Single nucleotide polymorphisms (SNPs) are inherited from parents and they measure heritable events

Single nucleotide polymorphisms (SNPs) are extensively used in case-control studies of practically all cancer types. They are used for the identification of inherited cancer susceptibility genes and those that may interact with environmental factors. However, being genetic markers, they are applicable only on heritable conditions, which is often a neglected fact. Based on the data in the nationwide Swedish Family-Cancer Database, we review familial risks for all main cancers and discuss the evidence for a heritable component in cancer. The available evidence is not conclusive but it is consistent in pointing to a minor heritable etiology in cancer, which will hamper the success of SNP-based association studies. Empirical familial risks should be used as guidance for the planning of SNP studies. We provide calculations for the assessment of familial risks for assumed allele frequencies and gene effects (odds ratios) for different modes of inheritance. Based on these data, we discuss the gene effects that could account for the unexplained proportion of familial breast and lung cancer. As a conclusion, we are concerned about the indiscriminate use of a genetic tool to cancers, which are mainly environmental in origin. We consider the likelihood of a successful application of SNPs in gene-environment studies small, unless established environmental risk factors are tested on proven candidate genes.     

后GWAS时代结直肠癌致病SNP功能机制的研究进展

结直肠癌(colorectal cancer, CRC)是受遗传与环境因素共同影响的复杂疾病,其中遗传因素发挥重要作用。至今,全基因组关联研究(genome-wide association studies, GWAS)已经发现了大量与结直肠癌风险相关的遗传变异。随之而来的后GWAS时代,越来越多的研究侧重于利用多组学数据和功能实验对潜在的致病位点进行解析。分析表明绝大多数风险单核苷酸多态性(single nucleotide polymorphism,SNP)位于非编码区,可能通过影响转录因子结合、表观遗传修饰、染色质可及性、基因组高级结构等,调控靶基因表达。本文对后GWAS时代结直肠癌致病位点的机制研究进行综述,阐述了后GWAS对于理解结直肠癌分子机制的重要意义,并探讨了结直肠癌GWAS的应用和前景,为实现GWAS成果转化提供参考。     

Using a Bayesian Hierarchical Model for Identifying Single Nucleotide Polymorphisms Associated with Childhood Acute Lymphoblastic Leukemia Risk in Case-Parent Triads

Childhood acute lymphoblastic leukemia (ALL) is a condition that arises from complex etiologies. The absence of consistent environmental risk factors and the presence of modest familial associations suggest ALL is a complex trait with an underlying genetic component. The identification of genetic factors associated with disease is complicated by complex genetic covariance structures and multiple testing issues. Both issues can be resolved with appropriate Bayesian variable selection methods. The present study was undertaken to extend our hierarchical Bayesian model for case-parent triads to incorporate single nucleotide polymorphisms (SNPs) and incorporate the biological grouping of SNPs within genes. Based on previous evidence that genetic variation in the folate metabolic pathway influences ALL risk, we evaluated 128 tagging SNPs in 16 folate metabolic genes among 118 ALL case-parent triads recruited from the Texas Children’s Cancer Center (Houston, TX) between 2003 and 2010. We used stochastic search gene suggestion (SSGS) in hierarchical Bayesian models to evaluate the association between folate metabolic SNPs and ALL. Using Bayes factors among these variants in childhood ALL case-parent triads, two SNPs were identified with a Bayes factor greater than 1. There was evidence that the minor alleles of NOS3 rs3918186 (OR = 2.16; 95% CI: 1.51-3.15) and SLC19A1 rs1051266 (OR = 2.07; 95% CI: 1.25-3.46) were positively associated with childhood ALL. Our findings are suggestive of the role of inherited genetic variation in the folate metabolic pathway on childhood ALL risk, and they also suggest the utility of Bayesian variable selection methods in the context of case-parent triads for evaluating the role of SNPs on disease risk.     

Molecular genetics of schizophrenia: past, present and future

Schizophrenia is a severe neuropsychiatric disorder with a polygenic mode of inheritance which is also governed by non-genetic factors. Candidate genes identified on the basis of biochemical and pharmacological evidence are being tested for linkage and association studies. Neurotransmitters, especially dopamine and serotonin have been widely implicated in its etiology. Genome scan of all human chromosomes with closely spaced polymorphic markers is being used for linkage studies. The completion and availability of the first draft of Human Genome Sequence has provided a treasure-trove that can be utilized to gain insight into the so far inaccessible regions of the human genome. Significant technological advances for identification of single nucleotide polymorphisms (SNPs) and use of microarrays have further strengthened research methodologies for genetic analysis of complex traits. In this review, we summarize the evolution of schizophrenia genetics from the past to the present, current trends and future direction of research.     

