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1.  Systems genetics approaches to understand complex traits 
Nature reviews. Genetics  2013;15(1):34-48.
Systems genetics is an approach to understand the flow of biological information that underlies complex traits. It uses a range of experimental and statistical methods to quantitate and integrate intermediate phenotypes, such as transcript, protein or metabolite levels, in populations that vary for traits of interest. Systems genetics studies have provided the first global view of the molecular architecture of complex traits and are useful for the identification of genes, pathways and networks that underlie common human diseases. Given the urgent need to understand how the thousands of loci that have been identified in genome-wide association studies contribute to disease susceptibility, systems genetics is likely to become an increasingly important approach to understanding both biology and disease.
doi:10.1038/nrg3575
PMCID: PMC3934510  PMID: 24296534
2.  Genome-wide Linkage Screen for Stature and Body-mass Index in 3.032 Families - Evidence for Sex- and Population-specific Genetic Effects 
Stature (adult body height), and body mass index (BMI) have a strong genetic component explaining observed variation in human populations, however, identifying those genetic components has been extremely challenging. It seems obvious that sample size is a critical determinant for successful identification of quantitative trait loci (QTL) that underlie the genetic architecture of these polygenic traits. The inherent shared environment and known genetic relationships in family studies provide clear advantages for gene mapping over studies utilizing unrelated individuals. To these ends, we combined the genotype and phenotype data from four previously performed family-based genome-wide screens resulting in a sample of 9.371 individuals from 3.032 African-American and European-American families and performed variance-components linkage analyses for stature and BMI. To our knowledge, this study represents the single largest family-based genome-wide linkage scan published for stature and BMI to date. This large study sample allowed us to pursue population-and sex-specific analyses as well. For stature we found evidence for linkage in previously reported loci on 11q23, 12q12, 15q25 and 18q23 as well as 15q26 and 19q13 which have not been linked to stature previously. For BMI we found evidence for two loci: one on 7q35 and another on 11q22 both of which have been previously linked to BMI in multiple populations. Our results show both the benefit of 1) combining data to maximize the sample size and 2) minimizing heterogeneity by analyzing subgroups where within-group variation can be reduced and suggest that the latter may be a more successful approach in genetic mapping.
doi:10.1038/ejhg.2008.152
PMCID: PMC2628452  PMID: 18781184
Body Height; Body Mass Index; Linkage mapping; Quantitative Trait Loci
3.  Genome-wide linkage screen for stature and body mass index in 3.032 families: evidence for sex- and population-specific genetic effects 
Stature (adult body height) and body mass index (BMI) have a strong genetic component explaining observed variation in human populations; however, identifying those genetic components has been extremely challenging. It seems obvious that sample size is a critical determinant for successful identification of quantitative trait loci (QTL) that underlie the genetic architecture of these polygenic traits. The inherent shared environment and known genetic relationships in family studies provide clear advantages for gene mapping over studies utilizing unrelated individuals. To these ends, we combined the genotype and phenotype data from four previously performed family-based genome-wide screens resulting in a sample of 9.371 individuals from 3.032 African-American and European-American families and performed variance-components linkage analyses for stature and BMI. To our knowledge, this study represents the single largest family-based genome-wide linkage scan published for stature and BMI to date. This large study sample allowed us to pursue population- and sex-specific analyses as well. For stature, we found evidence for linkage in previously reported loci on 11q23, 12q12, 15q25 and 18q23, as well as 15q26 and 19q13, which have not been linked to stature previously. For BMI, we found evidence for two loci: one on 7q35 and another on 11q22, both of which have been previously linked to BMI in multiple populations. Our results show both the benefit of (1) combining data to maximize the sample size and (2) minimizing heterogeneity by analyzing subgroups where within-group variation can be reduced and suggest that the latter may be a more successful approach in genetic mapping.
doi:10.1038/ejhg.2008.152
PMCID: PMC2628452  PMID: 18781184
body height; body mass index; linkage mapping; quantitative trait loci
4.  Strategic Approaches to Unraveling Genetic Causes of Cardiovascular Diseases 
Circulation research  2011;108(10):1252-1269.
