Copy number variants and genetic traits: closer to the resolution of phenotypic to genotypic variability

A considerable and unanticipated plasticity of the human genome, manifested as inter-individual copy number variation, has been discovered. These structural changes constitute a major source of inter-individual genetic variation that could explain variable penetrance of inherited (Mendelian and polygenic) diseases and variation in the phenotypic expression of aneuploidies and sporadic traits, and might represent a major factor in the aetiology of complex, multifactorial traits. For these reasons, an effort should be made to discover all common and rare copy number variants (CNVs) in the human population. This will also enable systematic exploration of both SNPs and CNVs in association studies to identify the genomic contributors to the common disorders and complex traits.     

Population genomics in Sardinia: a novel approach to hunt for genomic combinations underlying complex traits and diseases.

The availability of highly polymorphic markers permits testing whether complex traits and diseases result from genomic interactions between nonallelic normal variants at separate loci. Such variants may be identified by deviations from the expected distributions of alleles at a high number of polymorphic loci, when individuals with the phenotype of interest are compared to normal controls of the same breeding unit, provided that both groups share the same remote ancestry and had no ancestors in common for the last three to four generations. The circumstances needed for such studies are ideally met on the island of Sardinia. The recurrent finding of the same type of association in separate breeding units between the phenotype of interest and a given genotype should allow a distinction between true genetic identity by descent and randomly occurring identities, as these will be obviously different in separate breeding units. The availability of several breeding units located in sharply different ecological environments will permit assessment of the role of nature/nurture factors in the degree of manifestation of each newly discovered genotype/phenotype association. A pilot study to evaluate the proposed strategy has been carried out in the Sardinian village of Carloforte, a community of about 8,000 individuals who have remained genetically homogeneous. Fifty-five control samples have been genotyped with six tetranucleotide microsatellites and with a subset of the 400 markers contained in the ABI PRISM linkage mapping panel, version 2. The allele frequencies for these microsatellite markers have been determined for these 55 individuals and compared to those from a random sampling of subsets of these 55 persons. For the six tetranucleotide microsatellites, a subset of as few as 20 people displayed the same allele frequency distributions as observed with the original 55 unrelated individuals. In conclusion, when samples are chosen from the same breeding unit, the number of individuals sufficient to draw the genomic profile of an isolated population can be relatively small. Likewise, the number of probands with the phenotype of interest can be even smaller when they are ascertained with the same genealogical criteria as the normal controls. By comparing the genomic profile of the probands to a fraction of the control samples within each of several separate breeding units of common remote ancestry, the search for genotype/phenotype association for mono- and multifactorial traits and diseases should be simplified and yield unequivocal results.     

Modellvorstellungen zur Genetik multifaktorieller Krankheiten

In contrast to monogenic diseases, a straightforward genotype–phenotype relationship is unlikely for multifactorial diseases because of a number of genetic and nongenetic factors, including genetic heterogeneity, gene–gene and gene–environment interactions, and epigenetic mechanisms. As a consequence, the relative risk of particular genetic variants will generally be small, which implies that large sample sizes are required for their initial identification. No conclusions as to the frequency and diversity of the causative genetic variation can generally be drawn from the prevalence of a disease alone. Homogenization of the genetic background of the study population and the use of simple and clearly defined phenotypes together with “educated guesses” in candidate gene and gene–environment studies appear to be the most promising way to identify the genetic factors underlying multifactorial diseases. Replication of initial disease association findings, particularly for rare variants, should be carried out in populations that are genetically as similar as possible to the original population.     

Genetic association studies of complex traits: design and analysis issues

Most common diseases and many important quantitative traits are complex genetic traits, with multiple genetic and environmental variables contributing to the observed phenotype. Because of the multi-factorial nature of complex traits, each individual genetic variant generally has only a modest effect, and the interaction of genetic variants with each other or with environmental factors can potentially be quite important in determining the observed phenotype. It remains largely unknown what sort of genetic variants explain inherited variation in complex traits, but recent evidence suggests that common genetic variants will explain at least some of the inherited variation in susceptibility to common disease. Genetic association studies, in which the allele or genotype frequencies at markers are determined in affected individuals and compared with those of controls (either population- or family-based), may be an effective approach to detecting the effects of common variants with modest effects. With the explosion in single nucleotide polymorphism (SNP) discovery and genotyping technologies, large-scale association studies have become feasible, and small-scale association studies have become plentiful. We review the different types of association studies and discuss issues that are important to consider when performing and interpreting association studies of complex genetic traits. Heritable and accurately measured phenotypes, carefully matched large samples, well-chosen genetic markers, and adequate standards in genotyping, analysis, and interpretation are all integral parts of a high-quality association study.     

