Linkage analysis in the next-generation sequencing era

The Genetic Factors Influencing Cardiomyopathies and Heart Failure across the Allele Frequency Spectrum

Heart failure (HF) remains a major cause of mortality and morbidity worldwide. Understanding the genetic basis of HF allows for the development of disease-modifying therapies, more appropriate risk stratification, and personalised management of patients. The advent of next-generation sequencing has enabled genome-wide association studies; moving beyond rare variants identified in a Mendelian fashion and detecting common DNA variants associated with disease. We summarise the latest GWAS and rare variant data on mixed and refined HF aetiologies, and cardiomyopathies. We describe the recent understanding of the functional impact of titin variants and highlight FHOD3 as a novel cardiomyopathy-associated gene. We describe future directions of research in this field and how genetic data can be leveraged to improve the care of patients with HF.

Population genomics of helminth parasites

Next generation sequencing technologies have facilitated a shift from a few targeted loci in population genetic studies to whole genome approaches. Here, we review the types of questions and inferences regarding the population biology and evolution of parasitic helminths being addressed within the field of population genomics. Topics include parabiome, hybridization, population structure, loci under selection and linkage mapping. We highlight various advances, and note the current trends in the field, particularly a focus on human-related parasites despite the inherent biodiversity of helminth species. We conclude by advocating for a broader application of population genomics to reflect the taxonomic and life history breadth displayed by helminth parasites. As such, our basic knowledge about helminth population biology and evolution would be enhanced while the diversity of helminths in itself would facilitate population genomic comparative studies to address broader ecological and evolutionary concepts.

An exploration of linkage fine-mapping on sequences from case-control studies

Linkage analysis maps genetic loci for a heritable trait by identifying genomic regions with excess relatedness among individuals with similar trait values. Analysis may be conducted on related individuals from families, or on samples of unrelated individuals from a population. For allelically heterogeneous traits, population-based linkage analysis can be more powerful than genotypic-association analysis. Here, we focus on linkage analysis in a population sample, but use sequences rather than individuals as our unit of observation. Earlier investigations of sequence-based linkage mapping relied on known sequence relatedness, whereas we infer relatedness from the sequence data. We propose two ways to associate similarity in relatedness of sequences with similarity in their trait values and compare the resulting linkage methods to two genotypic-association methods. We also introduce a procedure to label case sequences as potential carriers or noncarriers of causal variants after an association has been found. This post hoc labeling of case sequences is based on inferred relatedness to other case sequences. Our simulation results indicate that methods based on sequence relatedness improve localization and perform as well as genotypic-association methods for detecting rare causal variants. Sequence-based linkage analysis therefore has potential to fine-map allelically heterogeneous disease traits.

Semi-automated assembly of high-quality diploid human reference genomes

The current human reference genome, GRCh38, represents over 20 years of effort to generate a high-quality assembly, which has benefitted society1,2. However, it still has many gaps and errors, and does not represent a biological genome as it is a blend of multiple individuals3,4. Recently, a high-quality telomere-to-telomere reference, CHM13, was generated with the latest long-read technologies, but it was derived from a hydatidiform mole cell line with a nearly homozygous genome5. To address these limitations, the Human Pangenome Reference Consortium formed with the goal of creating high-quality, cost-effective, diploid genome assemblies for a pangenome reference that represents human genetic diversity6. Here, in our first scientific report, we determined which combination of current genome sequencing and assembly approaches yield the most complete and accurate diploid genome assembly with minimal manual curation. Approaches that used highly accurate long reads and parent-child data with graph-based haplotype phasing during assembly outperformed those that did not. Developing a combination of the top-performing methods, we generated our first high-quality diploid reference assembly, containing only approximately four gaps per chromosome on average, with most chromosomes within ±1% of the length of CHM13. Nearly 48% of protein-coding genes have non-synonymous amino acid changes between haplotypes, and centromeric regions showed the highest diversity. Our findings serve as a foundation for assembling near-complete diploid human genomes at scale for a pangenome reference to capture global genetic variation from single nucleotides to structural rearrangements.

Linkage analysis identifies novel genetic modifiers of microbiome traits in families with inflammatory bowel disease

Dysbiosis of the gut microbiome is a hallmark of inflammatory bowel disease (IBD) and both, IBD risk and microbiome composition, have been found to be associated with genetic variation. Using data from families of IBD patients, we examined the association between genetic and microbiome similarity in a specific IBD context, followed by a genome-wide quantitative trait locus (QTL) linkage analysis of various microbiome traits using the same data. SNP genotypes as well as gut microbiome and phenotype data were obtained from the Kiel IBD family cohort (IBD-KC). The IBD-KC is an ongoing prospective study in Germany currently comprising 256 families with 455 IBD patients and 575 first- and second-degree relatives. Initially focusing upon known IBD risk loci, we noted a statistically significant (FDR<0.05) association between genetic similarity at SNP rs11741861 and overall microbiome dissimilarity among pairs of relatives discordant for IBD. In a genome-wide QTL analysis, 12 chromosomal regions were found to be linked to the abundance of one of seven microbial genera, namely Barnesiella (chromosome 4, region spanning 10.34 cM), Clostridium_XIVa (chr4, 3.86 cM; chr14, two regions spanning 7.05 and 13.02 cM respectively), Pseudoflavonifractor (chr7, 12.80 cM) Parasutterella (chr14, 8.26 cM), Ruminococcus (chr16, two overlapping regions spanning 8.01 and 16.87 cM, respectively), Roseburia (chr19, 7.99 cM), and Odoribacter (chr22, three regions spanning 0.89, 5.57 and 1.71 cM, respectively), as well as the Shannon index of α diversity (chr3, 1.47 cM). Our study thus shows that, in families of IBD patients, pairwise genetic similarity for at least one IBD risk locus is associated with overall microbiome dissimilarity among discordant pairs of relatives, and that hitherto unknown genetic modifiers of microbiome traits are located in at least 12 human genomic regions.

Identification of a Rare Novel KMT2C Mutation That Presents with Schizophrenia in a Multiplex Family

Schizophrenia is a complex genetic disorder involving many common variants with modest effects and rare mutations with high penetrance. Rare mutations associated with schizophrenia are highly heterogeneous and private for affected individuals and families. Identifying such mutations can help establish the molecular diagnosis, elucidate the pathogenesis, and provide helpful genetic counseling for affected patients and families. We performed a whole-exome sequencing analysis to search for rare pathogenic mutations co-segregating with schizophrenia transmitted in a dominant inheritance in a two-generation multiplex family. We identified a rare missense mutation H1574R (Histidine1574Arginine, rs199796552) of KMT2C (lysine methyltransferase 2C) co-segregating with affected members in this family. The mutation is a novel deleterious mutation of KMT2C, not reported before in the literature. The KMT2C encodes a histone 3 lysine 4 (H3K4)-specific methyltransferase and involves epigenetic regulation of brain gene expression. Mutations of KMT2C have been found in neurodevelopmental disorders, such as Kleefstra syndrome, intellectual disability, and autism spectrum disorders. Our finding suggests that schizophrenia might be one of the clinical phenotype spectra of KMT2C mutations, and KMT2C might be a novel risk gene for schizophrenia. Nevertheless, the co-segregation of this mutation with schizophrenia in this family might also be due to chance; functional assays of this mutation are needed to address this issue.