DNA sequence variants (DSVs) are major components of the “causal field” for virtually all-medical phenotypes, whether single-gene familial disorders or complex traits without a clear familial aggregation. The causal variants in single gene disorders are necessary and sufficient to impart large effects. In contrast, complex traits are due to a much more complicated network of contributory components that in aggregate increase the probability of disease. The conventional approach to identification of the causal variants for single gene disorders is genetic linkage. However, it does not offer sufficient resolution to map the causal genes in small size families or sporadic cases. The approach to genetic studies of complex traits entails candidate gene or Genome Wide Association Studies (GWAS). GWAS provides an unbiased survey of the effects of common genetic variants (common disease - common variant hypothesis). GWAS have led to identification of a large number of alleles for various cardiovascular diseases. However, common alleles account for a relatively small fraction of the total heritability of the traits. Accordingly, the focus has shifted toward identification of rare variants that might impart larger effect sizes (rare variant-common disease hypothesis). This shift is made feasible by recent advances in massively parallel DNA sequencing platforms, which afford the opportunity to identify virtually all common as well as rare alleles in individuals. In this review, we discuss various strategies that are used to delineate the genetic contribution to medically important cardiovascular phenotypes, emphasizing the utility of the new deep sequencing approaches.
doi:10.1161/CIRCRESAHA.110.236067
PMCID: PMC3115927  PMID: 21566222
Genetics; Next-Generation Sequencing; Complex traits; Polymorphism
5.  Molecular basis of a linkage peak: exome sequencing and family-based analysis identify a rare genetic variant in the ADIPOQ gene in the IRAS Family Study 
Human Molecular Genetics  2010;19(20):4112-4120.
Family-based linkage analysis has been a powerful tool for identification of genes contributing to traits with monogenic patterns of inheritance. These approaches have been of limited utility in identification of genes underlying complex traits. In contrast, searches for common genetic variants associated with complex traits have been highly successful. It is now widely recognized that common variations frequently explain only part of the inter-individual variation in populations. ‘Rare’ genetic variants have been hypothesized to contribute significantly to phenotypic variation in the population. We have developed a combination of family-based linkage, whole-exome sequencing, direct sequencing and association methods to efficiently identify rare variants of large effect. Key to the successful application of the method was the recognition that only a few families in a sample contribute significantly to a linkage signal. Thus, a search for mutations can be targeted to a small number of families in a chromosome interval restricted to the linkage peak. This approach has been used to identify a rare (1.1%) G45R mutation in the gene encoding adiponectin, ADIPOQ. This variant explains a strong linkage signal (LOD > 8.0) and accounts for ∼17% of the variance in plasma adiponectin levels in a sample of 1240 Hispanic Americans and 63% of the variance in families carrying the mutation. Individuals carrying the G45R mutation have mean adiponectin levels that are 19% of non-carriers. We propose that rare variants may be a common explanation for linkage peaks observed in complex trait genetics. This approach is applicable to a wide range of family studies and has potential to be a discovery tool for identification of novel genes influencing complex traits.
doi:10.1093/hmg/ddq327
PMCID: PMC2947405  PMID: 20688759
6.  Genome-wide Association Study of Porcine Hematological Parameters in a Large White × Minzhu F2 Resource Population 
Hematological traits, which are important indicators of immune function in animals, have been commonly examined as biomarkers of disease and disease severity in humans and animals. Genome-wide significant quantitative trait loci (QTLs) provide important information for use in breeding programs of animals such as pigs. QTLs for hematological parameters (hematological traits) have been detected in pig chromosomes, although these are often mapped by linkage analysis to large intervals making identification of the underlying mutation problematic. Single nucleotide polymorphisms (SNPs) are the common form of genetic variation among individuals and are thought to account for the majority of inherited traits. In this study, a genome-wide association study (GWAS) was performed to detect regions of association with hematological traits in a three-generation resource population produced by intercrossing Large White boars and Minzhu sows during the period from 2007 to 2011. Illumina PorcineSNP60 BeadChip technology was used to genotype each animal and seven hematological parameters were measured (hematocrit (HCT), hemoglobin (HGB), mean corpuscular hemoglobin (MCH), mean corpuscular hemoglobin concentration (MCHC), mean corpuscular volume (MCV), red blood cell count (RBC) and red blood cell volume distribution width (RDW)). Data were analyzed in a three step Genome-wide Rapid Association using the Mixed Model and Regression-Genomic Control (GRAMMAR-GC) method. A total of 62 genome-wide significant and three chromosome-wide significant SNPs associated with hematological parameters were detected in this GWAS. Seven and five SNPs were associated with HCT and HGB, respectively. These SNPs were all located within the region of 34.6-36.5 Mb on SSC7. Four SNPs within the region of 43.7-47.0 Mb and fifty-five SNPs within the region of 42.2-73.8 Mb on SSC8 showed significant association with MCH and MCV, respectively. At chromosome-wide significant level, one SNP at 29.2 Mb on SSC1 and two SNPs within the region of 26.0-26.2 Mb were found to be significantly associated with RBC and RDW, respectively. Many of the SNPs were located within previously reported QTL regions and appeared to narrow down the regions compared with previously described QTL intervals. In current research, a total of seven significant SNPs were found within six candidate genes SCUBE3, KDR, TDO, IGFBP7, ADAMTS3 and AFP. In addition, the KIT gene, which has been previously reported to relate to hematological parameters, was located within the region significantly associated with MCH and MCV and could be a candidate gene. These results of this study may lead to a better understanding of the molecular mechanisms of hematological parameters in pigs.
doi:10.7150/ijbs.4027
PMCID: PMC3385009  PMID: 22745577
genome-wide association study; porcine; hematological parameters
7.  The value of some Corsican sub-populations for genetic association studies 
BMC Medical Genetics  2008;9:73.
