Mapping Quantitative Trait Loci Using Linkage Disequilibrium: Marker- versus Trait-based Methods

Two approaches for mapping quantitative trait loci (QTL) using linkage disequilibrium at the population level were investigated. In the trait-based (TB) approach, the frequencies of marker alleles (or genotypes) are compared in individuals selected from the two tails of the trait distribution. The TB approach uses phenotypic information only in the selection step. In the marker-based (MB) approach, the quantitative trait values for the marker genotypes in the selected individuals are compared. The MB approach uses both the difference in marker allele (or genotype) frequencies and the phenotypic values of each marker genotype in the selected samples. We quantify the power of each approach and show that the power of the MB approach is greater than or equal to that of the TB approach. The advantage of the former is expected to increase with increasing number of traits phenotyped. Our accurate predictions obviate the need for elaborate simulation studies.     

Design and Analysis of Genetic Association Studies to Finely Map a Locus Identified by Linkage Analysis: Sample Size and Power Calculations

Association (e.g. case‐control) studies are often used to finely map loci identified by linkage analysis. We investigated the influence of various parameters on power and sample size requirements for such a study. Calculations were performed for various values of a high‐risk functional allele (f_A), frequency of a marker allele associated with the high risk allele (f₁), degree of linkage disquilibrium between functional and marker alleles (D′) and trait heritability attributable to the functional locus (h²). The calculations show that if cases and controls are selected from equal but opposite extreme quantiles of a quantitative trait, the primary determinants of power are h² and the specific quantiles selected. For a dichotomous trait, power also depends on population prevalence. Power is optimal if functional alleles are studied (f_A= f₁ and D′= 1.0) and can decrease substantially as D′ diverges from 1.0 or as f₁ diverges from f_A. These analyses suggest that association studies to finely map loci are most powerful if potential functional polymorphisms are identified a priori or if markers are typed to maximize haplotypic diversity. In the absence of such information, expected minimum power at a given location for a given sample size can be calculated by specifying a range of potential frequencies for f_A (e.g. 0.1‐0.9) and determining power for all markers within the region with specification of the expected D′ between the markers and the functional locus. This method is illustrated for a fine‐mapping project with 662 single nucleotide polymorphisms in 24 Mb. Regions differed by marker density and allele frequencies. Thus, in some, power was near its theoretical maximum and little additional information is expected from additional markers, while in others, additional markers appear to be necessary. These methods may be useful in the analysis and interpretation of fine‐mapping studies.     

A Simple Method to Calculate Resolving Power and Confidence Interval of QTL Map Location

Resolving power is defined as the 95% confidence interval for quantitative trait locus (QTL) map location that would be obtained when scoring an infinite number of markers in a given constellation of a marker-QTL mapping experiment. Resolving power can serve as a close estimate of the confidence interval of QTL map location, as well as a guide to the lower efficient limit of marker spacing in an initial marker-QTL mapping experiment. In the present study, an extensive series of simulations was carried out to provide estimates of resolving power, for backcross (BC) and F₂ designs, over a wide range of experimental sizes and of gene effects and dominance at the QTL. From the simulation results, the remarkably simple expressions, 3000/(mNd ²) (where m = 1 for BC and m = 2 for F₂; N = population size, and d = allele substitution effect) and 530/N (in terms of , the proportion of variance explained), were obtained for estimating resolving power. These expressions can provide a convenient guide to planning marker spacing in BC and F₂ marker-QTL linkage experiments and for placing confidence intervals about QTL map location obtained in such experiments.     

Using multivariate genetic modeling to detect pleiotropic quantitative trait loci

Large numbers of sibling pairs or other relatives are needed to detect linkage between a quantitative trait locus (QTL) and a marker, especially if the variance of the QTL is low relative to the total phenotypic variance of the trait. One strategy to increase the power to detect linkage is to reduce the environmental variance in the trait under analysis. This approach was explored by carrying out a series of simulation studies in which multivariate observations were used to estimate individual genotypic values at a QTL, that pleiotropically affected more than one trait. Simulations for different QTL allele frequencies with a completely informative marker showed that the power to detect the QTL increased substantially when estimates of individual genotypic values at the QTL were used in the linkage analysis instead of phenotypic observations. An advantage of this approach is that, rather than employing phenotypic selection, individuals with extreme genotypes may be selected when ascertaining a sample of extreme families.     