Whole-genome sequencing in French Canadians from Quebec

Genome-wide association studies (GWAS) have had a tremendous success in the identification of common DNA sequence variants associated with complex human diseases and traits. However, because of their design, GWAS are largely inappropriate to characterize the role of rare and low-frequency DNA variants on human phenotypic variation. Rarer genetic variation is geographically more restricted, supporting the need for local whole-genome sequencing (WGS) efforts to study these variants in specific populations. Here, we present the first large-scale low-pass WGS of the French-Canadian population. Specifically, we sequenced at ~5.6× coverage the whole genome of 1970 French Canadians recruited by the Montreal Heart Institute Biobank and identified 29 million bi-allelic variants (31 % novel), including 19 million variants with a minor allele frequency (MAF) <0.5 %. Genotypes from the WGS data are highly concordant with genotypes obtained by exome array on the same individuals (99.8 %), even when restricting this analysis to rare variants (MAF <0.5, 99.9 %) or heterozygous sites (98.9 %). To further validate our data set, we showed that we can effectively use it to replicate several genetic associations with myocardial infarction risk and blood lipid levels. Furthermore, we analyze the utility of our WGS data set to generate a French-Canadian-specific imputation reference panel and to infer population structure in the Province of Quebec. Our results illustrate the value of low-pass WGS to study the genetics of human diseases in the founder French-Canadian population.     

Common and rare genetic variants of human red blood cell enzymes in Italy

In the present paper we report on new data of the frequency of common and rare variants in the Italian population for ADA, AK-1, 6-PGD, EsA, EsB, EsD, PGM-1, PGM-2, SOD-A, AcP, GPT, and PGI. Moreover we present a comprehensive review of the available data on the electrophoretic variants of red cell enzymes in Italians. We find a considerable degree of genetic heterogeneity between the various populations living in the Peninsula and between the population of the Peninsula and of Sardinia. We also find that the estimates of the average heterozygosity are considerably smaller for the population of Sardinia as compared to Peninsula and Sicily. Finally, we report on the occurrence of several uncommon enzyme variants, which overall frequency is very similar to previously reported estimates for North European populations (Harris et al. 1974).     

Haplotype structure and evidence for positive selection at the human IL13 locus

Interleukin-13 (IL13) is believed to play an important role in the pathogenesis of atopy and allergic asthma. To better understand genetic variation at the IL13 locus, we resequenced a 5.1-kb genomic region spanning the entire locus and identified 26 single-nucleotide polymorphisms (SNPs) in 74 individuals from three major populations-Chinese, Caucasian, and African. Our survey suggests exceptionally high and significant geographic structure at the IL13 locus between African and outside Africa populations. This unusual pattern suggests that positive selection that acts in some local populations may have played a role on the IL13 locus. In support of this suggestion, we found a significant excess of high frequency-derived SNPs in the Chinese population and Caucasian population, respectively, as expected after a recent episode of positive selection. Further, the unusual haplotype structure indicates that different scenarios of the action of positive selection on the IL13 locus in different populations may exist. In the Caucasian population, the skewed haplotype distribution dominated by one common haplotype supports the hypothesis of simple directional selection. Whereas, in the Chinese population, the two-round hitchhiking hypothesis may explain the skewed haplotype structure with three dominant ones. These findings may provide insight into the likely relative roles of selection and population history in establishing present-day variation at the IL13 locus, and, motivate further studies of this locus as an important candidate in common diseases association studies.     

iHAP – integrated haplotype analysis pipeline for characterizing the haplotype structure of genes

Background  

The advent of genotype data from large-scale efforts that catalog the genetic variants of different populations have given rise to new avenues for multifactorial disease association studies. Recent work shows that genotype data from the International HapMap Project have a high degree of transferability to the wider population. This implies that the design of genotyping studies on local populations may be facilitated through inferences drawn from information contained in HapMap populations.     