Background
Genetic isolates with a history of a small founder population, long-lasting isolation and population bottlenecks represent exceptional resources in the identification of disease genes. In these populations the disease allele reveals Linkage Disequilibrium (LD) with markers over significant genetic intervals, therefore facilitating disease locus identification. In a previous study we examined the LD extension on the Xq13 region in three Corsican sub-populations from the inner mountainous region of the island. On the basis of those previous results we have proposed a multistep procedure to carry out studies aimed at the identification of genes involved in complex diseases in Corsica. A prerequisite to carry out the proposed multi-step procedure was the presence of different degrees of LD on the island and a common genetic derivation of the different Corsican sub-populations. In order to evaluate the existence of these conditions in the present paper we extended the analysis to the Corsican coastal populations.
Methods
Samples were analyzed using seven dinucleotide microsatellite markers on chromosome Xq13-21: DXS983, DXS986, DXS8092, DXS8082, DXS1225, DXS8037 and DXS995 spanning approximately 4.0 cM (13.3 Mb). We have also investigated the distribution of the DXS1225-DXS8082 haplotype which has been recently proposed as a good marker of population genetic history due to its low recombination rate.
Results
the results obtained indicate a decrease of LD on the island from the central mountainous toward the coastal sub-populations. In addition the analysis of the DXS1225-DXS8082 haplotype revealed: 1) the presence of a particular haplotype with high frequency; 2) the derivation from a common genetic pool of the sub-populations examined in the present study.
Conclusion
These results indicate the Corsican sub-populations useful for the fine mapping of genes contributing to complex diseases.
doi:10.1186/1471-2350-9-73
PMCID: PMC2518545  PMID: 18662385
8.  Genomics, Transcriptional Profiling and Heart Failure 
Associated with technological progress in DNA and mRNA profiling, advances in basic biology have led to a more complete and sophisticated understanding of interactions between genes, environment and affected tissues in the setting of complex and heterogeneous conditions like heart failure (HF). Ongoing identification of mutations causing hereditary hypertrophic and dilated cardiomyopathies has provided both pathophysiological insights and clinically applicable diagnostics for these relatively rare conditions. Genotyping clinical trial participants and genome wide association studies (GWAS) have accelerated the identification of much more common disease-modifying and treatment modifying genes that explain patient-to-patient differences that have long been recognized by practicing clinicians. At the same time, increasingly detailed characterization of gene expression within diseased tissues and circulating cells from animal models and patients are providing new insights into pathophysiology of HF that permit identification of novel diagnostic and therapeutic targets. In this rapidly evolving field, there is already ample support for the concept that genetic and expression profiling can enhance diagnostic sensitivity and specificity while providing a rational basis for prioritizing alternative therapeutic options in patients with cardiomyopathies and HF. Though the extensive characterizations provided by genomic and transcriptional profiling will increasingly challenge clinicians’ abilities to utilize complex and diverse information, advances in clinical information technology and user interfaces will permit greater individualization of prevention and treatment strategies to address the HF epidemic.
doi:10.1016/j.jacc.2008.12.064
PMCID: PMC2738978  PMID: 19422981
genomics; transcription; heart failure
9.  Advances in Genetical Genomics of Plants 
Current Genomics  2009;10(8):540-549.
Natural variation provides a valuable resource to study the genetic regulation of quantitative traits. In quantitative trait locus (QTL) analyses this variation, captured in segregating mapping populations, is used to identify the genomic regions affecting these traits. The identification of the causal genes underlying QTLs is a major challenge for which the detection of gene expression differences is of major importance. By combining genetics with large scale expression profiling (i.e. genetical genomics), resulting in expression QTLs (eQTLs), great progress can be made in connecting phenotypic variation to genotypic diversity. In this review we discuss examples from human, mouse, Drosophila, yeast and plant research to illustrate the advances in genetical genomics, with a focus on understanding the regulatory mechanisms underlying natural variation. With their tolerance to inbreeding, short generation time and ease to generate large families, plants are ideal subjects to test new concepts in genetics. The comprehensive resources which are available for Arabidopsis make it a favorite model plant but genetical genomics also found its way to important crop species like rice, barley and wheat. We discuss eQTL profiling with respect to cis and trans regulation and show how combined studies with other ‘omics’ technologies, such as metabolomics and proteomics may further augment current information on transcriptional, translational and metabolomic signaling pathways and enable reconstruction of detailed regulatory networks. The fast developments in the ‘omics’ area will offer great potential for genetical genomics to elucidate the genotype-phenotype relationships for both fundamental and applied research.