Molecular genetics of attention-deficit hyperactivity disorder (ADHD): an update

Attention-deficit hyperactivity disorder (ADHD) is a heritable and behavioral condition of childhood, affecting 5-10% of school-age children worldwide. Affected patients exhibit various behavioral problems such as carelessness, restlessness, disobedience and failure to stay quiet in class. The etiology of ADHD is not known. However, family, twin and adoption studies have provided strong evidence for a genetic etiology of the disorder. A genome-wide scan has identified six chromosomal loci with LOD scores suggestive of linkage. Animal studies suggest the involvement of the brain dopamine pathway and its alteration in ADHD but there is no direct evidence to support this hypothesis. In addition, there are at least 20 candidate genes of small effect that have been studied but none of them appear to be the major gene causing ADHD. Medical intervention along with psychosocial therapy proved to be beneficial for controlling ADHD, although some undesirable side effects have been encountered during medical treatment. In the future, identification of environmental factors, study of additive gene effects and the interaction of genes and environmental factors may provide better insight into the pathophysiology of ADHD. This may lead to an effective new treatment strategy.     

Complexities of cancer research: mouse genetic models

Cancer susceptibility is a complex interaction of an individual's genetic composition and environmental exposures. Huge strides have been made in understanding cancer over the past 100 yr, from recognition of cancer as a genetic disease, to identification of specific carcinogens, isolation of oncogenes, and recognition of tumor suppressors. A tremendous amount of knowledge has accumulated about the etiology of cancer. Cancer genetics has played a significant role in these discoveries. Analysis of high-risk familial cancers has led to the discovery of new tumor suppressor genes and important cancer pathways. These families, however, represent only a small fraction of cancer in the general population. Most cancer is instead probably the result of an intricate interaction of polymorphic susceptibility genes with the sea of environmental exposures that humans experience. Although the central cadre of cancer genes is known, little is understood about the peripheral genes that likely comprise the polymorphic susceptibility loci. The challenge for cancer genetics is therefore to move forward from the mendelian genetics of the rare familial cancer syndromes into the field of quantitative trait loci, susceptibility factors, and modifier genes. By identifying the genes that modulate an individual's susceptibility to cancer after an environmental exposure, researchers will be able to gain important insights into human biology, cancer prevention, and cancer treatment. This article summarizes the current state of quantitative trait genetic analysis and the tools, both proven and theoretical, that may be used to unravel one of the great challenges in cancer genetics.     

Genomewide linkage analysis in Costa Rican families implicates chromosome 15q14 as a candidate region for OCD

Obsessive compulsive disorder (OCD) has a complex etiology that encompasses both genetic and environmental factors. However, to date, despite the identification of several promising candidate genes and linkage regions, the genetic causes of OCD are largely unknown. The objective of this study was to conduct linkage studies of childhood-onset OCD, which is thought to have the strongest genetic etiology, in several OCD-affected families from the genetically isolated population of the Central Valley of Costa Rica (CVCR). The authors used parametric and non-parametric approaches to conduct genome-wide linkage analyses using 5,786 single nucleotide repeat polymorphisms (SNPs) in three CVCR families with multiple childhood-onset OCD-affected individuals. We identified areas of suggestive linkage (LOD score ≥ 2) on chromosomes 1p21, 15q14, 16q24, and 17p12. The strongest evidence for linkage was on chromosome 15q14 (LOD = 3.13), identified using parametric linkage analysis with a recessive model, and overlapping a region identified in a prior linkage study using a Caucasian population. Each CVCR family had a haplotype that co-segregated with OCD across a ~7 Mbp interval within this region, which contains 18 identified brain expressed genes, several of which are potentially relevant to OCD. Exonic sequencing of the strongest candidate gene in this region, the ryanodine receptor 3 (RYR3), identified several genetic variants of potential interest, although none co-segregated with OCD in all three families. These findings provide evidence that chromosome 15q14 is linked to OCD in families from the CVCR, and supports previous findings to suggest that this region may contain one or more OCD susceptibility loci.     

