用单元内混合家系相关法计算遗传力时的抽样误差估计

对应用单元内混合家系相关法计算遗传力时的抽样误差估计问题进行了探讨,推导出的抽样误差估计公式可用于遗传力的显著性检验．     

家畜抗性等级性状的QTL定位方法

在广义线性模型的框架内模拟研究了家畜抗性等级性状的QTL定位方法,QTL参数的估计采用最大似然方法,比较了阈模型方法与一般线性方法的QTL定位效率,并对影响等级性状QTL定位效率的主要因素（QTL效应、性状的遗传力）进行了模拟研究,实验设计为多个家系的女儿设计,资源群体大小为500头。研究结果表明：在QTL位置参数估计及检验功效方面,阈模型方法具有一定的优势,对抗性等级性状QTL定位的功效也高于线性方法。另外,性状遗传力和QTL效应的大小对QTL定位的准确度也有直接的影响,随着性状遗传力QTL效应的     

单元内混合家系相关法估算遗传力及遗传相关的探讨

本文取单元内同胞相关法和混合家系亲缘相关法估算畜禽遗传参数之长,推导出单元内混合家系相关法。用这种方法估算遗传参数,既能消除环境单元间的差异,又能充分利用全同胞和半同胞资料,具有扩大信息来源,提高估算准确性和计算方便的优点。     

利用“全同胞-半同胞”混合家系资料估测性状间的遗传相关

近年来,数量遗传学的理论发展很快,并且广泛应用于动植物的育种实践中,尤其在遗传参数的估计上,提出了不少新的方法。有关利用“全同胞-半同胞”混合家系资料估测性状遗传力的方法,吴常信(1983,会议资料)已做过专门论述。本文旨在推导利用“全同胞-半同胞”混合家系资料估测性状间遗传相关的公式,以充分利用资料所提供的信息。     

利用单元内方差分析法估计遗传参数的探讨

在遗传力的估计过程中,需将多种非遗传因素的影响从公畜间方差或者母畜间方差中剔除。在我国常使用的是盛志廉教授提出的单元内同胞相关法。本文对该法从理论上进行了更详细的证明,并将其推广到两层分类方差分析时的情况。同时还给出了当公母畜彼此间有亲缘关系时,利用单元内方差分析估计遗传力的方法。这些方法既可使遗传力的估计简便,又具有多因方差分析的功用。     

离散性状QTL区间定位的最大似然方法

采用最大似然区间定位法对阈模型与一般线性模型的QTL定位效率进行了比较,并对影响离散性状QTL检测效率的主要因素（QTL效应、性状的遗传力和表型发生率）进行了模拟研究,实验设计为多个家系的女儿设计．资源群体大小为500头。研究结果表明：在QTL参数估计及检验功效方面,阈模型方法具有较大的优势,对离散性状QTL定位的效率明显高于LM（Linear Model）方法,定位的准确性也较高。另外,性状遗传力、QTL效应的大小和性状表型发生率对QTL定位的准确度也有直接的影响,随着性状遗传力和表型发生率的提高,随着QTL效应的增大,QTL定位的效率也进一步提高。     

混合家系遗传参数估计方法研究

针对混合家系遗传参数估计,本文在假定公畜方差组分和母畜方差组分相等这一理论基础上,通过对方差分析的期望均方组成分析,提出了新的遗传力估计方法,以及某些特殊情况下的近似估计方法。通过一个估测实例比较了几种遗传力估计方法,结果表明,本文方法与全同胞组分估计最为接近,而且遗传力标准误最小,本文近似估计方法的效果也较好。对各种方法而言,资料越不平衡其差异越大。本文方法可以在一定程度上弥补全同胞分析时,因实际资料的公母畜方差组分差异过大的缺陷,具有实际可行性。此外,由于本文方法是用单因方差分析解决二因方差分析问题,计算更为简便,并可免于计算混合家系平均亲缘相关系数。     

51个红松子代家系苗期生长变异研究

为选育生长快、成活率高的优良红松子代家系,以吉林省龙井市开山屯林场的51个红松初级种子园子代家系为材料,对其苗高、地径、成活率及存活率进行测定分析。结果表明：除4年生地径的区组与家系交互作用外,各性状在家系间均达显著差异水平（P<0.05）;各性状表型变异系数变化范围为6.85%～29.89%;各性状的遗传力较高,除4年生地径（0.34）和存活率（0.37）外,遗传力均超过0.50,属于高遗传力;高变异系数,高遗传力,有利于家系的评价选择;相关性分析结果表明,各性状间相关关系均达极显著正相关水平。利用布雷津多性状综合评价法对51个红松子代家系进行评价,以10%的入选率,PK29、PK38、PK21、PK37和PK48五个家系入选。入选家系4年生苗高和地径平均值分别为29.26和0.93 cm,分别比总平均值高3.51和0.06 cm,遗传增益分别为10.08%和2.33%。本研究可以为红松优良家系评价提供理论基础和材料。     