Y-chromosomal variation in the Czech Republic

To analyze the contribution of the Czech population to the Y-chromosome diversity landscape of Europe and to reconstruct past demographic events, we typed 257 males from five locations for 21 UEPs. Moreover, 141 carriers of the three most common haplogroups were typed for 10 microsatellites and coalescent analyses applied. Sixteen Hg's characterized by derived alleles were identified, the most common being R1a-SRY(10831) and P-DYS257*(xR1a). The pool of haplogroups within I-M170 represented the third most common clade. Overall, the degree of population structure was low. The ages for Hg I-M170, P-DYS257*(xR1a), and R1a-SRY(10831) ap peared to be comparable and compatible with their presence during or soon after the LGM. A signal of population growth beginning in the first millennium B.C. was detected. Its similarity among the three most common Hg's indicated that growth was characteristic for a gene pool that already contained all of them. The Czech population appears to be influenced, to a very moderate extent, by genetic inputs from outside Europe in the post-Neolithic and historical times. Population growth postdated the archaeologically documented introduction of Neolithic technology and the estimated central value coincides with a period of repeated changes driven by the development of metal technologies and the associated social and trade organization.     

Examination of Candidate Exonic Variants for Association to Alzheimer Disease in the Amish

Alzheimer disease (AD) is the most common cause of dementia. As with many complex diseases, the identified variants do not explain the total expected genetic risk that is based on heritability estimates for AD. Isolated founder populations, such as the Amish, are advantageous for genetic studies as they overcome heterogeneity limitations associated with complex population studies. We determined that Amish AD cases harbored a significantly higher burden of the known risk alleles compared to Amish cognitively normal controls, but a significantly lower burden when compared to cases from a dataset of unrelated individuals. Whole-exome sequencing of a selected subset of the overall study population was used as a screening tool to identify variants located in the regions of the genome that are most likely to contribute risk. By then genotyping the top candidate variants from the known AD genes and from linkage regions implicated previous studies in the full dataset, new associations could be confirmed. The most significant result (p = 0.0012) was for rs73938538, a synonymous variant in LAMA1 within the previously identified linkage peak on chromosome 18. However, this association is specific to the Amish and did not generalize when tested in a dataset of unrelated individuals. These results suggest that additional risk variation in the Amish remains to be identified and likely resides outside of the classical protein coding gene regions.     

Family-based association studies for next-generation sequencing

An individual's disease risk is determined by the compounded action of both common variants, inherited from remote ancestors, that segregated within the population and rare variants, inherited from recent ancestors, that segregated mainly within pedigrees. Next-generation sequencing (NGS) technologies generate high-dimensional data that allow a nearly complete evaluation of genetic variation. Despite their promise, NGS technologies also suffer from remarkable limitations: high error rates, enrichment of rare variants, and a large proportion of missing values, as well as the fact that most current analytical methods are designed for population-based association studies. To meet the analytical challenges raised by NGS, we propose a general framework for sequence-based association studies that can use various types of family and unrelated-individual data sampled from any population structure and a universal procedure that can transform any population-based association test statistic for use in family-based association tests. We develop family-based functional principal-component analysis (FPCA) with or without smoothing, a generalized T(2), combined multivariate and collapsing (CMC) method, and single-marker association test statistics. Through intensive simulations, we demonstrate that the family-based smoothed FPCA (SFPCA) has the correct type I error rates and much more power to detect association of (1) common variants, (2) rare variants, (3) both common and rare variants, and (4) variants with opposite directions of effect from other population-based or family-based association analysis methods. The proposed statistics are applied to two data sets with pedigree structures. The results show that the smoothed FPCA has a much smaller p value than other statistics.     

Recent Mitochondrial DNA Mutations Increase the Risk of Developing Common Late-Onset Human Diseases

Mitochondrial DNA (mtDNA) is highly polymorphic at the population level, and specific mtDNA variants affect mitochondrial function. With emerging evidence that mitochondrial mechanisms are central to common human diseases, it is plausible that mtDNA variants contribute to the “missing heritability” of several complex traits. Given the central role of mtDNA genes in oxidative phosphorylation, the same genetic variants would be expected to alter the risk of developing several different disorders, but this has not been shown to date. Here we studied 38,638 individuals with 11 major diseases, and 17,483 healthy controls. Imputing missing variants from 7,729 complete mitochondrial genomes, we captured 40.41% of European mtDNA variation. We show that mtDNA variants modifying the risk of developing one disease also modify the risk of developing other diseases, thus providing independent replication of a disease association in different case and control cohorts. High-risk alleles were more common than protective alleles, indicating that mtDNA is not at equilibrium in the human population, and that recent mutations interact with nuclear loci to modify the risk of developing multiple common diseases.     

