Mining the HapMap to dissect complex traits

A report on the Keystone Symposium 'Genome Sequence Variation and the Inherited Basis of Common Disease and Complex Traits', Big Sky, USA, 8-13 January 2006.     

Whole-genome SNP association analysis of reproduction traits in the Finnish Landrace pig breed

Background

Good genetic progress for pig reproduction traits has been achieved using a quantitative genetics-based multi-trait BLUP evaluation system. At present, whole-genome single nucleotide polymorphisms (SNP) panels provide a new tool for pig selection. The purpose of this study was to identify SNP associated with reproduction traits in the Finnish Landrace pig breed using the Illumina PorcineSNP60 BeadChip.

Methods

Association of each SNP with different traits was tested with a weighted linear model, using SNP genotype as a covariate and animal as a random variable. Deregressed estimated breeding values of the progeny tested boars were used as the dependent variable and weights were based on their reliabilities. Statistical significance of the associations was based on Bonferroni-corrected P-values.

Results

Deregressed estimated breeding values were available for 328 genotyped boars. Of the 62 163 SNP in the chip, 57 868 SNP had a call rate > 0.9 and 7 632 SNP were monomorphic. Statistically significant results (P-value < 2.0E-06) were obtained for total number of piglets born in first and later parities and piglet mortality between birth and weaning in later parity, and suggestive associations (P-value < 4.0E-06) for piglet mortality between birth and weaning in first parity, number of stillborn piglets in later parity, first farrowing interval and second farrowing interval. Two of the statistically significant regions for total number of piglets born in first and later parities are located on chromosome 9 around 95 and 79 Mb. The estimated SNP effect in these regions was approximately one piglet between the two homozygote classes. By combining the two most significant SNP in these regions, favourable double homozygote animals are expected to have 1.3 piglets (P-value = 1.69E-08) more than unfavourable double homozygote animals. A region on chromosome 9 (66 Mb) was statistically significant for piglet mortality between birth and weaning in later parity (0.44 piglets between homozygotes, P-value = 6.94E-08).

Conclusions

Three separate regions on chromosome 9 gave significant results for litter size and pig mortality. The frequencies of favourable alleles of the significant SNP are moderate in the Finnish Landrace population and these SNP are thus valuable candidates for possible marker-assisted selection.     

Exploiting naturally occurring DNA variation and molecular profiling data to dissect disease and drug response traits

Identifying the key drivers of common human diseases and associated signaling pathways remains one of the primary objectives in the biomedical and life sciences. In this respect, common inbred strains of mice have played a crucial role, and recent advances in the development of genomics and bioinformatics tools have significantly enhanced their utility for this purpose. These advances have enabled a more holistic, network-oriented view of biological systems that facilitates elucidation of the underlying causes of disease and the best ways to target them. Success in reconstructing gene networks underlying disease traits (or other complex traits like drug response) and identifying the key drivers of these traits now largely rests on integrative approaches that combine data from multiple different sources. Such integrative genomics approaches that take into account genotypic, molecular profiling and clinical data in segregating mouse populations have recently been developed. Key to this integration has been the development and application of sophisticated algorithms to mine the diversity of data.     

Strategies to dissect parasite proteomes

Proteomes are complex and dynamic entities that are still poorly understood, but the application of proteomic technologies has become invaluable in many areas of biology, including parasitology. These technologies can be exploited to identify proteins in both complex or relatively simple samples, that formerly could only be characterized by targeted approaches such as Western blotting. Quantitative proteomic approaches can reveal modulations in protein expression that accompany phenotypes of interest. Proteomic approaches have been exploited to understand some of the molecular basis for host:parasite interactions and to elucidate phenotypes such as virulence, antigenicity and drug resistance. Many of the same technologies can also be more easily applied to targeted sub-proteomes. Examples from several studies on pathogen proteomes and sub-proteomes, from bacteria to helminths, are presented to illustrate the potential and limitations of proteomic technologies.     