doi:10.2174/138920209789503914
PMCID: PMC2817885  PMID: 20514216
Genetical genomics; e-QTL; network reconstruction; Arabidopsis thaliana; crop genetics.
10.  A genome-wide analysis of population structure in the Finnish Saami with implications for genetic association studies 
The understanding of patterns of genetic variation within and among human populations is a prerequisite for successful genetic association mapping studies of complex diseases and traits. Some populations are more favorable for association mapping studies than others. The Saami from northern Scandinavia and the Kola Peninsula represent a population isolate that, among European populations, has been less extensively sampled, despite some early interest for association mapping studies. In this paper, we report the results of a first genome-wide SNP-based study of genetic population structure in the Finnish Saami. Using data from the HapMap and the human genome diversity project (HGDP-CEPH) and recently developed statistical methods, we studied individual genetic ancestry. We quantified genetic differentiation between the Saami population and the HGDP-CEPH populations by calculating pair-wise FST statistics and by characterizing identity-by-state sharing for pair-wise population comparisons. This study affirms an east Asian contribution to the predominantly European-derived Saami gene pool. Using model-based individual ancestry analysis, the median estimated percentage of the genome with east Asian ancestry was 6% (first and third quartiles: 5 and 8%, respectively). We found that genetic similarity between population pairs roughly correlated with geographic distance. Among the European HGDP-CEPH populations, FST was smallest for the comparison with the Russians (FST=0.0098), and estimates for the other population comparisons ranged from 0.0129 to 0.0263. Our analysis also revealed fine-scale substructure within the Finnish Saami and warns against the confounding effects of both hidden population structure and undocumented relatedness in genetic association studies of isolated populations.
doi:10.1038/ejhg.2010.179
PMCID: PMC3062008  PMID: 21150888
Saami; genetic association studies; population structure; population isolates
11.  Genetic characterization of northeastern Italian population isolates in the context of broader European genetic diversity 
Population genetic studies on European populations have highlighted Italy as one of genetically most diverse regions. This is possibly due to the country's complex demographic history and large variability in terrain throughout the territory. This is the reason why Italy is enriched for population isolates, Sardinia being the best-known example. As the population isolates have a great potential in disease-causing genetic variants identification, we aimed to genetically characterize a region from northeastern Italy, which is known for isolated communities. Total of 1310 samples, collected from six geographically isolated villages, were genotyped at >145 000 single-nucleotide polymorphism positions. Newly genotyped data were analyzed jointly with the available genome-wide data sets of individuals of European descent, including several population isolates. Despite the linguistic differences and geographical isolation the village populations still show the greatest genetic similarity to other Italian samples. The genetic isolation and small effective population size of the village populations is manifested by higher levels of genomic homozygosity and elevated linkage disequilibrium. These estimates become even more striking when the detected substructure is taken into account. The observed level of genetic isolation in Friuli-Venezia Giulia region is more extreme according to several measures of isolation compared with Sardinians, French Basques and northern Finns, thus proving the status of an isolate.
doi:10.1038/ejhg.2012.229
PMCID: PMC3658181  PMID: 23249956
population genetics; isolated population; genetic distance
12.  An Evaluation of Statistical Approaches to Rare Variant Analysis in Genetic Association Studies 
Genetic Epidemiology  2009;34(2):188-193.
Genome-wide association (GWA) studies have proved to be extremely successful in identifying novel common polymorphisms contributing effects to the genetic component underlying complex traits. Nevertheless, one source of, as yet, undiscovered genetic determinants of complex traits are those mediated through the effects of rare variants. With the increasing availability of large-scale re-sequencing data for rare variant discovery, we have developed a novel statistical method for the detection of complex trait associations with these loci, based on searching for accumulations of minor alleles within the same functional unit. We have undertaken simulations to evaluate strategies for the identification of rare variant associations in population-based genetic studies when data are available from re-sequencing discovery efforts or from commercially available GWA chips. Our results demonstrate that methods based on accumulations of rare variants discovered through re-sequencing offer substantially greater power than conventional analysis of GWA data, and thus provide an exciting opportunity for future discovery of genetic determinants of complex traits. Genet. Epidemiol. 34: 188–193, 2010. © 2009 Wiley-Liss, Inc.
doi:10.1002/gepi.20450
PMCID: PMC2962811  PMID: 19810025
rare variant association; re-sequencing data; genome-wide association data
13.  Inter-rater reliability and concurrent validity of DSM-IV opioid dependence in a Hmong isolate using the Thai version of the Semi-Structured Assessment for Drug Dependence and Alcoholism (SSADDA) 
Addictive behaviors  2011;36(1-2):156-160.