Identification of New Rare Variants Associated With Familial Autoimmune Thyroid Diseases by Deep Sequencing of Linked Loci

CONTEXT

Genetic risk factors play a major role in the pathoetiology of autoimmune thyroid diseases (AITD). So far, only common risk variants have been identified in AITD susceptibility genes. Recently, rare genetic variants have emerged as important contributors to complex diseases, and we hypothesized that rare variants play a key role in the genetic susceptibility to AITD.

OBJECTIVE

We aimed to identify new rare variants that are associated with familial AITD.

METHODS

We performed deep sequencing of 3 previously mapped AITD-linked loci (10q, 12q, and 14q) in a dataset of 34 families in which AITD clustered (familial AITD).

RESULTS

We identified 13 rare variants, located in the inositol polyphosphate multikinase (IPMK) gene, that were associated with AITD (ie, both Graves' disease [GD] and Hashimoto's thyroiditis [HT]); 2 rare variants, within the dihydrolipoamide S-succinyltransferase (DLST) and zinc-finger FYVE domain-containing protein (ZFYVE1) genes, that were associated with GD only; and 3 rare variants, within the phosphoglycerate mutase 1 pseudogene 5 (PGAM1P5), LOC105369879, and methionine aminopeptidase 2 (METAP2) genes, that were associated with HT only.

CONCLUSION

Our study demonstrates that, in addition to common variants, rare variants also contribute to the genetic susceptibility to AITD. We identified new rare variants in 6 AITD susceptibility genes that predispose to familial AITD. Of these, 3 genes, IPMK, ZFYVE1, and METAP2, are mechanistically involved in immune pathways and have been previously shown to be associated with autoimmunity. These genes predispose to thyroid autoimmunity and may serve as potential therapeutic targets in the future.

An Investigation of the Role of Common and Rare Variants in a Large Italian Multiplex Family of Multiple Sclerosis Patients

Known multiple sclerosis (MS) susceptibility variants can only explain half of the disease's estimated heritability, whereas low-frequency and rare variants may partly account for the missing heritability. Thus, here we sought to determine the occurrence of rare functional variants in a large Italian MS multiplex family with five affected members. For this purpose, we combined linkage analysis and next-generation sequencing (NGS)-based whole exome and whole genome sequencing (WES and WGS, respectively). The genetic burden attributable to known common MS variants was also assessed by weighted genetic risk score (wGRS). We found a significantly higher burden of common variants in the affected family members compared to that observed among sporadic MS patients and healthy controls (HCs). We also identified 34 genes containing at least one low-frequency functional variant shared among all affected family members, showing a significant enrichment in genes involved in specific biological processes-particularly mRNA transport-or neurodegenerative diseases. Altogether, our findings point to a possible pathogenic role of different low-frequency functional MS variants belonging to shared pathways. We propose that these rare variants, together with other known common MS variants, may account for the high number of affected family members within this MS multiplex family.

Trace Reconstruction Problems in Computational Biology

The problem of reconstructing a string from its error-prone copies, the trace reconstruction problem, was introduced by Vladimir Levenshtein two decades ago. While there has been considerable theoretical work on trace reconstruction, practical solutions have only recently started to emerge in the context of two rapidly developing research areas: immunogenomics and DNA data storage. In immunogenomics, traces correspond to mutated copies of genes, with mutations generated naturally by the adaptive immune system. In DNA data storage, traces correspond to noisy copies of DNA molecules that encode digital data, with errors being artifacts of the data retrieval process. In this paper, we introduce several new trace generation models and open questions relevant to trace reconstruction for immunogenomics and DNA data storage, survey theoretical results on trace reconstruction, and highlight their connections to computational biology. Throughout, we discuss the applicability and shortcomings of known solutions and suggest future research directions.

The Complex and Diverse Genetic Architecture of Dilated Cardiomyopathy

Our insight into the diverse and complex nature of dilated cardiomyopathy (DCM) genetic architecture continues to evolve rapidly. The foundations of DCM genetics rest on marked locus and allelic heterogeneity. While DCM exhibits a Mendelian, monogenic architecture in some families, preliminary data from our studies and others suggests that at least 20% to 30% of DCM may have an oligogenic basis, meaning that multiple rare variants from different, unlinked loci, determine the DCM phenotype. It is also likely that low-frequency and common genetic variation contribute to DCM complexity, but neither has been examined within a rare variant context. Other types of genetic variation are also likely relevant for DCM, along with gene-by-environment interaction, now established for alcohol- and chemotherapy-related DCM. Collectively, this suggests that the genetic architecture of DCM is broader in scope and more complex than previously understood. All of this elevates the impact of DCM genetics research, as greater insight into the causes of DCM can lead to interventions to mitigate or even prevent it and thus avoid the morbid and mortal scourge of human heart failure.

The Molecular Genetics of Marfan Syndrome

Marfan syndrome (MFS) is a complex connective tissue disease that is primarily characterized by cardiovascular, ocular and skeletal systems disorders. Despite its rarity, MFS severely impacts the quality of life of the patients. It has been shown that molecular genetic factors serve critical roles in the pathogenesis of MFS. FBN1 is associated with MFS and the other genes such as FBN2, transforming growth factor beta (TGF-β) receptors (TGFBR1 and TGFBR2), latent TGF-β-binding protein 2 (LTBP2) and SKI, amongst others also have their associated syndromes, however high overlap may exist between these syndromes and MFS. Abnormalities in the TGF-β signaling pathway also contribute to the development of aneurysms in patients with MFS, although the detailed molecular mechanism remains unclear. Mutant FBN1 protein may cause unstableness in elastic structures, thereby perturbing the TGF-β signaling pathway, which regulates several processes in cells. Additionally, DNA methylation of FBN1 and histone acetylation in an MFS mouse model demonstrated that epigenetic factors play a regulatory role in MFS. The purpose of the present review is to provide an up-to-date understanding of MFS-related genes and relevant assessment technologies, with the aim of laying a foundation for the early diagnosis, consultation and treatment of MFS.

