numbers of individuals. The novel information on gametic variation will be useful in future animal and crop breeding programs.

Summary: Despite a lifetime prevalence of at least 5%, developmental stuttering, characterized by prolongations, blocks, and repetitions of speech sounds, remains a largely idiopathic speech disorder. Family, twin, and segregation studies overwhelmingly support a strong genetic influence on stuttering risk; however, its complex mode of inheritance combined with thus-far underpowered genetic studies contribute to the challenge of identifying and reproducing genes implicated in developmental stuttering susceptibility. We conducted a trans-ancestry genome-wide association study (GWAS) and meta-analysis of developmental stuttering in two primary datasets: The International Stuttering Project comprising 1,345 clinically ascertained cases from multiple global sites and 6,759 matched population controls from the biobank at Vanderbilt University Medical Center (VUMC), and 785 self-reported stuttering cases and 7,572 controls ascertained from The National Longitudinal Study of Adolescent to Adult Health (Add Health). Meta-analysis of these genome-wide association studies identified a genome-wide significant (GWS) signal for clinically reported developmental stuttering in the general population: a protective variant in the intronic or genic upstream region of SSUH2 (rs113284510, protective allele frequency = 7.49%, Z = −5.576, p = 2.46 × 10−8) that acts as an expression quantitative trait locus (eQTL) in esophagus-muscularis tissue by reducing its gene expression. In addition, we identified 15 loci reaching suggestive significance (p < 5 × 10−6). This foundational population-based genetic study of a common speech disorder reports the findings of a clinically ascertained study of developmental stuttering and highlights the need for further research.

Complex, multigenic biological traits are shaped by the emergent interaction of proteins being the main functional units at the molecular scale. Based on a phenomenological approach, algorithms for quantifying two different aspects of emergence were introduced (Wegner and Hao in Progr Biophys Mol Biol 161:54–61, 2021) describing: (i) pairwise reciprocal interactions of proteins mutually modifying their contribution to a complex trait (denoted as weak emergence), and (ii) formation of a new, complex trait by a set of n 'constitutive' proteins at concentrations exceeding individual threshold values (strong emergence). The latter algorithm is modified here to take account of protein redundancy with respect to a complex trait ('full redundancy'). Irreducibility is considered a necessary and sufficient criterion for strong biological emergence; if one constitutive protein is missing, or its concentration drops below the threshold the trait is lost. A definition based on 'unpredictability' is dismissed, because this criterion is irrelevant for the evolution of a complex trait, and apparent unpredictability may rather reflect our basic deficits in understanding unless we can provide an unequivocal proof for it. The phenomenological approach advocated here allows to identify hidden rules according to which strongly emergent traits may be organized. This is of high value for understanding the evolution of complex traits which seems to require the saltational advent of all constitutive proteins 'in one turn' to arrive at a functional trait providing for an improved fitness of the organism. Rather than being a purely random process, it may be guided by fundamental structural principles. [ABSTRACT FROM AUTHOR]

Male sexual orientation is influenced by environmental and complex genetic factors. Childhood gender nonconformity (CGN) is one of the strongest correlates of homosexuality with substantial familiality. We studied brothers in families with two or more homosexual brothers (409 concordant sibling pairs in 384 families, as well as their heterosexual brothers), who self-recalled their CGN. To map loci for CGN, we conducted a genome-wide linkage scan (GWLS) using SNP genotypes. The strongest linkage peaks, each with significant or suggestive two-point LOD scores and multipoint LOD score support, were on chromosomes 5q31 (maximum two-point LOD = 4.45), 6q12 (maximum two-point LOD = 3.64), 7q33 (maximum two-point LOD = 3.09), and 8q24 (maximum two-point LOD = 3.67), with the latter not overlapping with previously reported strongest linkage region for male sexual orientation on pericentromeric chromosome 8. Family-based association analyses were used to identify associated variants in the linkage regions, with a cluster of SNPs (minimum association p = 1.3 × 10–8) found at the 5q31 linkage peak. Genome-wide, clusters of multiple SNPs in the 10–6 to 10–8p-value range were found at chromosomes 5p13, 5q31, 7q32, 8p22, and 10q23, highlighting glutamate-related genes. This is the first reported GWLS and genome-wide association study on CGN. Further increasing genetic knowledge about CGN and its relationships to male sexual orientation should help advance our understanding of the biology of these associated traits. [ABSTRACT FROM AUTHOR]

Male sexual orientation is a scientifically and socially important trait shown by family and twin studies to be influenced by environmental and complex genetic factors. Individual genome-wide linkage studies (GWLS) have been conducted, but not jointly analyzed. Two main datasets account for > 90% of the published GWLS concordant sibling pairs on the trait and are jointly analyzed here: MGSOSO (Molecular Genetic Study of Sexual Orientation; 409 concordant sibling pairs in 384 families, Sanders et al. (2015)) and Hamer (155 concordant sibling pairs in 145 families, Mustanski et al. (2005)). We conducted multipoint linkage analyses with Merlin on the datasets separately since they were genotyped differently, integrated genetic marker positions, and combined the resultant LOD (logarithm of the odds) scores at each 1 cM grid position. We continue to find the strongest linkage support at pericentromeric chromosome 8 and chromosome Xq28. We also incorporated the remaining published GWLS dataset (on 55 families) by using meta-analytic approaches on published summary statistics. The meta-analysis has maximized the positional information from GWLS of currently available family resources and can help prioritize findings from genome-wide association studies (GWAS) and other approaches. Although increasing evidence highlights genetic contributions to male sexual orientation, our current understanding of contributory loci is still limited, consistent with the complexity of the trait. Further increasing genetic knowledge about male sexual orientation, especially via large GWAS, should help advance our understanding of the biology of this important trait. [ABSTRACT FROM AUTHOR]