Effectiveness of Shrinkage and Variable Selection Methods for the Prediction of Complex Human Traits using Data from Distantly Related Individuals

Genome‐wide association studies (GWAS) have detected large numbers of variants associated with complex human traits and diseases. However, the proportion of variance explained by GWAS‐significant single nucleotide polymorphisms has been usually small. This brought interest in the use of whole‐genome regression (WGR) methods. However, there has been limited research on the factors that affect prediction accuracy (PA) of WGRs when applied to human data of distantly related individuals. Here, we examine, using real human genotypes and simulated phenotypes, how trait complexity, marker‐quantitative trait loci (QTL) linkage disequilibrium (LD), and the model used affect the performance of WGRs. Our results indicated that the estimated rate of missing heritability is dependent on the extent of marker‐QTL LD. However, this parameter was not greatly affected by trait complexity. Regarding PA our results indicated that: (a) under perfect marker‐QTL LD WGR can achieve moderately high prediction accuracy, and with simple genetic architectures variable selection methods outperform shrinkage procedures and (b) under imperfect marker‐QTL LD, variable selection methods can achieved reasonably good PA with simple or moderately complex genetic architectures; however, the PA of these methods deteriorated as trait complexity increases and with highly complex traits variable selection and shrinkage methods both performed poorly. This was confirmed with an analysis of human height.     

Combined high resolution linkage and association mapping of quantitative trait loci

In this paper, we investigate variance component models of both linkage analysis and high resolution linkage disequilibrium (LD) mapping for quantitative trait loci (QTL). The models are based on both family pedigree and population data. We consider likelihoods which utilize flanking marker information, and carry out an analysis of model building and parameter estimations. The likelihoods jointly include recombination fractions, LD coefficients, the average allele substitution effect and allele dominant effect as parameters. Hence, the model simultaneously takes care of the linkage, LD or association and the effects of the putative trait locus. The models clearly demonstrate that linkage analysis and LD mapping are complementary, not exclusive, methods for QTL mapping. By power calculations and comparisons, we show the advantages of the proposed method: (1) population data can provide information for LD mapping, and family pedigree data can provide information for both linkage analysis and LD mapping; (2) using family pedigree data and a sparse marker map, one may investigate the prior suggestive linkage between trait locus and markers to obtain low resolution of the trait loci, because linkage analysis can locate a broad candidate region; (3) with the prior knowledge of suggestive linkage from linkage analysis, both population and family pedigree data can be used simultaneously in high resolution LD mapping based on a dense marker map, since LD mapping can increase the resolution for candidate regions; (4) models of high resolution LD mappings using two flanking markers have higher power than that of models of using only one marker in the analysis; (5) excluding the dominant variance from the analysis when it does exist would lose power; (6) by performing linkage interval mappings, one may get higher power than by using only one marker in the analysis.     

Combined Linkage and Association Mapping of Quantitative Trait Loci with Missing Completely at Random Genotype Data

In genetics study, the genotypes or phenotypes can be missing due to various reasons. In this paper, the impact of missing genotypes is investigated for high resolution combined linkage and association mapping of quantitative trait loci (QTL). We assume that the genotype data are missing completely at random (MCAR). Two regression models, “genotype effect model” and “additive effect model”, are proposed to model the association between the markers and the trait locus. If the marker genotype is not missing, the model is exactly the same as those of our previous study, i.e., the number of genotype or allele is used as weight to model the effect of the genotype or allele in single marker case. If the marker genotype is missing, the expected number of genotype or allele is used as weight to model the effect of the genotype or allele. By analytical formulae, we show that the “genotype effect model” can be used to model the additive and dominance effects simultaneously, and the “additive effect model” can only be used to model the additive effect. Based on the two models, F-test statistics are proposed to test association between the QTL and markers. The non-centrality parameter approximations of F-test statistics are derived to calculate power and to compare power, which show that the power of the F-tests is reduced due to the missingness. By simulation study, we show that the two models have reasonable type I error rates for a dataset of moderate sample size. However, the type I error rates can be very slightly inflated if all individuals with missing genotypes are removed from analysis. Hence, the proposed method can help to get correct type I error rates although it does not improve power. As a practical example, the method is applied to analyze the angiotensin-1 converting enzyme (ACE) data. Edited by Pak Sham.     

