Family-based versus unrelated case-control designs for genetic associations

The most simple and commonly used approach for genetic associations is the case-control study design of unrelated people. This design is susceptible to population stratification. This problem is obviated in family-based studies, but it is usually difficult to accumulate large enough samples of well-characterized families. We addressed empirically whether the two designs give similar estimates of association in 93 investigations where both unrelated case-control and family-based designs had been employed. Estimated odds ratios differed beyond chance between the two designs in only four instances (4%). The summary relative odds ratio (ROR) (the ratio of odds ratios obtained from unrelated case-control and family-based studies) was close to unity (0.96 [95% confidence interval, 0.91-1.01]). There was no heterogeneity in the ROR across studies (amount of heterogeneity beyond chance I(2) = 0%). Differences on whether results were nominally statistically significant (p < 0.05) or not with the two designs were common (opposite classification rates 14% and 17%); this reflected largely differences in power. Conclusions were largely similar in diverse subgroup analyses. Unrelated case-control and family-based designs give overall similar estimates of association. We cannot rule out rare large biases or common small biases.     

Further studies on associations between leprosy and genetic markers in human serum

Summary Seven serum proteins were typed on a sample of 910 individuals from Angola, Africa. The sample consisted of both leprosy patients and healthy controls from the same geographical area. Significant associations with leprosy were found for the Hp and Pi systems. The results are discussed.

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Zusammenfassung An einer Stichprobe von 910 Individuen aus Angola (Afrika) wurden sieben Serumprotein-Systeme untersucht. Diese Stichprobe umfaßt Leprakranke und gesunde Kontrollen aus dem gleichen geographischen Raum. Signifikante Assoziationen wurden nur zwischen Lepra und den Systemen Haptoglobin und Pi gefunden. Die Ergebnisse werden im einzelnen diskutiert.

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Supported by the Deutsche Forschungsgemeinschaft.     

The use of genetic correlations to evaluate associations between SNP markers and quantitative traits

Open-pollinated progeny of Corymbia citriodora established in replicated field trials were assessed for stem diameter, wood density, and pulp yield prior to genotyping single nucleotide polymorphisms (SNP) and testing the significance of associations between markers and assessment traits. Multiple individuals within each family were genotyped and phenotyped, which facilitated a comparison of standard association testing methods and an alternative method developed to relate markers to additive genetic effects. Narrow-sense heritability estimates indicated there was significant additive genetic variance within this population for assessment traits ( $ {\widehat{h}^{{2}}} = 0.{28}\;{\text{to}}\;0.{44} $ ) and genetic correlations between the three traits were negligible to moderate (r _G?=?0.08 to 0.50). The significance of association tests (p values) were compared for four different analyses based on two different approaches: (1) two software packages were used to fit standard univariate mixed models that include SNP-fixed effects, (2) bivariate and multivariate mixed models including each SNP as an additional selection trait were used. Within either the univariate or multivariate approach, correlations between the tests of significance approached +1; however, correspondence between the two approaches was less strong, although between-approach correlations remained significantly positive. Similar SNP markers would be selected using multivariate analyses and standard marker-trait association methods, where the former facilitates integration into the existing genetic analysis systems of applied breeding programs and may be used with either single markers or indices of markers created with genomic selection processes.     

Efficient study designs for test of genetic association using sibship data and unrelated cases and controls

Linkage mapping of complex diseases is often followed by association studies between phenotypes and marker genotypes through use of case-control or family-based designs. Given fixed genotyping resources, it is important to know which study designs are the most efficient. To address this problem, we extended the likelihood-based method of Li et al., which assesses whether there is linkage disequilibrium between a disease locus and a SNP, to accommodate sibships of arbitrary size and disease-phenotype configuration. A key advantage of our method is the ability to combine data from different family structures. We consider scenarios for which genotypes are available for unrelated cases, affected sib pairs (ASPs), or only one sibling per ASP. We construct designs that use cases only and others that use unaffected siblings or unrelated unaffected individuals as controls. Different combinations of cases and controls result in seven study designs. We compare the efficiency of these designs when the number of individuals to be genotyped is fixed. Our results suggest that (1) when the disease is influenced by a single gene, the one sibling per ASP-control design is the most efficient, followed by the ASP-control design, and familial cases contribute more association information than singleton cases; (2) when the disease is influenced by multiple genes, familial cases provide more association information than singleton cases, unless the effect of the locus being tested is much smaller than at least one other untested disease locus; and (3) the case-control design can be useful for detecting genes with small effect in the presence of genes with much larger effect. Our findings will be helpful for researchers designing and analyzing complex disease-association studies and will facilitate genotyping resource allocation.     