Healthspan is a complex trait, influenced by many genes and environmental factors that accelerate or delay aging, reduce or increase disease risk, and extend or reduce lifespan. Thus, assessing the role of genetic variation in aging requires an experimental strategy capable of modeling the genetic and biological complexity of human populations. The goal of the The Jackson Laboratory Nathan Shock Center (JAX NSC) is to provide research resources and training for geroscience investigators that seek to understand the role of genetics and genetic diversity on the fundamental process of aging and diseases of human aging using the laboratory mouse as a model system. The JAX NSC has available novel, deeply characterized populations of aged mice, performs state-of-the-art phenotyping of age-relevant traits, provides systems genetics analysis of complex data sets, and provides all of these resources to the geroscience community. The aged animal resources, phenotyping capacity, and genetic expertise available through the JAX NSC benefit the geroscience community by fostering cutting-edge, novel lines of research that otherwise would not be possible. Over the past 15 years, the JAX NSC has transformed aging research across the geroscience community, providing aging mouse resources and tissues to researchers. All JAX NSC data and tools are publicly disseminated on the Mouse Phenome Database and the JAX NSC website, thus ensuring that the resources generated and expertise acquired through the Center are readily available to the aging research community. The JAX NSC will continue to enhance its ability to perform innovative research using a mammalian model to illuminate novel genotype–phenotype relationships and provide a rational basis for designing effective risk assessments and therapeutic interventions to boost longevity and disease-free healthspan. [ABSTRACT FROM AUTHOR]

This work represents a novel mechanistic approach to simulate and study genomic networks with accompanying regulatory interactions and complex mechanisms of quantitative trait formation. The approach implemented in MeSCoT software is conceptually based on the omnigenic genetic model of quantitative (complex) trait, and closely imitates the basic in vivo mechanisms of quantitative trait realization. The software provides a framework to study molecular mechanisms of gene-by-gene and gene-by-environment interactions underlying quantitative trait's realization and allows detailed mechanistic studies of impact of genetic and phenotypic variance on gene regulation. MeSCoT performs a detailed simulation of genes' regulatory interactions for variable genomic architectures and generates complete set of transcriptional and translational data together with simulated quantitative trait values. Such data provide opportunities to study, for example, verification of novel statistical methods aiming to integrate intermediate phenotypes together with final phenotype in quantitative genetic analyses or to investigate novel approaches for exploiting gene-by-gene and gene-by-environment interactions. [ABSTRACT FROM AUTHOR]

Abstract Background Association studies in recently admixed populations are extremely useful to identify the genetic architecture of pigmentation, due to their high genotypic and phenotypic variation. However, to date only four Genome-Wide Association Studies (GWAS) have been carried out in these populations. Results We present a GWAS of skin pigmentation in an admixed sample from Cuba (N = 762). Additionally, we conducted a meta-analysis including the Cuban sample, and admixed samples from Cape Verde, Puerto Rico and African-Americans from San Francisco. This meta-analysis is one of the largest efforts so far to characterize the genetic basis of skin pigmentation in admixed populations (N = 2,104). We identified five genome-wide significant regions in the meta-analysis, and explored if the markers observed in these regions are associated with the expression of relevant pigmentary genes in human melanocyte cultures. In three of the regions identified in the meta-analysis (SLC24A5, SLC45A2, and GRM5/TYR), the association seems to be driven by non-synonymous variants (rs1426654, rs16891982, and rs1042602, respectively). The rs16891982 polymorphism is strongly associated with the expression of the SLC45A2 gene. In the GRM5/TYR region, in addition to the rs1042602 non-synonymous SNP located on the TYR gene, variants located in the nearby GRM5 gene have an independent effect on pigmentation, possibly through regulation of gene expression of the TYR gene. We also replicated an association recently described near the MFSD12 gene on chromosome 19 (lead variant rs112332856). Additionally, our analyses support the presence of multiple signals in the OCA2/HERC2/APBA2 region on chromosome 15. A clear causal candidate is the HERC2 intronic variant rs12913832, which has a profound influence on OCA2 expression. This variant has pleiotropic effects on eye, hair, and skin pigmentation. However, conditional and haplotype-based analyses indicate the presence of other variants with independent effects on melanin levels in OCA2 and APBA2. Finally, a follow-up of genome-wide signals identified in a recent GWAS for tanning response indicates that there is a substantial overlap in the genetic factors influencing skin pigmentation and tanning response. Conclusions Our meta-analysis of skin pigmentation GWAS in recently admixed populations provides new insights about the genetic architecture of this complex trait.

The Collaborative Cross (CC) is a mouse genetic reference population whose range of applications includes quantitative trait loci (QTL) mapping. The design of a CC QTL mapping study involves multiple decisions, including which and how many strains to use, and how many replicates per strain to phenotype, all viewed within the context of hypothesized QTL architecture. Until now, these decisions have been informed largely by early power analyses that were based on simulated, hypothetical CC genomes. Now that more than 50 CC strains are available and more than 70 CC genomes have been observed, it is possible to characterize power based on realized CC genomes. We report power analyses from extensive simulations and examine several key considerations: 1) the number of strains and biological replicates, 2) the QTL effect size, 3) the presence of population structure, and 4) the distribution of functionally distinct alleles among the founder strains at the QTL. We also provide general power estimates to aide in the design of future experiments. All analyses were conducted with our R package, SPARCC (Simulated Power Analysis in the Realized Collaborative Cross), developed for performing either large scale power analyses or those tailored to particular CC experiments.