Hongyu Zhao

Bio: Hongyu Zhao is an academic researcher from Yale University. The author has contributed to research in topics: Medicine & Genome-wide association study. The author has an hindex of 93, co-authored 710 publications receiving 33183 citations. Previous affiliations of Hongyu Zhao include University of North Carolina at Chapel Hill & Boston University.

Host-microbe interactions have shaped the genetic architecture of inflammatory bowel disease

Abstract: Crohn's disease and ulcerative colitis, the two common forms of inflammatory bowel disease (IBD), affect over 2.5 million people of European ancestry, with rising prevalence in other populations. Genome-wide association studies and subsequent meta-analyses of these two diseases as separate phenotypes have implicated previously unsuspected mechanisms, such as autophagy, in their pathogenesis and showed that some IBD loci are shared with other inflammatory diseases. Here we expand on the knowledge of relevant pathways by undertaking a meta-analysis of Crohn's disease and ulcerative colitis genome-wide association scans, followed by extensive validation of significant findings, with a combined total of more than 75,000 cases and controls. We identify 71 new associations, for a total of 163 IBD loci, that meet genome-wide significance thresholds. Most loci contribute to both phenotypes, and both directional (consistently favouring one allele over the course of human history) and balancing (favouring the retention of both alleles within populations) selection effects are evident. Many IBD loci are also implicated in other immune-mediated disorders, most notably with ankylosing spondylitis and psoriasis. We also observe considerable overlap between susceptibility loci for IBD and mycobacterial infection. Gene co-expression network analysis emphasizes this relationship, with pathways shared between host responses to mycobacteria and those predisposing to IBD.

Analysis of Transcription Factor HY5 Genomic Binding Sites Revealed Its Hierarchical Role in Light Regulation of Development

Abstract: The transcription factor LONG HYPOCOTYL5 (HY5) acts downstream of multiple families of the photoreceptors and promotes photomorphogenesis. Although it is well accepted that HY5 acts to regulate target gene expression, in vivo binding of HY5 to any of its target gene promoters has yet to be demonstrated. Here, we used a chromatin immunoprecipitation procedure to verify suspected in vivo HY5 binding sites. We demonstrated that in vivo association of HY5 with promoter targets is not altered under distinct light qualities or during light-to-dark transition. Coupled with DNA chip hybridization using a high-density 60-nucleotide oligomer microarray that contains one probe for every 500 nucleotides over the entire Arabidopsis thaliana genome, we mapped genome-wide in vivo HY5 binding sites. This analysis showed that HY5 binds preferentially to promoter regions in vivo and revealed >3000 chromosomal sites as putative HY5 binding targets. HY5 binding targets tend to be enriched in the early light-responsive genes and transcription factor genes. Our data thus support a model in which HY5 is a high hierarchical regulator of the transcriptional cascades for photomorphogenesis.

Rare independent mutations in renal salt handling genes contribute to blood pressure variation

Abstract: The effects of alleles in many genes are believed to contribute to common complex diseases such as hypertension. Whether risk alleles comprise a small number of common variants or many rare independent mutations at trait loci is largely unknown. We screened members of the Framingham Heart Study (FHS) for variation in three genes-SLC12A3 (NCCT), SLC12A1 (NKCC2) and KCNJ1 (ROMK)-causing rare recessive diseases featuring large reductions in blood pressure. Using comparative genomics, genetics and biochemistry, we identified subjects with mutations proven or inferred to be functional. These mutations, all heterozygous and rare, produce clinically significant blood pressure reduction and protect from development of hypertension. Our findings implicate many rare alleles that alter renal salt handling in blood pressure variation in the general population, and identify alleles with health benefit that are nonetheless under purifying selection. These findings have implications for the genetic architecture of hypertension and other common complex traits.

