Advances in statistical human genetics over the last 25 years

Linkage analysis in the next-generation sequencing era

Linkage analysis was developed to detect excess co-segregation of the putative alleles underlying a phenotype with the alleles at a marker locus in family data. Many different variations of this analysis and corresponding study design have been developed to detect this co-segregation. Linkage studies have been shown to have high power to detect loci that have alleles (or variants) with a large effect size, i.e. alleles that make large contributions to the risk of a disease or to the variation of a quantitative trait. However, alleles with a large effect size tend to be rare in the population. In contrast, association studies are designed to have high power to detect common alleles which tend to have a small effect size for most diseases or traits. Although genome-wide association studies have been successful in detecting many new loci with common alleles of small effect for many complex traits, these common variants often do not explain a large proportion of disease risk or variation of the trait. In the past, linkage studies were successful in detecting regions of the genome that were likely to harbor rare variants with large effect for many simple Mendelian diseases and for many complex traits. However, identifying the actual sequence variant(s) responsible for these linkage signals was challenging because of difficulties in sequencing the large regions implicated by each linkage peak. Current 'next-generation' DNA sequencing techniques have made it economically feasible to sequence all exons or the whole genomes of a reasonably large number of individuals. Studies have shown that rare variants are quite common in the general population, and it is now possible to combine these new DNA sequencing methods with linkage studies to identify rare causal variants with a large effect size. A brief review of linkage methods is presented here with examples of their relevance and usefulness for the interpretation of whole-exome and whole-genome sequence data.

Genetic advances in the study of speech and language disorders

Developmental speech and language disorders cover a wide range of childhood conditions with overlapping but heterogeneous phenotypes and underlying etiologies. This characteristic heterogeneity hinders accurate diagnosis, can complicate treatment strategies, and causes difficulties in the identification of causal factors. Nonetheless, over the last decade, genetic variants have been identified that may predispose certain individuals to different aspects of speech and language difficulties. In this review, we summarize advances in the genetic investigation of stuttering, speech-sound disorder (SSD), specific language impairment (SLI), and developmental verbal dyspraxia (DVD). We discuss how the identification and study of specific genes and pathways, including FOXP2, CNTNAP2, ATP2C2, CMIP, and lysosomal enzymes, may advance our understanding of the etiology of speech and language disorders and enable us to better understand the relationships between the different forms of impairment across the spectrum.

Efficiency robust statistics for genetic linkage and association studies under genetic model uncertainty

When testing genetic linkage and association, test statistics that follow a normal or Chi-square distributions are often used. These statistics are usually derived under a specific mode of inheritance (genetic model). Common genetic models include, but not limited to, the recessive, additive, multiplicative, and dominant models. For many diseases, their underlying genetic models are often unknown. Instead, a family of scientifically plausible genetic models may be available, which includes the four commonly used models. Hence, the optimal test is not available. Employing a single test statistic which is optimal for one model may suffer from substantial loss of power when the model is misspecified. In this situation efficient robust tests are useful. In this tutorial, we first review several commonly used robust statistics, including maximum efficiency robust tests, maximal tests, and constrained likelihood ratio tests for three common designs in genetic studies: (i) linkage analysis using affected sib-pairs, (ii) association studies using parents-offspring trios, and (iii) case-control association studies (unmatched and matched). Codes in the R statistical language for applying these robust statistics to test for linkage and association are presented with examples. We also provide some comparisons of the performance of the various robust tests via simulation studies. Guidelines for applications are also given for each study design. Finally, applications of robust tests to genome-wide association studies and meta-analysis are discussed.