Because isolated populations offer relative genetic and environmental homogeneity, they are important resources for mapping genes for complex traits. Reliable and valid phenotypic characterization of the disease in the population studied is essential. We examined diagnostic reliability and concurrent validity of DSM-IV opioid dependence (OD) in a Hmong population in Thailand with historically high rates of opium (and heroin) use.
578 Thai-speaking Hmong individuals were assessed for lifetime OD, using Thai versions of both the Semi-Structured Assessment for Drug Dependence and Alcoholism (Thai SSADDA) and the Mini-Neuropsychiatric Interview (Thai MINI; adapted for lifetime diagnoses). In a subsample of 123 individuals, two raters interviewed each subject independently within a 2-week period. Chance-corrected agreement on the OD diagnosis was assessed between raters and instruments.
Results showed excellent agreement for the DSM-IV diagnosis of OD both for the SSADDA (κ=0.97) and MINI: (κ=1.00) and between the SSADDA and MINI (κ=0.97).
Consistent with reliability assessments of English versions, our data demonstrate high reliability for Thai versions of the SSADDA and MINI in the diagnosis of OD. The high concordance between instruments supports the concurrent validity of the diagnosis.
Either interview provides reliable, valid OD diagnoses in Thai-speaking Hmong individuals.
doi:10.1016/j.addbeh.2010.08.031
PMCID: PMC2981662  PMID: 20888699
14.  SP18 Diagnostic and Research Uses of SNP Microarrays in Isolated Populations 
The Clinic for Special Children is a small non-profit pediatric medical facility that specializes in the diagnosis and treatment of genetic disorders among the Amish and Mennonite people of Pennsylvania. Our research leads to the discovery of pathogenic sequence variants and permits the rapid and cost-effective use of molecular genetic testing in our patient population. A current focus of our work is identification of all mutations segregating in the Plain populations of southeastern Pennsylvania for use in diagnostic testing. Presently, over 75 different mutations have been characterized in our patients.
The unique properties of young isolated populations such as the Amish and Mennonites facilitate rapid gene-mapping studies. Disease gene identification strategies, namely candidate gene localization and genome-wide mapping, have been employed with much success using Affymetrix GeneChip microarrays. Over the past 2 years, we have mapped four novel phenotypes and identified the causative gene: (1) sudden infant death with dysgenesis of the testes (SIDDT), (2) cortical dysplasia and focal epilepsy (CDFE), (3) a recessive cardiomyopathy, and (4) LYK5 deficiency. Eight other novel phenotypes have been mapped as well, but the gene has yet to be identified.
In addition to standard genotyping applications, DNA copy number changes are also detectable using SNP microarrays. The clinic laboratory has been exploring the utility of microarray copy number detection for performing “molecular karyotypes.” In our patients, we have detected several chromosomal abnormalities, some of which are below the resolution of standard karyotype analysis.
SNP microarray technology holds great promise as a useful diagnostic tool. The clinic laboratory is currently developing a custom diagnostic microarray that will accommodate several clinical applications on one platform. In addition to genome-wide SNP coverage for disease gene mapping and copy number analyses, this custom microarray will contain all known mutations from the Plain communities, permitting comprehensive molecular genetic testing.
PMCID: PMC2292047
15.  Genome-wide association mapping reveals a rich genetic architecture of complex traits in Oryza sativa 
Nature Communications  2011;2:467-.
Asian rice, Oryza sativa is a cultivated, inbreeding species that feeds over half of the world's population. Understanding the genetic basis of diverse physiological, developmental, and morphological traits provides the basis for improving yield, quality and sustainability of rice. Here we show the results of a genome-wide association study based on genotyping 44,100 SNP variants across 413 diverse accessions of O. sativa collected from 82 countries that were systematically phenotyped for 34 traits. Using cross-population-based mapping strategies, we identified dozens of common variants influencing numerous complex traits. Significant heterogeneity was observed in the genetic architecture associated with subpopulation structure and response to environment. This work establishes an open-source translational research platform for genome-wide association studies in rice that directly links molecular variation in genes and metabolic pathways with the germplasm resources needed to accelerate varietal development and crop improvement.
