David Emile Reich - US grants

DESCRIPTION (provided by applicant): Multiple sclerosis (MS) is an inflammatory disease of the CNS. It is thought that tissue injury occurs when activated, myelin-reactive T cells migrate into the CNS and cause damage to myelin, oligodendrocytes and axons. Ultimately, MS is a complex genetic disease as studies in twins, half-siblings, and adoptees indicate a strong family inheritability. However, large-scale studies attempting to identify genes affecting the disease have so far had limited success, calling for a more powerful search strategy. The classic method of finding genes--linkage mapping--works well for rare, single gene disorders that run simply in families. But linkage scans have failed to find the genes for more common, genetically complex diseases including MS. The approach most likely to work for gene discovery is the direct assessment of variation in populations and its association to disease. With present technology, the best-known way of doing this---haplotype mapping--is not practical because it requires studying too many sites in the genome. Because MS is significantly more common in Europeans than in Africans, a new approach, admixture mapping, may be a shortcut for using association studies to find disease genes. Specifically, we hypothesize that the intermediate genetic risk of MS in African Americans is derived almost entirely from their small percentage (10-40%) of European ancestry. By scanning along the genomes of African Americans with MS looking for regions of unusually high European ancestry, we can identify the 'European' gene segments that are likely to contain the genes that are related to MS risk. In this study, we propose to carry out the first whole-genome admixture scan for human disease genes, using 100-times fewer markers than a haplotype-based study. An admixture scan has the potential to rapidly identify disease regions especially for the subset of diseases that have different prevalences in two populations. The admixture mapping approach has only become feasible in the past year because of the large numbers of SNPs discovered with known frequencies in both African- and European-Americans. The SNP resources and novel analytical tools have now converged with large sample collections of African-American MS patients. The central aim of this project will be to carry out an admixture scan for MS genes in a sample of 1,000 African Americans with MS and 340well-matched controls. To follow-up all the genomic regions associated with disease, we will triple density of markers to increase statistical confidence in the results and refine the positions. We will then move to a targeted haplotype-based association study in the most interesting regions to clone new genes associated with MS.

[unreadable] DESCRIPTION (provided by applicant): Although 18 genetic variants have now been identified as conferring resistance to severe malaria, mostly in west Africans, only a subset have been replicated in multiple studies. Here we propose to assess genetic susceptibility to malaria in the Luo, an East African population from an ethnic background very different from the populations in which most of the associations were previously identified. We hypothesize that identifying genetic variation affecting susceptibility to malaria will lead to new drug targets, but that several of the candidate variants so far may not be real and thus need to be replicated. We also propose to search for new susceptibility variants, via the first whole-genome scan for malaria resistance genes. The first phase of this project will be to carry out a replication study for malaria susceptibility. We will study the 18 genetic variants previously associated to malaria resistance in 539 Luo cases and 477 matched controls. For the nine genes in which these variants occur, we will also create "haplotype maps" specific for the Luo population, and identify all the haplotypes of >5 percent frequency and assess whether any of them are associated to malaria resistance. The haplotype analysis in these genes is significant because it will allow us to identify new risk variants present only in East Africa, where few studies have been done. The second phase will be to search for a history of natural selection at nine genes associated with malaria resistance. We recently published a protocol to search for regions of the genome that have been affected by natural selection. We used this to establish evidence for selection at CD40 ligand and G6PD (two genes previously associated with malaria resistance). We now propose to use the same protocol to search for evidence of natural selection at all nine genes associated with malaria resistance in east Africans. As a second test for selection, we will also compare the frequencies of the malaria resistance haplotypes in the Luo to those in the Masai, a population that is also of Nilotic in origin like the Luo, but has historically lived in a low-malaria environment. The finding of a haplotype that is very different in frequency comparing the Luo and Masai, despite the similar ethnic background, would suggest the presence of a malaria resistance gene. The third phase will be to carry out a pilot whole-genome scan for malaria resistance genes. All the genes that were previously associated to resistance fo malaria were identified by the "candidate gene" approach, where a handful of genes that were specifically believed to be significant for malaria pathogenesis, were tested for association. However, it may be that some of the most interesting drug targets are in the mass of genes that have not yet been tested. We propose to carry out the first whole-genome association scan to find malaria resistance genes, using Affymetrix gene-chip technology to scan a panel of 50,000 variants in up to 200 Luo severe malaria cases, 200 healthy Luo controls, and 100 healthy Masai. We will search not only for differences in frequencies between Luo cases and controls, but also for regions that are strikingly different in frequency between the Luo and Masai despite their similar ethnic origin, suggesting selection for malaria resistance in Luo in the past few thousand years. [unreadable] [unreadable] [unreadable]