Type I and type II error rates for quantitative trait loci (QTL) mapping studies using recombinant inbred mouse strains

Effective mapping strategies for quantitative traits must allow for the detection of the more important quantitative trait loci (QTLs) while minimizing false positives. Type I (false-positive) and Type II (false-negative) error rates were estimated from a computer simulation of QTL mapping in the BXD recombinant inbred (RI) set comprising 26 strains of mice, and comparisons made with theoretical predictions. The results are generally applicable to other RI sets when corrections are made for differing strain numbers and marker densities. Regardless of the number or magnitude of simulated QTLs contributing to the trait variance, thep value necessary to provide genome-wide. 05 Type I error protection was found to be aboutp=.0001. To provide adequate protection against both Type I (α=.0001) and Type II (β=.2) errors, a QTL would have to account for more than half of the between-strain (genetic) variance if the BXD or similar set was used alone. In contrast, a two-step mapping strategy was also considered, where RI strains are used as a preliminary screen for QTLs to be specifically tested (confirmed) in an F₂ (or other) population. In this case, QTLs accounting for ∼16% of the between-strain variance could be detected with an 80% probability in the BXD set when α=0.2. To balance the competing goals of minimizing Type I and II errors, an economical strategy is to adopt a more stringent α initially for the RI screen, since this requires only a limited genome search in the F₂ of the RI-implicated regions (∼10% of the F₂ genome whenp<.01 in the RIs). If confirmed QTLs do not account in the aggregate for a sufficient proportion of the genetic variance, then a more relaxed α value can be used in the RI screen to increase the statistical power. This flexibility in setting RI α values is appropriate only when adequate protection against Type I errors comes from the F₂ (or other) confirmation test(s).     

DNA markers associated with high versus low IQ: The IQ quantitative trait loci (QTL) project

General cognitive ability (intelligence, often indexed by IQ scores) is one of the most highly heritable behavioral dimensions. In an attempt to identify some of the many genes (quantitative trait loci; QTL) responsible for the substantial heritability of this quantitative trait, the IQ QTL Project uses an allelic association strategy. Allelic frequencies are compared for the high and low extremes of the IQ dimension using DNA markers in or near genes that are likely to be relevant to neural functioning. Permanent cell lines have been established for low-IQ (mean IQ=82;N=18), middle-IQ (mean IQ=105;N=21), and high-IQ (mean IQ=130;N=24) groups and for a replication sample consisting of even more extreme low-IQ (mean IQ=59;N=17) and high-IQ (mean IQ=142;N=27) groups. Subjects are Caucasian children tested from 6 to 12 years of age. This first report of the IQ QTL Project presents allelic association results for 46 two-allele markers and for 26 comparisons for 14 multiple-allele markers. Two markers yielded significant (p<.01) allelic frequency differences between the high- and the low-IQ groups in the combined sample—a new HLA marker for a gene unique to the human species and a new brain-expressed triplet repeat marker (CTGB33). The prospects for harnessing the power of molecular genetic techniques to identify QTL for quantitative dimensions of human behavior are discussed.     

Finding disease genes: a fast and flexible approach for analyzing high-throughput data

Linkage disequilibrium (LD) is the non-random distribution of alleles across the genome, and it can create serious problems for modern linkage studies. In particular, computational feasibility is often obtained at the expense of power, precision, and/or accuracy. In our new approach, we combine linkage results over multiple marker subsets to provide fast, efficient, and robust analyses, without compromising power, precision, or accuracy. Allele frequencies and LD in the densely spaced markers are used to construct subsamples that are highly informative for linkage. We have tested our approach extensively, and implemented it in the software package EAGLET (Efficient Analysis of Genetic Linkage: Estimation and Testing). Relative to several commonly used methods we show that EAGLET has increased power to detect disease genes across a range of trait models, LD patterns, and family structures using both simulated and real data. In particular, when the underlying LD pattern is derived from real data, we find that EAGLET outperforms several commonly used linkage methods. In-depth analysis of family data, simulated with linkage and under the real-data derived LD pattern, showed that EAGLET had 78.1% power to detect a dominant disease with incomplete penetrance, whereas the method that uses one marker per cM had 69.7% power, and the cluster-based approach implemented in MERLIN had 76.7% power. In this same setting, EAGLET was three times faster than MERLIN, and it narrowed the MERLIN-based confidence interval for trait location by 29%. Overall, EAGLET gives researchers a fast, accurate, and powerful new tool for analyzing high-throughput linkage data, and large extended families are easily accommodated.     