Understanding the genetics and physiology of domesticated species is important for crop improvement. By studying natural variation and the phenotypic traits of 413 diverse accessions of rice, Zhao et al. identify many common genetic variants that influence quantitative traits such as seed size and flowering time.
doi:10.1038/ncomms1467
PMCID: PMC3195253  PMID: 21915109
16.  The HapMap Resource is Providing New Insights into Ourselves and its Application to Pharmacogenomics 
The exploration of quantitative variation in complex traits such as gene expression and drug response in human populations has become one of the major priorities for medical genetics. The International HapMap Project provides a key resource of genotypic data on human lymphoblastoid cell lines derived from four major world populations of European, African, Chinese and Japanese ancestry for researchers to associate with various phenotypic data to find genes affecting health, disease and response to drugs. Recent progress in dissecting genetic contribution to natural variation in gene expression within and among human populations and variation in drug response are two examples in which researchers have utilized the HapMap resource. The HapMap Project provides new insights into the human genome and has applicability to pharmacogenomics studies leading to personalized medicine.
PMCID: PMC2288550  PMID: 18392109
HapMap; lymphoblastoid cell lines; genotype; gene expression; population genetics
17.  The HapMap Resource is Providing New Insights into Ourselves and its Application to Pharmacogenomics 
The exploration of quantitative variation in complex traits such as gene expression and drug response in human populations has become one of the major priorities for medical genetics. The International HapMap Project provides a key resource of genotypic data on human lymphoblastoid cell lines derived from four major world populations of European, African, Chinese and Japanese ancestry for researchers to associate with various phenotypic data to find genes affecting health, disease and response to drugs. Recent progress in dissecting genetic contribution to natural variation in gene expression within and among human populations and variation in drug response are two examples in which researchers have utilized the HapMap resource. The HapMap Project provides new insights into the human genome and has applicability to pharmacogenomics studies leading to personalized medicine.
PMCID: PMC2288550  PMID: 18392109
HapMap; Lymphoblastoid cell lines; Genotype; Gene expression; Population genetics
18.  solQTL: a tool for QTL analysis, visualization and linking to genomes at SGN database 
BMC Bioinformatics  2010;11:525.
Background
A common approach to understanding the genetic basis of complex traits is through identification of associated quantitative trait loci (QTL). Fine mapping QTLs requires several generations of backcrosses and analysis of large populations, which is time-consuming and costly effort. Furthermore, as entire genomes are being sequenced and an increasing amount of genetic and expression data are being generated, a challenge remains: linking phenotypic variation to the underlying genomic variation. To identify candidate genes and understand the molecular basis underlying the phenotypic variation of traits, bioinformatic approaches are needed to exploit information such as genetic map, expression and whole genome sequence data of organisms in biological databases.
Description
The Sol Genomics Network (SGN, http://solgenomics.net) is a primary repository for phenotypic, genetic, genomic, expression and metabolic data for the Solanaceae family and other related Asterids species and houses a variety of bioinformatics tools. SGN has implemented a new approach to QTL data organization, storage, analysis, and cross-links with other relevant data in internal and external databases. The new QTL module, solQTL, http://solgenomics.net/qtl/, employs a user-friendly web interface for uploading raw phenotype and genotype data to the database, R/QTL mapping software for on-the-fly QTL analysis and algorithms for online visualization and cross-referencing of QTLs to relevant datasets and tools such as the SGN Comparative Map Viewer and Genome Browser. Here, we describe the development of the solQTL module and demonstrate its application.
Conclusions
solQTL allows Solanaceae researchers to upload raw genotype and phenotype data to SGN, perform QTL analysis and dynamically cross-link to relevant genetic, expression and genome annotations. Exploration and synthesis of the relevant data is expected to help facilitate identification of candidate genes underlying phenotypic variation and markers more closely linked to QTLs. solQTL is freely available on SGN and can be used in private or public mode.
doi:10.1186/1471-2105-11-525
PMCID: PMC2984588  PMID: 20964836
19.  Heritability and Demographic Analyses in the Large Isolated Population of Val Borbera Suggest Advantages in Mapping Complex Traits Genes 
PLoS ONE  2009;4(10):e7554.
Background
Isolated populations are a useful resource for mapping complex traits due to shared stable environment, reduced genetic complexity and extended Linkage Disequilibrium (LD) compared to the general population. Here we describe a large genetic isolate from the North West Apennines, the mountain range that runs through Italy from the North West Alps to the South.
Methodology/Principal Findings
The study involved 1,803 people living in 7 villages of the upper Borbera Valley. For this large population cohort, data from genealogy reconstruction, medical questionnaires, blood, anthropometric and bone status QUS parameters were evaluated. Demographic and epidemiological analyses indicated a substantial genetic component contributing to each trait variation as well as overlapping genetic determinants and family clustering for some traits.
