Genetic analysis of the ELOVL6 gene polymorphism associated with type 2 diabetes mellitus

Recent animal studies have indicated that overexpression of the elongation of long-chain fatty acids family member 6 (Elovl6) gene can cause insulin resistance and β-cell dysfunction. These are the major factors involved in the development of type 2 diabetes mellitus (T2DM). To identify the relationship between single nucleotide polymorphisms (SNP) ofELOVL6 and T2DM pathogenesis, we conducted a case-control study of 610 Han Chinese individuals (328 newly diagnosed T2DM and 282 healthy subjects). Insulin resistance and islet first-phase secretion function were evaluated by assessment of insulin resistance in a homeostasis model (HOMA-IR) and an arginine stimulation test. Three SNPs of the ELOVL6 gene were genotyped with polymerase chain reaction-restriction fragment length polymorphism, with DNA sequencing used to confirm the results. Only genotypes TT and CT of the ELOVL6 SNP rs12504538 were detected in the samples. Genotype CC was not observed. The T2DM group had a higher frequency of the C allele and the CT genotype than the control group. Subjects with the CT genotype had higher HOMA-IR values than those with the TT genotype. In addition, no statistical significance was observed between the genotype and allele frequencies of the control and T2DM groups for SNPs rs17041272 and rs6824447. The study indicated that the ELOVL6 gene polymorphism rs12504538 is associated with an increased risk of T2DM, because it causes an increase in insulin resistance.

Associations between CD36 gene polymorphisms and susceptibility to coronary artery heart disease

Associations between polymorphisms of the CD36 gene and susceptibility to coronary artery heart disease (CHD) are not clear. We assessed allele frequencies and genotype distributions of CD36 gene polymorphisms in 112 CHD patients and 129 control patients using semi-quantitative polymerase chain reaction (PCR) and restriction fragment length polymorphism (RFLP) analysis. Additionally, we detected CD36 mRNA expression by real-time quantitative PCR, and we quantified plasma levels of oxidized low-density lipoprotein (ox-LDL) using an enzyme-linked immunosorbent assay (ELISA). There were no significant differences between the two groups (P>0.05) in allele frequencies of rs1761667 or in genotype distribution and allele frequencies of rs3173798. The genotype distribution of rs1761667 significantly differed between CHD patients and controls (P=0.034), with a significantly higher frequency of the AG genotype in the CHD group compared to the control group (P=0.011). The plasma levels of ox-LDL in patients with the AG genotype were remarkably higher than those with the GG and AA genotypes (P=0.010). In a randomized sample taken from patients in the two groups, the CD36 mRNA expression of the CHD patients was higher than that of the controls. In CHD patients, the CD36 mRNA expression in AG genotype patients was remarkably higher than in those with an AA genotype (P=0.005). After adjusted logistic regression analysis, the AG genotype of rs1761667 was associated with an increased risk of CHD (OR=2.337, 95% CI=1.336-4.087, P=0.003). In conclusion, the rs1761667 polymorphism may be closely associated with developing CHD in the Chongqing Han population of China, and an AG genotype may be a genetic susceptibility factor for CHD.

Haplotype analysis suggest common founders in carriers of the recurrent BRCA2 mutation, 3398delAAAAG, in French Canadian hereditary breast and/ovarian cancer families

Affiliation: Faculté de médicine, Université de Montréal

Enlarging a training set for genomic selction by imputation of un-genotyped animals in populations of varying genetic architecture

