Current Topics in Human Genetics: Studies in Complex Diseases


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Shimizu, J. Goto, I. Nishino, T. Toda, S. Morishita, S. Mao, C. Reuter, M. Ruzhnikov, A. Beck, E. Farrow, L. Emrick, J. Rosenfeld, K. Mackenzie, L. Robak, M. Wheeler, L. Burrage, M. Jain, D. Calame, M. Graf, S. Masters, B. Lee, I. Thiffault, P. Agarwal, J. Bernstein, H. Bellen, H. Chao, Undiagnosed Diseases Network.

Khan, P. Weng, A. Majmundar, T. Lim, S. Shril, J. Martinez-Agosto, N. Mann, Y. Jin, V. Aggarwal, A. Onuchic-Whitford, F. Buerger, J. Musgrove, B. Beck, K. Riedhammer, M. Benz, J. Hoefele, H. Rehm, D. MacArthur, S. Mane, V. Hildebrandt, S. Sanna-Cherchi, E. Beck, A. Basar, H. Oda, D. Uehara, J. Inazawa, E. Macnamara, P. D'Souza, J. Bodurtha, W. Mu, K. Baranano, T. Kosho, M. Kempers, M. Walkiewicz, R. Wang, C. Tifft, I. Aksentijevich, A. Werner, D. Are we making any difference in the genomic literacy of K students, graduate students, healthcare providers, and the general public?

What is standing in the way of the utopian genomically literate populace? As genetics professionals and educators, we recognize the importance of being genomically literate. However, as the pace of genetic and genomic knowledge and technologies increases, gaps in genetic and genomic literacy between genetics professionals and other subsets of the population increases.

This session will explore the effectiveness of our genetics and genomics education pipeline. Through both short presentations and a large group panel discussion, this session will foster audience member engagement in a conversation about what is working and what barriers are in the way of a truly genomically literate society. HudsonAlpha Inst Biotech, Huntsville.

Rochester Inst Technol. Johns Hopkins Sch Med, Baltimore. Despite progress in cataloging disease-causing genes for various multifactorial disorders, their underlying biology has been difficult to unravel. As large-scale exome and whole-genome sequencing data become available, finding disease-associated variants is becoming easier, but not finding their biological effects. Solving this puzzle will require us to place genes in their correct spatio-temporal context, deciphering their regulatory control and building their gene regulatory networks GRNs.

Building these networks will help us to connect the many coding and non-coding variants by elucidating the highly integrative and interactive properties of the human genome. In this session, we will bring together diverse speakers highlighting the multiple ways of connecting various components of the genome that contribute to a phenotype and disease state.

We will discuss work that integrates both computational and experimental approaches to understand how disruption of critical genes and non-coding elements involved in specific processes leads to distinct effects across different cells and tissues. Figuring out these discrete effects will lead to a better understanding of normal developmental processes and the complexities of cellular response in disease.

The various talks will demonstrate how comprehension of these networks has an important lesson for systems biology: the whole idea of a system is to provide robustness from variation and mutations, but a well-integrated system can have the opposite effect and lead to disease even when only one primary component is perturbed due to effect amplification through the network.

New York Univ Sch Med. Univ Washington, Seattle. Over the years, genetics has grown from a purely academic discipline to one that revolutionized forensics, genealogy, and medicine. As genomics becomes more accessible in the consumer and legal worlds, it has also become entangled in national conversations about race and ethnicity, family reunification, criminal justice, and data privacy. The use of DNA has moved well beyond the clinical and research context and now plays an important role in forensics, disaster response, and border security.

This session will bring together experts from diverse fields who will discuss how genomic data are incorporated in their work in various settings, the policies or lack thereof that shape this work, and the difficulties in protecting genetic information used in these settings. It will introduce attendees to the importance and limits of genomic information in forensics especially with the emergence of forensic genealogy , the challenges of sharing DNA data across borders for the identification of missing migrants, and the policy considerations for employing DNA as a biometric for border security.

The closing panel will serve as an open forum for discussion on the strengths and weaknesses of relevant policies, as well as opportunities for new policy development around topics such as genomic privacy, security, confidentiality, and transparency. Texas State Univ, San Marcos. Geisinger Hlth Syst, Danville. Duke Univ, Durham. The expansion of the human neocortex in anatomically modern humans has, in part, led to our unique cognitive and social abilities.

Genome sequencing of our extant relatives, archaic humans, and current human populations has identified loci undergoing selection along the human lineage. However, the specific genetic changes leading to cortical expansion are very poorly understood. Because neuropsychiatric disorders impact cognitive and behavioral domains that are uniquely developed in humans, comparative neurogenetics may also provide fundamental insights into the biology of risk for neuropsychiatric disorders.

Functional analyses, described in this session, allow us to annotate the effects of genetic variants under selection on human brain structure, function, and development. Nadav Ahituv will present the functional impact of fixed changes within human accelerated regions of the genome in human and chimpanzee iPSC-derived neural progenitor cells. Amanda Tilot will present a large-scale assessment of how Neanderthal introgression continues to influence a neuroimaging-derived brain-shape phenotype that reflects a uniquely human neurodevelopmental process.

