Variation In Complex Phenotypes Research Articles

Expanded phenotypic spectrum of neurodevelopmental and neurodegenerative disorder Bryant-Li-Bhoj syndrome with 38 additional individuals

Bryant-Li-Bhoj syndrome (BLBS), which became OMIM-classified in 2022 (OMIM: 619720, 619721), is caused by germline variants in the two genes that encode histone H3.3 (H3-3A/H3F3A and H3-3B/H3F3B) [1–4]. This syndrome is characterized by developmental delay/intellectual disability, craniofacial anomalies, hyper/hypotonia, and abnormal neuroimaging [1, 5]. BLBS was initially categorized as a progressive neurodegenerative syndrome caused by de novo heterozygous variants in either H3-3A or H3-3B [1–4]. Here, we analyze the data of the 58 previously published individuals along 38 unpublished, unrelated individuals. In this larger cohort of 96 people, we identify causative missense, synonymous, and stop-loss variants. We also expand upon the phenotypic characterization by elaborating on the neurodevelopmental component of BLBS. Notably, phenotypic heterogeneity was present even amongst individuals harboring the same variant. To explore the complex phenotypic variation in this expanded cohort, the relationships between syndromic phenotypes with three variables of interest were interrogated: sex, gene containing the causative variant, and variant location in the H3.3 protein. While specific genotype-phenotype correlations have not been conclusively delineated, the results presented here suggest that the location of the variants within the H3.3 protein and the affected gene (H3-3A or H3-3B) contribute more to the severity of distinct phenotypes than sex. Since these variables do not account for all BLBS phenotypic variability, these findings suggest that additional factors may play a role in modifying the phenotypes of affected individuals. Histones are poised at the interface of genetics and epigenetics, highlighting the potential role for gene-environment interactions and the importance of future research.

Leveraging epigenomes and three-dimensional genome organization for interpreting regulatory variation.

Understanding the impact of regulatory variants on complex phenotypes is a significant challenge because the genes and pathways that are targeted by such variants and the cell type context in which regulatory variants operate are typically unknown. Cell-type-specific long-range regulatory interactions that occur between a distal regulatory sequence and a gene offer a powerful framework for examining the impact of regulatory variants on complex phenotypes. However, high-resolution maps of such long-range interactions are available only for a handful of cell types. Furthermore, identifying specific gene subnetworks or pathways that are targeted by a set of variants is a significant challenge. We have developed L-HiC-Reg, a Random Forests regression method to predict high-resolution contact counts in new cell types, and a network-based framework to identify candidate cell-type-specific gene networks targeted by a set of variants from a genome-wide association study (GWAS). We applied our approach to predict interactions in 55 Roadmap Epigenomics Mapping Consortium cell types, which we used to interpret regulatory single nucleotide polymorphisms (SNPs) in the NHGRI-EBI GWAS catalogue. Using our approach, we performed an in-depth characterization of fifteen different phenotypes including schizophrenia, coronary artery disease (CAD) and Crohn's disease. We found differentially wired subnetworks consisting of known as well as novel gene targets of regulatory SNPs. Taken together, our compendium of interactions and the associated network-based analysis pipeline leverages long-range regulatory interactions to examine the context-specific impact of regulatory variation in complex phenotypes.

Rare genetic variants explain missing heritability in smoking.

Common genetic variants explain less variation in complex phenotypes than inferred from family-based studies, and there is a debate on the source of this 'missing heritability'. We investigated the contribution of rare genetic variants to tobacco use with whole-genome sequences from up to 26,257 unrelated individuals of European ancestries and 11,743 individuals of African ancestries. Across four smoking traits, single-nucleotide-polymorphism-based heritability ([Formula: see text]) was estimated from 0.13 to 0.28 (s.e., 0.10-0.13) in European ancestries, with 35-74% of it attributable to rare variants with minor allele frequencies between 0.01% and 1%. These heritability estimates are 1.5-4 times higher than past estimates based on common variants alone and accounted for 60% to 100% of our pedigree-based estimates of narrow-sense heritability ([Formula: see text], 0.18-0.34). In the African ancestry samples, [Formula: see text] was estimated from 0.03 to 0.33 (s.e., 0.09-0.14) across the four smoking traits. These results suggest that rare variants are important contributors to the heritability of smoking.