Genome-wide association studies for seven production traits highlight genomic regions useful to dissect dry-cured ham quality and production traits in Duroc heavy pigs

Protected designation of origin dry-cured hams are obtained from heavy pigs (slaughtered at about 160 kg of live weight). A specific breeding program designed to improve meat quality for this production has included as key traits the level of intermuscular fat between the leg muscles and ham weight loss during the seasoning period together with a balance between fat and lean cuts. In this study we carried out genome-wide association studies for seven traits used in the genetic merit of Italian Duroc heavy pigs, five related to meat and carcass quality traits (visible intermuscular fat, ham weight loss at first salting, backfat thickness, ham weight and lean cuts), and two related to performance and efficiency traits (average daily gain and feed : gain ratio). A total of 573 performance-tested pigs were genotyped with the Illumina PorcineSNP60 BeadChip and genome-wide association analyses were carried out using the Bayes B approach with the 1 Mb window option of GenSel and random residuals for each of the seven traits. Detected windows were supported by independent single nucleotide polymorphism analyses with a linear mixed model (LMM) approach on the same animals for the same traits. A total of 30 windows identifying different quantitative trait loci (QTL) were detected and among those, 27 were confirmed by LMM in one of these traits. Among the confirmed windows, three QTL were reported for visible intermuscular fat, seven for ham weight loss at first salting and five and four for backfat thickness and lean cut, respectively. A total of eight QTL were detected for the other production traits. No overlapping QTL were reported except for one window on porcine chromosome 10 between lean cuts and ham weight that contained the CACNB2 gene that has been already associated with loin marbling score in other Duroc pigs. Several regions contained genes that have been already associated with production traits in other pig breeds, including Duroc lines, related to fat deposition or muscle structure. This work reports, for the first time, genome-wide association study results for several traits in Italian Duroc heavy pigs. These results will be useful to dissect the genetic basis for dry-cured ham production traits that determine the total genetic merit index of Italian Duroc pigs.     

The use of genetic correlations to evaluate associations between SNP markers and quantitative traits

Open-pollinated progeny of Corymbia citriodora established in replicated field trials were assessed for stem diameter, wood density, and pulp yield prior to genotyping single nucleotide polymorphisms (SNP) and testing the significance of associations between markers and assessment traits. Multiple individuals within each family were genotyped and phenotyped, which facilitated a comparison of standard association testing methods and an alternative method developed to relate markers to additive genetic effects. Narrow-sense heritability estimates indicated there was significant additive genetic variance within this population for assessment traits ( $ {\widehat{h}^{{2}}} = 0.{28}\;{\text{to}}\;0.{44} $ ) and genetic correlations between the three traits were negligible to moderate (r _G?=?0.08 to 0.50). The significance of association tests (p values) were compared for four different analyses based on two different approaches: (1) two software packages were used to fit standard univariate mixed models that include SNP-fixed effects, (2) bivariate and multivariate mixed models including each SNP as an additional selection trait were used. Within either the univariate or multivariate approach, correlations between the tests of significance approached +1; however, correspondence between the two approaches was less strong, although between-approach correlations remained significantly positive. Similar SNP markers would be selected using multivariate analyses and standard marker-trait association methods, where the former facilitates integration into the existing genetic analysis systems of applied breeding programs and may be used with either single markers or indices of markers created with genomic selection processes.     

SNP and haplotype analysis reveal IGF2 variants associated with growth traits in Chinese Qinchuan cattle

Insulin-like growth factor 2 (IGF2) is a potent cell growth and differentiation factor and is implicated in mammals’ growth and development. The objective of this study was to evaluate the effects of the mutations in the bovine IGF2 with growth traits in Chinese Qinchuan cattle. Four single nucleotide polymorphisms (SNPs) were detected of the bovine IGF2 by DNA pool sequencing and forced polymerase chain reaction–restriction fragment length polymorphism (forced PCR–RFLP) methods. We also investigated haplotype structure and linkage disequilibrium (LD) coefficients for four SNPs in 817 individuals representing two main cattle breeds from China. The result of haplotype analysis showed eight different haplotypes and 27 combined genotypes within the study population. The statistical analyses indicated that the four SNPs, combined genotypes and haplotypes are associated with the withers height, body length, chest breadth, chest depth and body weight in Qinchuan cattle population (P < 0.05 or <0.01). The mutant-type variants and mutant haplotype (Hap 8: ATGG; likely to be the beneficial QTN allele) was superior for growth traits; the heterozygote diplotype was associated with higher growth traits compared to wild-type homozygote. Our results provide evidence that polymorphisms in the IGF2 gene are associated with growth traits, and may be used for marker-assisted selection in beef cattle breeding program.     