The expected power of genome-wide linkage disequilibrium testing using single nucleotide polymorphism markers for detecting a low-frequency disease variant

The expected power of genome-wide linkage disequilibrium (LD) testing for a low-frequency disease variant was examined using a simple genetic model in which the degree of LD between the disease variant and the adjacent single nucleotide polymorphism (SNP) marker decreases in proportion to the number of generations since the LD-generating event. In this study, the frequency of the SNP marker being in complete LD with a low-frequency disease variant at the LD-generating event was regarded as the random variable having the probability distribution expected from the neutral infinite sites model, which enables us to derive the formula for calculating the expected power of genome-wide LD testing without determining the allele frequency of the associated SNP marker. Such a treatment is essential for the evaluation of the power of LD testing, because the frequency of the associated marker allele is always unknown. The main results obtained are as follows: (1) genome-wide LD testing with a case-control design could identify a disease variant with a high penetrance, while a low-frequency disease variant showing a low penetrance is difficult to detect; (2) although the degree of LD increases as the number of markers increases, the power of LD testing does not necessarily increase after the significance level is adjusted by the Šidák correction or the Bonferroni correction based on the number of testings; (3) the use of SNP markers with only high-frequency minor alleles is more powerful for detecting LD even with a low-frequency disease variant than the use of SNP markers with both high- and low-frequency minor alleles. Thus, the study design of LD testing must be evaluated prior to the investigation. The present study will provide a guideline for determining the number of SNP markers and the range of SNP allele frequencies suitable for genome-wide LD testing.     

Possible causal relationships between cerebellar patterns of foliation and hindlimb coordination in laboratory mice: a quantitative trait locus analysis

The cerebellum is involved in a large set of integrative functions including memory, affect, and motricity. The cerebellar patterns of foliation and their causal relationships with motricity were investigated via a wide genome scan approach and quantitative trait locus (QTL) strategy. QTLs were mapped in an F₂ population derived from NZB/B1NJ and C57BL/6By inbred strains of mice for cerebellar fissures in the four vermal lobules (intraculminate, uvula, declival, and intracentral) and for hindpaw slips in a bar crossing test. No linkage was detected for uvula and intracentral fissures. We found five QTLs linked to declival fissure: Cpfd-1q and Cpfd-2q (chromosome 1), Cpfd-3q (chromosome 5), Cpfd-4q (chromosome 9), and Cpfd-5q (chromosome 13). Two QTLs were also mapped for intraculminate fissure Cpfi-1q (chromosome 4) and Cpfi-2q (chromosome 1). Most of the confidence intervals of these QTLs included genes that were previously identified for their implication in the physiological mechanisms underlying cerebellar patterns of foliation. Only one significant QTL was found for the measure of hindpaw coordination (Tne-1q). It was linked with Cpfd-1q and Cpfd-2q on the telomeric part of chromosome 1.     

High resolution mapping of quantitative trait loci by linkage disequilibrium analysis

Two methods, linkage analysis and linkage disequilibrium (LD) mapping or association study, are usually utilised for mapping quantitative trait loci (QTL). Linkage mapping is appropriate for low resolution mapping to localise trait loci to broad chromosome regions within a few cM (<10 cM), and is based on family data. Linkage disequilibrium mapping, on the other hand, is useful in high resolution or fine mapping, and is based on both population and family data. Using only one marker, one may carry out single-point linkage analysis and linkage disequilibrium mapping. Using two or more markers, it is possible to flank the QTL by multipoint analysis. The development and thus availability of dense marker maps, such as single nucleotide polymorphisms (SNP) in human genome, presents a tremendous opportunity for multipoint fine mapping. In this article, we propose a regression approach of mapping QTL by linkage disequilibrium mapping based on population data. Assuming that two marker loci flank one quantitative trait locus, a two-point linear regression is proposed to analyse population data. We derive analytical formulas of parameter estimations, and non-centrality parameters of appropriate tests of genetic effects and linkage disequilibrium coefficients. The merit of the method is shown by the power calculation and comparison. The two-point regression model can capture much more linkage and linkage disequilibrium information than that derived when only one marker is used. For a complex disease with heritability h(2)> or =0.15, a study with sample size of 250 can provide high power for QTL detection under moderate linkage disequilibria.     

