Estimating the frequencies and population specificities of risk alleles

描述（由申请人提供）：了解性状的遗传结构——导致人际差异的遗传变异的频率、数量和影响——是过去五十年来统计、分子和进化遗传学的中心目标之一。双胞胎/家族研究表明，包括精神障碍在内的大多数特征都是高度可遗传的，最近的全基因组关联研究（GWAS）已经发现了数千个与这些特征可靠相关的单核苷酸多态性（snp），即将到来的全基因组序列数据将允许对性状遗传基础的遗传变异进行更彻底的调查。然而，在这些数据的洪流中，关于性状遗传结构的基本问题仍然没有答案，或者没有得到很好的描述。尽管双胞胎/家庭研究已经详细描述了数百种性状的遗传能力，但这种遗传能力在多大程度上是由于遗传变异的加性效应而产生的，这一点仍然不清楚。尽管GWAS已经证明大量的遗传变异一定是性状遗传的原因，但常见（全世界的人共有）与罕见（特定于人群或大家庭）遗传变异的相对重要性仍不清楚。最后，预测一个种族或群体特征的遗传变异是否通常预测其他种族或群体的相同特征尚不清楚。随着该领域在未来几年转向全基因组测序，现在比以往任何时候都更有必要更好地理解这些关于性状遗传结构的基本问题。这样做应该有助于指导未来的分析和投资决策。我们提出的方法的发展，将有助于研究人员大大减少周围的遗传结构的不确定性

DESCRIPTION (provided by applicant): Understanding the genetic architecture of traits-the frequencies, numbers, and effects of genetic variants that cause interpersonal differences-has been one of the central goals of statistical, molecular and evolutionary genetics over the last fify years. Twin/family studies have showed that most traits, including mental disorders, are highly heritable, recent genome-wide association studies (GWAS) have discovered thousands of single nucleotide polymorphisms (SNPs) reliably associated with these traits, and forthcoming whole-genome sequence data will allow a much more thorough investigation into genetic variants that underlie trait heritability. In the midst of this deluge of data, however, fundamenta questions about the genetic architecture of traits remain unanswered or are poorly characterized. Although twin/family studies have detailed the heritability of hundreds of traits, the degree to which this heritability is due to additive effects of genetic variants remains unclea. Although GWAS has demonstrated that a huge number of genetic variants must be responsible for trait heritability, the relative importance of common (shared by people worldwide) versus rare (specific to populations or extended families) genetic variants remains unclear. Finally, it is unclear whether genetic variants that predict traits in one ethnicity or population typically predit those same traits in other ethnicities or populations. As the field turns to whole-genome sequencing in the years ahead, it is crucial, now more than ever, to have a better understanding of these fundamental questions about the genetic architecture of traits. Doing so should help guide future analytic and investment decisions. We propose the development of methodologies that will help investigators greatly reduce the uncertainty surrounding the genetic architecture of traits using existing SNP data and, as it becomes available, sequence data. First, we demonstrate a method that allows the full additive genetic variation of a trait to be accurately estimated using simulated SNP data, and describe several advances that we will work on in order to make this method feasible to use on real SNP data. Second, we describe how sequence data can be used to accurately estimate the importance of common versus rare genetic variants, and propose the development of a method that will allow this approach to be used on existing SNP data. Third, we show a method that allows investigators to understand the degree to which SNPs that predict a trait in one ethnicity also predict that trait in another ethnicity, and we propose developing two extensions of this that (a) clarify why such differences occur and (b) make this approach applicable to understanding the specificity of SNP associations between subpopulations. Finally, we will apply these methods to the three largest case-control SNP datasets on Major Depressive Disorder, Bipolar Disorder, and Schizophrenia. By project's end, we anticipate having tools that allow for a much clearer understanding of the genetic architecture of these and other heritable phenotypes.