Predicting unobserved phenotypes for complex traits from whole-genome SNP data

Genome-wide association studies (GWAS) for quantitative traits and disease in humans and other species have shown that there are many loci that contribute to the observed resemblance between relatives. GWAS to date have mostly focussed on discovery of genes or regulatory regions habouring causative polymorphisms, using single SNP analyses and setting stringent type-I error rates. Genome-wide marker data can also be used to predict genetic values and therefore predict phenotypes. Here, we propose a Bayesian method that utilises all marker data simultaneously to predict phenotypes. We apply the method to three traits: coat colour, %CD8 cells, and mean cell haemoglobin, measured in a heterogeneous stock mouse population. We find that a model that contains both additive and dominance effects, estimated from genome-wide marker data, is successful in predicting unobserved phenotypes and is significantly better than a prediction based upon the phenotypes of close relatives. Correlations between predicted and actual phenotypes were in the range of 0.4 to 0.9 when half of the number of families was used to estimate effects and the other half for prediction. Posterior probabilities of SNPs being associated with coat colour were high for regions that are known to contain loci for this trait. The prediction of phenotypes using large samples, high-density SNP data, and appropriate statistical methodology is feasible and can be applied in human medicine, forensics, or artificial selection programs.     

How to dissect the Node of Ranvier

Voltage-gated sodium channels are unique in that they combine action potential conduction with cell adhesion. Mammalian sodium channels are heterotrimers, composed of a central, pore-forming α subunit and two auxiliary β subunits. The α subunits are members of a large gene family containing the voltage-gated sodium, potassium, and calcium channels. Sodium channel α subunits form a gene subfamily with at least 11 members. Mutations in sodium channel α subunit genes have been linked to paroxysmal disorders such as epilepsy, long QT syndrome (LQT), and hyperkalemic periodic paralysis in humans, and motor endplate disease and cerebellar ataxia in mice. Three genes encode the sodium channel β subunits with at least one alternative splice product. Unlike the pore-forming α subunits, the sodium channel β subunits are not structurally related to β subunits of calcium and potassium channels. Sodium channel β subunits are multifunctional. They modulate channel gating and regulate the level of channel expression at the plasma membrane. We have shown that β subunits also function as cell adhesion molecules (CAMs) in terms of interaction with extracellular matrix molecules, regulation of cell migration, cellular aggregation, and interaction with the cytoskeleton. A mutation in SCN1B has been shown to cause GEFS + 1 epilepsy in human families. We propose that the sodium channel signalling complex at nodes of Ranvier involves β subunits as channel modulators as well as CAMs, other CAMs such as neurofascin and contactin, RPTPβ, and extracellular matrix molecules such as tenascin.     

Functional genomics and SNP analysis of human genes encoding proline metabolic enzymes

Proline metabolism in mammals involves two other amino acids, glutamate and ornithine, and five enzymatic activities, Δ¹-pyrroline-5-carboxylate (P5C) reductase (P5CR), proline oxidase, P5C dehydrogenase, P5C synthase and ornithine-δ-aminotransferase (OAT). With the exception of OAT, which catalyzes a reversible reaction, the other four enzymes are unidirectional, suggesting that proline metabolism is purpose-driven, tightly regulated, and compartmentalized. In addition, this tri-amino-acid system also links with three other pivotal metabolic systems, namely the TCA cycle, urea cycle, and pentose phosphate pathway. Abnormalities in proline metabolism are relevant in several diseases: six monogenic inborn errors involving metabolism and/or transport of proline and its immediate metabolites have been described. Recent advances in the Human Genome Project, in silico database mining techniques, and research in dissecting the molecular basis of proline metabolism prompted us to utilize functional genomic approaches to analyze human genes which encode proline metabolic enzymes in the context of gene structure, regulation of gene expression, mRNA variants, protein isoforms, and single nucleotide polymorphisms.     