Background: The most common application of imputation is to infer genotypes of a high-density panel of markers on animals that are genotyped for a low-density panel. However, the increase in accuracy of genomic predictions resulting from an increase in the number of markers tends to reach a plateau beyond a certain density. Another application of imputation is to increase the size of the training set with un-genotyped animals. This strategy can be particularly successful when a set of closely related individuals are genotyped. ----- Methods: Imputation on completely un-genotyped dams was performed using known genotypes from the sire of each dam, one offspring and the offspring’s sire. Two methods were applied based on either allele or haplotype frequencies to infer genotypes at ambiguous loci. Results of these methods and of two available software packages were compared. Quality of imputation under different population structures was assessed. The impact of using imputed dams to enlarge training sets on the accuracy of genomic predictions was evaluated for different populations, heritabilities and sizes of training sets. ----- Results: Imputation accuracy ranged from 0.52 to 0.93 depending on the population structure and the method used. The method that used allele frequencies performed better than the method based on haplotype frequencies. Accuracy of imputation was higher for populations with higher levels of linkage disequilibrium and with larger proportions of markers with more extreme allele frequencies. Inclusion of imputed dams in the training set increased the accuracy of genomic predictions. Gains in accuracy ranged from close to zero to 37.14%, depending on the simulated scenario. Generally, the larger the accuracy already obtained with the genotyped training set, the lower the increase in accuracy achieved by adding imputed dams. ----- Conclusions: Whenever a reference population resembling the family configuration considered here is available, imputation can be used to achieve an extra increase in accuracy of genomic predictions by enlarging the training set with completely un-genotyped dams. This strategy was shown to be particularly useful for populations with lower levels of linkage disequilibrium, for genomic selection on traits with low heritability, and for species or breeds for which the size of the reference population is limited.

Hardy-Weinberg Equilibrium and the Ternary Plot

The Hardy-Weinberg law, formulated about 100 years ago, states that under certain assumptions, the three genotypes AA, AB and BB at a bi-allelic locus are expected to occur in the proportions p2, 2pq, and q2 respectively, where p is the allele frequency of A, and q = 1-p. There are many statistical tests being used to check whether empirical marker data obeys the Hardy-Weinberg principle. Among these are the classical xi-square test (with or without continuity correction), the likelihood ratio test, Fisher's Exact test, and exact tests in combination with Monte Carlo and Markov Chain algorithms. Tests for Hardy-Weinberg equilibrium (HWE) are numerical in nature, requiring the computation of a test statistic and a p-value. There is however, ample space for the use of graphics in HWE tests, in particular for the ternary plot. Nowadays, many genetical studies are using genetical markers known as Single Nucleotide Polymorphisms (SNPs). SNP data comes in the form of counts, but from the counts one typically computes genotype frequencies and allele frequencies. These frequencies satisfy the unit-sum constraint, and their analysis therefore falls within the realm of compositional data analysis (Aitchison, 1986). SNPs are usually bi-allelic, which implies that the genotype frequencies can be adequately represented in a ternary plot. Compositions that are in exact HWE describe a parabola in the ternary plot. Compositions for which HWE cannot be rejected in a statistical test are typically “close" to the parabola, whereas compositions that differ significantly from HWE are “far". By rewriting the statistics used to test for HWE in terms of heterozygote frequencies, acceptance regions for HWE can be obtained that can be depicted in the ternary plot. This way, compositions can be tested for HWE purely on the basis of their position in the ternary plot (Graffelman & Morales, 2008). This leads to nice graphical representations where large numbers of SNPs can be tested for HWE in a single graph. Several examples of graphical tests for HWE (implemented in R software), will be shown, using SNP data from different human populations

Búsqueda de selección en el polimorfismo 677C>T (c.665C>T) del gen de la metilentetrahidrofolato reductasa (MTHFR) en una población Colombiana

