Suzanne M. Leal

Evolution and Functional Impact of Rare Coding Variation from Deep Sequencing of Human Exomes

A Deep Look Into Our Genes Recent debates have focused on the degree of genetic variation and its impact upon health at the genomic level in humans (see the Perspective by Casals and Bertranpetit). Tennessen et al. (p. 64, published online 17 May), looking at all of the protein-coding genes in the human genome, and Nelson et al. (p. 100, published online 17 May), looking at genes that encode drug targets, address this question through deep sequencing efforts on samples from multiple individuals. The findings suggest that most human variation is rare, not shared between populations, and that rare variants are likely to play a role in human health. Most functionally consequential variants in protein-coding genes are rare and, thus, difficult to find. As a first step toward understanding how rare variants contribute to risk for complex diseases, we sequenced 15,585 human protein-coding genes to an average median depth of 111× in 2440 individuals of European (n = 1351) and African (n = 1088) ancestry. We identified over 500,000 single-nucleotide variants (SNVs), the majority of which were rare (86% with a minor allele frequency less than 0.5%), previously unknown (82%), and population-specific (82%). On average, 2.3% of the 13,595 SNVs each person carried were predicted to affect protein function of ~313 genes per genome, and ~95.7% of SNVs predicted to be functionally important were rare. This excess of rare functional variants is due to the combined effects of explosive, recent accelerated population growth and weak purifying selection. Furthermore, we show that large sample sizes will be required to associate rare variants with complex traits.

Methods for Detecting Associations with Rare Variants for Common Diseases: Application to Analysis of Sequence Data

Although whole-genome association studies using tagSNPs are a powerful approach for detecting common variants, they are underpowered for detecting associations with rare variants. Recent studies have demonstrated that common diseases can be due to functional variants with a wide spectrum of allele frequencies, ranging from rare to common. An effective way to identify rare variants is through direct sequencing. The development of cost-effective sequencing technologies enables association studies to use sequence data from candidate genes and, in the future, from the entire genome. Although methods used for analysis of common variants are applicable to sequence data, their performance might not be optimal. In this study, it is shown that the collapsing method, which involves collapsing genotypes across variants and applying a univariate test, is powerful for analyzing rare variants, whereas multivariate analysis is robust against inclusion of noncausal variants. Both methods are superior to analyzing each variant individually with univariate tests. In order to unify the advantages of both collapsing and multiple-marker tests, we developed the Combined Multivariate and Collapsing (CMC) method and demonstrated that the CMC method is both powerful and robust. The CMC method can be applied to either candidate-gene or whole-genome sequence data.

Guidelines for investigating causality of sequence variants in human disease

The discovery of rare genetic variants is accelerating, and clear guidelines for distinguishing disease-causing sequence variants from the many potentially functional variants present in any human genome are urgently needed. Without rigorous standards we risk an acceleration of false-positive reports of causality, which would impede the translation of genomic research findings into the clinical diagnostic setting and hinder biological understanding of disease. Here we discuss the key challenges of assessing sequence variants in human disease, integrating both gene-level and variant-level support for causality. We propose guidelines for summarizing confidence in variant pathogenicity and highlight several areas that require further resource development.

Analysis of 6,515 exomes reveals the recent origin of most human protein-coding variants

Establishing the age of each mutation segregating in contemporary human populations is important to fully understand our evolutionary history and will help to facilitate the development of new approaches for disease-gene discovery. Large-scale surveys of human genetic variation have reported signatures of recent explosive population growth, notable for an excess of rare genetic variants, suggesting that many mutations arose recently. To more quantitatively assess the distribution of mutation ages, we resequenced 15,336 genes in 6,515 individuals of European American and African American ancestry and inferred the age of 1,146,401 autosomal single nucleotide variants (SNVs). We estimate that approximately 73% of all protein-coding SNVs and approximately 86% of SNVs predicted to be deleterious arose in the past 5,000–10,000 years. The average age of deleterious SNVs varied significantly across molecular pathways, and disease genes contained a significantly higher proportion of recently arisen deleterious SNVs than other genes. Furthermore, European Americans had an excess of deleterious variants in essential and Mendelian disease genes compared to African Americans, consistent with weaker purifying selection due to the Out-of-Africa dispersal. Our results better delimit the historical details of human protein-coding variation, show the profound effect of recent human history on the burden of deleterious SNVs segregating in contemporary populations, and provide important practical information that can be used to prioritize variants in disease-gene discovery.