Jason Stein will present a series of studies that determine the functional impact of selected alleles on cortical structure using GWAS of cortical structure via MRI. Armin Raznahan will present work that combines in vivo neuroimaging and postmortem transcriptomic maps of the human brain to identify candidate mediators of regional cortical expansion in humans and the role of these expanded systems in neurodevelopmental disorders.

Collectively, these presentations will showcase the rapid understanding of genetic variants impacting brain structure and development across human evolution in the burgeoning field of comparative neurogenetics. Univ North Carolina Chapel Hill. NIMH, Bethesda. Moderators : Daniel C. Functional validation of genomic variants associated with disease is a vital yet increasingly difficult task in the face of rapidly accelerated genomic discovery.

Crucially, it is now far easier and more cost-effective to identify candidate disease-causing variants than it is to determine their biological consequences and mechanisms of pathogenesis. Fortunately, numerous experimental tools and approaches have emerged to investigate the impact of rare variants at a molecular level. Here, we highlight the use of scalable model systems that employ cutting-edge molecular toolkits to interpret human genetic lesions.

These include coding and non-coding changes as well as point mutations or copy number variants. Our session features experts in diverse model systems, but the speakers are unified by a common goal of interpreting variation found in humans. Attendees will learn how: 1 CRISPR-enabled enhancer reporter assays have enabled testing thousands of candidate enhancer variants in mice; 2 Zebrafish assays have facilitated gene discovery and copy number variant dissection in rare pediatric syndromes; 3 three-dimensional organoids grown from patient iPS cells make it possible to assess the impact of genomic variants on brain structure and physiology; and 4 Drosophila models offer a tractable means to accelerate both gene discovery and therapeutic development in undiagnosed disorders.

In summary, our session will demonstrate how elegant biological approaches can address the major challenges of interpretation and serve as a necessary complement to in silico predictions for the human genetics community. Lawrence Berkeley Natl Lab. Duke Univ Med Ctr, Durham. Nationwide Children's Hosp, Columbus.

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Texas Children's Hosp, Houston. Little data is known about the genetic bases of recurrent reproductive loss and infertility mainly due to their genetic heterogeneity. In fact, the genetics of reproductive loss and infertility has a lot of delays relative to other medical disciplines such as neurology, biochemistry, ophthalmology, and pediatrics.

The advent of next-generation sequencing NGS in the last decade has opened an unprecedented opportunity to tackle genetically heterogeneous conditions and allowed the recent identification of several genes responsible for infertility and reproductive loss in humans. This session will address the genetic bases of female and male reproductive failure and show the impact of NGS on the identification of several of their responsible genes. The first speaker, Aleksandar Rajkovic, will describe his work on gonadal development, germ cell differentiation, and folliculogenesis.

He will show how germ cell-specific transcriptional regulators determine the pool of primordial follicles, their activation, reproductive life span, and how their mutations lead to premature ovarian failure. The second speaker, Lei Wang, will talk about the genetic basis of female infertility. He will describe his work on the identification of novel Mendelian phenotypes and genes responsible for abnormalities in oocyte maturation, fertilization, and early embryonic development.

The third speaker, Rima Slim, will present her work on a particular form of pregnancy loss, called molar pregnancy. She will describe current knowledge about four causative genes for recurrent moles, their roles in its pathogenesis, and link to recurrent miscarriages and female and male infertility. The fourth speaker, Pierre Ray, will focus on the genetics of male infertility and his identification of several genes responsible for abnormalities in spermatogenesis.

The session assembles top experts in their fields, from four different countries, who will bring different academic and medical perspectives since the four countries are under different health care systems and regulations. Fudan Univ, Shanghai, China. Univ Grenoble Alpes, France. Identity-by-Descent IBD segments are fundamental genetic concepts and relevant to many aspects of human genetics. While traditionally IBD studies were mostly theoretical studies and on small samples, they have recently become more data-driven thanks to the availability of large cohorts with genotypes and new computational methods.

In particular, modern IBD methods have begun to offer new insights into genetic genealogy, recent population history, and disease risks due to rare variants and haplotypes. This session will showcase cutting-edge analytical methods and the new results brought by these methods. A short introduction will be followed by four talks. David Balding will offer insights into theoretical frameworks for IBD and relatedness and applications in association analysis and heritability.

Sharon Browning will discuss inference of population history from IBD segments. Ardalan Naseri will review recent efficient methods for identifying IBD segments from very large genotyped samples. Eurie Hong will describe a large collection of genotyped cohorts available at a direct-to-consumer company, their findings, and the implications for society. Univ Melbourne, Australia. Moderators : Scott J. We continue to see increased availability of both next-generation sequencing and additional biomedical data.