De novo mutations in histone-modifying genes in congenital heart disease

Abstract: Exome sequencing of patients with congenital heart disease (CHD) and their unaffected parents reveals an excess of strong-effect, protein-altering de novo mutations in genes expressed in the developing heart, many of which regulate chromatin modification in key developmental genes; collectively, these mutations are predicted to account for approximately 10% of severe CHD cases. This paper demonstrates that de novo mutations with large effect have a role in the pathogenesis of at least 10% of cases of congenital heart disease (CHD). Using exome sequence analysis in parent–offspring trios Richard Lifton and colleagues compared the frequency of de novo mutations, identified by exome sequencing, in 362 CHD parent–offspring trios and 264 control trios. Gene ontology analysis demonstrated significant enrichment of de novo protein-altering mutation of genes involved in chromatin modification, notably a marked enrichment of genes involved in the production, removal and reading of methylation of histone H3K4 and H3K27. Congenital heart disease (CHD) is the most frequent birth defect, affecting 0.8% of live births1. Many cases occur sporadically and impair reproductive fitness, suggesting a role for de novo mutations. Here we compare the incidence of de novo mutations in 362 severe CHD cases and 264 controls by analysing exome sequencing of parent–offspring trios. CHD cases show a significant excess of protein-altering de novo mutations in genes expressed in the developing heart, with an odds ratio of 7.5 for damaging (premature termination, frameshift, splice site) mutations. Similar odds ratios are seen across the main classes of severe CHD. We find a marked excess of de novo mutations in genes involved in the production, removal or reading of histone 3 lysine 4 (H3K4) methylation, or ubiquitination of H2BK120, which is required for H3K4 methylation2,3,4. There are also two de novo mutations in SMAD2, which regulates H3K27 methylation in the embryonic left–right organizer5. The combination of both activating (H3K4 methylation) and inactivating (H3K27 methylation) chromatin marks characterizes ‘poised’ promoters and enhancers, which regulate expression of key developmental genes6. These findings implicate de novo point mutations in several hundreds of genes that collectively contribute to approximately 10% of severe CHD.

Initial sequencing and analysis of the human genome.

Abstract: The human genome holds an extraordinary trove of information about human development, physiology, medicine and evolution. Here we report the results of an international collaboration to produce and make freely available a draft sequence of the human genome. We also present an initial analysis of the data, describing some of the insights that can be gleaned from the sequence.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Human biochemical genetics

Abstract: So far in this course we have dealt entirely with the evolution of characters that are controlled by simple Mendelian inheritance at a single locus. There are notes on the course website about gametic disequilibrium and how allele frequencies change at two loci simultaneously, but we didn’t discuss them. In every example we’ve considered we’ve imagined that we could understand something about evolution by examining the evolution of a single gene. That’s the domain of classical population genetics. For the next few weeks we’re going to be exploring a field that’s actually older than classical population genetics, although the approach we’ll be taking to it involves the use of population genetic machinery. If you know a little about the history of evolutionary biology, you may know that after the rediscovery of Mendel’s work in 1900 there was a heated debate between the “biometricians” (e.g., Galton and Pearson) and the “Mendelians” (e.g., de Vries, Correns, Bateson, and Morgan). Biometricians asserted that the really important variation in evolution didn’t follow Mendelian rules. Height, weight, skin color, and similar traits seemed to

Biological insights from 108 schizophrenia-associated genetic loci

Abstract: Schizophrenia is a highly heritable disorder. Genetic risk is conferred by a large number of alleles, including common alleles of small effect that might be detected by genome-wide association studies. Here we report a multi-stage schizophrenia genome-wide association study of up to 36,989 cases and 113,075 controls. We identify 128 independent associations spanning 108 conservatively defined loci that meet genome-wide significance, 83 of which have not been previously reported. Associations were enriched among genes expressed in brain, providing biological plausibility for the findings. Many findings have the potential to provide entirely new insights into aetiology, but associations at DRD2 and several genes involved in glutamatergic neurotransmission highlight molecules of known and potential therapeutic relevance to schizophrenia, and are consistent with leading pathophysiological hypotheses. Independent of genes expressed in brain, associations were enriched among genes expressed in tissues that have important roles in immunity, providing support for the speculated link between the immune system and schizophrenia.