Conclusions/Significance
The data provide evidence for significant heritability of medical relevant traits that will be important in mapping quantitative traits. We suggest that this population isolate is suitable to identify rare variants associated with complex phenotypes that may be difficult to study in larger but more heterogeneous populations.
doi:10.1371/journal.pone.0007554
PMCID: PMC2761731  PMID: 19847309
20.  Genetic Variants Associated with Complex Human Diseases Show Wide Variation across Multiple Populations 
Public Health Genomics  2009;13(2):72-79.
Background
The wide use of genome wide association studies (GWAS) has led to the successful identification of multiple genetic susceptibility variants to several complex human diseases. Given the limited amount of data on genetic variation at these loci in populations of non-European origin, we investigated population variation among 11 population groups for loci showing strong and consistent association from GWAS with several complex human diseases.
Methods
Data from the International HapMap Project Phase 3, comprising 11 population groups, were used to estimate allele frequencies at loci showing strong and consistent association from GWAS with any of 26 complex human diseases and traits. Allele frequency summary statistics and FST at each locus were used to estimate population differentiation.
Results
There is wide variation in allele frequencies and FST across the 11 population groups for susceptibility loci to these complex human diseases and traits. Allele frequencies varied widely across populations, often by as much as 20- to 40-fold. FST, as a measure of population differentiation, also varied widely across the loci studied (for example, 0.019 to 0.201 for type 2 diabetes, 0.022 to 0.520 for prostate cancer loci, and 0.006 to 0.520 for serum lipid levels).
Conclusions
The public health risk posed by any of these risk alleles is likely to show wide variation across populations simply as a function of its frequency, and this risk difference may be amplified by gene-gene and gene-environment interactions. These analyses offer compelling reasons for including multiple human populations from different parts of the world in the international effort to use genomic tools to understand disease etiology and differential distribution of diseases across ethnic groups.
doi:10.1159/000218711
PMCID: PMC2835382  PMID: 19439916
Complex disease; Genome wide association studies; Population genetics
21.  Integration of genetic and genomic methods for identification of genes and gene variants encoding QTLs in the nonhuman primate 
Methods (San Diego, Calif.)  2009;49(1):63-69.
We have developed an integrated approach, using genetic and genomic methods, in conjunction with resources from the Southwest National Primate Research Center (SNPRC) baboon colony, for the identification of genes and their functional variants that encode quantitative trait loci (QTL). In addition, we use comparative genomic methods to overcome the paucity of baboon specific reagents and to augment translation of our findings in a nonhuman primate (NHP) to the human population. We are using the baboon as a model to study the genetics of cardiovascular disease (CVD). A key step for understanding gene-environment interactions in cardiovascular disease is the identification of genes and gene variants that influence CVD phenotypes. We have developed a sequential methodology that takes advantage of the SNPRC pedigreed baboon colony, the annotated human genome, and current genomic and bioinformatic tools. The process of functional polymorphism identification for genes encoding QTLs involves comparison of expression profiles for genes and predicted genes in the genomic region of the QTL for individuals discordant for the phenotypic trait mapping to the QTL. After comparison, genes of interest are prioritized, and functional polymorphisms are identified in candidate genes by genotyping and quantitative trait nucleotide analysis. This approach reduces the time and labor necessary to prioritize and identify genes and their polymorphisms influencing variation in a quantitative trait compared with traditional positional cloning methods.
doi:10.1016/j.ymeth.2009.06.009
PMCID: PMC2760456  PMID: 19596448
Nonhuman primate (NHP); quantitative trait loci (QTL); cardiovascular disease (CVD); functional polymorphism; discordant sib-pairs; gene array; gene networks
22.  Of mice and men: molecular genetics of congenital heart disease 
Congenital heart disease (CHD) affects nearly 1 % of the population. It is a complex disease, which may be caused by multiple genetic and environmental factors. Studies in human genetics have led to the identification of more than 50 human genes, involved in isolated CHD or genetic syndromes, where CHD is part of the phenotype. Furthermore, mapping of genomic copy number variants and exome sequencing of CHD patients have led to the identification of a large number of candidate disease genes. Experiments in animal models, particularly in mice, have been used to verify human disease genes and to gain further insight into the molecular pathology behind CHD. The picture emerging from these studies suggest that genetic lesions associated with CHD affect a broad range of cellular signaling components, from ligands and receptors, across down-stream effector molecules to transcription factors and co-factors, including chromatin modifiers.