Pedigree-Based Gene Mapping Supports Previous Loci and Reveals Novel Suggestive Loci in Specific Language Impairment

Purpose Specific language impairment (SLI) is characterized by a delay in language acquisition despite a lack of other developmental delays or hearing loss. Genetics of SLI is poorly understood. The purpose of this study is to identify SLI genetic loci through family-based linkage mapping. Method We performed genome-wide parametric linkage analysis in six families segregating with SLI. An age-appropriate standardized omnibus language measure was used to categorically define the SLI phenotype. Results A suggestive linkage region replicated a previous region of interest with the highest logarithm of odds (LOD) score of 2.40 at 14q11.2-q13.3 in Family 489. A paternal parent-of-origin effect associated with SLI and language phenotypes on a nonsynonymous single nucleotide polymorphism (SNP) in NOP9 (14q12) was reported previously. Linkage analysis identified a new SLI locus at 15q24.3-25.3 with the highest parametric LOD score of 3.06 in Family 315 under a recessive mode of inheritance. Suggestive evidence of linkage was also revealed at 4q31.23-q35.2 in Family 300, with the highest LOD score of 2.41. Genetic linkage was not identified in the other three families included in parametric linkage analysis. Conclusions These results are the first to report genome-wide suggestive linkage with a total language standard score on an age-appropriate omnibus language measure across a wide age range. Our findings confirm previous reports of a language-associated locus on chromosome 14q, report new SLI loci, and validate the pedigree-based parametric linkage analysis approach to mapping genes for SLI. Supplemental Material https://doi.org/10.23641/asha.13203218.

Next Generation Sequencing and Bioinformatics Analysis of Family Genetic Inheritance

Mendelian and complex genetic trait diseases continue to burden and affect society both socially and economically. The lack of effective tests has hampered diagnosis thus, the affected lack proper prognosis. Mendelian diseases are caused by genetic mutations in a singular gene while complex trait diseases are caused by the accumulation of mutations in either linked or unlinked genomic regions. Significant advances have been made in identifying novel diseases associated mutations especially with the introduction of next generation and third generation sequencing. Regardless, some diseases are still without diagnosis as most tests rely on SNP genotyping panels developed from population based genetic analyses. Analysis of family genetic inheritance using whole genomes, whole exomes or a panel of genes has been shown to be effective in identifying disease-causing mutations. In this review, we discuss next generation and third generation sequencing platforms, bioinformatic tools and genetic resources commonly used to analyze family based genomic data with a focus on identifying inherited or novel disease-causing mutations. Additionally, we also highlight the analytical, ethical and regulatory challenges associated with analyzing personal genomes which constitute the data used for family genetic inheritance.

Human leukocyte antigen association with familial steroid-sensitive nephrotic syndrome

Steroid-sensitive nephrotic syndrome (SSNS) is the most common form of nephrotic syndrome in childhood. Cases with the familial occurrence of SSNS suggest that genetics may play a role in the disease. Human leucocyte antigen (HLA) alleles have been associated with SSNS. We present genetic findings in nine families (44 participants), each with at least two affected siblings. A total of 19 patients were affected with familial SSNS. Six of nine families showed linkage to markers on chromosome 6p (27.29-33.97 Mbp) (Hg19), especially to markers D6S1629 and D6S1560 on HLA dense region in this location. Interestingly, we also found linkage of disease phenotype of familial SSNS on chromosome 15 (91.7-96.9 Mbp) (Hg19) with a logarithm of odds (LOD) score Z = 3.02.Conclusion: Our findings confirm the linkage of HLA markers on chromosome 6, which strengthens the association of HLA alleles in SSNS. What is Known: • Human leukocyte antigen (HLA) alleles have been associated with idiopathic steroid-sensitive nephrotic syndrome (SSNS). Only few studies have investigated the association between HLA alleles and familial SSNS. What is New: • We present evidence of linkage of familial SSNS to chromosome 6p (27.29-33.97 Mbp) (Hg19), especially to markers D6S1629 and D6S1560 on HLA dense region in this location. We also found linkage of the disease phenotype of familial SSNS on chromosome 15 (91.7-96.9 Mbp) (Hg19) with a logarithm of odds (LOD) score of Z = 3.02 following autosomal recessive inheritance pattern.

Review: Understanding Rare Genetic Diseases in Low Resource Regions Like Jammu and Kashmir - India

Rare diseases (RDs) are the clinical conditions affecting a few percentage of individuals in a general population compared to other diseases. Limited clinical information and a lack of reliable epidemiological data make their timely diagnosis and therapeutic management difficult. Emerging Next-Generation DNA Sequencing technologies have enhanced our horizons on patho-physiological understanding of many of the RDs and ushered us into an era of diagnostic and therapeutic research related to this ignored health challenge. Unfortunately, relevant research is meager in developing countries which lack a reliable estimate of the exact burden of most of the RDs. India is to be considered as the "Pandora's Box of genetic disorders." Owing to its huge population heterogeneity and high inbreeding or endogamy rates, a higher burden of rare recessive genetic diseases is expected and supported by the literature findings that endogamy is highly detrimental to health as it enhances the degree of homozygosity of recessive alleles in the general population. The population of a low resource region Jammu and Kashmir (J&K) - India, is highly inbred. Some of its population groups variably practice consanguinity. In context with the region's typical geographical topography, highly inbred population structure and unique but heterogeneous gene pool, a huge burden of known and uncharacterized genetic disorders is expected. Unfortunately, many suspected cases of genetic disorders remain undiagnosed or misdiagnosed due to lack of appropriate clinical as well as diagnostic resources in the region, causing patients to face a huge psycho-socio-economic crisis and many a time suffer life-long with their ailment. In this review, the major challenges associated with RDs are highlighted in general and an account on the methods that can be adopted for conducting fruitful molecular genetic studies in genetically vulnerable and low resource regions is also provided, with an example of a region like J&K - India.

Synergistic Chemotherapy Drug Response Is a Genetic Trait in Lymphoblastoid Cell Lines

Lymphoblastoid cell lines (LCLs) are a highly successful model for evaluating the genetic etiology of cancer drug response, but applications using this model have typically focused on single drugs. Combination therapy is quite common in modern chemotherapy treatment since drugs often work synergistically, and it is an important progression in the use of the LCL model to expand work for drug combinations. In the present work, we demonstrate that synergy occurs and can be quantified in LCLs across a range of clinically important drug combinations. Lymphoblastoid cell lines have been commonly employed in association mapping in cancer pharmacogenomics, but it is so far untested as to whether synergistic effects have a genetic etiology. Here we use cell lines from extended pedigrees to demonstrate that there is a substantial heritable component to synergistic drug response. Additionally, we perform linkage mapping in these pedigrees to identify putative regions linked to this important phenotype. This demonstration supports the premise of expanding the use of the LCL model to perform association mapping for combination therapies.

ONETOOL for the analysis of family-based big data

Motivation

Despite the need for separate tools to analyze family-based data, there are only a handful of tools optimized for family-based big data compared to the number of tools available for analyzing population-based data.