Despite the growth of these resources, we have not seen the sweeping improvements in health that some anticipated. This is due to a variety of challenges related to the translation of genome sciences into clinical care and practice at the patient, provider, health systems, and payer levels for genetic testing. To address several translational challenges, it is critical to 1 understand the evolving marketplace and test quality, 2 understand how genomic information influences how patients and providers pursue healthcare services, 3 apply health economic methods to assess the value utilizing genomic information, and 4 identify strategies and methods for evaluation of genetic tests and tools from various stakeholder perspectives.

Considering these challenges, Gillian Hooker will introduce the genetic testing marketplace and discuss attributes associated with test value. Next, Ragan Hart will discuss decision analysis for discerning cost-effectiveness of next-generation sequencing tests, highlight limitations of current value assessment methods, and report on proposed solutions. Additionally, Kurt Christensen will discuss approaches to conducting economic evaluations alongside clinical studies. Finally, Erick Lin will discuss an evidence review process for assessing value associated with diagnostic tests.

The presentations will be followed by a panel discussion with all of the speakers. This session intends to offer insights from various experts who are developing and applying solutions as they relate to these questions and how to best utilize genetic data and health data for translational impact in clinical practice.

Concert Genet, Franklin. Stanford Univ. Moderators : Anna O. Copious amounts of rich, diverse clinical data have become increasingly available for research through electronic health record EHR databanks and clinical repositories. EHRs provide a dense source of longitudinal data that can be integrated for research tasks such as cohort development, outcome ascertainment, and clinical translation.

EHR-coupled biorepositories combined with advancements in machine learning approaches and natural language processing provide a unique opportunity for studying the complex architecture of health and disease by using the biomedical products of clinical care. This session will focus on the ways that EHR-extracted data can be leveraged in driving discovery in precision health and genomics. Applications include developing refined phenotype algorithms, elucidating patterns for the purpose of homogeneous patient stratification, using phenome-wide association studies PheWAS to find novel relationships among diseases, discovering phenotype-genotype associations, evaluating drug usage, identifying potential drug interactions, and uncovering pleiotropic relationships.

This session will also tackle efficient research strategies needed to address challenges and limitations associated with EHR-derived data and analyses including the handling of heterogeneous, multivariate and highly dimensional data, addressing missing attributes, and the computational feasibility of analytic approaches. Overall, the EHR provides an invaluable resource of information that can be leveraged to build accurate predictive models that will aid in uncovering the genetic basis of common diseases as well as potentially advancing diagnosis, treatment, and prevention of disease.

Case Western Reserve Univ, Cleveland. Univ Pennsylvania, Philadelphia. Columbia Univ, New York. Guillen Sacoto, A. Begtrup, R. Willaert, A. Crunk, E. Heise, L. Rhodes, C. Kucera, L. Havens, J.


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Stark, C. Sue, T. Tan, E. Tantsis, M. Tchan, S. White, M. Wilson, D. Wright, K. Jones, B. Bennetts, S. Ichikawa, B. Wu, R. Oetjens, M. Kelly, A. Strum, R. Regeneron Genetics Center, C. Martin, D. Head, I. Manoli, Y. Gucek, C. Song, S. Nagamani, D. Nguyen, I. Grafe, E. Munivez, M. Jiang, P. Esposito, J.

Goodwin, E. Strudthoff, S. McGuire, V. Shenava, S. Rosenfeld, B. Lee, Brittle Bone Disorders Consortium. Peterson, B. Bimber, L. Colgin, A. Johnson, A. Lewis, B. Vernon, W. Thompson, R. Manuel, A. Aiudi, J. Jones, J. Carr, B. Koob, K. Karanjeet, K. Gardner, M. Madsen, S. Knight, M. Cessna, R. Factor, C. Sweeney, B. Caan, L.

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  6. Zhang, J. Hoskins, K. Connelly, D. Eiser, R. Stolzenberg-Solomon, B. Wolpin, S. Chanock, G. Petersen, J. Shi, C. Westlake, L. Amundadottir, PanScan, PanC4. Wiggins, J. Pearson, M. Black, A. Dunbier, T. Merriman, L. Morton, N. Dogan-Artun, G. MacLeod, C. Bartels, M. Piazza, S. Mack, X. Wang, R. Gimple, Q. Wu, Z. Faber, B. Rubin, S. Shetty, S. Angers, P. Dirks, M. Lupien, J. Rich, P. Mikulasova, K. Fung, N. Karataraki, B. Walker, G. Morgan, C. Ashby, A. Corcoran, S. Hambleton, D. Rico, L. Dean, M. Viard, M. Yeager, V. Naranbhai, M. Kellis, J. Davila-Velderrain, S. Mohammadi, Y.

    Park, L. He, C. Boix, M. Kousi, J. Mantero, K. Galani, L. Ho, H. Mathys, J. Young, D. Bennett, L. Nguyen, A. Charney, X. He, K. Kendler, P. Sullivan, S. Bacanu, B. Riley, E. Gerges, T. Singh, M. Goldman, S. Berretta, S. McCarroll, M. Collado Torres, E. Burke, A. Peterson, J. Shin, R. Straub, A. Rajpurohit, S. Semick, W. Ulrich, A. Price, C. Valencia, R. Tao, A. Deep-Soboslay, T.