Duplications of the neuropeptide receptor gene VIPR2 confer significant risk for schizophrenia

Rare copy number variants (CNVs) have a prominent role in the aetiology of schizophrenia and other neuropsychiatric disorders. Substantial risk for schizophrenia is conferred by large (>500-kilobase) CNVs at several loci, including microdeletions at 1q21.1 (ref. 2), 3q29 (ref. 3), 15q13.3 (ref. 2) and 22q11.2 (ref. 4) and microduplication at 16p11.2 (ref. 5). However, these CNVs collectively account for a small fraction (2–4%) of cases, and the relevant genes and neurobiological mechanisms are not well understood. Here we performed a large two-stage genome-wide scan of rare CNVs and report the significant association of copy number gains at chromosome 7q36.3 with schizophrenia. Microduplications with variable breakpoints occurred within a 362-kilobase region and were detected in 29 of 8,290 (0.35%) patients versus 2 of 7,431 (0.03%) controls in the combined sample. All duplications overlapped or were located within 89 kilobases upstream of the vasoactive intestinal peptide receptor gene VIPR2. VIPR2 transcription and cyclic-AMP signalling were significantly increased in cultured lymphocytes from patients with microduplications of 7q36.3. These findings implicate altered vasoactive intestinal peptide signalling in the pathogenesis of schizophrenia and indicate the VPAC2 receptor as a potential target for the development of new antipsychotic drugs.

Homozygous mutations in LPIN2 are responsible for the syndrome of chronic recurrent multifocal osteomyelitis and congenital dyserythropoietic anaemia (Majeed syndrome)

Background: Majeed syndrome is an autosomal recessive, autoinflammatory disorder characterised by chronic recurrent multifocal osteomyelitis and congenital dyserythropoietic anaemia. The objectives of this study were to map, identify, and characterise the Majeed syndrome causal gene and to speculate on its function and role in skin and bone inflammation. Methods: Six individuals with Majeed syndrome from two unrelated families were identified for this study. Homozygosity mapping and parametric linkage analysis were employed for the localisation of the gene responsible for Majeed syndrome. Direct sequencing was utilised for the identification of mutations within the genes contained in the region of linkage. Expression studies and in silico characterisation of the identified causal gene and its protein were carried out. Results: The phenotype of Majeed syndrome includes inflammation of the bone and skin, recurrent fevers, and dyserythropoietic anaemia. The clinical picture of the six affected individuals is briefly reviewed. The gene was mapped to a 5.5 cM interval (1.8 Mb) on chromosome 18p. Examination of genes in this interval led to the identification of homozygous mutations in LPIN2 in affected individuals from the two families. LPIN2 was found to be expressed in almost all tissues. The function of LPIN2 and its role in inflammation remains unknown. Conclusions: We conclude that homozygous mutations in LPIN2 result in Majeed syndrome. Understanding the aberrant immune response in this condition will shed light on the aetiology of other inflammatory disorders of multifactorial aetiology including isolated chronic recurrent multifocal osteomyelitis, Sweet syndrome, and psoriasis.

The Genetic Basis of Mendelian Phenotypes: Discoveries, Challenges, and Opportunities

Discovering the genetic basis of a Mendelian phenotype establishes a causal link between genotype and phenotype, making possible carrier and population screening and direct diagnosis. Such discoveries also contribute to our knowledge of gene function, gene regulation, development, and biological mechanisms that can be used for developing new therapeutics. As of February 2015, 2,937 genes underlying 4,163 Mendelian phenotypes have been discovered, but the genes underlying ∼50% (i.e., 3,152) of all known Mendelian phenotypes are still unknown, and many more Mendelian conditions have yet to be recognized. This is a formidable gap in biomedical knowledge. Accordingly, in December 2011, the NIH established the Centers for Mendelian Genomics (CMGs) to provide the collaborative framework and infrastructure necessary for undertaking large-scale whole-exome sequencing and discovery of the genetic variants responsible for Mendelian phenotypes. In partnership with 529 investigators from 261 institutions in 36 countries, the CMGs assessed 18,863 samples from 8,838 families representing 579 known and 470 novel Mendelian phenotypes as of January 2015. This collaborative effort has identified 956 genes, including 375 not previously associated with human health, that underlie a Mendelian phenotype. These results provide insight into study design and analytical strategies, identify novel mechanisms of disease, and reveal the extensive clinical variability of Mendelian phenotypes. Discovering the gene underlying every Mendelian phenotype will require tackling challenges such as worldwide ascertainment and phenotypic characterization of families affected by Mendelian conditions, improvement in sequencing and analytical techniques, and pervasive sharing of phenotypic and genomic data among researchers, clinicians, and families.