Using SNP markers to dissect linkage disequilibrium at a major quantitative trait locus for resistance to the potato cyst nematode Globodera pallida on potato chromosome V

The damage caused by the parasitic root cyst nematode Globodera pallida is a major yield-limiting factor in potato cultivation . Breeding for resistance is facilitated by the PCR-based marker ‘HC’, which is diagnostic for an allele conferring high resistance against G. pallida pathotype Pa2/3 that has been introgressed from the wild potato species Solanum vernei into the Solanum tuberosum tetraploid breeding pool. The major quantitative trait locus (QTL) controlling this nematode resistance maps on potato chromosome V in a hot spot for resistance to various pathogens including nematodes and the oomycete Phytophthora infestans. An unstructured sample of 79 tetraploid, highly heterozygous varieties and breeding clones was selected based on presence (41 genotypes) or absence (38 genotypes) of the HC marker. Testing the clones for resistance to G. pallida confirmed the diagnostic power of the HC marker. The 79 individuals were genotyped for 100 single nucleotide polymorphisms (SNPs) at 10 loci distributed over 38 cM on chromosome V. Forty-five SNPs at six loci spanning 2 cM in the interval between markers GP21-GP179 were associated with resistance to G. pallida. Based on linkage disequilibrium (LD) between SNP markers, six LD groups comprising between 2 and 18 SNPs were identified. The LD groups indicated the existence of multiple alleles at a single resistance locus or at several, physically linked resistance loci. LD group C comprising 18 SNPs corresponded to the ‘HC’ marker. LD group E included 16 SNPs and showed an association peak, which positioned one nematode resistance locus physically close to the R1 gene family. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

SNP analysis to results (SNPator): a web-based environment oriented to statistical genomics analyses upon SNP data

Single nucleotide polymorphisms (SNPs) are the most widely used marker in studies to assess associations between genetic variants and complex traits or diseases. They are also becoming increasingly important in the study of the evolution and history of humans and other species. The analysis and processing of SNPs obtained thanks to high-throughput technologies imply the time consuming and costly use of different, complex and usually format-incompatible software. SNPator is a user-friendly web-based SNP data analysis suite that integrates, among many other algorithms, the most common steps of a SNP association study. It frees the user from the need to have large computer facilities and an in depth knowledge of genetic software installation and management. Genotype data is directly read from the output files of the usual genotyping platforms. Phenotypic data on the samples can also be easily uploaded. Many different quality control and analysis procedures can be performed either by using built-in SNPator algorithms or by calling standard genetic software. AVAILABILITY: Access is granted from the SNPator webpage http://www.snpator.org.     

A genetic approach to dissect sexually dimorphic behaviors

It has been known since antiquity that gender-specific behaviors are regulated by the gonads. We now know that testosterone is required for the appropriate display of male patterns of behavior. Estrogen and progesterone, on the other hand, are essential for female typical responses. Research from several groups also indicates that estrogen signaling is required for male typical behaviors. This finding raises the issue of the relative contribution of these two hormonal systems in the control of male typical behavioral displays. In this review we discuss the findings that led to these conclusions and suggest various genetic strategies that may be required to understand the relative roles of testosterone and estrogen signaling in the control of gender-specific behavior.     

Genetic approaches to dissect plant nonhost resistance mechanisms

Nonhost resistance (NHR) refers to the immunity of most tested genotypes of a plant species to most tested variants of a pathogen species. Thus, NHR is broad spectrum and durable in nature and constitutes a major safety barrier against invasion of a myriad of potentially pathogenic microbes in any plants including domesticated crops. Genetic study of NHR is generally more difficult compared to host resistance mainly because NHR is genetically more complicated and often lacks intraspecific polymorphisms. Nevertheless, substantial progress has been made towards the understanding of the molecular basis of NHR in the past two decades using various approaches. Not surprisingly, molecular mechanisms of NHR revealed so far encompasses pathogen-associated molecular pattern-triggered immunity and effector-triggered immunity. In this review, we briefly discuss the inherent difficulty in genetic studies of NHR and summarize the main approaches that have been taken to identify genes contributing to NHR. We also discuss new enabling strategies for dissecting multilayered NHR in model plants with a focus on NHR against filamentous pathogens, especially biotrophic pathogens such as powdery mildew and rust fungi.