Bayesian variable selection in searching for additive and dominant effects in genome-wide data

Although complex diseases and traits are thought to have multifactorial genetic basis, the common methods in genome-wide association analyses test each variant for association independent of the others. This computational simplification may lead to reduced power to identify variants with small effect sizes and requires correcting for multiple hypothesis tests with complex relationships. However, advances in computational methods and increase in computational resources are enabling the computation of models that adhere more closely to the theory of multifactorial inheritance. Here, a Bayesian variable selection and model averaging approach is formulated for searching for additive and dominant genetic effects. The approach considers simultaneously all available variants for inclusion as predictors in a linear genotype-phenotype mapping and averages over the uncertainty in the variable selection. This leads to naturally interpretable summary quantities on the significances of the variants and their contribution to the genetic basis of the studied trait. We first characterize the behavior of the approach in simulations. The results indicate a gain in the causal variant identification performance when additive and dominant variation are simulated, with a negligible loss of power in purely additive case. An application to the analysis of high- and low-density lipoprotein cholesterol levels in a dataset of 3895 Finns is then presented, demonstrating the feasibility of the approach at the current scale of single-nucleotide polymorphism data. We describe a Markov chain Monte Carlo algorithm for the computation and give suggestions on the specification of prior parameters using commonly available prior information. An open-source software implementing the method is available at http://www.lce.hut.fi/research/mm/bmagwa/ and https://github.com/to-mi/.     

Two point mutations are responsible for G6PD polymorphism in Sardinia.

The human X-linked gene encoding glucose 6-phosphate dehydrogenase (G6PD) is highly polymorphic; more than 300 G6PD variants have been identified. G6PD deficiency in different geographical areas appears to have arisen through independent mutational events, but within the same population it may also be heterogeneous. One example is the island of Sardinia, where careful clinical and biochemical studies have identified four different G6PD variants. We cloned and sequenced the four G6PD variants from Sardinia and found that only two mutations are responsible for G6PD deficiency in this area: one mutation is the cause of the G6PD Seattle-like phenotype, a milder form of G6PD deficiency; the other mutation is responsible for all forms of very severe G6PD deficiency in Sardinia and, possibly, in the Mediterranean.     

Genetic data on Alghero population (Sardinia): Contrast between biological and cultural evidence

Data on 20 genetic polymorphisms (61 alleles) in the Algehero population on the northwestern coast of Sardinia are presented and discussed in relation to its linguistic peculiarity inside the island. Since the Aragonese (Spain) conquest of Sardinia in the 13th century, the Catalan language, the same as that spoken in Northeastern Spain and certain districts of Southern France, has been used in Alghero even until today. Analysis for heterogeneity of gene frequency distributions indicates that the genetic information obtained on Alghero is adequate to discriminate Sardinians from other neighbouring populations. Genetic variation between populations measured through genetic distances and principal-component analysis shows that the present-day population of Alghero is much closer genetically to Sardinians than to Catalonians. Our genetic results do not support any interpretation of the linguistic affinities between Alghero and Catalonia at present as indicative of biological kinship. © 1994 Wiley-Liss, Inc.     

Inclusion of gene-gene and gene-environment interactions unlikely to dramatically improve risk prediction for complex diseases

Genome-wide association studies have identified hundreds of common genetic variants associated with the risk of multifactorial diseases. However, their impact on discrimination and risk prediction is limited. It has been suggested that the identification of gene-gene (G-G) and gene-environment (G-E) interactions would improve disease prediction and facilitate prevention. We conducted a simulation study to explore the potential improvement in discrimination if G-G and G-E interactions exist and are known. We used three diseases (breast cancer, type 2 diabetes, and rheumatoid arthritis) as motivating examples. We show that the inclusion of G-G and G-E interaction effects in risk-prediction models is unlikely to dramatically improve the discrimination ability of these models.     

Generalization and Dilution of Association Results from European GWAS in Populations of Non-European Ancestry: The PAGE Study

The vast majority of genome-wide association study (GWAS) findings reported to date are from populations with European Ancestry (EA), and it is not yet clear how broadly the genetic associations described will generalize to populations of diverse ancestry. The Population Architecture Using Genomics and Epidemiology (PAGE) study is a consortium of multi-ancestry, population-based studies formed with the objective of refining our understanding of the genetic architecture of common traits emerging from GWAS. In the present analysis of five common diseases and traits, including body mass index, type 2 diabetes, and lipid levels, we compare direction and magnitude of effects for GWAS-identified variants in multiple non-EA populations against EA findings. We demonstrate that, in all populations analyzed, a significant majority of GWAS-identified variants have allelic associations in the same direction as in EA, with none showing a statistically significant effect in the opposite direction, after adjustment for multiple testing. However, 25% of tagSNPs identified in EA GWAS have significantly different effect sizes in at least one non-EA population, and these differential effects were most frequent in African Americans where all differential effects were diluted toward the null. We demonstrate that differential LD between tagSNPs and functional variants within populations contributes significantly to dilute effect sizes in this population. Although most variants identified from GWAS in EA populations generalize to all non-EA populations assessed, genetic models derived from GWAS findings in EA may generate spurious results in non-EA populations due to differential effect sizes. Regardless of the origin of the differential effects, caution should be exercised in applying any genetic risk prediction model based on tagSNPs outside of the ancestry group in which it was derived. Models based directly on functional variation may generalize more robustly, but the identification of functional variants remains challenging.     