    Hyde, J. Kleinman, D. Weinberger, A. Jaffe, BrainSeq Consortium.

    Overview - Annals of Human Genetics - Wiley Online Library

    Li, J. Zhu, R. Zhou, B. Daly, on behalf of Global Biobank Meta-analysis Initiative. Pividori, A. Barbeira, H. Feng, T. Ge, C. Smoller, B. Park, S. Damrauer, J. Ritchie, D. Rader, Regeneron Genetics Center. Sakaue, M. Kanai, J. Karjalainen, M. Akiyama, M. Kurki, N.

    Matoba, A. Takahashi, M. Hirata, M. Kubo, K. Matsuda, Y. Murakami, M. Daly, Y. Kamatani, Y. Okada, FinnGen Project. Moderators : Julio D. Hartiala, Y. Han, Q. Jia, Z. Kurt, P. Huang, N. Woodward, J. Gukasyan, D. Smith, M. Seldin, C. Pan, M. Mehrabian, A. Lusis, P. Bazeley, A. Quyyumi, M. Scholz, J. Thiery, W. Howe, F. Asselbergs, R.

    Patel, L. Nieminen, J. Laurikka, X. Yang, W. Tang, S. Hazen, H. Hu, J. Haessler, P. Auer, K. Wiggins, A. Moscati, A. Beiser, N. Heard-Costa, L. Raffield, J. Chung, S. Marini, C. Anderson, J. Rosand, H. Xu, L. Lange, A. Correa, S. Seshadri, S. Rich, R. Do, R. Loos, J. Bis, T. Assimes, B. Silver, S. Liu, R. Jackson, S. Mitchell, M. Fornage, A. Reiner, C. Zhang, Y. Veturi, A. Verma, T. Drivas, W. Chung, D. Crosslin, J. Denny, D. Fasel, H. Hakonarson, S. Hebbring, G. Jarvik, I. Kullo, E. Larson, S. Pendergrass, L. Rasmussen-Torvik, D. Schaid, P. Sleiman, J. Smoller, I. Stanaway, W.

    Wei, C. Weng, M. Gukasyan, Q. Jia, N. Woodward, R. Zhu, Y. Han, L. Stolze, Z. Kurt, X. Yang, C. Romanoski, J. Hartiala, H. Stolze, M. Whalen, A. Conklin, A. Solomon, M. Wuennemann, T. Fotsing Tadjo, M. Beaudoin, K. Lo, G. Bertrand, K. Xiong, C. Thangavel, J. Ba-Abbad, R. Carss, F. Raymond, K. Wang, Y. Li, F. Porto, A. Webster, G. Arno, R. Ullah, D. Dotto, I. Meo, P. Magini, M. Maresca, L. Caporali, F. Palombo, F. Tagliavini, E. Baugh, B. Macao, Z. Szilagyi, C. Peron, M. Gustafson, C. Morgia, P. Meta-analyses, combining the results of individual GWAS, have greater power and identify more disease-associated variants Manolio, The properties of LD in the human genome and development of reliable imputation methods have greatly facilitated meta-analysis for common diseases.

    Different commercial chips use different sets of tag SNPs in the design. Consequently, analysis of overlapping SNPs across genotyping platforms has limitations. Imputing genotypes from a standard reference panel in each study allows data to be combined for meta-analysis across studies. Results for individual SNPs must be carefully examined and care taken to control for differences in allele frequencies between groups that could lead to false-positive associations.

    However, replication of association across multiple studies and population groups provides the most reliable evidence of true genetic associations Manolio, In general, results from well-conducted individual studies that meet the stringent thresholds for genome-wide significance have been confirmed in subsequent meta-analyses of the same disease. In addition, the combined results identify many novel associations. Meta-analyses such as those for age at menarche and menopause are often carried out within large international consortia. Early examples of very large studies combined data for measurements routinely collected in multiple studies such as height and weight or clinical phenotypes collected on many individuals Stranger et al.

    More recently, large international efforts have worked to combine datasets for common diseases with a substantial public health burden to increase power for gene discovery. The results from this and studies in other diseases demonstrate that increasing numbers of genomic regions associated with disease risk are identified as study size increases Visscher et al.

    In general, genes or regions with the largest effects are identified in initial studies and markers reported in subsequent studies identify genes with progressively smaller effect sizes. Nevertheless, the large meta-analyses provide insights into genetic architecture and the genes and pathways contributing to disease risk. Developments in genomics and genetics that enabled large GWAS have discovered many variants affecting risk of common diseases.

    The distribution of effect sizes affecting common diseases is highly skewed towards small effect sizes Stranger et al. This has led some commentators to question the value and potential of these results to transform our understanding of common diseases see Visscher et al.

    These are important questions. What is the value of many variants of small effect? How can results be translated into better prevention and treatment? Do small effect sizes mean we need to apply alternative approaches to understand genetic contributions to complex disease? Is there still a place for GWAS in future studies? In answering these questions and planning future studies, it is important to understand that the distribution of small effect sizes is not unexpected and agrees with theoretical models Stranger et al.