TGFB2 mutations cause familial thoracic aortic aneurysms and dissections associated with mild systemic features of Marfan syndrome

A predisposition for thoracic aortic aneurysms leading to acute aortic dissections can be inherited in families in an autosomal dominant manner. Genome-wide linkage analysis of two large unrelated families with thoracic aortic disease followed by whole-exome sequencing of affected relatives identified causative mutations in TGFB2. These mutations—a frameshift mutation in exon 6 and a nonsense mutation in exon 4—segregated with disease with a combined logarithm of odds (LOD) score of 7.7. Sanger sequencing of 276 probands from families with inherited thoracic aortic disease identified 2 additional TGFB2 mutations. TGFB2 encodes transforming growth factor (TGF)-β2, and the mutations are predicted to cause haploinsufficiency for TGFB2; however, aortic tissue from cases paradoxically shows increased TGF-β2 expression and immunostaining. Thus, haploinsufficiency for TGFB2 predisposes to thoracic aortic disease, suggesting that the initial pathway driving disease is decreased cellular TGF-β2 levels leading to a secondary increase in TGF-β2 production in the diseased aorta.

Mitochondrial genetic background modifies breast cancer risk.

Inefficient mitochondrial electron transport chain (ETC) function has been implicated in the vicious cycle of reactive oxygen species (ROS) production that may predispose an individual to late onset diseases, such as diabetes, hypertension, and cancer. Mitochondrial DNA (mtDNA) variations may affect the efficiency of ETC and ROS production, thus contributing to cancer risk. To test this hypothesis, we genotyped 69 mtDNA variations in 156 unrelated European-American females with familial breast cancer and 260 age-matched European-American female controls. Fishers exact test was done for each single-nucleotide polymorphism (SNP)/haplogroup and the P values were adjusted for multiple testing using permutation. Odds ratio (OR) and its 95% confidence interval (95% CI) were calculated using the Sheehe correction. Among the 69 variations, 29 were detected in the study subjects. Three SNPs, G9055A (OR, 3.03; 95% CI, 1.63-5.63; P = 0.0004, adjusted P = 0.0057), A10398G (OR, 1.79; 95% CI, 1.14-2.81; P = 0.01, adjusted P = 0.19), and T16519C (OR, 1.98; 95% CI, 1.25-3.12; P = 0.0030, adjusted P = 0.0366), were found to increase breast cancer risk; whereas T3197C (OR, 0.31; 95% CI, 0.13-0.75; P = 0.0043, adjusted P = 0.0526) and G13708A (OR, 0.47; 95% CI, 0.24-0.92; P = 0.022, adjusted P = 0.267) were found to decrease breast cancer risk. Overall, individuals classified as haplogroup K show a significant increase in the risk of developing breast cancer (OR, 3.03; 95% CI, 1.63-5.63; P = 0.0004, adjusted P = 0.0057), whereas individuals bearing haplogroup U have a significant decrease in breast cancer risk (OR, 0.37; 95% CI, 0.19-0.73; P = 0.0023, adjusted P = 0.03). Our results suggest that mitochondrial genetic background plays a role in modifying an individuals risk to breast cancer.

A Novel Adaptive Method for the Analysis of Next-Generation Sequencing Data to Detect Complex Trait Associations with Rare Variants Due to Gene Main Effects and Interactions

There is solid evidence that rare variants contribute to complex disease etiology. Next-generation sequencing technologies make it possible to uncover rare variants within candidate genes, exomes, and genomes. Working in a novel framework, the kernel-based adaptive cluster (KBAC) was developed to perform powerful gene/locus based rare variant association testing. The KBAC combines variant classification and association testing in a coherent framework. Covariates can also be incorporated in the analysis to control for potential confounders including age, sex, and population substructure. To evaluate the power of KBAC: 1) variant data was simulated using rigorous population genetic models for both Europeans and Africans, with parameters estimated from sequence data, and 2) phenotypes were generated using models motivated by complex diseases including breast cancer and Hirschsprungs disease. It is demonstrated that the KBAC has superior power compared to other rare variant analysis methods, such as the combined multivariate and collapsing and weight sum statistic. In the presence of variant misclassification and gene interaction, association testing using KBAC is particularly advantageous. The KBAC method was also applied to test for associations, using sequence data from the Dallas Heart Study, between energy metabolism traits and rare variants in ANGPTL 3,4,5 and 6 genes. A number of novel associations were identified, including the associations of high density lipoprotein and very low density lipoprotein with ANGPTL4. The KBAC method is implemented in a user-friendly R package.