NcRNA-eQTL: a database to systematically evaluate the effects of SNPs on non-coding RNA expression across cancer types.

Numerous studies indicate that non-coding RNAs (ncRNAs) have critical functions across biological processes, and single-nucleotide polymorphisms (SNPs) could contribute to diseases or traits through influencing ncRNA expression. However, the associations between SNPs and ncRNA expression are largely unknown. Therefore, genome-wide expression quantitative trait loci (eQTL) analysis to assess the effects of SNPs on ncRNA expression, especially in multiple cancer types, will help to understand how risk alleles contribute toward tumorigenesis and cancer development. Using genotype data and expression profiles of ncRNAs of >8700 samples from The Cancer Genome Atlas (TCGA), we developed a computational pipeline to systematically identify ncRNA-related eQTLs (ncRNA-eQTLs) across 33 cancer types. We identified a total of 6 133 278 and 721 122 eQTL-ncRNA pairs in cis-eQTL and trans-eQTL analyses, respectively. Further survival analyses identified 8312 eQTLs associated with patient survival times. Furthermore, we linked ncRNA-eQTLs to genome-wide association study (GWAS) data and found 262 332 ncRNA-eQTLs overlapping with known disease- and trait-associated loci. Finally, a user-friendly database, ncRNA-eQTL (http://ibi.hzau.edu.cn/ncRNA-eQTL), was developed for free searching, browsing and downloading of all ncRNA-eQTLs. We anticipate that such an integrative and comprehensive resource will improve our understanding of the mechanistic basis of human complex phenotypic variation, especially for ncRNA- and cancer-related studies.

Genome-Wide Association Studies to Improve Wood Properties: Challenges and Prospects.

Wood formation is an excellent model system for quantitative trait analysis due to the strong associations between the transcriptional and metabolic traits that contribute to this complex process. Investigating the genetic architecture and regulatory mechanisms underlying wood formation will enhance our understanding of the quantitative genetics and genomics of complex phenotypic variation. Genome-wide association studies (GWASs) represent an ideal statistical strategy for dissecting the genetic basis of complex quantitative traits. However, elucidating the molecular mechanisms underlying many favorable loci that contribute to wood formation and optimizing GWAS design remain challenging in this omics era. In this review, we summarize the recent progress in GWAS-based functional genomics of wood property traits in major timber species such as Eucalyptus, Populus, and various coniferous species. We discuss several appropriate experimental designs for extensive GWAS in a given undomesticated tree population, such as omics-wide association studies and high-throughput phenotyping technologies. We also explain why more attention should be paid to rare allelic and major structural variation. Finally, we explore the potential use of GWAS for the molecular breeding of trees. Such studies will help provide an integrated understanding of complex quantitative traits and should enable the molecular design of new cultivars.

Reconstructing the molecular phylogeny of giant sengis (Macroscelidea; Macroscelididae; Rhynchocyon)

Giant sengis (Macroscelidea; Macroscelididae; Rhynchocyon), also known as giant elephant-shrews, are small-bodied mammals that range from central through eastern Africa. Previous research on giant sengi systematics has relied primarily on pelage color and geographic distribution. Because some species have complex phenotypic variation and large geographic ranges, we used molecular markers to evaluate the phylogeny and taxonomy of the genus, which currently includes four species: R. chrysopygus, R. cirnei (six subspecies), R. petersi (two subspecies), and R. udzungwensis. We extracted DNA from fresh and historical museum samples from all taxa except one R. cirnei subspecies, and we generated and analyzed approximately 4700 aligned nucleotides (2685 bases of mitochondrial DNA and 2019 bases of nuclear DNA) to reconstruct a molecular phylogeny. We genetically evaluate Rhynchocyon spp. sequences previously published on GenBank, propose that the captive R. petersi population in North American zoos is likely R. p. adersi, and suggest that hybridization among taxa is not widespread in Rhynchocyon. The DNA sample we have from the distinctive but undescribed giant sengi from the Boni forest of northern coastal Kenya is unexpectedly nearly identical to R. chrysopygus, which will require further study. Our analyses support the current morphology-based taxonomy, with each recognized species forming a monophyletic clade, but we propose elevating R. c. stuhlmanni to a full species.