Results

ONETOOL implements the properties of well-known existing family data analysis tools and recently developed methods in a computationally efficient manner, and so is suitable for analyzing the vast amount of variant data available from sequencing family members, providing a rich choice of analysis methods for big data on families.

Availability and implementation

ONETOOL is freely available from http://healthstat.snu.ac.kr/software/onetool/.

Supplementary information

Supplementary data are available at Bioinformatics online.

Genome-wide association studies and polygenic risk scores for skin cancer: clinically useful yet?

BACKGROUND

Genome-wide association studies (GWAS) have identified thousands of susceptibility variants, although most have been associated with small individual risk estimates that offer little predictive value. However, combining multiple variants into polygenic risk scores (PRS) may be more informative. Multiple studies have developed PRS composed of GWAS-identified variants for cutaneous cancers. This review highlights data from these studies.

OBJECTIVES

To review published GWAS and PRS studies for melanoma, cutaneous squamous cell carcinoma (cSCC) and basal cell carcinoma (BCC), and discuss their potential clinical utility.

METHODS

We searched PubMed and the National Human Genome Research Institute-European Bioinformatics Institute GWAS catalogue to identify relevant studies.

RESULTS

Results from 21 GWAS (11 melanoma, 3 cSCC, 7 BCC) and 11 PRS studies are summarized. Six loci in pigmentation genes overlap between these three cancers (ASIP/RALY, IRF4, MC1R, OCA2, SLC45A2 and TYR). Additional loci overlap for cSCC/BCC and BCC/melanoma, but no other loci are shared between cSCC and melanoma. PRS for melanoma show roughly two-to-threefold increases in risk and modest improvements in risk prediction (2-7% increases). PRS are associated with twofold and threefold increases in risk of cSCC and BCC, respectively, with small improvements (2% increase) in predictive ability.

CONCLUSIONS

Existing data indicate that PRS may offer small, but potentially meaningful, improvements to risk prediction. Additional research is needed to clarify the potential utility of PRS in cutaneous carcinomas. Clinical translation will require well-powered validation studies incorporating known risk factors to evaluate PRS as tools for screening. What's already known about this topic? Over 50 susceptibility loci for melanoma, basal cell carcinoma (BCC) and cutaneous squamous cell carcinoma (cSCC) have been identified in genome-wide association studies (GWAS). Polygenic risk scores (PRS) using variants identified from GWAS have also been developed for melanoma, BCC and cSCC, and investigated with respect to clinical risk prediction. What does this study add? This review provides an overview of GWAS findings and the potential clinical utility of PRS for melanoma, BCC and cSCC.

Rediscovering the value of families for psychiatric genetics research

As it is likely that both common and rare genetic variation are important for complex disease risk, studies that examine the full range of the allelic frequency distribution should be utilized to dissect the genetic influences on mental illness. The rate limiting factor for inferring an association between a variant and a phenotype is inevitably the total number of copies of the minor allele captured in the studied sample. For rare variation, with minor allele frequencies of 0.5% or less, very large samples of unrelated individuals are necessary to unambiguously associate a locus with an illness. Unfortunately, such large samples are often cost prohibitive. However, by using alternative analytic strategies and studying related individuals, particularly those from large multiplex families, it is possible to reduce the required sample size while maintaining statistical power. We contend that using whole genome sequence (WGS) in extended pedigrees provides a cost-effective strategy for psychiatric gene mapping that complements common variant approaches and WGS in unrelated individuals. This was our impetus for forming the "Pedigree-Based Whole Genome Sequencing of Affective and Psychotic Disorders" consortium. In this review, we provide a rationale for the use of WGS with pedigrees in modern psychiatric genetics research. We begin with a focused review of the current literature, followed by a short history of family-based research in psychiatry. Next, we describe several advantages of pedigrees for WGS research, including power estimates, methods for studying the environment, and endophenotypes. We conclude with a brief description of our consortium and its goals.

Recombination fraction and genetic linkage among key disease resistance genes (Co-42 /Phg-2 and Co-5/"P.ult") in common bean

Anthracnose (Colletotrichum lindemuthianum), Angular leaf spot (Pseudocercospora griseola) and Pythium root rot are important pathogens affecting common bean production in the tropics. A promising strategy to manage these diseases consists of combining several resistance (R) genes into one cultivar. The aim of the study was to determine genetic linkage between gene pairs, Co-4² /Phg-2, on bean-chromosome Pv08 and Co-5/"P.ult" on-chromosome Pv07, to increase the efficiency of dual selection of resistance genes for major bean diseases, with molecular markers. The level of recombination was determined by tracking molecular markers for both BC3F6 and F2 generations. Recombination fraction r, among gene pairs, the likelihood of linkage, L(r), and logarithm of odds (LOD) scores were computed using the statistical relationship of likelihood which assumes a binomial distribution. The SCAR marker pair SAB3/PYAA19 for the gene pair Co-5/"P.ult" exhibited moderate linkage (r = 32 cM with a high LOD score of 9.2) for BC₃F₆ population, but relatively stronger linkage for the F₂ population (r = 21 cM with a high LOD score of 18.7). However, the linkage among SCAR marker pair SH18/SN02, for the gene pair Co-4² /Phg-2 was incomplete for BC₃F₆ population (r = 47 cM with a low LOD score of 0.16) as well as F₂ population (r = 44 cM with a low LOD score of 0.7). Generally, the weak or incomplete genetic linkage between marker pairs studied showed that all the four genes mentioned earlier have to be tagged with a corresponding linked marker during selection. The approaches used in this study will contribute to two loci linkage mapping techniques in segregating plant populations.

A high-dimensional linkage analysis model for characterizing crossover interference

Linkage analysis has played an important role in understanding genome structure and evolution. However, two-point linkage analysis widely used for genetic map construction can rarely chart a detailed picture of genome organization because it fails to identify the dependence of crossovers distributed along the length of a chromosome, a phenomenon known as crossover interference. Multi-point analysis, proven to be more advantageous in gene ordering and genetic distance estimation for dominant markers than two-point analysis, is equipped with a capacity to discern and quantify crossover interference. Here, we review a statistical model for four-point analysis, which, beyond three-point analysis, can characterize crossover interference that takes place not only between two adjacent chromosomal intervals, but also over multiple successive intervals. This procedure provides an analytical tool to elucidate the detailed landscape of crossover interference over the genome and further infer the evolution of genome structure and organization.

Landscaping Crossover Interference Across a Genome

The evolutionary success of eukaryotic organisms crucially depends on the capacity to produce genetic diversity through reciprocal exchanges of each chromosome pair, or crossovers (COs), during meiosis. It has been recognized that COs arise more evenly across a given chromosome than at random. This phenomenon, termed CO interference, occurs pervasively in eukaryotes and may confer a selective advantage. We describe here a multipoint linkage analysis procedure for segregating families to quantify the strength of CO interference over the genome, and extend this procedure to illustrate the landscape of CO interference in natural populations. We further discuss the crucial role of CO interference in amplifying and maintaining genetic diversity through sex-, stress-, and age-induced differentiation.