Mapping the Genetic Variation of Regional Brain Volumes as Explained by All Common SNPs from the ADNI Study

Typically twin studies are used to investigate the aggregate effects of genetic and environmental influences on brain phenotypic measures. Although some phenotypic measures are highly heritable in twin studies, SNPs (single nucleotide polymorphisms) identified by genome-wide association studies (GWAS) account for only a small fraction of the heritability of these measures. We mapped the genetic variation (the proportion of phenotypic variance explained by variation among SNPs) of volumes of pre-defined regions across the whole brain, as explained by 512,905 SNPs genotyped on 747 adult participants from the Alzheimer''s Disease Neuroimaging Initiative (ADNI). We found that 85% of the variance of intracranial volume (ICV) (p = 0.04) was explained by considering all SNPs simultaneously, and after adjusting for ICV, total grey matter (GM) and white matter (WM) volumes had genetic variation estimates near zero (p = 0.5). We found varying estimates of genetic variation across 93 non-overlapping regions, with asymmetry in estimates between the left and right cerebral hemispheres. Several regions reported in previous studies to be related to Alzheimer''s disease progression were estimated to have a large proportion of volumetric variance explained by the SNPs.     

Familial risks for common diseases: etiologic clues and guidance to gene identification

Familial clustering of a disease is a direct indicator of a possible heritable cause, provided that environmental sharing can be excluded. If the familial clustering is lacking, the likelihood of a heritable influence is also small. In the era of genome scans, the consideration of data on heritability should be important in the assessment of the likely success of the genome scan. The availability of a Multigeneration Register in Sweden provides a reliable access to families throughout the last century. This Register has been extensively used to study a number of different diseases through linkage to the Hospital Discharge Register. In the present article we review the obtained and some unpublished results for nine main disease classes. For each of these, familial risks are given for four disease subtypes. As measures of familial clustering we use risks between siblings, twins and spouses. Disease correlation between spouses suggests environmental sharing and a higher correlation between siblings and particularly twins shows heritable effects. We will also comment on the established susceptibility genes and the risks conferred by them. The data suggest high heritabilities for chronic obstructive pulmonary disease, asthma, noninfective enteritis and colitis, cerebral palsy and endocrine and metabolic diseases. Among the performed first-generation genome scans on various diseases, the success appears to be related to the a priori heritability estimates. To our knowledge this is a first attempt to summarize familial risks for a large number of diseases using data from a single population on which reasonable uniform diagnostic criteria have been applied.     

Current strategies in the search for low penetrance genes in cancer

The genetic etiology of most cancers remains largely unclear and it has been hypothesised that common genetic variants with modest effects on disease susceptibility cause the bulk of this unexplained risk. Case-control association studies are considered the most effective strategy to identify these low-penetrance genes. While traditionally, such studies have focused on putative functional single nucleotide polymorphisms (SNPs) in candidate genes, a more comprehensive approach can now be taken, as a result of a number of recent developments: the mapping of the human genome, including the identification of almost ten million SNPs; and the development of high-throughput genotyping technologies that enable hundreds of thousands of SNPs to be genotyped in a single reaction, in multiple subjects and at an affordable cost. All common genomic variation can be captured by genotyping SNPs in gene-, pathway- or genome-wide-based strategies and these are now being applied to many diseases, including cancer. We present an outline of each of these approaches, including recent published examples, and discuss a number of challenges that remain to be addressed.     

A genome-wide association study of facial morphology identifies novel genetic loci in Han Chinese

The human face is a heritable surface with many complex sensory organs. In recent years, many genetic loci associated with facial features have been reported in different populations, yet there is a lack of studies on the Han Chinese population. Here, we report a genome-wide association study of 3 D normal human faces of 2,659 Han Chinese with autosegment phenotypes of facial morphology. We identify singlenucleotide polymorphisms(SNPs) encompassing four genomic regions showing significant associations with different facial regions, including SNPs in DENND1 B associated with the chin, SNPs among PISRT1 associated with eyes, SNPs between DCHS2 and SFRP2 associated with the nose, and SNPs in VPS13 B associated with the nose. We replicate 24 SNPs from previously reported genetic loci in different populations, whose candidate genes are DCHS2, SUPT3 H, HOXD1, SOX9, PAX3, and EDAR. These results provide a more comprehensive understanding of the genetic basis of variation in human facial morphology.     