Genetic Mapping by Bulk Segregant Analysis in Drosophila: Experimental Design and Simulation-Based Inference.

Identifying the genomic regions that underlie complex phenotypic variation is a key challenge in modern biology. Many approaches to quantitative trait locus mapping in animal and plant species suffer from limited power and genomic resolution. Here, I investigate whether bulk segregant analysis (BSA), which has been successfully applied for yeast, may have utility in the genomic era for trait mapping in Drosophila (and other organisms that can be experimentally bred in similar numbers). I perform simulations to investigate the statistical signal of a quantitative trait locus (QTL) in a wide range of BSA and introgression mapping (IM) experiments. BSA consistently provides more accurate mapping signals than IM (in addition to allowing the mapping of multiple traits from the same experimental population). The performance of BSA and IM is maximized by having multiple independent crosses, more generations of interbreeding, larger numbers of breeding individuals, and greater genotyping effort, but is less affected by the proportion of individuals selected for phenotypic extreme pools. I also introduce a prototype analysis method for simulation-based inference for BSA mapping (SIBSAM). This method identifies significant QTL and estimates their genomic confidence intervals and relative effect sizes. Importantly, it also tests whether overlapping peaks should be considered as two distinct QTL. This approach will facilitate improved trait mapping in Drosophila and other species for which hundreds or thousands of offspring (but not millions) can be studied.

Intrinsic Gastrointestinal Macrophages: Their Phenotype and Role in Gastrointestinal Motility.

There is an increasing awareness of the role of macrophages in the regulation and maintenance of gastrointestinal function in health and disease. This work has proceeded in the context of an increased understanding of the complex phenotypic variation in macrophages throughout the body and has shown previously unidentified roles for macrophages in diseases such as gastroparesis, postoperative ileus, and inflammatory bowel disease. Opportunities for exploiting the phenotypic modulation of tissue resident macrophages have been identified as possible therapies for some of these diseases. In addition, macrophages are an established component of the innate immune system and can respond to variations and changes in the intestinal microbiome and potentially mediate part of the impact of the microbiota on intestinal health. We reviewed the latest work on novel concepts in defining macrophage phenotype, discuss possible mechanisms of action for tissue-resident macrophages in the gut, address the significance of microbiome effects on macrophage phenotype, and review the known and possible roles of macrophages in motility disorders of the gastrointestinal tract.

Genetic Architecture of Abdominal Pigmentation in Drosophila melanogaster

Pigmentation varies within and between species and is often adaptive. The amount of pigmentation on the abdomen of Drosophila melanogaster is a relatively simple morphological trait, which serves as a model for mapping the genetic basis of variation in complex phenotypes. Here, we assessed natural variation in female abdominal pigmentation in 175 sequenced inbred lines of the Drosophila melanogaster Genetic Reference Panel, derived from the Raleigh, NC population. We quantified the proportion of melanization on the two most posterior abdominal segments, tergites 5 and 6 (T5, T6). We found significant genetic variation in the proportion of melanization and high broad-sense heritabilities for each tergite. Genome-wide association studies identified over 150 DNA variants associated with the proportion of melanization on T5 (84), T6 (34), and the difference between T5 and T6 (35). Several of the top variants associated with variation in pigmentation are in tan, ebony, and bric-a-brac1, genes known to affect D. melanogaster abdominal pigmentation. Mutational analyses and targeted RNAi-knockdown showed that 17 out of 28 (61%) novel candidate genes implicated by the genome-wide association study affected abdominal pigmentation. Several of these genes are involved in developmental and regulatory pathways, chitin production, cuticle structure, and vesicle formation and transport. These findings show that genetic variation may affect multiple steps in pathways involved in tergite development and melanization. Variation in these novel candidates may serve as targets for adaptive evolution and sexual selection in D. melanogaster.

Digging for gold nuggets: uncovering novel candidate genes for variation in gastrointestinal nematode burden in a wild bird species.