    The results reflect the underlying genetic architecture showing that genetic risk of complex diseases is due to many variants with small effects. One proposed outcome of gene discovery is to use information from associated variants for predictions of individual disease risk. This scenario contrasts with that of common variants underlying common diseases, for which effect sizes are small and the frequencies of risk alleles differ only slightly between cases and controls.

    Consequently, for common diseases, individual variants have little diagnostic value Fugger et al. To date, even combining results from many variants has provided limited value because only a proportion of causative loci have been identified and there are substantial environmental effects that contribute to most common diseases. In fact, useful levels of prediction may only be approached when predictors are estimated from very large samples, order s of magnitude greater than currently available Dudbridge, Hence, although prediction will become more feasible as sample sizes continue to grow, the current and real translational value of gene discovery in complex diseases lies in identification of genes and biological pathways affecting disease that present new targets for intervention Fugger et al.

    The gene regions themselves are the targets for future studies and the effects of naturally occurring variation are not a good predictor of diagnostic value or the effects of direct therapeutic interventions on target genes. Indeed therapeutic interventions or new diagnostics may be directed to other genes in a relevant pathway affecting disease risk that have no natural variants affecting disease risk.

    The goal of large scale association studies is, therefore, to identify the disease causing variants, characterize their functional effects and determine the genes and pathways responsible for disease risk. The associated variants are likely picking up a signal from the causal variant s , guilt by association and not the true culprit.

    As discussed above, LD patterns in the human genome helped in the gene discovery phase by allowing us to type representative tagging SNPs. Full imputation of all common variants means that the likely causal variant s may be in the list, but the same patterns of LD that allowed imputation make the next steps of tracking down the causal variant s more challenging. The initial results therefore represent a starting point.

    The next important step for individual regions is to identify the specific genes and pathways implicated in disease risk. At present, there is no definitive database to look up a set of SNPs and determine which SNP s is most likely to have functional effects. One approach is to look for allele-specific differences in expression of genes or individual transcripts in the region. Genetic differences contributing to variation in gene expression are known as expression quantitative trait loci eQTLs. Several studies show that complex trait-associated variants overlap with eQTL variants Stranger et al.

    The eQTLs can be close to the gene affected cis effects or the SNPs can affect gene expression at remote points on the same or different chromosomes trans effects. The power to detect trans -eQTLs is much lower than for cis -eQTLs partly because they are likely to have smaller effect sizes, and partly because of the need to adjust for the many more statistical tests conducted in trans analyses , and few studies have sufficient power to detect such trans effects.

    Some SNPs influenced multiple trans -genes. These results support the view that disease-associated variants identified by GWAS can function through effects on transcription of both closely related genes and genes on other chromosomes. Some eQTL datasets are publically available e. However, many available datasets may not be relevant to diseases and traits associated with reproduction and there is a need to develop eQTL datasets for relevant tissues like the endometrium. The international ENCODE project has made major advances in better understanding genome regulation through a systematic approach to characterizing functional elements in the genome Dunham et al.

    A recent series of important papers report results of systematic mapping of regions of transcription, transcription factor-binding sites, chromatin structure and histone modification in a range of cell lines Dunham et al. The results demonstrate that a large proportion of the non-coding region of the genome introns and intergenic sequences contain regulatory elements. These data are available in genome browsers and can be used to search for the overlap between disease-associated variants and functional elements to prioritize SNPs and genes or SNPs for follow-up functional studies.

    Although extensive, the complete datasets are only available for a limited number of cell lines. Identifying the functional variants for reproductive traits will require better understanding of tissue-specific gene regulation and changes in the regulation during development. One important direction for future studies in reproduction is to conduct genomic experiments in relevant cell types and tissues to identify eQTLs, map functional elements and better characterize gene regulation in tissues relevant to reproductive activity and fertility.

    For example, of Given that common SNPs do not generally tag rare genetic variation, it is highly likely that the common GWAS signals significantly associated with complex diseases are not due to functional coding variants; however, current GWAS designs will have missed low-frequency coding variants and other low-frequency functional variants contributing to disease risk.

    There is increasing evidence that low-frequency variants LFVs do contribute to disease risk. Growth differentiation factor 9 GDF9 and bone morphogenetic protein 15 BMP15 are expressed in oocytes and play critical roles in the regulation of ovarian follicle development. Sequencing the coding region of GDF9 in women from families with a high frequency of dizygotic twins identified novel LFVs that change amino acid composition, or introduce premature stop codons Fig.

    The LFVs in GDF9 associated with increased dizygotic twinning included two premature stop codons and two mis-sense variants, ranged in frequency from 0. The c. Power for gene discovery is partly a function of allele frequency and larger samples are required to identify LFVs contributing to disease risk. Genotyping chips with this exome content provide a rapid, low-cost method to genotype most variants in protein coding regions in a large number of individuals.