Multiple rare genetic variants co-segregating with familial IgA nephropathy all act within a single immune-related network

BACKGROUND

IgA nephropathy (IgAN) is a common complex disease with a strong genetic involvement. We aimed to identify novel, rare, highly penetrant risk variants combining family-based linkage analysis with whole-exome sequencing (WES).

METHODS

Linkage analysis of 16 kindreds of South Italian ancestry was performed using an 'affected-only' strategy. Eight most informative trios composed of two familial cases and an intrafamilial control were selected for WES. High-priority variants in linked regions were identified and validated using Sanger sequencing. Custom TaqMan assays were designed and carried out in the 16 kindreds and an independent cohort of 240 IgAN patients and 113 control subjects.

RESULTS

We found suggestive linkage signals in 12 loci. After sequential filtering and validation of WES data, we identified 24 private or extremely rare (MAF <0.0003) linked variants segregating with IgAN status. These were present within coding or regulatory regions of 23 genes that merged into a common functional network. The genes were interconnected by AKT, CTNNB1, NFKB, MYC and UBC, key modulators of WNT/β-catenin and PI3K/Akt pathways, which are implicated in IgAN pathogenesis. Overlaying publicly available expression data, genes/proteins with expression notably altered in IgAN were included in this immune-related network. In particular, the network included the glucocorticoid receptor gene, NR3C1, which is the target of corticosteroid therapy routinely used in the treatment of IgAN.

CONCLUSION

Our findings suggest that disease susceptibility could be influenced by multiple rare variants acting in a common network that could provide the starting point for the identification of potential drug targets for personalized therapy.

Cognitive, Linguistic, and Motor Abilities in a Multigenerational Family with Childhood Apraxia of Speech

Objective

This study describes the phenotype in a large family with a strong, multigenerational history of severe speech sound disorder (SSD) persisting into adolescence and adulthood in approximately half the cases. Aims were to determine whether a core phenotype, broader than speech, separated persistent from resolved SSD cases; and to ascertain the uniqueness of the phenotype relative to published cases.

Method

Eleven members of the PM family (9–55 years) were assessed across cognitive, language, literacy, speech, phonological processing, numeracy, and motor domains. Between group comparisons were made using the Mann–WhitneyU-test (p < 0.01). Participant performances were compared to normative data using standardized tests and to the limited published data on persistent SSD phenotypes.

Results

Significant group differences were evident on multiple speech, language, literacy, phonological processing, and verbal intellect measures without any overlapping scores. Persistent cases performed within the impaired range on multiple measures. Phonological memory impairment and subtle literacy weakness were present in resolved SSD cases.

Conclusion

A core phenotype distinguished persistent from resolved SSD cases that was characterized by a multiple verbal trait disorder, including Childhood Apraxia of Speech. Several phenotypic differences differentiated the persistent SSD phenotype in the PM family from the few previously reported studies of large families with SSD, including the absence of comorbid dysarthria and marked orofacial apraxia. This study highlights how comprehensive phenotyping can advance the behavioral study of disorders, in addition to forming a solid basis for future genetic and neural studies.

Mutations in the TMCO3 Gene are Associated with Cornea Guttata and Anterior Polar Cataract

The molecular basis for cornea guttata and anterior polar cataract remains idiopathic in most cases. In this study, our aim was to identify the disease-associated gene in Chinese patients with these conditions. Patients with the conditions from two Chinese families, and ten sporadic patients, were investigated. Genome-wide linkage and exome sequencing analyses showed transmembrane and coiled-coil domain 3 (TMCO3) as the disease candidate gene for a coding heterozygous mutation c.41C > T, resulting in a P14L amino acid change that co-segregated with the disease phenotype as discovered in Family A. TMCO3 belongs to the monovalent cation: protein antiporter 2 transporter family, a moderately large group whose members all share a very similar function under normal physiological conditions. The gene is expressed in the human cornea, lens capsule, and choroid-retinal pigment epithelium. This study reveals, for the first time, that mutations in TMCO3 are associated with cornea guttata and anterior polar cataract, warranting further investigation into the pathogenesis of this disorder.

Genetic Epidemiology and Public Health: The Evolution From Theory to Technology

Genetic epidemiology represents a hybrid of epidemiologic designs and statistical models that explicitly consider both genetic and environmental risk factors for disease. It is a relatively new field in public health; the term was first coined only 35 years ago. In this short time, the field has been through a major evolution, changing from a field driven by theory, without the technology for genetic measurement or computational capacity to apply much of the designs and methods developed, to a field driven by rapidly expanding technology in genomic measurement and computational analyses while epidemiologic theory struggles to keep up. In this commentary, we describe 4 different eras of genetic epidemiology, spanning this evolution from theory to technology, what we have learned, what we have added to the broader field of public health, and what remains to be done.

Family-based approaches: design, imputation, analysis, and beyond

Participants in the family-based analysis group at Genetic Analysis Workshop 19 addressed diverse topics, all of which used the family data. Topics addressed included questions of study design and data quality control (QC), genotype imputation to augment available sequence data, and linkage and/or association analyses. Results show that pedigree-based tests that are sensitive to genotype error may be useful for QC. Imputation quality improved with inclusion of small amounts of pedigree information used to phase the data in evaluation of 5 commonly used approaches for imputation in samples of (typically) unrelated subjects. It improved still further when pedigree-based imputation using larger pedigrees was also added. An important distinction was made between methods that do versus do not make use of Mendelian transmission in pedigrees, because this serves as a key difference between underlying models and assumptions. Methods that model relatedness generally had higher power in association testing than did analyses that carry out testing in the presence of a transmission model, but this may reflect details of implementation and/or ability of more general methods to jointly include data from larger pedigrees. In either case, for single nucleotide polymorphism-set approaches, weights that incorporate information on functional effects may be more useful than those that are based only on allele frequencies. The overall results demonstrate that family data continue to provide important information in the search for trait loci.

Genome-wide linkage analyses of non-Hispanic white families identify novel loci for familial late-onset Alzheimer's disease

INTRODUCTION

Few high penetrance variants that explain risk in late-onset Alzheimer's disease (LOAD) families have been found.

METHODS

We performed genome-wide linkage and identity-by-descent (IBD) analyses on 41 non-Hispanic white families exhibiting likely dominant inheritance of LOAD, and having no mutations at known familial Alzheimer's disease (AD) loci, and a low burden of APOE ε4 alleles.