A classical setting for associations between markers and loci affecting quantitative traits

We examine the relationships between a genetic marker and a locus affecting a quantitative trait by decomposing the genetic effects of the marker locus into additive and dominance effects under a classical genetic model. We discuss the structure of the associations between the marker and the trait locus, paying attention to non-random union of gametes, multiple alleles at the marker and trait loci, and non-additivity of allelic effects at the trait locus. We consider that this greater-than-usual level of generality leads to additional insights, in a way reminiscent of Cockerham's decomposition of genetic variance into five terms: three terms in addition to the usual additive and dominance terms. Using our framework, we examine several common tests of association between a marker and a trait.     

Population genetic structure and trait associations in forest savory using molecular,morphological and phytochemical markers

In this investigation, morphological, phytochemical and ISSR markers were used to estimate the relationships among and within seven populations of white savory (Satureja mutica), belonging to four provinces in Iran. The individuals were phenotypically diverse, which stamen length, corolla length, corolla diameter, calyx length, bract length, inflorescence length, calyx length and bracteole width were characteristics with the highest variation. Leaf dimensions were in significant correlation with flower and inflorescence characteristics. Chemical compounds of essential oils were found variable in various individuals and all samples were principally composed of phenolic constituents (carvacrol and/or thymol). As a consequence, the plants were classified into two major chemotypes including carvacrol and thymol. A total of 197 band positions were produced by 14 ISSR primers, of which 176 were found polymorphic with 88.91% polymorphism. ISSR genetic similarity values among individuals ranged between 0.45 and 0.94 which was indicative of a high level of genetic variation. Multiple regression analysis (MRA) revealed that phytochemical compositions as dependent variable, showed statistically significant correlation and in association with leaf and flower traits as independent variable, indicating a main role of leaf and flower on production of these compounds. Also, several ISSR fragments were found associated with some morphological traits and phytochemical compositions. The high diversity within and among populations of S. mutica according to different data systems could provide useful information for conservation and selection of cross-parents in breeding programs.     

A study of fluctuating dermatoglyphic asymmetry in the sibs and parents of cleft lip propositi.

Fluctuating asymmetry was studied in cleft lip propositi and their normal sibs and parents. The traits examined were a-b ridge counts and fingerprint patterns. Propositi with a family history of this congenital malformation and their normal sibs and parents were significantly different from the controls for this type of asymmetry. Propositi without a family history and their normal sibs and parents were similar to the controls. These results support the hypothesis that familial and sporadic cases of congenital cleft lip are different entities and give evidence for a genetic mechanism in the parents and sibs of the familial cases that may account for this congenital disorder and, concomitantly, increased fluctuating asymmetry.     

Reliability of statistical associations between genes and disease

Many statistical associations between a disease and alleles of specific genes have proven to be irreproducible. In part, this irreproducibility can be attributed to a lack of replication before publication and the fact that, until recently, the relationship between statistical significance and various measures of reproducibility was not widely understood. This review proposes a classification system, the Better Associations for Disease and GEnes (BADGE) system, for describing genetic associations. The BADGE classes, first class through fifth class, are based on the P value of the association. A first-class association, with P<2×10⁻⁷, is expected to be reproducible even in the absence of other evidence supporting the association. A fifth-class association corresponds to conventional statistical significance (P<5×10⁻²), which provides almost no assurance of reproducibility. Three intervening classes, described as second-, third-, and fourth-class associations, are defined by P values separated by factors of 20 or 25 from these extremes.     