[unreadable] DESCRIPTION (provided by applicant): Whole-genome association mapping, with all its theoretical power to detect genetic variants that contribute to common disease, is finally becoming practical. Most methods for analyzing data from these studies have envisioned scans with hundreds of thousands of SNPs in a relatively homogeneous population such as European Americans. However, the differences that exist among human populations also need to be taken into account. Even in a population that is relatively homogeneous, cases and controls may have different ancestral histories, which will result in "population stratification", or the population may be recently "admixed" as is the case for African-Americans and Hispanics. We propose to develop tools & methods for Population Substructure Analysis (PSSA) to deal with these issues in a disease-mapping scenario. [unreadable] [unreadable] (1) Our first aim will be to improve our already published methods and software (ANCESTRYMAP) for admixture mapping. Admixture mapping is a method for carrying out a genome-wide association study in a population of recent mixed ancestry such as African or Hispanic Americans, with far fewer markers than are needed for a homogeneous population. In the past two years great strides have been made in turning admixture mapping into a practical method, and we expect to continue to extend its applicability. [unreadable] [unreadable] (2) Our second aim will address the problem that whole-genome association scans with hundreds of thousands of SNPs will be severely compromised in their power to study a minority population such as African or Hispanic Americans unless methods are developed that search for association after inferring an individual's ancestry state at each point in the genome. A key aim of PSSA is to build methods that allow fully-powered whole-genome association scans in minority groups. [unreadable] [unreadable] (3) Our third aim will be to provide a novel approach for correcting of population stratification in whole-genome association scans. Population stratification refers to systematic differences in ancestry between cases and controls, which can lead to allele frequency differences and false-positive associations. Building on previous work we introduce new methods to measure and correct for stratification. We believe our new techniques will provide near-optimal power, and will be computationally efficient. We intend to make all these tools publicly available for the scientific community. [unreadable] [unreadable] [unreadable] [unreadable]

DESCRIPTION (provided by applicant): Mixture between populations is a fundamental process that shapes biology, genetic variation, and the risk for disease. Despite its importance, the analytical methods that are available to study mixture on a genome-wide scale are limited. This makes it a priority to develop methods that can analyze this history in the large data sets that are now practical to generate. No federal grant currently supports the development of such methods. This grant proposes to develop methods and make software available for studying mixture. The Aims are: (1) To develop tools that make inferences about mixture based on allele frequency and haplotype frequency differences. (2) To develop tools that estimate dates of mixture based on admixture linkage disequilibrium and genetic divergence data. We expect that this grant will be of value in three areas. (a) It will support the development of methods and user-friendly software that will be important for evolutionary and medical genetics. (b) It will support work that will result in insights relevant to finding disease genes in human populations that are recently or anciently admixed. (c) It will lead to new insights about human history as well as the history of other organisms. The connection to medical genetics is particularly important. Our laboratory's past work on the evolutionary history of populations has been intertwined with our work on disease gene mapping, and the approaches that we developed in both areas were synergistic. In particular, we have leveraged the history of admixture in human populations to make new gene discoveries and to understand variation in disease risk across populations. We expect to be able to make further connections between evolutionary and medical genetics by developing sophisticated approaches for understanding and modeling population mixture.