doi:10.1007/s00018-013-1430-1
PMCID: PMC3958813  PMID: 23934094
Congenital heart disease; CHD; Disease genes; Copy number variants; CNVs
23.  Nutrition and Physical Activity in Aging, Obesity, and Cancer 
The analysis of complex genetic traits, including mapping and identification of causative genes, has long been an enigma of genetic biology, whether in the animal sciences or in medical sciences. Traits of agricultural interest and traits of medical interest are often under the influence of both environmental factors and multiple genes, each with modest contributions to the total variance in the trait. Although the number of known mutations underlying complex traits is still relatively small, advances in genomics have greatly enhanced traditional pathways to their analysis and gene mining. The candidate gene approach, linkage analysis, and association studies are all significantly more powerful with recent advances in genome mapping, sequencing, and analysis of individual variation. Avenues to gene discovery are discussed with emphasis on genome wide association studies (GWAS) and the use of single nucleotide polymorphisms (SNPs) as revealed by increasingly powerful commercially available microarrays.
doi:10.1111/j.1749-6632.2012.06733.x
PMCID: PMC3483623  PMID: 23050961
genomics; complex traits; animals, disease resistance; GWAS
24.  Natural Genetic Variation in Arabidopsis: Tools, Traits and Prospects for Evolutionary Ecology 
Annals of Botany  2007;99(6):1043-1054.
Background
The model plant Arabidopsis thaliana (Arabidopsis) shows a wide range of genetic and trait variation among wild accessions. Because of its unparalleled biological and genomic resources, the potential of Arabidopsis for molecular genetic analysis of this natural variation has increased dramatically in recent years.
Scope
Advanced genomics has accelerated molecular phylogenetic analysis and gene identification by quantitative trait loci (QTL) mapping and/or association mapping in Arabidopsis. In particular, QTL mapping utilizing natural accessions is now becoming a major strategy of gene isolation, offering an alternative to artificial mutant lines. Furthermore, the genomic information is used by researchers to uncover the signature of natural selection acting on the genes that contribute to phenotypic variation. The evolutionary significance of such genes has been evaluated in traits such as disease resistance and flowering time. However, although molecular hallmarks of selection have been found for the genes in question, a corresponding ecological scenario of adaptive evolution has been difficult to prove. Ecological strategies, including reciprocal transplant experiments and competition experiments, and utilizing near-isogenic lines of alleles of interest will be a powerful tool to measure the relative fitness of phenotypic and/or allelic variants.
Conclusions
As the plant model organism, Arabidopsis provides a wealth of molecular background information for evolutionary genetics. Because genetic diversity between and within Arabidopsis populations is much higher than anticipated, combining this background information with ecological approaches might well establish Arabidopsis as a model organism for plant evolutionary ecology.
doi:10.1093/aob/mcl281
PMCID: PMC3243570  PMID: 17259228
Arabidopsis; natural genetic variation; natural trait variation; QTL mapping; LD mapping; plant development; plant evolution; molecular ecology
25.  A genome-wide analysis of 'Bounty' descendants implicates several novel variants in migraine susceptibility 
Neurogenetics  2012;13(3):261-266.
Migraine is a common neurological disease with a complex genetic aetiology. The disease affects ~12% of the Caucasian population and females are three times more likely than males to be diagnosed. In an effort to identify loci involved in migraine susceptibility, we performed a pedigree-based genome-wide association study of the isolated population of Norfolk Island, which has a high prevalence of migraine. This unique population originates from a small number of British and Polynesian founders who are descendents of the Bounty mutiny and forms a very large multigenerational pedigree (Bellis et al.; Human Genetics, 124(5):543–5542, 2008). These population genetic features may facilitate disease gene mapping strategies (Peltonen et al.; Nat Rev Genet, 1(3):182–90, 2000. In this study, we identified a high heritability of migraine in the Norfolk Island population (h2=0.53, P=0.016). We performed a pedigree-based GWAS and utilised a statistical and pathological prioritisation approach to implicate a number of variants in migraine. An SNP located in the zinc finger protein 555 (ZNF555) gene (rs4807347) showed evidence of statistical association in our Norfolk Island pedigree (P=9.6×10−6) as well as replication in a large independent and unrelated cohort with >500 migraineurs. In addition, we utilised a biological prioritisation to implicate four SNPs, in within the ADARB2 gene, two SNPs within the GRM7 gene and a single SNP in close proximity to a HTR7 gene. Association of SNPs within these neurotransmitter-related genes suggests a disrupted serotoninergic system that is perhaps specific to the Norfolk Island pedigree, but that might provide clues to understanding migraine more generally.
doi:10.1007/s10048-012-0325-x
PMCID: PMC3604878  PMID: 22678113
Migraine; Association; Gene

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