Se realizó un estudio genético – poblacional en dos grupos etarios de población colombiana con la finalidad de evaluar las diferencias genéticas relacionadas con el polimorfismo MTHFR 677CT en busca de eventos genéticos que soporten la persistencia de este polimorfismo en la especie humana debido que este ha sido asociado con múltiples enfermedades. De esta manera se genotipificaron los individuos, se analizaron los genotipos, frecuencias alélicas y se realizaron diferentes pruebas genéticas-poblacionales. Contrario a lo observado en poblaciones Colombianas revisadas se identificó la ausencia del Equilibrio Hardy-Weinberg en el grupo de los niños y estructuras poblacionales entre los adultos lo que sugiere diferentes historias demográficas y culturales entre estos dos grupos poblacionales al tiempo, lo que soporta la hipótesis de un evento de selección sobre el polimorfismo en nuestra población. De igual manera nuestros datos fueron analizados junto con estudios previos a nivel nacional y mundial lo cual sustenta que el posible evento selectivo es debido a que el aporte de ácido fólico se ha incrementado durante las últimas dos décadas como consecuencia de las campañas de fortificación de las harinas y suplementación a las embarazadas con ácido fólico, por lo tanto aquí se propone un modelo de selección que se ajusta a los datos encontrados en este trabajo se establece una relación entre los patrones nutricionales de la especie humana a través de la historia que explica las diferencias en frecuencias de este polimorfismo a nivel espacial y temporal.  

Genotipificación de HLA-B en pacientes colombianos afectados por el síndrome Stevens-Johnson y la Necrólisis Epidérmica Tóxica

Las reacciones alérgicas a medicamentos cutáneas severas (RAM) como el Síndrome Stevens Johnson (SJS) y la Necrólisis Epidérmica Tóxica (NET),caracterizadas por exantema, erosión de la piel y las membranas mucosas, flictenas, desprendimiento de la piel secundario a la muerte de queratinocitos y compromiso ocular. Son infrecuentes en la población pero con elevada morbi-mortalidad, se presentan luego de la administración de diferentes fármacos. En Asia se ha asociado el alelo HLA-B*15:02 como marcador genético para SJS. En Colombia no hay datos de la incidencia de estas RAM, ni de la relación con medicamentos específicos o potenciales y tampoco estudios de aproximación genómica de genes de susceptibilidad.

Selection and sticklebacks

Over the last decade, there has been increasing circumstantial evidence for the action of natural selection in the genome, arising largely from molecular genetic surveys of large numbers of markers. In nonmodel organisms without densely mapped markers, a frequently used method is to identify loci that have unusually high or low levels of genetic differentiation, or low genetic diversity relative to other populations. The paper by Makinen et al. (2008a) in this issue of Molecular Ecology reports the results of a survey of microsatellite allele frequencies at more than 100 loci in seven populations of the three-spined stickleback (Gasterosteus aculeatus). They show that a microsatellite locus and two indel markers located within the intron of the Eda gene, known to control the number of lateral plates in the stickleback (Fig. 1), tend to be much more highly genetically differentiated than other loci, a finding that is consistent with the action of local selection. They identify a further two independent candidates for local selection, and, most intriguingly, they further suggest that up to 15% of their loci may provide evidence of balancing selection.

An approximate Bayesian computation approach to overcome biases that arise when using amplified fragment length polymorphism markers to study population structure

There is great interest in using amplified fragment length polymorphism (AFLP) markers because they are inexpensive and easy to produce. It is, therefore, possible to generate a large number of markers that have a wide coverage of species genotnes. Several statistical methods have been proposed to study the genetic structure using AFLP's but they assume Hardy-Weinberg equilibrium and do not estimate the inbreeding coefficient, F-IS. A Bayesian method has been proposed by Holsinger and colleagues that relaxes these simplifying assumptions but we have identified two sources of bias that can influence estimates based on these markers: (i) the use of a uniform prior on ancestral allele frequencies and (ii) the ascertainment bias of AFLP markers. We present a new Bayesian method that avoids these biases by using an implementation based on the approximate Bayesian computation (ABC) algorithm. This new method estimates population-specific F-IS and F-ST values and offers users the possibility of taking into account the criteria for selecting the markers that are used in the analyses. The software is available at our web site (http://www-leca.uif-grenoble.fi-/logiciels.htm). Finally, we provide advice on how to avoid the effects of ascertainment bias.