RESULTS

Two-point parametric linkage analysis identified 14 significantly linked regions, including three novel linkage regions for LOAD (5q32, 11q12.2-11q14.1, and 14q13.3), one of which replicates a genome-wide association LOAD locus, the MS4A6A-MS4A4E gene cluster at 11q12.2. Five of the 14 regions (3q25.31, 4q34.1, 8q22.3, 11q12.2-14.1, and 19q13.41) are supported by strong multipoint results (logarithm of odds [LOD*] ≥1.5). Nonparametric multipoint analyses produced an additional significant locus at 14q32.2 (LOD* = 4.18). The 1-LOD confidence interval for this region contains one gene, C14orf177, and the microRNA Mir_320, whereas IBD analyses implicates an additional gene BCL11B, a regulator of brain-derived neurotrophic signaling, a pathway associated with pathogenesis of several neurodegenerative diseases.

DISCUSSION

Examination of these regions after whole-genome sequencing may identify highly penetrant variants for familial LOAD.

Linkage and whole genome sequencing identify a locus on 6q25-26 for formal thought disorder and implicate MEF2A regulation

Formal thought disorder is a major feature of schizophrenia and other psychotic disorders. It is heritable, found in healthy relatives of patients with schizophrenia and other mental disorders but knowledge of specific genetic factors is lacking. The aim of this study was to search for biologically relevant high-risk variants. Formal thought disorder was assessed in participants in the Copenhagen Schizophrenia Linkage Study (N=236), a unique high-risk family study comprised of six large pedigrees. Microsatellite linkage analysis of formal thought disorder was performed and subsequent haplotype analysis of the implicated region using phased microsatellite and SNP genotypes. Whole genome sequencing (N=3) was used in the attempt to identify causative variants in the linkage region. Linkage analysis of formal thought disorder resulted in a single peak at chromosome 6(q26-q27) centred on marker D6S1277, with a maximum LOD score of 4.0. Phasing and fine mapping of the linkage peak identified a 5.5Mb haplotype (chr6:162242322-167753547, hg18) in 31 individuals, all belonging to the same pedigree sharing the haplotype from a common ancestor. The haplotype segregated with increased total thought disorder index score (P=4.9 × 10(-5)) and qualitatively severe forms of thought disturbances. Whole genome sequencing identified a novel nucleotide deletion (chr6:164377205 AG>A, hg18) predicted to disrupt the potential binding of the transcription factor MEF2A. The MEF2A binding site is located between two genes previously reported to associate with schizophrenia, QKI (HGNC:21100) and PDE10A (HGNC:8772). The findings are consistent with MEF2A deregulation conferring risk of formal thought disorder.

Introduction to the analysis of next generation sequencing data and its application to venous thromboembolism

Despite knowledge of various inherited risk factors associated with venous thromboembolism (VTE), no definite cause can be found in about 50% of patients. The application of data-driven searches such as GWAS has not been able to identify genetic variants with implications for clinical care, and unexplained heritability remains. In the past years, the development of several so-called next generation sequencing (NGS) platforms is offering the possibility of generating fast, inexpensive and accurate genomic information. However, so far their application to VTE has been very limited. Here we review basic concepts of NGS data analysis and explore the application of NGS technology to VTE. We provide both computational and biological viewpoints to discuss potentials and challenges of NGS-based studies.

Whole-genome single nucleotide polymorphism-based linkage analysis in spondyloarthritis multiplex families reveals a new susceptibility locus in 13q13

OBJECTIVE

Spondyloarthritis (SpA) is a chronic inflammatory disorder with high heritability but with complex genetics. Apart from HLA-B27, most of the underlying genetic components remain to be identified. We conducted a whole-genome high-density non-parametric linkage analysis to identify new genetic factors of susceptibility to SpA.

METHODS

914 subjects including 462 with SpA from 143 multiplex families were genotyped using Affymetrix 250K microarrays. After quality control, 189 368 single nucleotide polymorphisms (SNPs) were kept for further analyses. Both non-parametric and parametric linkage analyses were performed using Merlin software. Association was tested with Unphased.

RESULTS

Non-parametric linkage analysis identified two regions significantly linked to SpA: the major histocompatibility complex (LODmax=24.77) and a new 13q13 locus (LODmax=5.03). Additionally, eight loci achieved suggestive LOD scores, including the previously identified SPA2 locus at 9q33 (LODmax=3.51). Parametric analysis supported a codominant model in 13q13 with a maximum heterogeneity LOD, 'HLOD' score of 3.084 (α=0.28). Identification of meiotic recombination events around the 13q13 linkage peak in affected subjects from the 43 best-linked families allowed us to map the disease interval between 38.753 and 40.040 Mb. Family-based association analysis of the SNPs inside this interval in the best-linked families identified a SNP near FREM2 (rs1945502) which reached a p value close to statistical significance (corrected p=0.08).

CONCLUSION

We report here for the first time a significant linkage between 13q13 and SpA. Identification of susceptibility factor inside this chromosomal region through targeted sequencing in linked families is underway.

Genetic linkage analysis in the age of whole-genome sequencing

For many years, linkage analysis was the primary tool used for the genetic mapping of Mendelian and complex traits with familial aggregation. Linkage analysis was largely supplanted by the wide adoption of genome-wide association studies (GWASs). However, with the recent increased use of whole-genome sequencing (WGS), linkage analysis is again emerging as an important and powerful analysis method for the identification of genes involved in disease aetiology, often in conjunction with WGS filtering approaches. Here, we review the principles of linkage analysis and provide practical guidelines for carrying out linkage studies using WGS data.

KCNJ11: Genetic Polymorphisms and Risk of Diabetes Mellitus

Diabetes mellitus (DM) is a major worldwide health problem and its prevalence has been rapidly increasing in the last century. It is caused by defects in insulin secretion or insulin action or both, leading to hyperglycemia. Of the various types of DM, type 2 occurs most frequently. Multiple genes and their interactions are involved in the insulin secretion pathway. Insulin secretion is mediated through the ATP-sensitive potassium (KATP) channel in pancreatic beta cells. This channel is a heteromeric protein, composed of four inward-rectifier potassium ion channel (Kir6.2) tetramers, which form the pore of the KATP channel, as well as sulfonylurea receptor 1 subunits surrounding the pore. Kir6.2 is encoded by the potassium inwardly rectifying channel, subfamily J, member 11 (KCNJ11) gene, a member of the potassium channel genes. Numerous studies have reported the involvement of single nucleotide polymorphisms of the KCNJ11 gene and their interactions in the susceptibility to DM. This review discusses the current evidence for the contribution of common KCNJ11 genetic variants to the development of DM. Future studies should concentrate on understanding the exact role played by these risk variants in the development of DM.