The extent to which genotypic variation at a priori identified candidate genes can explain variation in complex phenotypes is a major debate in evolutionary biology. Whereas some high-profile genes such as the MHC or MC1R clearly do account for variation in ecologically relevant characters, many complex phenotypes such as response to parasite infection may well be underpinned by a large number of genes, each of small and effectively undetectable effect. Here, we characterize a suite of novel candidate genes for variation in gastrointestinal nematode (Trichostrongylus tenuis) burden among red grouse (Lagopus lagopus scotica) individuals across a network of moors in north-east Scotland. We test for associations between parasite load and genotypic variation in twelve genes previously identified to be differentially expressed in experimentally infected red grouse or genetically differentiated among red grouse populations with overall different parasite loads. These genes are associated with a broad physiological response including immune system processes. Based on individual-level generalized linear models, genotypic variants in nine genes were significantly associated with parasite load, with effect sizes accounting for differences of 514-666 worms per bird. All but one of these variants were synonymous or untranslated, suggesting that these may be linked to protein-coding variants or affect regulatory processes. In contrast, population-level analyses revealed few and inconsistent associations with parasite load, and little evidence of signatures of natural selection. We discuss the broader significance of these contrasting results in the context of the utility of population genomics and landscape genomics approaches in detecting adaptive genomic signatures.

Distribution and medical impact of loss-of-function variants in the Finnish founder population.

Exome sequencing studies in complex diseases are challenged by the allelic heterogeneity, large number and modest effect sizes of associated variants on disease risk and the presence of large numbers of neutral variants, even in phenotypically relevant genes. Isolated populations with recent bottlenecks offer advantages for studying rare variants in complex diseases as they have deleterious variants that are present at higher frequencies as well as a substantial reduction in rare neutral variation. To explore the potential of the Finnish founder population for studying low-frequency (0.5–5%) variants in complex diseases, we compared exome sequence data on 3,000 Finns to the same number of non-Finnish Europeans and discovered that, despite having fewer variable sites overall, the average Finn has more low-frequency loss-of-function variants and complete gene knockouts. We then used several well-characterized Finnish population cohorts to study the phenotypic effects of 83 enriched loss-of-function variants across 60 phenotypes in 36,262 Finns. Using a deep set of quantitative traits collected on these cohorts, we show 5 associations (p<5×10−8) including splice variants in LPA that lowered plasma lipoprotein(a) levels (P = 1.5×10−117). Through accessing the national medical records of these participants, we evaluate the LPA finding via Mendelian randomization and confirm that these splice variants confer protection from cardiovascular disease (OR = 0.84, P = 3×10−4), demonstrating for the first time the correlation between very low levels of LPA in humans with potential therapeutic implications for cardiovascular diseases. More generally, this study articulates substantial advantages for studying the role of rare variation in complex phenotypes in founder populations like the Finns and by combining a unique population genetic history with data from large population cohorts and centralized research access to National Health Registers.

Integrative Modeling of eQTLs and Cis-Regulatory Elements Suggests Mechanisms Underlying Cell Type Specificity of eQTLs

Genetic variants in cis-regulatory elements or trans-acting regulators frequently influence the quantity and spatiotemporal distribution of gene transcription. Recent interest in expression quantitative trait locus (eQTL) mapping has paralleled the adoption of genome-wide association studies (GWAS) for the analysis of complex traits and disease in humans. Under the hypothesis that many GWAS associations tag non-coding SNPs with small effects, and that these SNPs exert phenotypic control by modifying gene expression, it has become common to interpret GWAS associations using eQTL data. To fully exploit the mechanistic interpretability of eQTL-GWAS comparisons, an improved understanding of the genetic architecture and causal mechanisms of cell type specificity of eQTLs is required. We address this need by performing an eQTL analysis in three parts: first we identified eQTLs from eleven studies on seven cell types; then we integrated eQTL data with cis-regulatory element (CRE) data from the ENCODE project; finally we built a set of classifiers to predict the cell type specificity of eQTLs. The cell type specificity of eQTLs is associated with eQTL SNP overlap with hundreds of cell type specific CRE classes, including enhancer, promoter, and repressive chromatin marks, regions of open chromatin, and many classes of DNA binding proteins. These associations provide insight into the molecular mechanisms generating the cell type specificity of eQTLs and the mode of regulation of corresponding eQTLs. Using a random forest classifier with cell specific CRE-SNP overlap as features, we demonstrate the feasibility of predicting the cell type specificity of eQTLs. We then demonstrate that CREs from a trait-associated cell type can be used to annotate GWAS associations in the absence of eQTL data for that cell type. We anticipate that such integrative, predictive modeling of cell specificity will improve our ability to understand the mechanistic basis of human complex phenotypic variation.