    Disease-related variants in exons that change protein composition through amino acid substitutions alter stop signals or splicing will provide direct evidence of the specific genes contributing to disease risk. Variants may also indicate the likely functional consequences of the altered proteins. The fine mapping and functional studies required to determine the specific genes affected by common non-coding variants will not be necessary. Consequently, low-frequency coding variants could provide a more direct path to develop more effective preventative and therapeutic strategies.

    Major advances in sequencing technology have broad applications in genetics and genomics. Sequencing can speed up identification of causal variants in rare Mendelian disease, help understand the functional role of genetic variation and facilitate discovery of further disease-associated variants. Examples include continued discovery of common and rare variants through sequencing Manolio, and the wide application of DNA sequencing in the ENCODE project to identify functional regions of the genome Dunham et al. As sequencing costs fall, some commentators have suggested that GWAS will largely be replaced by sequencing.

    However, the cost of genotyping remains cheaper than sequencing and the challenges and cost of analysis for genotype calling in sequence data limit the applications of sequencing. As we seek to understand the role of LFVs, very large samples must be studied and projects can be conducted on a much larger scale with current genotyping technology.

    The future of gene-mapping studies is likely to see the parallel use of sequencing and genotyping for continued discovery of disease-associated variants. The number of discovered variants is strongly correlated with experimental sample size, where an ever-increasing sample size will increase the number of discovered variants Visscher et al. International efforts combining results of many studies in big meta-analyses have been the best approach to gene discovery for common diseases. In contrast, efforts for many reproductive diseases are based on modest samples, and have therefore detected only a small number of significant associations.

    Important exceptions include traits such as age at menarche that has been measured in many studies or breast cancer where large international consortia studying different aspects of breast cancer have actively recruited patients and combined studies to greatly increase the size of studies for gene discovery. Some argue that further studies show diminishing returns and we expect that effect sizes of subsequent discoveries will be smaller. However, effect size for an individual variant does not reflect the importance of the pathway to the disease or ability to develop diagnostic or therapeutic outcomes.

    Discovery of additional variants increases the chances for finding tractable targets for immediate follow-up and clinical outcomes. Therefore, diseases with a genetic component associated with reproduction will continue to benefit from additional GWAS studies and large meta-analyses to define more of the genetic variants that contribute to disease risk. Discovery of LFVs associated with disease by genotyping exome chips or by high throughput sequencing will help understand functional pathways leading to disease.

    However, many of the current large collections have limited phenotype and clinical information. Small clinic-based samples with detailed information on individual patients are not large enough for gene discovery and larger sample collections generally lack detailed information on treatments and risk factors.

    The large meta-analyses combine data sets where disease phenotypes and risk factors may have been recorded in different ways. Differences in disease definition are likely to be important and averaging across studies with different methods of ascertaining disease cases may lead to under-estimation of effect size for some variants. Consequently, the results from GWAS data are limited by the minimal phenotypic and clinical information collected for most sample sets.

    Phenotypic measurements are expensive and it will be a challenge to generate these rich datasets. Combination of datasets with detailed harmonized phenotypic and clinical information combined with current genomics tools will yield valuable insights into disease risks, disease classification and co-morbidity. Gene discoveries from GWAS do not generally provide results that can be translated immediately into the clinic.

    They are the starting point to understand disease biology and have already provided novel insights into the pathogenesis of several diseases. Variants that increase the risk of type 2 diabetes influence beta-cell development and function and focus attention on insulin secretion in the development of disease Grarup et al.

    Discoveries in IBD have highlighted the importance of the autophagy pathway in disease development Rioux et al. Results for endometriosis suggest effects on estrogen response and cell growth rather than inflammation Nyholt et al. Genetic variants in the interleukin 23 and interleukin 17 pathways are associated with susceptibility to psoriasis suggesting that targeting this pathway might have therapeutic benefit. Monoclonal antibodies neutralizing these genes have been shown to be effective in treating psoriasis and several compounds targeting this pathway are in clinical development Fugger et al.

    Genetic and environmental factors both influence the risk of complex diseases and understanding environmental risk factors has also proved difficult. The advances in gene discovery will be useful in defining some of these environmental risk factors. One example is studies on the role of autophagy related like 1 Saccharomyces cerevisiae ATG16L1 in the risk of Crohn's disease. A knock down of Atg16l1 in mice induces a phenotype similar, but not identical, to Crohn's disease Cadwell et al. Mice raised in a specific pathogen free environment do not have the phenotype, but symptoms return in the presence of a specific mouse norovirus Cadwell et al.

    In endometriosis there are suggestions of effects of environmental toxins and interactions with genotype, but the topic remains controversial Pauwels et al. Gene discovery has occurred rapidly over the last 5 years and immediate translation of these discoveries is not realistic. The biological insights into disease risk factors do provide new drug targets. However, development and testing of new drugs can take many years. One approach is drug repositioning through the analysis of GWAS results to identify alternative indications for existing drugs Sanseau et al.

    Data from the published GWAS catalogue were used to construct a list of GWAS genes associated with disease traits and investigate whether these genes are targeted by drugs already launched or in development. These proportions were significantly higher than genes across the genome. Moreover, of the genes implicated from GWAS This is 2. Examples include well-validated targets and associated drugs such as 3-hydroxymehtylglutaryl-CoA reductase HMGCR , the target for statins lowering cholesterol Sanseau et al.