The neuro-muscular system in cercaria with different patterns of locomotion

The neuro-muscular system (NMS) of cercariae with different swimming patterns was studied with immunocytochemical methods and confocal scanning laser microscopy. Specimens of the continuously swimming Cercaria parvicaudata, Maritrema subdolum and Himasthla elongata were compared with specimens of the intermittently swimming Cryptocotyle lingua and the attached Podocotyle atomon. The patterns of F-actin in the musculature, 5-HT immunoreactive (-IR), FMRFamide-IR neuronal elements, α-tubulin-IR elements in the nervous and sensory systems and DAPI-stained nuclei were investigated. The general plan of the NMS was similar in all cercariae studied. No major structural differences in the patterns of muscle fibres were observed. However, in the tail of C. lingua, transverse muscle fibres connecting the bands of longitudinal muscles were found. No major structural differences in the 5-HT- or FMRFamide-IR nervous systems were observed. The number of 5-HT-IR neurones in the cercarial bodies varied between 12 and 14. The number and distribution of the α-tubulin-IR processes on the cercarial bodies and tails differed from each other. The relation between the number and structure of the α-tubulin-IR processes and the host finding strategy of the cercariae is discussed. A detailed schematic picture of the NMS in the tails of C. lingua and M. subdolum is presented.     

Hypothetical quantitative trait loci (QTL) for circadian period of locomotor activity in CXB recombinant inbred strains of mice

The locomotor activity of male mice (Mus musculus) of 13 CXB (BALB/cBy × C57BL/6J) recombinant inbred (RI) strains and their progenitor strains was monitored for 4 to 6 weeks by infrared photoelectric beams under constant dark. The circadian period (τ) of locomotor activity was calculated and used in quantitative trait locus (QTL) analysis of strains' means. Results were compared with potential QTL found in a previous study of the BXD RI series. The mean τ of 13 CXB RI mouse strains (three to six animals per strain) in constant dark had a unimodal distribution suggesting polygenic inheritance. A number of potential QTL were found for this trait. There were two associations atp<.001,H23 on chromosome 3 andPmv16 on chromosome 16. A region of chromosome 1 was associated with τ in both CXB and BXD RI series. There was also a conjunction with a locus determined from QTL analysis of the previously reported τ of wheel running activity in seven CXB RI strains (Schwartz and Zimmerman, 1990).     

Quantitative trait loci associated with the behavioral response of B×D recombinant inbred mice to restraint stress: A preliminary communication

Quantitative trait loci (QTL) analysis was used to make provisional identification of loci containing genes influencing vulnerability to stress. The effect of restraint stress on openfield activity was measured in C57BL/6J and DBA/2J inbred strains of mice and in 22 B×D recombinant inbred strains of mice. QTL analyses were performed by correlating the behavioral delta scores for each group with the strain distribution pattern of 1300 markers for the B×D mice. A significant association was found between postrestraint rearings during min 5 through 8 in the open field and theLamb2 marker on chromosome 1 (r=.718,p<.0001). Significant associations at thep<.0001 level were also found between baseline open-field rearings of control mice during min 0 through 5 and theZp3, Ache, andMr66-1 markers on chromosome 5, baseline open-field rearings of control mice during min 5 through 8 and thePmv42 marker on chromosome 15, and open-field rearings of experimental mice during min 0 through 5 and theD11Ncvs61 marker on chromosome 11.     

A Quantitative Genetic Analysis of Slow-Wave Sleep in Influenza-Infected CXB Recombinant Inbred Mice

Influenza-infected C57BL/6J mice spend increased amounts of time in slow-wave sleep (SWS) during the dark phase of the circadian cycle compared to healthy mice. In contrast, infected BALB/cByJ mice show a normal or reduced time in SWS, particularly during the light phase. To identify genetic loci with linkage to these traits, we measured sleep in 13 CXB recombinant inbred (RI) strains derived from a cross between C57BL/6ByJ and BALB/cByJ mice. The probability density distribution of sleep patterns of influenza-infected CXB RI mice showed modes that correspond roughly with the parental modes during the dark phase of the circadian cycle and are intermediate or C57BL/6-like during the light phase. These patterns are consistent with the presence of a low number of major effect quantitative trait loci (QTLs). Chromosomal regions with provisional association to strain variation in influenza-induced SWS patterns were identified. In particular, a 10- to 12-cM interval on Chr 6 between D6Mit74 and D6Mit188 contains a QTL (LRS = 16.6 at 1 cM proximal to D6Mit316; genomewide p<.05) that influences the SWS response to influenza infection during the light phase. We have provisionally named this QTL Srilp1 (sleep response to influenza, light phase 1). Candidate genes for mediation of this phenotype include Ghrhr (growth hormone releasing hormone receptor), Crhr2 (corticotropin releasing hormone receptor 2), and Cd8a (an epitope on cytotoxic T lymphocytes). Several other intervals achieved suggestive probability scores that are sufficient to warrant further analysis either with additional RI strains or with F₂ panels. The analysis also suggests that dark phase and light phase responses are regulated by different genetic factors.     