Aberrant Gene Expression in Humans

Gene expression as an intermediate molecular phenotype has been a focus of research interest. In particular, studies of expression quantitative trait loci (eQTL) have offered promise for understanding gene regulation through the discovery of genetic variants that explain variation in gene expression levels. Existing eQTL methods are designed for assessing the effects of common variants, but not rare variants. Here, we address the problem by establishing a novel analytical framework for evaluating the effects of rare or private variants on gene expression. Our method starts from the identification of outlier individuals that show markedly different gene expression from the majority of a population, and then reveals the contributions of private SNPs to the aberrant gene expression in these outliers. Using population-scale mRNA sequencing data, we identify outlier individuals using a multivariate approach. We find that outlier individuals are more readily detected with respect to gene sets that include genes involved in cellular regulation and signal transduction, and less likely to be detected with respect to the gene sets with genes involved in metabolic pathways and other fundamental molecular functions. Analysis of polymorphic data suggests that private SNPs of outlier individuals are enriched in the enhancer and promoter regions of corresponding aberrantly-expressed genes, suggesting a specific regulatory role of private SNPs, while the commonly-occurring regulatory genetic variants (i.e., eQTL SNPs) show little evidence of involvement. Additional data suggest that non-genetic factors may also underlie aberrant gene expression. Taken together, our findings advance a novel viewpoint relevant to situations wherein common eQTLs fail to predict gene expression when heritable, rare inter-individual variation exists. The analytical framework we describe, taking into consideration the reality of differential phenotypic robustness, may be valuable for investigating complex traits and conditions.     

SNP-based pathway enrichment analysis for genome-wide association studies

Background  

Recently we have witnessed a surge of interest in using genome-wide association studies (GWAS) to discover the genetic basis of complex diseases. Many genetic variations, mostly in the form of single nucleotide polymorphisms (SNPs), have been identified in a wide spectrum of diseases, including diabetes, cancer, and psychiatric diseases. A common theme arising from these studies is that the genetic variations discovered by GWAS can only explain a small fraction of the genetic risks associated with the complex diseases. New strategies and statistical approaches are needed to address this lack of explanation. One such approach is the pathway analysis, which considers the genetic variations underlying a biological pathway, rather than separately as in the traditional GWAS studies. A critical challenge in the pathway analysis is how to combine evidences of association over multiple SNPs within a gene and multiple genes within a pathway. Most current methods choose the most significant SNP from each gene as a representative, ignoring the joint action of multiple SNPs within a gene. This approach leads to preferential identification of genes with a greater number of SNPs.     

Parkinsonism genes: culprits and clues

Parkinson's disease (PD) is characterized by a unique clinical constellation that includes: slowness, rigidity, gait difficulty, and tremor at rest. Pathological studies have linked this presentation to the loss of midbrain dopamine neurons (Gelb et al. 1999) although other neuronal populations are also targeted in PD. Epidemiological data implicate both genetic and environmental factors in the etiology of the disease. The identification of a series of genes that underlie relatively rare, familial forms of Parkinsonism (a clinical term that encompasses 'sporadic' PD, familial Parkinson's-like forms, as well as other related syndromes) has brought excitement to the field. Three of the mutated familial Parkinsonism (FP) genes: Parkin, DJ-1, and PINK1, typically present with apparent autosomal recessive inheritance and are implicated in mitochondria and oxidative stress-related survival pathways. Two other FP genes: alpha-Synuclein (alphaSyn) and LRRK2, present in an autosomal dominant pattern and are associated with prominent intracellular protein inclusions. A series of recent publications suggest novel pathways that may link the FP genes.     

Genetic epidemiology of multistage carcinogenesis

It is commonly believed that cancer is a multistage, polygenic disease. Even though conceptually appealing, the evidence supporting the multistage theory remains limited. Most known tumor suppresser genes are associated with monogenic dominant cancers following a two-hit pathway. We review results from a recent twin study on 90000 individuals that give support to the multistage theory. Statistically significant heritability estimates were shown for cancers of the colorectum (35%), breast (27%), and prostate (42%). These estimates are much higher than those obtained from family studies in which parents and offspring, or sibs are compared. The difference can be accounted for by the involvement of many genes. A polygenic cancer would show small effects in family studies but large effects in twin studies. We present calculations on the decrease in familial risks when the number of genes involved increases or when the penetrance decreases. We test the apparent number of stages involved in the main cancers from the Swedish Family-Cancer Database. The logarithms of the slopes suggest large differences in the apparent numbers of mutations involved in different cancers. The number of mutations required appears to be less in familial breast cancer compared to sporadic breast cancer. Study designs for gene identification should be revised to accommodate polygenic cancers.     