Does plant architectural complexity increase with increasing habitat complexity? A test with a pioneer shrub in the Brazilian Cerrado

Understanding variation in plant traits in heterogeneous habitats is important to predict responses to changing environments, but trait-environment associations are poorly known along ecological gradients. We tested the hypothesis that plant architectural complexity increases with habitat complexity along a soil fertility gradient in a Cerrado (Neotropical savanna) area in southeastern Brazil. Plant architecture and productivity (estimated as the total number of healthy infructescences) of Miconia albicans (SW.) Triana were examined in three types of vegetation which together form a natural gradient of increasing soil fertility, tree density and canopy cover: grasslands (campo sujo, CS), shrublands (cerrado sensu strico, CE) and woodlands (cerradão, CD). As expected, plants growing at the CS were shorter and had a lower branching pattern, whereas plants at the CD were the tallest. Unexpectedly, however, CD plants did not show higher architectural complexity compared to CE plants. Higher architectural similarity between CE and CD plants compared to similarity between CS and CE plants suggests reduced expression of functional architectural traits under shade. Plants growing at the CE produced more quaternary shoots, leading to a larger number of infructescences. This higher plant productivity in CE indicates that trait variation in ecological gradients is more complex than previously thought. Nematode-induced galls accounted for fruit destruction in 76.5% infructescences across physiognomies, but percentage of attack was poorly related to architectural variables. Our data suggest shade-induced limitation in M. albicans architecture, and point to complex phenotypic variation in heterogeneous habitats in Neotropical savannas.

Genetic background and tumour susceptibility in mouse models

Dear Editor, Genetically engineered mouse (GEM) models of tumourigenesis have revolutionised our understanding of the molecular mechanisms of cancer. They have been instrumental in the identification and characterisation of oncogenes and tumour suppressors and have provided invaluable tools for recapitulating human cancers. However, a major caveat of using GEM models is the phenotypic variation that arises due to the influence of genetic background.1, 2 Even after extensive backcrossing (>10 times), genes physically linked to the deleted gene can be the same as those of the ES cells. As we discuss here, differences in genetic background have been shown to significantly impact tumour susceptibility, which can lead to misinterpretation of results. This strain-dependent phenotypic variation can manifest as completely different phenotypes or differential penetrance.1, 2 This issue of differential tumour susceptibility caused by the influence of genetic background is highlighted in a recent study by Biswas et al.3 published in Cell Death and Differentiation. The authors demonstrated that loss of Bid delays tumourigenesis in Atm-deficient mice by generating compound knockout mice (Atm−/−Bid−/−). This was achieved by cross-breeding Atm+/− mice (129S/Sv background) to Bid−/− mice (C57BL/6 background). Subsequently, tumour onset in Atm−/−Bid−/− mice in a mixed genetic background (129S/Sv:C57BL/6) was compared to the Atm−/− parental strain in a pure 129S/Sv background. Given the tumour suppressive influence of the C57BL/6 background, which renders this strain resistant to many types of tumours,1, 4 the effects of Bid-deficiency on tumour susceptibility in this study are difficult to interpret. In order to accurately compare congenic strains potentially confounding influence of mixed genetic backgrounds must be controlled for by ensuring all genotypes are comprised of the same genetic background. In the study by Biswas et al.,3 the appropriate controls seem not included to definitively rule out the possibility that C57BL/6 background contributes to the delayed tumourigenesis observed in Atm−/−Bid−/− mice. Many studies employing GEM models have demonstrated how genetic background can cause substantial phenotypic variation.1, 2 A particularly striking example is the NMYC transgenic mouse model of neuroblastoma. Differential neuroblastoma penetrance between NMYC transgenic mice of different genetic backgrounds was reported.5 Serial backcrossing to a C57BL/6 background caused a reduced penetrance in contrast to serial backcrossing to a 129Sv/J background, which caused an increased penetrance. This phenomenon has also been observed in other studies that compare tumour incidence in different genetic backgrounds. For example, tumour susceptibility in p53−/− and Atm−/− mice has also been shown to be dependent on genetic background. In a 129/Sv background, these knockout strains were found to have increased tumour penetrance compared to when they were backcrossed into a C57BL/6 background6, 7. Consistent with these observations, in our own experience in specific pathogen-free (SPF) conditions, we have observed that backcrossing once to a C57BL/6 background from a pure 129/Sv background significantly delays median tumour onset by 50–60% (Dorstyn et al., unpublished data). Taken together, these observations demonstrate the extent by which genetic background can impact on tumour susceptibility and highlights the importance of controlling for these effects. Interestingly, multiple studies have provided evidence that environmental factors (for example, SPF versus non-SPF facilities, diet, and sanitation) are a major determinant of phenotypic variation between mouse models used in different studies.8, 9 Although environmental factors no doubt influence tumour susceptibility (in addition to other phenotypes), it is highly unlikely that this completely explains the complex phenotypic variation observed in many studies. Rather, a combination of influences from environmental and genetic factors most likely explain these discrepancies. Regardless of to what extent each of these factors contributes to phenotypic variation, suitable controls to rule out the effects of genetic background are essential for studies involving GEM models. Given the wealth of evidence supporting a role for genetic background in contributing to phenotypic variation, such studies that exclude these controls should be interpreted cautiously.