A supervised approach for predicting patient survival with gene expression data

Rapid development in genomics in recent years has allowed the simultaneous measurement of the expression levels of thousands of genes using DNA microarrays. This has offered tremendous potential for growth in our understanding of the pathophysiology of many diseases. When microarray studies also contain information about an outcome variable such as time to an event or death, one of the goals of an investigator is to understand how the expression levels of genes (covariates) relate to the time-to-event (referred to as survival time) in the course of a disease.In this article, we consider the case where the number of covariates, p, exceeds the number of observations, N, a setting typical of microarray gene expression data. For a given vector of responses representing survival times of N subjects and the corresponding p × N gene expression matrix, we examine the problem of predicting the survival probability when N ≪ p. This is an ill-conditioned problem further compounded by the presence of possibly censored survival times. We propose a model that combines the partial least squares approach for dimensionality reduction with the accelerated failure time model, a widely used log-linear model for linking censored survival time to covariates. We develop parametric methods to account for censoring as well as for predicting patient survival probabilities. We illustrate the applicability of our methods using cancer microarray data and explore the biological relevance of our results using pathway analysis. Finally, we evaluate the performance of our methods using extensive simulation studies.

Matrix metalloprotease polymorphisms are associated with gas transfer in alpha 1 antitrypsin deficiency

Alpha-1-antitrypsin deficiency (AATD) is associated with variable development of emphysema and other features of chronic obstructive pulmonary disease (COPD). Matrix metalloproteinases (MMPs) are believed to be important in the pathophysiology of COPD, and may therefore confer susceptibility to this phenotype in patients with AATD.

Objectives: to assess the role of polymorphism of MMP1, MMP3 and MMP12 in AATD phenotypes.

Methods: 424 PiZZ subjects from the UK AATD Registry were assessed for history of chronic bronchitis (CB), post-bronchodilator lung function impairment and decline of lung function. Tag single nucleotide polymorphisms (SNPs) for MMP1, MMP3 and MMP12 were chosen using HapMap (r2>0.8, MAF>0.05) and were genotyped using TaqMan® genotyping technologies. Quantitative genetic association was assessed using regression modelling to correct for covariates.

Results: in patients with AATD, carriers of the G allele of rs678815 ( MMP3) had lower gas transfer (KCO) ( P = 0.025, B =-7.766) than the homozygous wild type, while carriers of the T allele of rs470358 ( MMP1) had higher KCO ( P = 0.025, B = 6.130).

Conclusions: variations in MMP1 and MMP3 are associated with gas transfer in AATD, supporting a previous family study showing linkage of KCO to this gene region. Replication of these preliminary data is now required particularly if MMP inhibitors are to be considered as a therapeutic option.

Connecting QTLS to the g-matrix of evolutionary quantitative genetics

Evolutionary quantitative genetics has recently advanced in two distinct streams. Many biologists address evolutionary questions by estimating phenotypic selection and genetic (co)variances (G matrices). Simultaneously, an increasing number of studies have applied quantitative trait locus (QTL) mapping methods to dissect variation. Both conceptual and practical difficulties have isolated these two foci of quantitative genetics. A conceptual integration follows from the recognition that QTL allele frequencies are the essential variables relating the G-matrix to marker-based mapping experiments. Breeding designs initiated from randomly selected parental genotypes can be used to estimate QTL-specific genetic (co)variances. These statistics appropriately distill allelic variation and provide an explicit population context for QTL mapping estimates. Within this framework, one can parse the G-matrix into a set of mutually exclusive genomic components and ask whether these parts are similar or dissimilar in their respective features, for example the magnitude of phenotypic effects and the extent and nature of pleiotropy. As these features are critical determinants of sustained response to selection, the integration of QTL mapping methods into G-matrix estimation can provide a concrete, genetically based experimental program to investigate the evolutionary potential of natural populations.

Association of interacting genes in the toll-like receptor signaling pathway and the antibody response to pertussis vaccination

BACKGROUND

Activation of the Toll-like receptor (TLR) signaling pathway through TLR4 may be important in the induction of protective immunity against Bordetella pertussis with TLR4-mediated activation of dendritic and B cells, induction of cytokine expression, and reversal of tolerance as crucial steps. We examined whether single nucleotide polymorphisms (SNPs) in genes of the TLR4 pathway and their interaction are associated with the response to whole-cell vaccine (WCV) pertussis vaccination in 490 one-year-old children.