Two-locus genome-wide linkage scan for prostate cancer susceptibility genes with an interaction effect

Prostate cancer represents a significant worldwide public health burden. Epidemiological and genetic epidemiological studies have consistently provided data supporting the existence of inherited prostate cancer susceptibility genes. Segregation analyses of prostate cancer suggest that a multigene model may best explain familial clustering of this disease. Therefore, modeling gene–gene interactions in linkage analysis may improve the power to detect chromosomal regions harboring these disease susceptibility genes. In this study, we systematically screened for prostate cancer linkage by modeling two-locus gene–gene interactions for all possible pairs of loci across the genome in 426 prostate cancer families from Johns Hopkins Hospital, University of Michigan, University of Umeå, and University of Tampere. We found suggestive evidence for an epistatic interaction for six sets of loci (target chromosome-wide/reference marker-specific P≤0.0001). Evidence for these interactions was found in two independent subsets from within the 426 families. While the validity of these results requires confirmation from independent studies and the identification of the specific genes underlying this linkage evidence, our approach of systematically assessing gene–gene interactions across the entire genome represents a promising alternative approach for gene identification for prostate cancer.     

Challenges and complexities in estimating both the functional impact and the disease risk associated with the extensive genetic variation in human DNA repair genes

Individual risk and the population incidence of disease result from the interaction of genetic susceptibility and exposure. DNA repair is an example of a cellular process where genetic variation in families with extreme predisposition is documented to be associated with high disease likelihood, including syndromes of premature aging and cancer. Although the identification and characterization of new genes or variants in cancer families continues to be important, the focus of this paper is the current status of efforts to define the impact of polymorphic amino acid substitutions in DNA repair genes on individual and population cancer risk. There is increasing evidence that mild reductions in DNA repair capacity, assumed to be the consequence of common genetic variation, affect cancer predisposition. The extensive variation being found in the coding regions of DNA repair genes and the large number of genes in each of the major repair pathways results in complex genotypes with potential to impact cancer risk in the general population. The implications of this complexity for molecular epidemiology studies, as well as concepts that may make these challenges more manageable, are discussed. The concepts include both experimental and computational approaches that could be employed to develop predictors of disease susceptibility based on DNA repair genotype, focusing initially on studies to assess functional impact on individual proteins and pathways and then on molecular epidemiology studies to assess exposure-dependent health risk. In closing, we raise some of the non-technical challenges to the utilization of the full richness of the genetic variation to reduce disease occurrence and ultimately improve health care.     

Bipolar I disorder and schizophrenia: a 440-single-nucleotide polymorphism screen of 64 candidate genes among Ashkenazi Jewish case-parent trios

Bipolar, schizophrenia, and schizoaffective disorders are common, highly heritable psychiatric disorders, for which familial coaggregation, as well as epidemiological and genetic evidence, suggests overlapping etiologies. No definitive susceptibility genes have yet been identified for any of these disorders. Genetic heterogeneity, combined with phenotypic imprecision and poor marker coverage, has contributed to the difficulty in defining risk variants. We focused on families of Ashkenazi Jewish descent, to reduce genetic heterogeneity, and, as a precursor to genomewide association studies, we undertook a single-nucleotide polymorphism (SNP) genotyping screen of 64 candidate genes (440 SNPs) chosen on the basis of previous linkage or of association and/or biological relevance. We genotyped an average of 6.9 SNPs per gene, with an average density of 1 SNP per 11.9 kb in 323 bipolar I disorder and 274 schizophrenia or schizoaffective Ashkenazi case-parent trios. Using single-SNP and haplotype-based transmission/disequilibrium tests, we ranked genes on the basis of strength of association (P<.01). Six genes (DAO, GRM3, GRM4, GRIN2B, IL2RB, and TUBA8) met this criterion for bipolar I disorder; only DAO has been previously associated with bipolar disorder. Six genes (RGS4, SCA1, GRM4, DPYSL2, NOS1, and GRID1) met this criterion for schizophrenia or schizoaffective disorder; five replicate previous associations, and one, GRID1, shows a novel association with schizophrenia. In addition, six genes (DPYSL2, DTNBP1, G30/G72, GRID1, GRM4, and NOS1) showed overlapping suggestive evidence of association in both disorders. These results may help to prioritize candidate genes for future study from among the many suspected/proposed for schizophrenia and bipolar disorders. They provide further support for shared genetic susceptibility between these two disorders that involve glutamate-signaling pathways.