Nonlinear Fitness Consequences of Variation in Expression Level of a Eukaryotic Gene

Levels of gene expression show considerable variation in eukaryotes, but no fine-scale maps have been made of the fitness consequences of such variation in controlled genetic backgrounds and environments. To address this, we assayed fitness at many levels of up- and down-regulated expression of a single essential gene, LCB2, involved in sphingolipid synthesis in budding yeast Saccharomyces cerevisiae. Reduced LCB2 expression rapidly decreases cellular fitness, yet increased expression has little effect. The wild-type expression level is therefore perched on the edge of a nonlinear fitness cliff. LCB2 is upregulated when cells are exposed to osmotic stress; consistent with this, the entire fitness curve is shifted upward to higher expression under osmotic stress, illustrating the selective force behind gene regulation. Expression levels of LCB2 are lower in wild yeast strains than in the experimental lab strain, suggesting that higher levels in the lab strain may be idiosyncratic. Reports indicate that the effect sizes of alleles contributing to variation in complex phenotypes differ among environments and genetic backgrounds; our results suggest that such differences may be explained as simple shifts in the position of nonlinear fitness curves.

Phenotype mining in CNV carriers from a population cohort †

Phenotype mining is a novel approach for elucidating the genetic basis of complex phenotypic variation. It involves a search of rich phenotype databases for measures correlated with genetic variation, as identified in genome-wide genotyping or sequencing studies. An initial implementation of phenotype mining in a prospective unselected population cohort, the Northern Finland 1966 Birth Cohort (NFBC1966), identifies neurodevelopment-related traits-intellectual deficits, poor school performance and hearing abnormalities-which are more frequent among individuals with large (>500 kb) deletions than among other cohort members. Observation of extensive shared single nucleotide polymorphism haplotypes around deletions suggests an opportunity to expand phenotype mining from cohort samples to the populations from which they derive.