? DESCRIPTION (provided by applicant) Population history shapes human biology, genetic variation, and disease risk. Despite its importance, analytical methods to study history on a genome-wide scale are still limited in both their resolution and qualitative ability to reconstruct aspects of the past. This makes it a prioriy to develop methods that can analyze history in the large data sets that are now practical to generate. These new methods also need to deal with the major population mixture events that have been documented in the last five years as a result of the genomic and ancient DNA data revolutions. Thus, it is no longer adequate to simply propose models of population relationships without mixture. Instead, the hypotheses that need to be tested for consistency with data are ones that involve major, already-known mixture events. The toolkit for studying the historical events-whilst taking into account known admixture events-is at present inadequate. This proposal aims to build on and extend the work of R01 GM100233, which from 2012-2015 supported the joint PIs' central research program on developing methods for studying human population history, resulting in 31 publications directly linked to the grant. We now propose four new Aims: (1) To develop tests for population mixture that work even without explicit phylogenetic models; (2) To build a comprehensive toolkit for studying population history using linkage disequilibrium; (3) To capitalize on the power of the joint allele frequency spectrum for studies of human history; (4) To enable powerful comparisons of X and the autosomes to reveal sex-biased demography. This grant will be of value in three ways. (a) It will support the development of methods and user- friendly software that will be important for both evolutionary and medical genetics. (b) It will support work that will result in insights relevant to finding disease genes in human populations that are recently or anciently admixed. (c) It will lead to new discoveries about human history. The link to medical genetics is important. In the past, we have been successful at drawing a direct connection between our laboratory's work on detecting and characterizing population mixture, and human biology including genetic susceptibility to disease. We leveraged the history of admixture in African Americans to make new disease gene discoveries (for example, risk factors for prostate cancer), to understand variation in disease ris across populations, and to document differences in the biology of recombination between African Americans and people who do not have West African ancestry, which is predicted to lead to different levels of risk for diseases associated with errors in recombination. We anticipate that the approaches for understanding and modeling population history that we develop with the support of this grant will continue to synergize with the latest research on human genetic variation and disease risk.

PROJECT SUMMARY / ABSTRACT Population history shapes human biology, genetic variation, and disease risk. Despite its importance, analytical methods to study history on a genome-wide scale are limited in both their resolution and qualitative ability to reconstruct aspects of the past. This makes it a priority to develop new methods that are able to model the major population mixture events that have been documented as a result of the genomic and ancient DNA data revolutions in the last decade. At present, the world's ancient DNA data suffer from major technical biases that are not properly controlled for, the analytical toolkit for studying population mixture with modern and ancient DNA is inadequate, and we are only beginning to develop methods that realize the potential of ancient DNA to reveal as much about biology as about history. This proposal aims to address these needs by extending funding for grant R01 GM100233, which from 2012-2020 supported the PIs' central research program on developing methods for studying human population history and leveraging this information to learn about biology, resulting in 70 publications linked to the grant. We propose three new Aims: (1) To create an unbiased pipeline for processing ancient DNA, and to use it reanalyze the world's data; (2) To extend the capabilities of admixture graphs for population history inference; (3) To introduce new tools for analyzing population history and biology. This grant will be of value in four ways. (a) It will support the development of methods and user-friendly and well-documented software that will be important for evolutionary and medical genetics. (b) It will support work that will result in insights relevant to finding disease genes in human populations that are recently or anciently admixed. (c) It will lead to new discoveries about human history. (d) It will produce a publicly available reprocessed version of the world's ancient DNA data that will be of broad use to the community. The link to medical genetics is important. In the past, we have been successful at drawing a direct connection between our laboratory's work on detecting and characterizing population mixture, and human biology including genetic susceptibility to disease. We leveraged the history of admixture in African Americans to make new disease gene discoveries (for example, risk factors for prostate cancer), to understand variation in disease risk across populations, and to document differences in the biology of recombination between African Americans and people who do not have West African ancestry, which are predicted to lead to different levels of risk for diseases associated with errors in recombination. Our focus on drawing connections between population history and disease risk also highlighted the opportunities for discovery of recessive disease genes in thousands of founder groups in South Asia. We anticipate that the methods and resources we develop with the support of this grant will continue to synergize with the latest research on human variation and disease risk.