Heterozygosity effects in studies of genetic markers and disease

Examples were discussed where heterozygosity was associated with increased or decreased disease risks and where the apparent mechanism is direct functional involvement of gene products and not linkage disequilibrium. Special attention was paid to the impact of Hp (haptoglobin) heterozygosity on a number of different multifactorial disorders. When phenotype distributions in patients show large deviations from the Hardy-Weinberg equilibrium significant differences between patients and controls may be found concerning phenotype distributions but not with respect to the frequencies of alleles and phenotypic factors. The common method of studying ratios of phenotypic factors by pooling homo- and heterozygotes is in principle a conservative approach which tends to underestimate the strength of associations and to obscure heterozygosity effects. A significant deviation from the Hardy-Weinberg equilibrium in a marker system examined in a group of patients is in itself a sensitive indicator of phenotypic association with the disease in question.     

Use of unlinked genetic markers to detect population stratification in association studies.

We examine the issue of population stratification in association-mapping studies. In case-control studies of association, population subdivision or recent admixture of populations can lead to spurious associations between a phenotype and unlinked candidate loci. Using a model of sampling from a structured population, we show that if population stratification exists, it can be detected by use of unlinked marker loci. We show that the case-control-study design, using unrelated control individuals, is a valid approach for association mapping, provided that marker loci unlinked to the candidate locus are included in the study, to test for stratification. We suggest guidelines as to the number of unlinked marker loci to use.     

A covering method for detecting genetic associations between rare variants and common phenotypes

Genome wide association (GWA) studies, which test for association between common genetic markers and a disease phenotype, have shown varying degrees of success. While many factors could potentially confound GWA studies, we focus on the possibility that multiple, rare variants (RVs) may act in concert to influence disease etiology. Here, we describe an algorithm for RV analysis, RareCover. The algorithm combines a disparate collection of RVs with low effect and modest penetrance. Further, it does not require the rare variants be adjacent in location. Extensive simulations over a range of assumed penetrance and population attributable risk (PAR) values illustrate the power of our approach over other published methods, including the collapsing and weighted-collapsing strategies. To showcase the method, we apply RareCover to re-sequencing data from a cohort of 289 individuals at the extremes of Body Mass Index distribution (NCT00263042). Individual samples were re-sequenced at two genes, FAAH and MGLL, known to be involved in endocannabinoid metabolism (187Kbp for 148 obese and 150 controls). The RareCover analysis identifies exactly one significantly associated region in each gene, each about 5 Kbp in the upstream regulatory regions. The data suggests that the RVs help disrupt the expression of the two genes, leading to lowered metabolism of the corresponding cannabinoids. Overall, our results point to the power of including RVs in measuring genetic associations.     

Alpha-globin gene markers identify genetic differences between Australian aborigines and Melanesians.

Australian aborigines exhibit a number of alpha-globin cluster rearrangements involving both alpha- and zeta-globin genes. alpha+-Thalassemia (-alpha/) in this population is heterogeneous and includes the 3.7 types I, II, and III gene deletions. The alpha alpha alpha/ and zeta zeta zeta/ rearrangements are each found in association with two haplotypes, indicating origins from at least two separate DNA crossover events. Differences in alpha-globin cluster rearrangements and in haplotypes between Australian aborigines, Papua New Guinea highlanders and island Melanesians, are consistent with multiple colonizing events into Australia.     

Simple,robust linkage tests for affected sibs.

Parametric-linkage analysis applied to large pedigrees with many affected individuals has helped in the identification of highly penetrant genes; but, for diseases lacking a clear Mendelian inheritance pattern or caused by several genes of low to moderate penetrance, a more robust strategy is nonparametric analysis applied to small sets of affected relatives, such as affected sib pairs. Here we show that the robustness of affected-sib-pair tests is related to the shape of the constraint set for the sibs'' identity-by-descent (IBD) probabilities. We also derive a set of constraints for the IBD probabilities of affected sib triples and use common features of the shapes of the two constrain sets to introduce new nonparametric tests (called "minmax" tests) that are more robust than those in current use. Asymptotic-power computations support the robustness of the proposed minmax tests.