The genetic basis of adrenal gland weight and structure in BXD recombinant inbred mice

Adrenal gland function is mediated through secreted hormones, which play a vital role in the autonomic and hypothalamic-pituitary-adrenal (HPA)-axis-mediated stress response. The genetic underpinnings of the stress response can be approached using a quantitative trait locus (QTL) analysis. This method has been used to investigate genomic regions associated with variation in complex phenotypes, but it has not been used to explore the structure of the adrenal. We used QTL analyses to identify candidate genes underlying adrenal weight and adrenal cortical zone and medulla widths. We used 64 BXD recombinant inbred (RI) strains of mice (n = 528) and 2 parental strains (C57BL/6J and DBA/2J; n = 20) to measure adrenal weights and adrenal zone widths. For adrenal weight, we found significant QTLs on chromosome 3 for females (Fawq1) and Chr 4 for males (Mawq1) and suggestive QTLs on Chrs 1, 3, 10, and 14 for females and Chrs 2, 4, 10, 17, and X for males. We identified a significant QTL on Chr 10 (Mawdq1) and a suggestive QTL on Chr 13 for male adrenal total width. For male adrenal medulla width, we found a significant QTL on Chr 5 (Mmwdq1) and a suggestive QTL on Chr 1. We also identified significant QTLs on Chrs 10 (Mxwdq1) and 14 (Mxwdq2) for male X-zone width. There are 113 genes that mapped within the significant QTL intervals, and we identified 4 candidate genes associated with adrenal structure and/or function. In summary, this study is an important first step for detecting genetic factors influencing the structure of the adrenal component of the HPA axis using QTL analyses, which may relate to adrenal function and provide further insights into elucidating genes critical for stress-related phenotypes.

Graduality and innovation in the evolution of complex phenotypes: insights from development

The neo-Darwinian paradigm benefits from the assumption that phenotypic variation is gradual and that phenotype and genotype have a relatively simple relationship. These assumptions are historically inherited from the times of the neo-Darwinian synthesis and, consequently, do not include present understanding about development. In this study, understanding about the dynamics of pattern formation is used to explore to that extent phenotypic variation can be expected to be gradual and simply related to molecular variation. Variation in simple phenotypes seems to fit neo-Darwinian assumptions but variation in complex phenotypes does not. Instead, variation in complex phenotypes would have a tendency to relatively less gradual evolution, even at microevolutionary time scales, that would make phylogenetic reconstructions more difficult. In addition, they will have a tendency to exhibit specific trends in innovation rates over group radiations with early accelerations and late decelerations. This work also explores further consequences of these results in our understanding of phenotypic evolution.

Cleaning genotype data.

The identification of genes contributing to variation in complex phenotypes requires genetic data of high fidelity. Thus, the identification of pedigree and genotyping errors is a crucial prerequisite to the analysis of data from a genome scan for disease genes. The problem has been given little attention in most gene hunting papers; the focus has often been on eliminating mendelian inconsistencies in order that the analysis may proceed, rather than on achieving the best possible data. Though a number of computer programs are available to assist in the identification of genotyping and pedigree errors, the process is still not completely automated. While the Collaborative Study on the Genetics of Alcoholism (COGA) data set for GAW11 is completely compatible with Mendel's rules, there are still some errors present. We inspected the COGA data for the presence of additional errors, and identified five possible pedigree errors.

Expression of decorin in human tissues and cell lines and defined chromosomal assignment of the gene locus (DCN).

Earlier studies had shown that the expression of the gene coding for one eminent connective tissue proteoglycan, decorin (DCN), is deficient in the fibroblasts of 3 out of 15 Marfan patients (Pulkkinen et al., 1990). To obtain more information on the expression of this gene, various human tissues and cell lines were studied. High mRNA levels of decorin were detected in aorta, lung, skin, kidney, smooth muscle, and placenta, whereas significantly lower mRNA levels were found in the rest of the tissues analyzed. Two sizes of transcripts were observed in all tissues. The two transcripts of decorin most probably do not represent two different genes, since in situ hybridization gave only one strong signal, placing the gene in 12q21----q22. No tissue-specific differences in the two mRNA species of decorin were detected. This is in contrast to the gene of versican, another connective tissue proteoglycan gene, that was analyzed as a control; high expression of a longer transcript of the versican gene was found in brain and smooth muscle, whereas the shorter transcript was predominant in all other tissues studied. DCN was actively transcribed in cultured mesenchymal cells, whereas in cells of endothelial or epithelial origin, the transcription level was undetectable. These tissue- and cell type-specific variations in the expression of DCN may help to explain the complex phenotypic variation typical of individuals with Marfan syndrome.