Identifying adaptive genetic divergence among populations from genome scans

The identification of signatures of natural selection in genomic surveys has become an area of intense research, stimulated by the increasing ease with which genetic markers can be typed. Loci identified as subject to selection may be functionally important, and hence (weak) candidates for involvement in disease causation. They can also be useful in determining the adaptive differentiation of populations, and exploring hypotheses about speciation. Adaptive differentiation has traditionally been identified from differences in allele frequencies among different populations, summarised by an estimate of F-ST. Low outliers relative to an appropriate neutral population-genetics model indicate loci subject to balancing selection, whereas high outliers suggest adaptive (directional) selection. However, the problem of identifying statistically significant departures from neutrality is complicated by confounding effects on the distribution of F-ST estimates, and current methods have not yet been tested in large-scale simulation experiments. Here, we simulate data from a structured population at many unlinked, diallelic loci that are predominantly neutral but with some loci subject to adaptive or balancing selection. We develop a hierarchical-Bayesian method, implemented via Markov chain Monte Carlo (MCMC), and assess its performance in distinguishing the loci simulated under selection from the neutral loci. We also compare this performance with that of a frequentist method, based on moment-based estimates of F-ST. We find that both methods can identify loci subject to adaptive selection when the selection coefficient is at least five times the migration rate. Neither method could reliably distinguish loci under balancing selection in our simulations, even when the selection coefficient is twenty times the migration rate.

Comparative evaluation of a new effective population size estimator based on approximate Bayesian computation

We describe and evaluate a new estimator of the effective population size (N-e), a critical parameter in evolutionary and conservation biology. This new "SummStat" N-e. estimator is based upon the use of summary statistics in an approximate Bayesian computation framework to infer N-e. Simulations of a Wright-Fisher population with known N-e show that the SummStat estimator is useful across a realistic range of individuals and loci sampled, generations between samples, and N-e values. We also address the paucity of information about the relative performance of N-e estimators by comparing the SUMMStat estimator to two recently developed likelihood-based estimators and a traditional moment-based estimator. The SummStat estimator is the least biased of the four estimators compared. In 32 of 36 parameter combinations investigated rising initial allele frequencies drawn from a Dirichlet distribution, it has the lowest bias. The relative mean square error (RMSE) of the SummStat estimator was generally intermediate to the others. All of the estimators had RMSE > 1 when small samples (n = 20, five loci) were collected a generation apart. In contrast, when samples were separated by three or more generations and Ne less than or equal to 50, the SummStat and likelihood-based estimators all had greatly reduced RMSE. Under the conditions simulated, SummStat confidence intervals were more conservative than the likelihood-based estimators and more likely to include true N-e. The greatest strength of the SummStat estimator is its flexible structure. This flexibility allows it to incorporate any, potentially informative summary statistic from Population genetic data.

Population subdivision and molecular sequence variation: Theory and analysis of Drosophila ananassae data

Population subdivision complicates analysis of molecular variation. Even if neutrality is assumed, three evolutionary forces need to be considered: migration, mutation, and drift. Simplification can be achieved by assuming that the process of migration among and drift within subpopulations is occurring fast compared to Mutation and drift in the entire population. This allows a two-step approach in the analysis: (i) analysis of population subdivision and (ii) analysis of molecular variation in the migrant pool. We model population subdivision using an infinite island model, where we allow the migration/drift parameter Theta to vary among populations. Thus, central and peripheral populations can be differentiated. For inference of Theta, we use a coalescence approach, implemented via a Markov chain Monte Carlo (MCMC) integration method that allows estimation of allele frequencies in the migrant pool. The second step of this approach (analysis of molecular variation in the migrant pool) uses the estimated allele frequencies in the migrant pool for the study of molecular variation. We apply this method to a Drosophila ananassae sequence data set. We find little indication of isolation by distance, but large differences in the migration parameter among populations. The population as a whole seems to be expanding. A population from Bogor (Java, Indonesia) shows the highest variation and seems closest to the species center.