    This analysis of drug repositioning highlights the power of GWAS studies in defining new drug targets and providing biological insights to help streamline drug development. Genetic profiles can also be used in important ways to investigate genetic co-morbidity and to evaluate use of current diagnostic criteria in closely related disease conditions. We have shown for endometriosis Painter et al. Association results must pass stringent thresholds for significance and be replicated in independent studies before risk variants are accepted as contributing to disease risk.

    Only a few of the top hits meet these criteria in most genome-wide studies. However, many other variants lie just below the threshold. Larger studies help to discover more of the risk variants, but the application of multivariate statistical approaches to the entire marker dataset can be used in other important ways to understand the nature of genetic contributions to disease risk.

    It is often difficult to determine the relationship between disease classes with strongly overlapping symptoms. In genetic studies of endometriosis, the Revised American Fertility Society rAFS classification system is commonly used to stage disease severity and assigns patients to one of four stages I—IV on the basis of the extent of the disease and the associated adhesions present Montgomery et al. Other classification systems have been proposed including ovarian versus peritoneal disease, and deep infiltrating versus superficial disease.

    Whether these sub-classes represent the natural history of one disorder, or are in fact different disease sub-types, is an important consideration in endometriosis research. Analysis of genome-wide marker data can assess the genetic contribution to individual disease sub-classes and also the shared genetic contribution to each subclass and provide new insights into the different disease presentations.

    Large samples with detailed data on symptoms and disease classification will facilitate these studies and may provide important insights for future diagnosis and treatment. Another approach is to use genome marker data to evaluate comorbidity between disease conditions.

    Epidemiological studies can be difficult to interpret because there may be problems with ascertainment and large cohorts must be recruited to have sufficient numbers of patients with both conditions to enable firm conclusions to be drawn. For example, investigating co-morbidity of ovarian cancer or endometrial cancer with endometriosis is problematic because of the potential incidental diagnosis of endometriosis at laparoscopy as part of investigations of symptoms for ovarian or endometrial cancer. The advent of genome-wide marker data offers an alternative approach by evaluating shared genetic contributions to disease traits directly using the GWAS genotypes.

    Epidemiological evidence also suggests comorbidity between schizophrenia and cardiovascular disease. Leveraging the large GWAS studies conducted on cardiovascular disease identified additional loci associated with schizophrenia Andreassen et al. The overlap in genetic risk is important since there is significant mortality from cardiovascular disease in patients with schizophrenia suggesting the need to better monitor cardiovascular disease in these patients Gegenava and Kavtaradze, ; Laursen et al.

    Analysis of GWAS data across disease studies can lead to a better understanding of the shared genetic contributions to disease and possible re-assessment of diagnostic criteria. This could be an important avenue for translation of genomics to improve clinical practice. Genome-wide association studies provide a powerful approach for the discovery of genes or variants contributing to risk of complex diseases.

    Results for multiple traits and diseases are reported in over publications and documented in the Catalog of Published Genome-Wide Association Studies at the National Human Genome Research Institute. Included in these studies are results for over 30 traits and diseases related to reproduction documenting many novel findings.

    Results generally show that genetic contributions to complex disease come from many gene regions across the genome, each with small effects on disease risk. Consequently, studies on large samples are essential to identify the many individual variants affecting disease risk. Combined studies have been undertaken for traits like age at menarche, breast cancer and prostate cancer. However, most studies for diseases associated with reproduction have been relatively small. Results show genetic data can also help define sub-types of disease and co-morbidity with other traits and diseases.

    Consequently, future genetic marker studies in large samples with detailed phenotypic and clinical information will yield valuable insights into disease risks, disease classification and co-morbidity for many diseases associated with reproduction. The value of GWAS has been questioned by some commentators because variants discovered have such small effects. Even when combined, the small numbers of variants identified thus far have little diagnostic value for individuals because of their small effects coupled with environmental influences.

    However, the real translational value of gene discovery in complex traits lies in discovery of genes and biological pathways affecting disease that present new targets for intervention. The initial results therefore represent a starting point, and for diseases like endometriosis, the first step in defining causal pathways to disease.

    Much work remains to determine the mechanisms in each defined region. Eighty per cent of markers associated with common disease lie in intronic and intergenic regions with no easy functional explanation for increased disease risk. Indeed, GWAS studies have revealed how much we still have to learn about the control of gene transcription. Genomic studies such as the ENCODE project are helping to fill the gap and as functional studies progress, laboratories with specialized knowledge of specific genes and pathways can help unravel important mechanisms leading to disease.

    Studies of rare coding variants affecting risk may also help. Novel genes and pathways provide new targets for biomarker discovery and new drug targets for drug development or repositioning of drugs currently on the market or in clinical trials.

    Human Genetics

    Genetic variants can help understand important environmental risk factors for targeted intervention. Genetic studies have much to contribute to future studies in reproduction. However, the real benefits will only be realized by convergence of genetics, genomics and biological research in well-phenotyped datasets to develop better methods of diagnosis and treatment for the many common diseases associated with reproduction.