马尾松半同胞子代苗期与幼林生长变异分析及家系选择

为探讨马尾松改良种子园半同胞家系子代的生长变异情况,测定分析了26个家系1 年生苗高、地径和0.5年生、1.5 年生及2.5 年生幼林的树高、地径、胸径、材积和冠幅等生长指标。结果表明,各性状在家系间均存在极显著差异,参试群体的树高、胸径、材积和冠幅的家系遗传力大于0.8,且前三个性状的单株遗传力大于0.4,说明上述性状是遗传力较高的性状。2.5年生材积生长量显著高于普通生产种的优良家系有19个,入选率为73%,树高、胸径和材积的遗传增益分别为2.76%、3.60%和8.49%,与初级种子园混合种和普通生产种相比,其材积分别提高55.28%和218.28%。     

果蝇D.auraria 5个姊妹种的进化方向新探

为提高主基因+多基因混合遗传分析的精度,降低试验误差,采用重复内分组随机区组设计,对低遗传力性状的B12和B22或F23家系平均数资料进行遗传分析.通过AIC准则和适合性检验比较无主基因(A-0)、1对主基因(A)、2对主基因(B)、多基因(C)、1对主基因+多基因(D)和2对主基因+多基因(E)模型以鉴定其遗传模式.采用IECM算法估计混合模型参数.通过油菜HSTC14×宁油7号初花期F23家系平均数资料阐明该方法.     

Genetics of Callous-Unemotional Behavior in Children

Callous-unemotional behavior (CU) is currently under consideration as a subtyping index for conduct disorder diagnosis. Twin studies routinely estimate the heritability of CU as greater than 50%. It is now possible to estimate genetic influence using DNA alone from samples of unrelated individuals, not relying on the assumptions of the twin method. Here we use this new DNA method (implemented in a software package called Genome-wide Complex Trait Analysis, GCTA) for the first time to estimate genetic influence on CU. We also report the first genome-wide association (GWA) study of CU as a quantitative trait. We compare these DNA results to those from twin analyses using the same measure and the same community sample of 2,930 children rated by their teachers at ages 7, 9 and 12. GCTA estimates of heritability were near zero, even though twin analysis of CU in this sample confirmed the high heritability of CU reported in the literature, and even though GCTA estimates of heritability were substantial for cognitive and anthropological traits in this sample. No significant associations were found in GWA analysis, which, like GCTA, only detects additive effects of common DNA variants. The phrase ‘missing heritability’ was coined to refer to the gap between variance associated with DNA variants identified in GWA studies versus twin study heritability. However, GCTA heritability, not twin study heritability, is the ceiling for GWA studies because both GCTA and GWA are limited to the overall additive effects of common DNA variants, whereas twin studies are not. This GCTA ceiling is very low for CU in our study, despite its high twin study heritability estimate. The gap between GCTA and twin study heritabilities will make it challenging to identify genes responsible for the heritability of CU.     

Selection of sires to reduce sampling variance in the estimates of heritability by half-sib correlation

Summary Standard methods to estimate heritability by half-sib correlation are biased if selection has operated in the parental generation. In this paper a simple method to correct for selection of animals used as sires is described. By selection of both the top and the bottom ranking sires, the sampling variances of the corrected estimates of heritability are substantially reduced. Algebraic expressions to predict the sampling variance of the estimates of heritability using selected sires are derived. Theoretical predictions were checked by Monte-Carlo simulation. The results may have application in the design of experiments to estimate heritabilities.     

Partitioning the Heritability of Tourette Syndrome and Obsessive Compulsive Disorder Reveals Differences in Genetic Architecture

The direct estimation of heritability from genome-wide common variant data as implemented in the program Genome-wide Complex Trait Analysis (GCTA) has provided a means to quantify heritability attributable to all interrogated variants. We have quantified the variance in liability to disease explained by all SNPs for two phenotypically-related neurobehavioral disorders, obsessive-compulsive disorder (OCD) and Tourette Syndrome (TS), using GCTA. Our analysis yielded a heritability point estimate of 0.58 (se = 0.09, p = 5.64e-12) for TS, and 0.37 (se = 0.07, p = 1.5e-07) for OCD. In addition, we conducted multiple genomic partitioning analyses to identify genomic elements that concentrate this heritability. We examined genomic architectures of TS and OCD by chromosome, MAF bin, and functional annotations. In addition, we assessed heritability for early onset and adult onset OCD. Among other notable results, we found that SNPs with a minor allele frequency of less than 5% accounted for 21% of the TS heritability and 0% of the OCD heritability. Additionally, we identified a significant contribution to TS and OCD heritability by variants significantly associated with gene expression in two regions of the brain (parietal cortex and cerebellum) for which we had available expression quantitative trait loci (eQTLs). Finally we analyzed the genetic correlation between TS and OCD, revealing a genetic correlation of 0.41 (se = 0.15, p = 0.002). These results are very close to previous heritability estimates for TS and OCD based on twin and family studies, suggesting that very little, if any, heritability is truly missing (i.e., unassayed) from TS and OCD GWAS studies of common variation. The results also indicate that there is some genetic overlap between these two phenotypically-related neuropsychiatric disorders, but suggest that the two disorders have distinct genetic architectures.     