Combinations of Susceptibility Genes Are Associated with Higher Risk for Multiple Sclerosis and Imply Disease Course Specificity

Multiple sclerosis (MS) is a chronic autoimmune disease of the central nervous system that predominantly affects young adults. The genetic contributions to this multifactorial disease were underscored by a genome wide association study (GWAS) conducted by the International Multiple Sclerosis Genetic Consortium in a multinational cohort prompting the discovery of 57 non-MHC MS-associated common genetic variants. Hitherto, few of these newly reported variants have been replicated in larger independent patient cohorts. We genotyped a cohort of 1033 MS patients and 644 healthy controls with a consistent genetic background for the 57 non-MHC variants reported to be associated with MS by the first large GWAS as well as the HLA DRB1*1501 tagging SNP rs3135388. We robustly replicated three of the 57 non-MHC reported MS-associated single nucleotide polymorphisms (SNPs). In addition, our study revealed several genotype-genotype combinations with an evidently higher degree of disease association than the genotypes of the single SNPs. We further correlated well-defined clinical phenotypes, i.e. ataxia, visual impairment due to optic neuritis and paresis with single SNPs and genotype combinations, and identified several associations. The results may open new avenues for clinical implications of the MS associated genetic variants reported from large GWAS.     

Canonical Correlation Analysis for Gene-Based Pleiotropy Discovery

Genome-wide association studies have identified a wealth of genetic variants involved in complex traits and multifactorial diseases. There is now considerable interest in testing variants for association with multiple phenotypes (pleiotropy) and for testing multiple variants for association with a single phenotype (gene-based association tests). Such approaches can increase statistical power by combining evidence for association over multiple phenotypes or genetic variants respectively. Canonical Correlation Analysis (CCA) measures the correlation between two sets of multidimensional variables, and thus offers the potential to combine these two approaches. To apply CCA, we must restrict the number of attributes relative to the number of samples. Hence we consider modules of genetic variation that can comprise a gene, a pathway or another biologically relevant grouping, and/or a set of phenotypes. In order to do this, we use an attribute selection strategy based on a binary genetic algorithm. Applied to a UK-based prospective cohort study of 4286 women (the British Women''s Heart and Health Study), we find improved statistical power in the detection of previously reported genetic associations, and identify a number of novel pleiotropic associations between genetic variants and phenotypes. New discoveries include gene-based association of NSF with triglyceride levels and several genes (ACSM3, ERI2, IL18RAP, IL23RAP and NRG1) with left ventricular hypertrophy phenotypes. In multiple-phenotype analyses we find association of NRG1 with left ventricular hypertrophy phenotypes, fibrinogen and urea and pleiotropic relationships of F7 and F10 with Factor VII, Factor IX and cholesterol levels.     

Genetics,geography, and culture: The population of S. Pietro Island (Sardinia,Italy)

An interesting aspect of the island of Sardinia (Italy) is the wide range of genetic variability within the island itself. The variability is widened by the presence of some populations of different ethnic origin who speak a language other than Sardinian. This work deals with the study of the genetic structure of the Carloforte population which inhabits the tiny island of S. Pietro 4 km off the southwest coast of Sardinia. S. Pietro was first populated in 1738 by emigrants coming from the island of Tabarka (Tunisia) who spoke an archaic form of the Ligurian dialect. Data on genetic polymorphisms in the Carloforte population are presented and discussed in relation to some Sardinian and Italian populations. Data on demographic and matrimonial structure are also presented. The genetic analyses show the Carloforte population as being clearly separated from both Sardinians and continental Italians. The isolation of Carloforte, highlighted by language diversity, endogamy, and consanguinity levels and marriage area, supports the idea of genetic diversity linked to cultural peculiarity. © 1996 Wiley-Liss, Inc.