    Funding to pay the Open Access publication charges for this article was provided by the Wellcome Trust. National Center for Biotechnology Information , U. Mol Hum Reprod. Published online Aug Zondervan , 2, 3 and D. Nyholt 1. Author information Article notes Copyright and License information Disclaimer. This article has been cited by other articles in PMC.

    Abstract Genetic factors contribute to risk of many common diseases affecting reproduction and fertility. Keywords: reproductive traits, GWAS, gene discovery, translation, review. Introduction Genetic inheritance influences risk for many reproductive traits and diseases. Genome-wide association studies GWAS methods provide a powerful approach for mapping disease genes.

    Open in a separate window. Endometriosis Endometriosis is a common gynaecological disease associated with severe pelvic pain and subfertility. Uterine fibroids Uterine fibroids, also known as leiomyomas, are common benign tumours of the female reproductive tract. Age at menarche and age at menopause Age at menarche and age at natural menopause in women define the beginning and the end of reproductive life. Genetic architecture of common diseases The genetic architecture for a disease or trait is defined as the number of loci affecting the trait, the distribution of effect sizes, interactions between the genes or loci and interactions with the environment Stranger et al.

    Distribution of effect sizes Empirical observations confirm theoretical expectation that individual variants associated with common diseases have small effects on disease risk. Linkage disequilibrium and estimating genotypes for all common SNPs Patterns of common genetic variation in the human genome were characterized for a number of different ethnic groups in a major study by the International HapMap Consortium Frazer et al. Meta-analyses Meta-analyses, combining the results of individual GWAS, have greater power and identify more disease-associated variants Manolio, The future of genetic studies Developments in genomics and genetics that enabled large GWAS have discovered many variants affecting risk of common diseases.

    Gene discovery and functional biology The goal of large scale association studies is, therefore, to identify the disease causing variants, characterize their functional effects and determine the genes and pathways responsible for disease risk. DNA sequencing studies Major advances in sequencing technology have broad applications in genetics and genomics. The role of large studies with detailed phenotypic data The number of discovered variants is strongly correlated with experimental sample size, where an ever-increasing sample size will increase the number of discovered variants Visscher et al.

    Genotype—phenotype relationships Genetic and environmental factors both influence the risk of complex diseases and understanding environmental risk factors has also proved difficult. New drug targets Gene discovery has occurred rapidly over the last 5 years and immediate translation of these discoveries is not realistic.

    Applications of GWAS data beyond the top hits Genetic profiles can also be used in important ways to investigate genetic co-morbidity and to evaluate use of current diagnostic criteria in closely related disease conditions. Summary and conclusion Genome-wide association studies provide a powerful approach for the discovery of genes or variants contributing to risk of complex diseases.

    Authors' roles G. Funding G. Conflict of interest None declared. Meta-analysis of genome-wide association scans for genetic susceptibility to endometriosis in Japanese population. J Hum Genet. Genome-wide association study link novel Loci to endometriosis. PLoS One. Improved detection of common variants associated with schizophrenia by leveraging pleiotropy with cardiovascular-disease risk factors.

    Am J Hum Genet. Genome-wide association defines more than 30 distinct susceptibility loci for Crohn's disease. Nat Genet. Family-based genome-wide association studies. Genetics for clinicians: from candidate genes to whole genome scans technological advances Best Pract Res Clin Endocrinol Metab. Discovering genotypes underlying human phenotypes: past successes for mendelian disease, future approaches for complex disease.

    Common sequence variants on 20q Uterine leiomyomata: etiology, symptomatology, and management. Fertil Steril. Virus-plus-susceptibility gene interaction determines Crohn's disease gene Atg16L1 phenotypes in intestine. A genome-wide association study identifies three loci associated with susceptibility to uterine fibroids. A haplotype map of the human genome. Genome-wide association and longitudinal analyses reveal genetic loci linking pubertal height growth, pubertal timing and childhood adiposity.

    Hum Mol Genet. High-resolution haplotype structure in the human genome. Power and predictive accuracy of polygenic risk scores. PLoS Genet. Estimation of significance thresholds for genomewide association scans. Genet Epidemiol. An integrated encyclopedia of DNA elements in the human genome. Thirty new loci for age at menarche identified by a meta-analysis of genome-wide association studies.

    Current Topics in Human Genetics: Studies in Complex Diseases Current Topics in Human Genetics: Studies in Complex Diseases
    Current Topics in Human Genetics: Studies in Complex Diseases Current Topics in Human Genetics: Studies in Complex Diseases
    Current Topics in Human Genetics: Studies in Complex Diseases Current Topics in Human Genetics: Studies in Complex Diseases
    Current Topics in Human Genetics: Studies in Complex Diseases Current Topics in Human Genetics: Studies in Complex Diseases
    Current Topics in Human Genetics: Studies in Complex Diseases Current Topics in Human Genetics: Studies in Complex Diseases

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