METHODOLOGY/PRINCIPAL FINDINGS

We analyzed associations of 75 haplotype-tagging SNPs in genes in the TLR4 signaling pathway with pertussis toxin (PT)-IgG titers. We found significant associations between the PT-IgG titer and SNPs in CD14, TLR4, TOLLIP, TIRAP, IRAK3, IRAK4, TICAM1, and TNFRSF4 in one or more of the analyses. The strongest evidence for association was found for two SNPs (rs5744034 and rs5743894) in TOLLIP that were almost completely in linkage disequilibrium, provided statistically significant associations in all tests with the lowest p-values, and displayed a dominant mode of inheritance. However, none of these single gene associations would withstand correction for multiple testing. In addition, Multifactor Dimensionality Reduction Analysis, an approach that does not need correction for multiple testing, showed significant and strong two and three locus interactions between SNPs in TOLLIP (rs4963060), TLR4 (rs6478317) and IRAK1 (rs1059703).

CONCLUSIONS/SIGNIFICANCE

We have identified significant interactions between genes in the TLR pathway in the induction of vaccine-induced immunity. These interactions underline that these genes are functionally related and together form a true biological relationship in a protein-protein interaction network. Practically all our findings may be explained by genetic variation in directly or indirectly interacting proteins at the extra- and intracytoplasmic sites of the cell membrane of antigen-presenting cells, B cells, or both. Fine tuning of interacting proteins in the TLR pathway appears important for the induction of an optimal vaccine response.

Multistage Sampling for Genetic Studies

In the past, to study Mendelian diseases, segregating families have been carefully ascertained for segregation analysis, followed by collecting extended multiplex families for linkage analysis. This would then be followed by association studies, using independent case-control samples and/or additional family data. Recently, for complex diseases, the initial sampling has been for a genome-wide linkage analysis, often using independent sib-pairs or nuclear families, to identify candidate regions for follow-up with association studies, again using case-control samples and/or additional family data. We now have the ability to conduct genome-wide association studies using 100,000-500,000 diallelic genetic markers. For such studies we focus especially on efficient two-stage association sampling designs, which can retain nearly optimal statistical power at about half the genotyping cost. Similarly, beginning an association study by genotyping pooled samples may also be a viable option if the cost of accurately pooling DNA samples outweighs genotyping costs. Finally, we note that the sampling of family data for linkage analysis is not a practice that should be automatically discontinued.

Establishing a neurological-psychiatric biobank: banking, informatics, ethics

The recent development of genetic databases and biobanks in a number of countries reflects scientist's beliefs in the future health benefits to be derived from genetic research. The NEPSYBANK is a national program of the Hungarian Clinical Neurogenetic Society with comprehensive participation of the Neurology and Psychiatry Departments of Medical Universities and the National Institute of Psychiatry and Neurology. The NEPSYBANK forms a part of the national biobank project (www.biobank.hu). The goal is to establish nationwide collaboration and common biobanking standards on quality, access, and protection of integrity in the field of neurology and psychiatry. Biological materials and databases are already collected in stroke, epilepsy, multiple sclerosis, motoneuron diseases, dementia, movement disorders, schizophrenia, and alcohol addiction. In peripheral neuropathies, neuropathic pain syndromes, muscle diseases, migraine, myasthenia gravis, depression, panic disease, anxiety, autism, and software development is in progress. The resources have been expanded by continued prospective collection of samples and data and important bottlenecks in sample purification, sample retrieval, in protection of the integrity of the research participants, as well as in guaranteeing the security and confidentiality of the participant's information have been harmonized. The development of uniform consent management, comprehensive sample overview and quality standards for health care-related biobanking may provide a unique opportunity for Hungary in molecular clinically oriented research. The program is a diseased-based research biobank with comprehensive collection of phenotypic and environmental information as well as biobanking of DNA, RNA or buffy coat, plasma, and erythrocytes stored at -80 degrees C. The biobank has a neuropathological part as well: storing conventional pathology and biopsy specimens. The analytical and informational demands being created by biobanking requires a "connectivity of community" that has not traditionally been present in the life sciences. As you put more resources into something, your silos tend to become taller, and we need to avoid this. The life science and healthcare community should be ignored working in individual "silos."