Nutrigenetics and nutrigenomics insights into diabetes etiopathogenesis

Diabetes mellitus (DM) is considered a global pandemic, and the incidence of DM continues to grow worldwide. Nutrients and dietary patterns are central issues in the prevention, development and treatment of this disease. The pathogenesis of DM is not completely understood, but nutrient-gene interactions at different levels, genetic predisposition and dietary factors appear to be involved. Nutritional genomics studies generally focus on dietary patterns according to genetic variations, the role of gene-nutrient interactions, gene-diet-phenotype interactions and epigenetic modifications caused by nutrients; these studies will facilitate an understanding of the early molecular events that occur in DM and will contribute to the identification of better biomarkers and diagnostics tools. In particular, this approach will help to develop tailored diets that maximize the use of nutrients and other functional ingredients present in food, which will aid in the prevention and delay of DM and its complications. This review discusses the current state of nutrigenetics, nutrigenomics and epigenomics research on DM. Here, we provide an overview of the role of gene variants and nutrient interactions, the importance of nutrients and dietary patterns on gene expression, how epigenetic changes and micro RNAs (miRNAs) can alter cellular signaling in response to nutrients and the dietary interventions that may help to prevent the onset of DM.

Challenges of linkage analysis in the era of whole-genome sequencing

Whole-genome sequencing (WGS) is becoming an affordable technology for the study of the genetics of complex traits. With any new technology, experimental designs and statistical methods, both old and new, must be evaluated. One design seeing a resurgence of interest is the use of families. Genetic Analysis Workshop 18 provided the opportunity to evaluate statistical methods applied to WGS data for family-based studies. We summarize the results of five contributions that used linkage in the context of WGS. The investigators took differing approaches, including assessment of false-positive rates in classic two-point linkage, the effects of heterogeneity on linkage and association tests, and the use of linkage to focus association tests. We describe the primary findings of each contribution and note challenges that are not new to those working in family designs or specific to WGS data; for example, choice of phenotype definition, covariate adjustment, and use of longitudinal data may produce different results, making comparisons challenging. We detail new issues brought about by WGS, such as the elevated genome-wide false-positive rate for classic two-point parametric linkage analysis, computational demands in multipoint calculations, and lack of clarity in how to best use linkage to focus association testing. Finally, we comment on when linkage may be helpful for WGS, highlighting where additional research is needed; for example, although linkage analysis has been successful in the study of rare variants of large effect, how to best use family information in the context of rare variants of moderate effect remains an open research question.

Linkage analysis and the study of Mendelian disease in the era of whole exome and genome sequencing

Whole exome and whole genome sequencing are now routinely used in the study of inherited disease, and some of their major successes have been the identification of genes involved in disease predisposition in pedigrees where disease seems to follow Mendelian inheritance patterns. These successes include scenarios where only a single individual was sequenced and raise the question whether linkage analysis has become superfluous. Linkage analysis requires genome-wide genotyping on family-based data, and traditionally the linkage analysis was performed before the targeting sequencing stage. However, methods are emerging that seek to exploit the capability of linkage analysis to integrate data both across individuals and across pedigrees. This ability has been exploited to select samples used for sequencing studies and to identify among the variants uncovered by sequencing those mapping to regions likely to contain the gene of interest and, more generally, to improve variant detection. So, although the formal isolated linkage analysis stage is less commonly seen, when uncovering the genetic basis of Mendelian disease, methods relying heavily on genetic linkage analysis principles are being integrated directly into the whole mapping process ranging from sample selection to variant calling and filtering.

Linkage analysis incorporating gene-age interactions identifies seven novel lipid loci: the Family Blood Pressure Program

OBJECTIVE

To detect novel loci with age-dependent effects on fasting (≥ 8 h) levels of total cholesterol, high-density lipoprotein, low-density lipoprotein, and triglycerides using 3600 African Americans, 1283 Asians, 3218 European Americans, and 2026 Mexican Americans from the Family Blood Pressure Program (FBPP).

METHODS

Within each subgroup (defined by network, race, and sex), we employed stepwise linear regression (retention p ≤ 0.05) to adjust lipid levels for age, age-squared, age-cubed, body-mass-index, current smoking status, current drinking status, field center, estrogen therapy (females only), as well as antidiabetic, antihypertensive, and antilipidemic medication use. For each trait, we pooled the standardized male and female residuals within each network and race and fit a generalized variance components model that incorporated gene-age interactions. We conducted FBPP-wide and race-specific meta-analyses by combining the p-values of each linkage marker across subgroups using a modified Fisher's method.

RESULTS

We identified seven novel loci with age-dependent effects; four total cholesterol loci from the meta-analysis of Mexican Americans (on chromosomes 2q24.1, 4q21.21, 8q22.2, and 12p11.23) and three high-density lipoprotein loci from the meta-analysis of all FBPP subgroups (on chromosomes 1p12, 14q11.2, and 21q21.1). These loci lacked significant genome-wide linkage or association evidence in the literature and had logarithm of odds (LOD) score ≥ 3 in the meta-analysis with LOD ≥ 1 in at least two network and race subgroups (exclusively of non-European descent).

CONCLUSION

Incorporating gene-age interactions into the analysis of lipids using multi-ethnic cohorts can enhance gene discovery. These interaction loci can guide the selection of families for sequencing studies of lipid-associated variants.

Visualization of haplotype sharing patterns in pedigree samples

OBJECTIVES

A particular approach to the visualization of descent of founder DNA copies in a pedigree has been suggested, which helps to understand haplotype sharing patterns among subjects of interest. However, the approach does not provide the information in an ideal format to show haplotype sharing patterns. Therefore, we aimed to find an efficient way to visualize such sharing patterns and to demonstrate that our tool provides useful information for finding an informative subset of subjects for a sequence study.

METHODS

The visualization package, SharedHap, computes and visualizes a novel metric, the SharedHap proportion, which quantifies haplotype sharing among a set of subjects of interest. We applied SharedHap to simulated and real pedigree datasets to illustrate the approach.

RESULTS

SharedHap successfully represents haplotype sharing patterns that contribute to linkage signals in both simulated and real datasets. Using the visualizations we were also able to find ideal sets of subjects for sequencing studies.

CONCLUSIONS

Our novel metric that can be computed using the SharedHap package provides useful information about haplotype sharing patterns among subjects of interest. The visualization of the SharedHap proportion provides useful information in pedigree studies, allowing for a better selection of candidate subjects for use in further sequencing studies.

Identification of rare variants for hypertension with incorporation of linkage information

We conducted linkage analysis using the genome-wide association study data on chromosome 3, and then assessed association between hypertension and rare variants of genes located in the regions showing evidence of linkage. The rare variants were collapsed if their minor allele frequencies were less than or equal to the thresholds: 0.01, 0.03, or 0.05. In the collapsing process, they were either unweighted or weighted by the nonparametric linkage log of odds scores in 2 different schemes: exponential weighting and cumulative weighting. Logistic regression models using the generalized estimating equations approach were used to assess association between the collapsed rare variants and hypertension adjusting for age and gender. Evidence of association from the weighted and unweighted collapsing schemes with minor allele frequencies ≤0.01, after accounting for multiple testing, was found for genes DOCK3 (p = 0.0090), ARMC8 (p = 1.29E-5), KCNAB1 (p = 5.8E-4), and MYRIP (p = 5.79E-6). DOCK3 and MYRIP are newly discovered. Incorporating linkage scores as weights was found to help identify rare causal variants with a large effect size.