An Effective Method to Identify Heritable Components from Multivariate Phenotypes

Multivariate phenotypes may be characterized collectively by a variety of low level traits, such as in the diagnosis of a disease that relies on multiple disease indicators. Such multivariate phenotypes are often used in genetic association studies. If highly heritable components of a multivariate phenotype can be identified, it can maximize the likelihood of finding genetic associations. Existing methods for phenotype refinement perform unsupervised cluster analysis on low-level traits and hence do not assess heritability. Existing heritable component analytics either cannot utilize general pedigrees or have to estimate the entire covariance matrix of low-level traits from limited samples, which leads to inaccurate estimates and is often computationally prohibitive. It is also difficult for these methods to exclude fixed effects from other covariates such as age, sex and race, in order to identify truly heritable components. We propose to search for a combination of low-level traits and directly maximize the heritability of this combined trait. A quadratic optimization problem is thus derived where the objective function is formulated by decomposing the traditional maximum likelihood method for estimating the heritability of a quantitative trait. The proposed approach can generate linearly-combined traits of high heritability that has been corrected for the fixed effects of covariates. The effectiveness of the proposed approach is demonstrated in simulations and by a case study of cocaine dependence. Our approach was computationally efficient and derived traits of higher heritability than those by other methods. Additional association analysis with the derived cocaine-use trait identified genetic markers that were replicated in an independent sample, further confirming the utility and advantage of the proposed approach.     

Testing marker-based estimates of heritability in the wild

Marker-based estimates of heritability are an attractive alternative to pedigree-based methods for estimating quantitative genetic parameters in field studies where it is difficult or impossible to determine relationships and pedigrees. Here I test the ability of the marker-based method to estimate heritability of a suite of traits in a wild population of bighorn sheep (Ovis canadensis) using marker data from 32 microsatellite loci. I compared marker-based estimates with estimates obtained using a pedigree and the animal model. Marker-based estimates of heritability were imprecise and downwardly biased. The high degree of uncertainty in marker-based estimates suggests that the method may be sufficient to detect the presence of genetic variance for highly heritable traits, but not sufficiently reliable to estimate genetic parameters.     

Selection and genetic (co)variance in bighorn sheep

Genetic theory predicts that directional selection should deplete additive genetic variance for traits closely related to fitness, and may favor the maintenance of alleles with antagonistically pleiotropic effects on fitness-related traits. Trait heritability is therefore expected to decline with the degree of association with fitness, and some genetic correlations between selected traits are expected to be negative. Here we demonstrate a negative relationship between trait heritability and association with lifetime reproductive success in a wild population of bighorn sheep (Ovis canadensis) at Ram Mountain, Alberta, Canada. Lower heritability for fitness-related traits, however, was not wholly a consequence of declining genetic variance, because those traits showed high levels of residual variance. Genetic correlations estimated between pairs of traits with significant heritability were positive. Principal component analyses suggest that positive relationships between morphometric traits constitute the main axis of genetic variation. Trade-offs in the form of negative genetic or phenotypic correlations among the traits we have measured do not appear to constrain the potential for evolution in this population.     

遗传与基因表达数据的整合—— eQTL的方法及应用

高通量的基因型分析和芯片技术的发展使人们能够进一步研究哪些遗传差异最终影响基因的表达。通过表达数量性状座位(eQTL)作图方法可对基因表达水平的遗传基础进行解析。与传统的QTL分析方法一样, eQTL的主要目标是鉴别表达性状座位所在的染色体区域。但由于表达谱数据成千上万, 而传统的QTL分析方法最多分析几十个性状, 因此需要考虑这类实验设计的特点以及统计分析方法。本文详细介绍了eQTL定位过程及其研究方法, 重点从个体选择、基因芯片实验设计、基因表达数据的获得与标准化、作图方法及结果分析等方面进行了综述, 指出了当前eQTL研究存在的问题和局限性。最后介绍了eQTL研究在估计基因表达遗传率、挖掘候选基因、构建基因调控网络、理解基因间及基因与环境的互作的应用进展。