Physiological characteristics of elite short- and long-distance triathletes

The purpose of this study was to compare the physiological responses in cycling and running of elite short-distance (ShD) and long-distance (LD) triathletes. Fifteen elite male triathletes participating in the World Championships were divided into two groups (ShD and LD) and performed a laboratory trial that comprised submaximal treadmill running, maximal then submaximal ergometry cycling and then an additional submaximal run. 'In situ' best ShD triathlon performances were also analysed for each athlete. ShD demonstrated a significantly faster swim time than LD whereas V˙O_2max (ml kg^–1 min^–1), cycling economy (W l^–1 min^–1), peak power output ( , W) and ventilatory threshold (%V˙O_2max) were all similar between ShD and LD. Moreover, there were no differences between the two groups in the change (%) in running economy from the first to the second running bout. Swimming time was correlated to (r=–0.76; P<0.05) and economy (r=–0.89; P<0.01) in the ShD athletes. Also, cycling time in the triathlon was correlated to (r=–0.83; P<0.05) in LD. In conclusion, ShD triathletes had a faster swimming time but did not exhibit different maximal or submaximal physiological characteristics measured in cycling and running than LD triathletes. Electronic Publication     

Further genetic heterogeneity of human red cell phosphoglucomutase-1: a non-electrophoretic polymorphism

The electrophoretic patterns of human red cell phosphoglucomutase (PGM) were determined by standard starch-gel electrophoresis on two aliquots of haemolysate, one of which was previously heat-treated. Samples from 67 families and 417 unrelated healthy subjects were examined. Heat denaturation studies combined with electrophoresis showed a greater heterogeneity of phosphoglucomutase-1 (PGM₁) isozymes than that revealed by electrophoresis alone. Both the PGM} and the PGM? isozymes turned out to be either heat-resistant (tr) or heat-sensitive (ts) and this new phenotypic property segregated along with the electrophoretic allele with which it was originally associated. Comparison of red cell PGM₁ patterns of 217 PGM₁2-l heterozygous individuals, analysed both as described in this paper and by acid starch-gel electrophoresis, which also distinguishes two common PGM¹¹ (PGM₁^1S and PGM₁^1F) and two common PGM₁²(PGM₁^2Sand PGM₁^2F) allelic products, has shown that the two sets of four alleles do not coincide. Thus eight different PGM₁ alleles were identified. The PGM₁^1Str, PGM₁^1Sts, PGM₁^1Ftr, PGM₁^1Fts, PGM₁^2Str, PGM₁^2Sts and PGM₁^2Ftsgene frequencies were estimated as 0523,0–066,0099, 0–029, 0–224, 0–012, 0–043, 0–004, respectively. Three polymorphic sites are hypothesized within the PGM₁ structural gene and the observed frequencies of the eight alleles discussed in terms of ‘disequilibrium’ among these sites. This is the second example of a human enzyme isoelectrophoretic polymorphism revealed by research specifically aimed at detecting electrophoretically cryptic genetic variations. The technique used in this study appears to offer a reliable means of detecting isoelectrophoretic variants for proteins already known to be electrophoretically polymorphic.     

Genetic markers associated with high versus low performance on episodic memory tasks

Associations were studied between six serum protein polymorphisms (C3, BF, HP, ORM, TF, and GC) and high versus low scoring on episodic memory tasks in an attempt to identify QTL (quantitative trait loci) contributing to the heritability of this quantitative trait. Since a highly significant sex difference (p=.00002) was found with respect to the distribution of high and low scoring, with men showing a poorer performance, associations were studied separately for males and females. In females significant differences (p<.05) between the high and the low groups were found in four of six marker systems (C3, HP, TF, and CG), whereas in males a significant difference was found only in the HP system. Significant differences from population frequencies were also found more frequently in females than in males. The strongest marker associations were found with complement C3 and the acute-phase reactant HP, which suggests that immune response factors may be of importance in preserving episodic memory function. The overall results appear to indicate that episodic memory is a multifactorial and heritable quantitative trait where sex is an important determinant.