False-positive rates in two-point parametric linkage analysis

Two-point linkage analyses of whole genome sequence data are a promising approach to identify rare variants that segregate with complex diseases in large pedigrees because, in theory, the causal variants have been genotyped. We used whole genome sequence data and simulated traits provided by Genetic Analysis Workshop 18 to evaluate the proportion of false-positive findings in a binary trait using classic two-point parametric linkage analysis. False-positive genome-wide significant log of odds (LOD) scores were identified in more than 80% of 50 replicates for a binary phenotype generated by dichotomizing a quantitative trait that was simulated with a polygenic component (that was not based on any of the provided whole genome sequence genotypes). In contrast, when the trait was truly nongenetic (created by randomly assigning affected-unaffected status), the number of false-positive results was well controlled. These results suggest that when using two-point linkage analyses on whole genome sequence data, one should carefully examine regions yielding significant two-point LOD scores with multipoint analysis and that a more stringent significance threshold may be needed.

An exploration of heterogeneity in genetic analysis of complex pedigrees: linkage and association using whole genome sequencing data in the MAP4 region

We conduct pedigree-based linkage and association analyses of simulated systolic blood pressure data in the nonascertained large Mexican American pedigrees provided by Genetic Analysis Workshop 18, focusing on observed sequence variants in MAP4 on chromosome 3. Because pedigrees are large and sequence data have been completed by imputation, it is feasible to conduct analysis for each pedigree separately as well as for all pedigrees combined. We are interested in quantifying and explaining between-pedigree heterogeneity in linkage and association signals. To this end, we first examine minor allele frequency differences between pedigrees. In some of the pedigrees, rare and low-frequency variants occur at a higher prevalence than in all pedigrees combined. In simulation replicate 1, we conduct variance-components linkage and association analysis of all 894 MAP4 variants to compare analytic approaches in single pedigree and combined analysis. In all 200 replicates, we similarly examine the 15 causal variants in MAP4 known under the generating model. We illustrate how random allele frequency variation among pedigrees leads to heterogeneity in pedigree-specific linkage and association signals.

Statistical power and significance testing in large-scale genetic studies

Significance testing was developed as an objective method for summarizing statistical evidence for a hypothesis. It has been widely adopted in genetic studies, including genome-wide association studies and, more recently, exome sequencing studies. However, significance testing in both genome-wide and exome-wide studies must adopt stringent significance thresholds to allow multiple testing, and it is useful only when studies have adequate statistical power, which depends on the characteristics of the phenotype and the putative genetic variant, as well as the study design. Here, we review the principles and applications of significance testing and power calculation, including recently proposed gene-based tests for rare variants.

The next-generation sequencing revolution and its impact on genomics

Genomics is a relatively new scientific discipline, having DNA sequencing as its core technology. As technology has improved the cost and scale of genome characterization over sequencing's 40-year history, the scope of inquiry has commensurately broadened. Massively parallel sequencing has proven revolutionary, shifting the paradigm of genomics to address biological questions at a genome-wide scale. Sequencing now empowers clinical diagnostics and other aspects of medical care, including disease risk, therapeutic identification, and prenatal testing. This Review explores the current state of genomics in the massively parallel sequencing era.

Gene × smoking interactions on human brain gene expression: finding common mechanisms in adolescents and adults

BACKGROUND

Numerous studies have examined gene × environment interactions (G × E) in cognitive and behavioral domains. However, these studies have been limited in that they have not been able to directly assess differential patterns of gene expression in the human brain. Here, we assessed G × E interactions using two publically available datasets to assess if DNA variation is associated with post-mortem brain gene expression changes based on smoking behavior, a biobehavioral construct that is part of a complex system of genetic and environmental influences.

METHODS

We conducted an expression quantitative trait locus (eQTL) study on two independent human brain gene expression datasets assessing G × E for selected psychiatric genes and smoking status. We employed linear regression to model the significance of the Gene × Smoking interaction term, followed by meta-analysis across datasets.

RESULTS

Overall, we observed that the effect of DNA variation on gene expression is moderated by smoking status. Expression of 16 genes was significantly associated with single nucleotide polymorphisms that demonstrated G × E effects. The strongest finding (p = 1.9 × 10⁻¹¹) was neurexin 3-alpha (NRXN3), a synaptic cell-cell adhesion molecule involved in maintenance of neural connections (such as the maintenance of smoking behavior). Other significant G × E associations include four glutamate genes.

CONCLUSIONS

This is one of the first studies to demonstrate G × E effects within the human brain. In particular, this study implicated NRXN3 in the maintenance of smoking. The effect of smoking on NRXN3 expression and downstream behavior is different based upon SNP genotype, indicating that DNA profiles based on SNPs could be useful in understanding the effects of smoking behaviors. These results suggest that better measurement of psychiatric conditions, and the environment in post-mortem brain studies may yield an important avenue for understanding the biological mechanisms of G × E interactions in psychiatry.

Taiwan Schizophrenia Linkage Study: lessons learned from endophenotype-based genome-wide linkage scans and perspective

Taiwan Schizophrenia Linkage Study (TSLS) was initiated with a linkage strategy for locating multiple genes, each of small to moderate effect, and aimed to recruit a large enough sample of pairs of affected siblings and their families ascertained from a multisite study. With a sample of 607 families successfully recruited, a total of 2,242 individuals (1,207 affected and 1,035 unaffected) from 557 families were genotyped using 386 microsatellite markers spaced at an average of 9-cM intervals. Here the author reviews the establishment of TSLS and initial signal derived from linkage scan using the diagnosis of schizophrenia. Based on the limited success of the initial linkage analysis, a sufficient-component causal model is proposed to incorporate endophenotypes and genes for schizophrenia. Four types of candidate endophenotype measured in TSLS, including schizotypal personality, Continuous Performance Test, Wisconsin Card Sorting Test, and niacin skin flush test, are briefly described. The author discusses different strategies of linkage analysis incorporating these endophenotypes, including quantitative trait loci (QTL) linkage analysis, clustering-derived subgroups, ordered subset analysis (OSA), and latent classes for linkage scan. Then the author summarizes the linkage signals generated from seven studies of endophenotype-based linkage analysis using TSLS, including QTL scan of neurocognitive performance, QTL scan of niacin skin flush, the family cluster of attention deficit and execution deficit, OSA by schizophrenia-schizotypy factors, nested OSA by age at onset and neurocognitive performance, and the latent class of deficit schizophrenia for linkage analysis. The perspective of combining next-generation sequencing with linkage analysis of families is also discussed.