Measuring departures from Hardy-Weinberg: a Markov chain Monte Carlo method for estimating the inbreeding coefficient

Many well-established statistical methods in genetics were developed in a climate of severe constraints on computational power. Recent advances in simulation methodology now bring modern, flexible statistical methods within the reach of scientists having access to a desktop workstation. We illustrate the potential advantages now available by considering the problem of assessing departures from Hardy-Weinberg (HW) equilibrium. Several hypothesis tests of HW have been established, as well as a variety of point estimation methods for the parameter which measures departures from HW under the inbreeding model. We propose a computational, Bayesian method for assessing departures from HW, which has a number of important advantages over existing approaches. The method incorporates the effects-of uncertainty about the nuisance parameters--the allele frequencies--as well as the boundary constraints on f (which are functions of the nuisance parameters). Results are naturally presented visually, exploiting the graphics capabilities of modern computer environments to allow straightforward interpretation. Perhaps most importantly, the method is founded on a flexible, likelihood-based modelling framework, which can incorporate the inbreeding model if appropriate, but also allows the assumptions of the model to he investigated and, if necessary, relaxed. Under appropriate conditions, information can be shared across loci and, possibly, across populations, leading to more precise estimation. The advantages of the method are illustrated by application both to simulated data and to data analysed by alternative methods in the recent literature.

A genome wide association study of mathematical ability reveals an association at chromosome 3q29, a locus associated with autism and learning difficulties: a preliminary study

Mathematical ability is heritable, but few studies have directly investigated its molecular genetic basis. Here we aimed to identify specific genetic contributions to variation in mathematical ability. We carried out a genome wide association scan using pooled DNA in two groups of U.K. samples, based on end of secondary/high school national academic exam achievement: high (n = 419) versus low (n = 183) mathematical ability while controlling for their verbal ability. Significant differences in allele frequencies between these groups were searched for in 906,600 SNPs using the Affymetrix GeneChip Human Mapping version 6.0 array. After meeting a threshold of p<1.5×10-5, 12 SNPs from the pooled association analysis were individually genotyped in 542 of the participants and analyzed to validate the initial associations (lowest p-value 1.14 ×10-6). In this analysis, one of the SNPs (rs789859) showed significant association after Bonferroni correction, and four (rs10873824, rs4144887, rs12130910 rs2809115) were nominally significant (lowest p-value 3.278 × 10-4). Three of the SNPs of interest are located within, or near to, known genes (FAM43A, SFT2D1, C14orf64). The SNP that showed the strongest association, rs789859, is located in a region on chromosome 3q29 that has been previously linked to learning difficulties and autism. rs789859 lies 1.3 kbp downstream of LSG1, and 700 bp upstream of FAM43A, mapping within the potential promoter/regulatory region of the latter. To our knowledge, this is only the second study to investigate the association of genetic variants with mathematical ability, and it highlights a number of interesting markers for future study.

Assessment of DGAT1 and LEP gene polymorphisms in three Nelore (Bos indicus) lines selected for growth and their relationship with growth and carcass traits

The aim of this study was to analyze LEP and DGAT1 gene polymorphisms in 3 Nelore lines selected for growth and to evaluate their effects on growth and carcass traits. Traits analyzed were birth, weaning, and yearling weight, rump height, LM area, backfat thickness, and rump fat thickness obtained by ultrasound. Two SNP in the LEP gene [LEP 1620(A/G) and LEP 305(T/C)] and the K232A mutation in the DGAT1 gene were analyzed. The sample consisted of 357 Nelore heifers from 2 lines selected for yearling weight and a control line, established in 1980, at the Estacao Experimental de Zootecnia de Sertaozinho (Sertaozinho, Brazil). Three genotypes were obtained for each marker. Differences in allele frequencies among the 3 lines were only observed for the DGAT1 K232A polymorphism, with the frequency of the A allele being greater in the control line than in the selected lines. The DGAT1 K232A mutation was associated only with rump height, whereas LEP 1620(A/G) was associated with weaning weight and LEP 305(T/C) with birth weight and backfat thickness. However, more studies, with larger data sets, are necessary before these makers can be used for marker-assisted selection.